ANALYSIS AND OPTIMIZATION OF CONTAINER LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT
A Thesis submitted to Gujarat Technological University
for the Award of
Doctor of Philosophy
in
Mechanical Engineering
By
UTPAL VINODCHANDRA SHAH Enrollment No. 139997119018
under supervision of
Dr. (Prof.) G.H.Upadhyay
GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD
July 2018
ANALYSIS AND OPTIMIZATION OF CONTAINER LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT
A Thesis submitted to Gujarat Technological University
for the Award of
Doctor of Philosophy
in
Mechanical Engineering
By
UTPAL VINODCHANDRA SHAH Enrollment No. 139997119018
under supervision of
Dr. (Prof.) G.H.Upadhyay
GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD
July 2018
© UTPAL VINODCHANDRA SHAH
DECLARATION
I declare that the thesis entitled “ANALYSIS AND OPTIMIZATION OF CONTAINER
LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT” submitted by me
for the degree of Doctor of Philosophy is the record of research work carried out by me
during the period from June 2014 to July 2018 under the supervision of Dr. (Prof.) G. H.
Upadhyay and this has not formed the basis for the award of any degree, diploma,
associateship, fellowship, titles in this or any other University or other institution of higher
learning.
I further declare that the material obtained from other sources has been duly acknowledged
in the thesis. I shall be solely responsible for any plagiarism or other irregularities, if
noticed in the thesis.
Signature of the Research Scholar: …………………………… Date: 25-07-2018
Name of Research Scholar: UTPAL VINODCHANDRA SHAH
Place : Ahmedabad
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I certify that the work incorporated in the thesis “ANALYSIS AND OPTIMIZATION
OF CONTAINER LIFTING DEVICE USED FOR SOLID WASTE
MANAGEMENT” submitted by Shri UTPAL VINODCHANDRA SHAH was
carried out by the candidate under my supervision/guidance. To the best of my
knowledge: (i) the candidate has not submitted the same research work to any other
institution for any degree/diploma, Associateship, Fellowship or other similar titles (ii)
the thesis submitted is a record of original research work done by the Research Scholar
during the period of study under my supervision, and (iii) the thesis represents
independent research work on the part of the Research Scholar.
Signature of Supervisor: ……………………………… Date: 25-07-2018
Name of Supervisor: Dr. (Prof.) G. H. Upadhyay
Place: Ahmedabad
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(Enrollment No. 139997119018) entitled “ANALYSIS AND OPTIMIZATION OF
CONTAINER LIFTING DEVICE USED FOR SOLID WASTE MANAGEMENT” was
conducted on 25 – 07 – 2018, Wednesday at Gujarat Technological University.
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I
ABSTRACT
In all mega cities and municipal corporations, HCV chassis are used for loading and
unloading the containers having size more than 5.5 cubic meters. But for all towns and
nagarpalikas, these may not be preferred due to space limitations and narrow size roads. So
in the case of towns and nagarpalikas, tractor driven container lifting device may be used,
which will lift up to 4.5 cubic meter containers. They can use containers of up to 4 ‐ 4.5 cubic
meters capacity, which will make optimum use of the tractors. Even for more space
limitations small containers mounted on LCVs chassis can also be used.
It has been observed that mostly all municipal corporations are using 5.5 and 6 cubic meter
containers operated by a truck as the main source of waste collection and transportation.
But at ULBs, small towns and villages, it is very difficult to operate truck operated containers
because of following reasons:
1) Quantity of Solid waste to be handled is less as compare to Municipal Corporation.
2) Cost of truck operated container lifting device is also very high.
3) Narrow size of the roads and space limitations also do not allow using such solid
waste handling systems.
In this work, small container having capacity of 4.5 cubic meters is fabricated so that it can
be operated by tractor operated container lifting device. The idea was to design and
develop new container lifting device that can be used to handle 4.5 cubic meter container
which is to be operated by tractor. As tractor is used to operate container lifting device, in
the spare time it can be used to operate trolleys, water tanker etc. at the Nagarpalikas. In
short, tractor can be used to perform multiple activities at Nagarpalika/ULB level. This
combination can be made at less than half of cost in comparison to the truck operated
container lifting device.
The main focus of the research work is the design and development of container lifting
device that can handle 4.5 cubic meter container with the help of tractor. Work has been
carried out for design, failure analysis, vibration and FEA of various components of CLD, i.e.
hydraulic cylinder, link chain, different joints, cross bars, mechanical jack, leaf spring etc.
Hydraulic cylinder is the most critical components of CLD, which is further optimized
considering single and multi‐objective optimization problem using MATLAB.
II
The optimized design and modifications suggested in this work related to the CLD are
successfully implemented by GUDC and it is used in all ULBs in the state of Gujarat for solid
waste management. This CLD system is used to handle 4.5 cubic meter container with the
help of tractor to collect and transport municipal solid waste by various Nagarpalikas in
Gujarat State.
III
ACKNOWLEDGEMENT
I am indebted to various people and organizations during my doctoral research since I
received this opportunity in 2013. I would like to express my deepest gratitude to Dr. (Prof.)
G. H. Upadhyay, (Professor & Head, Mechanical Engineering Department, L. D. College of
Engineering, Ahmedabad) for his great support and invaluable guidance throughout my
research. I remember during my post graduation study when I had interaction with him. It
was then; I was inspired to work for Ph.D. under his guidance.
I would like to extend my gratitude to Dr.(Prof.) A. B. Dhruv, Dr. (Prof.) J. A. Vadher, DPC
members of research monitoring committee for this research for their valuable suggestions.
Their deep insights into the problem and ability to provide guidance and solutions have
been of immense value in improving the quality of my research work at all stages.
Under the broad vision of Government of Gujarat officials from GUDC, they formed team
called TAG (Technical Approval Group) since year 2008 for the MSWM (Municipal Solid
Waste Management) in the state of Gujarat. In that team I got a chance to work with all
technical experts from different municipal corporations, like Mr. Vijay Mistry, (Joint Director
(Mechanical), Ahmedabad Municipal Corporation) where I learned a lot regarding
equipments used for MSWM (Municipal Solid Waste Management). I recognize that this
research would not have been possible without the support of GUDC, AMC and AUDA
officials from the Government of Gujarat and I express my sincere thanks to them.
I would like to express a deep sense of gratitude to my respected Dr. G. P. Vadodaria
(Principal, L D College of Engineering, Ahmedabad), Dr. M N Patel (Ex – Principal, L D College
of Engineering & Ex – Vice Chancellor Gujarat University), Dr.(Prof.) A. M. Prabhakar
(Principal, GEC, Modasa) for allowing me to utilize the resources of institute and for
continuous support.
I would like to express my deepest sense of gratitude for support, continuous advice,
intelligent approach, constructive criticism, whole hearted and ever available help and
encouragement throughout the Ph.D. program by Dr. (Prof.) B. J. Shah, Prof. G. B. Modha
and Dr. (Prof.) B. C. Khatri.
IV
Special thanks also go to Prof. S. P. Patel, Prof. P.D. Patel and Prof. R. J. Jani for encouraging
and supporting me to complete this research work.
I also acknowledge Shri D. N. Shah and other workshop staff for constant support during
vibration analysis of hydraulic cylinders of CLD using FFT analyser to complete this research
work.
Last but not least I would like to express my deep sense of gratitude to my parents and
family members whose support in all time made me mentally free to concentrate my thesis
work.
UTPAL VINODCHANDRA SHAH
V
TABLE OF CONTENT Chapter Title Page No. No.
Abstract I
Acknowledgement III
Table of content V
List of Abbreviations VIII
List of Symbols IX
List of Figures X
List of Tables XIII
1. Introduction 1
1.1 Introduction for municipal solid waste management 1
1.2 Solid waste management in Indian scenario 2
1.3 MSW – A growing challenge 5
1.4 Transportation of Waste 5
1.5 Transport of Waste in Open Vehicles 6
1.6 Major drawbacks of the SWM transport system 7
1.7 Introduction to Container Lifting Device used for Solid Waste 8
Management
1.8 Objectives of the study 9
1.9 Scope of the work 9
1.10 Description of the research work 9
1.11 Tools to be used 10
2. Review of literature 11
2.1 Introduction to literature review 11
2.2 Literature Review on Solid Waste Management 11
2.3 Literature review on Design and FEA of CLD 13
2.4 Literature review on Optimization of hydraulic cylinders 19
2.5 Performance problem identification through survey 21
2.6 Research Gap 22
3. Design Methodology 23
3.1 Introduction 23
VI
Chapter Title Page No. No.
3.2 Design of Hydraulic Cylinder 23
3.2.1 Material used for Hydraulic Cylinder 24
3.2.2 Theoretical Design of Cylinder Tube 24
3.2.3 Design of Thick Cylinder 25
3.3 Theoretical Design of Piston and Piston Rod 26
3.4 Design of Cross‐Rod 26
3.5 Design of Rear Mechanical Jack of CLD Model 27
3.5.1 Loading location 27
3.5.2 Design of welded joint 29
3.5.3 Design of locking pin 29
3.6 Design of leaf spring 29
3.6.1 Materials for leaf spring 32
4. Design calculations 33
4.1 Design calculations – Anchor pin for 4.5 cubic meter container 33
4.2 Design calculations of hydraulic cylinder 36
4.3 Design for pin joint 38
4.4 Design Calculation of Cross‐Rod 39
4.5 Design calculation for Rear mechanical Jack 40
4.6 Design calculations of leaf spring for CLD model 42
4.7 Selection of hoisting chain link for container lifting 44
4.7.1 Materials for chain link 45
5. Modelling and analysis of container lifting device 47
5.1 3D modelling 47
5.1.1 About SolidWorks 2014 47
5.2 Finite element analysis (FEA) 47
5.3 Transient Structural Analysis of Container Lifter Model in Ansys 48
5.3.1 Steps for transient analysis in Ansys Workbench 49
5.4 Dynamic Analysis of Container Lifting Device 52
5.4.1 Rigid dynamic analysis of CLD model 54
5.4.2 Transient dynamic analysis of CLD model 57
5.5 Static Analysis of Leaf Spring 65
VII
Chapter Title Page No. No.
5.6 Static Analysis of Mechanical Jack 67
5.7 FFT Analyzer used for vibration measurement of hydraulic cylinder 68
5.8 OMNITREND software and its applications 72
5.9 VIBXPERT – FFT (Fast Fourier Transform) data collector and signal 73
Analyzer
6. Optimization in Hydraulic Cylinder 77
6.1 Introduction 77
6.2 Optimization Technique – Genetic Algorithm 78
6.2.1 Outline of genetic algorithm 79
6.3 Optimization in Hydraulic Cylinder Design – A Case Study 80
6.4 Single Objective Optimization Problem – Nonlinear Constrained 82
Minimization
6.5 Multi Objective Optimization using Genetic Algorithm 83
6.6 Difference with Single objective optimization 84
6.6.1 Two goals instead of one 85
6.6.2 Dealing with two search spaces 85
6.6.3 No artificial fix‐ups 86
6.7 Multi – objective optimization 86
6.8 Conclusion of optimization 93
7. Conclusion and future scope of research 94
7.1 Conclusion of research work 94
7.2 Future scope of research work 95
ANNEXURE I 97
ANNEXURE II 100
REFERENCES 102
LIST OF PUBLICATIONS 108
VIII
LIST OF ABBREVIATIONS
Sr. No. Abbreviations Meaning
1 AMC Ahmedabad Municipal Corporation
2 APDL ANSYS Parametric Design Language
3 ASTM American Society for Testing Materials
4 AUDA Ahmedabad Urban Development Authority
5 CFD Computational Fluid Dynamics
6 CLD Container Lifting Device
7 FEA Finite Element Analysis
8 FFT Fast Fourier Transform
9 FOS Factor of safety
10 GUDC Gujarat Urban Development Corporation
11 HCV Heavy Commercial vehicles
12 ISO International Organisation for Standardization
13 LCV Light Commercial Vehicles
14 MSWM Municipal Solid Waste Management
15 SWC Safe Working Capacity
16 ULB Urban Local Bodies
17 WLL Working Load Limit
IX
LIST OF SYMBOLS
Symbol Description
% Percentage
Poisson’s ratio
r Radial stress
t Tensile stress
id Internal diameter
od External diameter
Shear stress
mg milligram
gm gram
mm Millimetres
hr Hour
y Yield stress
[L] Lower Bound
[U] Upper Bound
X
LIST OF FIGURES
Figure No. Title of Figure Page No.
Fig. 1.1 Process of solid waste management 2
Fig. 1.2 Waste transportation in open vehicles 6
Fig. 1.3 Inappropriate secondary storage 6
Fig. 1.4 Manually waste loading in open tractors in small towns 6
Fig. 1.5 Manual loading of waste in open trucks in large cities 6
Fig. 1.6 Truck mounted container lifting device 8
Fig. 2.1 Variation of experimental stress of steel and composite leaf Springs 15
Fig. 2.2 Load‐deflection curves for steel and composite leaf springs 15
Fig. 2.3 The state of loading condition with respect to the installation of 15
crane chain
Fig. 2.4 Tensile stress–strain behaviour of the Grade‐80 steel chain 17
Fig. 2.5 Mating fracture of surfaces at welded portion 17
Fig. 2.6 Different types of eye design used for leaf spring 18
Fig. 3.1 Forces acting on hydraulic cylinder 23
Fig. 3.2 Thick cylindrical shell stress distribution over its thickness 25
Fig. 3.3 Cross‐rod fixed at both ends 26
Fig. 3.4 3D model of mechanical rear jack 27
Fig. 3.5 Eccentric loading of column 28
Fig. 3.6 Resulting stress distribution 28
Fig. 3.7 Column welded to body of CLD 29
Fig. 3.8 Shear of locking pin 29
Fig. 3.9 Semi – elliptical leaf spring 30
Fig. 4.1 Drawing of 4.5 cubic meter container used for CLD 34
Fig. 4.2 Drawing of anchor pin used in 4.5 cubic meter Container 35
Fig. 4.3 Cylindrical pin joints 38
Fig. 4.4 Drawing of Cross‐rod used in CLD 39
Fig. 4.5 Cross – section area of column section 41
Fig. 4.6 Model of chain link 45
Fig. 5.1 SolidWorks 3‐D model of container lifting device 53
XI
Figure No. Title of Figure Page No.
Fig. 5.2 Joints between individual components of CLD model 54
Fig. 5.3 Remote force, constrain, joint velocity, acceleration shown in CLD model 55
Fig. 5.4 Force shown on translation joint probe 56
Fig. 5.5 Translation joint force change with time 56
Fig. 5.6 Fine meshing of CLD model 57
Fig. 5.7 Time varying force applied on piston 59
Fig. 5.8 Time varying reaction force applied on cylinder 59
Fig. 5.9 Remote force, constrain, joint velocity, acceleration shown in CLD 59 model for transient analysis
Fig. 5.10 Von‐Mises stress contour generated in CLD model 60
Fig. 5.11 Maximum Von‐Mises stress generate in CLD model 60
Fig. 5.12 Maximum value of Von‐Mises stress change with time 61
Fig. 5.13 Equivalent elastic strain contour generated in CLD model 61
Fig. 5.14 Maximum value of Equivalent elastic strain change with time 61
Fig. 5.15 Total deformation of CLD model 62
Fig. 5.16 Total deformation of CLD model with respect to time 62
Fig. 5.17 Cylinder‐ base total revolute joint probe force 62
Fig. 5.18 Time varying total cylinder‐ base revolute joint force 63
Fig. 5.19 Big link‐ base total revolute joint probe force 63
Fig. 5.20 Time varying total big link – base revolute joint force 63
Fig. 5.21 Big link – piston rod total revolute joint probe force 64
Fig. 5.22 Time varying total big link‐ piston rod revolute joint force 64
Fig. 5.23 Safety factor contour generate in CLD model 64
Fig. 5.24 Minimum time varying safety factor 65
Fig. 5.25 3 – D model of leaf spring 65
Fig. 5.26 Load and constrain applied to model of leaf spring 66
Fig. 5.27 Stress generated in leaf spring 66
Fig. 5.28 Strain generated in leaf spring 66
Fig. 5.29 Load and constrain applied to model of mechanical jack 67
Fig. 5.30 Stress generated in mechanical jack 67
Fig. 5.31 Strain generated in mechanical jack 68
XII
Figure No. Title of Figure Page No.
Fig. 5.32 Hydraulic Cylinder 69
Fig. 5.33 Hydraulic cylinder with bush 69
Fig. 5.34 OMNITREND Software 72
Fig. 5.35 FFT spectrum of Hydraulic cylinder 74
Fig. 5.36 Photographs of actual readings taken of CLD by FFT analyzer 76
Fig. 6.1 Optimization using MATLAB for the function: Minimization of 82
force value (f) exerted on piston
Fig. 6.2 Optimization using MATLAB for the function: Minimization of 83
Cross‐sectional area (A) of the Hydraulic Cylinder
Fig. 6.3 Mapping from Parameter Space into Objective Function Space 87
Fig. 6.4 Set of Non‐inferior Solutions 87
Fig. 6.5 Pareto optimization using Genetic Algorithm plot of Stress 89 generated (N/mm2) v/s Force on Piston (N)
Fig. 6.6 Pareto optimization using Genetic Algorithm plot of Force (N) 91 v/s Thickness of Cylinder (mm)
Fig. 6.7 Pareto optimization using Genetic Algorithm plot of Force (N) 93 v/s Cross‐sectional area of Cylinder (mm2)
XIII
LIST OF TABLES
Table No. Title of Table Page No.
Table 2.1 Parameter comparison between standard eye and casted eye 18
Table 4.1 Grade 80 alloy chain standard by national association of 46
chain manufacturer
Table 5.1 Rigid dynamic analysis setup for CLD model 52
Table 5.2 Material and its property for individual component of CLD 53
Table 5.3 Joint applied to different pair of CLD component 54
Table 5.4 Analysis setting in rigid dynamic analysis of CLD model 55
Table 5.5 Value of translation joint force with respect to time 57
Table 5.6 Transient dynamic analysis setting 58
Table 5.7 Transient dynamic analysis setup of CLD model 58
Table 5.8 Typical measurements of RMS values 75
Table 6.1 Comparison of Classical and Genetic Algorithm 79
Table 6.2 Value of each variable Internal diameter (d), Thickness of Cylinder (t) 88
and Internal Pressure (p) after each iteration
Table 6.3 Value of each variable Internal diameter (d), Stress of Cylinder (s) 90
and Internal Pressure (p) after each iteration
Table 6.4 Value of each variable internal diameter (d), Thickness of Cylinder (t) 92
and Internal Pressure (p) after each iteration
Table 7.1 Comparison of results with allowable value 94
1
CHAPTER 1
INTRODUCTION
1.1 Introduction for Municipal Solid Waste Management
Municipal solid waste (MSW) includes waste from market waste, yard waste,
agricultural wastes, households waste, non‐hazardous solid waste from industrial, street
sweepings, institutional and commercial establishments (excluding bio‐medical waste in
present context). Community – Industrial hazardous waste, infective waste are not
considered as MSW and should be collected – processed separately. Management and
Handling Rules 2000 defines MSW as “commercial and residential wastes generated in
municipal or notified areas in either solid or semi‐solid form excluding industrial hazardous
wastes but including treated bio‐medical wastes”. MSW related various other definitions are
defined in MSW Rules 2000, which are given in ANNEXURE I. MSW management covers the
functions of collection, transfer – transportation, processing – recycling and final disposal of
solid waste. Safe and cost‐effective management of MSW is a significant environmental
challenge for modern society. Imperfectly managed waste disposal is directly affected to the
human health and environment.
Preferably MSW management shall include the ethics of waste minimization,
recycling, resource recovery as well as an integrated processing – disposal facility, leading to
effective service delivery in a justifiable manner. Solid waste should be managed at all
stages starting from waste generation to the final waste disposal. An integrated solid waste
management plan would consist of:
understanding the present waste management practices
recognizing waste management needs
priorities shall be set for the required actions
budget need should be identified
different stakeholders shall be coordinated
determining progress in terms of targets achieved
as the plan advances priorities shall be modified
communication and coordination with the external/local agencies to achieve the
desired targets.
Introduction
2
For the purpose of EIA (Environmental Impact Assessments) Notification, common
municipal solid waste management facilities may be observed and compare with centralized
MSW facility for any given town, city, region. It is further to mention a common facility need
not have surrounding ULBs included.
1.2 Solid waste management in Indian scenario
The rapid urbanization is changing the nature of solid waste management from a low
priority, localized issue to a general social and environmental problem with risks to public
health and environment. Usually, MSW is disposed of in low‐lying regions without taking
any safety measures or effective controls. Therefore, MSWM is one of the most important
environmental problems of Indian megacities. The MSWM system comprises five activities,
i.e. waste generation, collection, transportation, processing/treatment and disposal.
Fig. 1.1 Process of solid waste management[16]
MSW management is constrained by respective organisation weakness, less funding,
improper management and operational systems, public laziness, lack of municipal staff. This
can be made self – sustained by increasing the income of Municipal Corporation through
taxation. [12]
In Indian towns, MSW storage is to be done at a central place. Persons deposit their
waste in bins – enclosures located at street corners at specific intervals. The waste
containers generally are constructed of metal, concrete, or brickwork. In most of the towns
and cities indiscriminate littering of roads and drains are found. Community storage may
reduce the cost of waste collection, but chances of scattering remains will be the major
issue. Scavenging of the wastes by stray animals and rag pickers may lead to further
scattering of solid waste.
It is often observed by the experienced municipal experts that the lack of public
alertness among city residents is proving to be a major hurdle in maintaining the cleanliness.
This problem is most severe in slums and in areas where the lower and middle income
groups reside. Because of the poor conditions people are not using proper ways of
Solid waste management in Indian scenario
3
temporary storage of wastes. In some of these types of areas, NGOs are involved in making
arrangements for waste collection from households leading to improvement in the street
cleanliness.
For transportation of waste, different types of vehicles, varying from bullock carts to
compactors are used. In large cities open body trucks of 5 to 9 tons capacity are in common
use, but in smaller towns, tractor‐trailers are used despite being slow. In few large cities,
compactor vehicles are also used for transporting the solid waste. The waste is transported
mostly by municipality owned vehicles; though in some big towns, private contractor
vehicles are also hired to expand the working. These vehicles are generally maintained in
the municipal workshop along with other vehicles of the corporation but will get less priority
in maintenance. Most of these municipality workshops have facilities for minor repairs only.
Although preventive maintenance is necessary in these types of vehicles, but this aspect is
often neglected.
In India, thousands of urban residents make their living upon waste processing by
working in small industries, which recycle tin cans, plastics, leather, glass, bottles, bones,
hair, metal etc., recovered from MSW. Most of the material containing metals, unsoiled
paper, plastics, glass, cardboard, etc. are marketable and hence recycled by householders
themselves or by the rag‐pickers. By the time waste reaches the community bins, it contains
only a small portion of recyclable material and consists mainly of vegetables – fruit peelings,
used toiletries, scraps of soiled paper –plastic and inert material such as sand and stones,
etc.
The greater proportion of organic matter in MSW indicates that the biological
processing of waste such as composting. There are several problems found in composting
like transportation, poor recognition by farmers (may be for the quality concerns),
marketing, price etc. There are several efforts are being taken to promote waste
segregation and composting recently. With a majority of Indian successful combustion
reaction cannot be obtained so supplementary fuel will be required to aid waste
combustion.
Generally in all towns, waste is being disposed of in low‐lying areas. Waste disposal
sites are preferred on the basis of their closeness to the collection areas and new dumping
sites are normally identified only when the existing ones are filled. The waste is simply
Introduction
4
dumped at such sites and, except in the major cities. Rarely bulldozers are used for
compaction at the disposal site. In megacities, they are used mainly for leveling of the
deposited waste. In many cities and towns proper weighing, filling and soil layering are not
usual practice. A soil cover is also hardly ever provided, except at the time of final closure of
the site. Most of the disposal sites are unfenced and the waste picking is commonly in
vogue, posing problems in the operation of the sites. The rag‐pickers usually have a
collective practice to light a fire on the dumpsite either to reduce the menacing flies, volume
or odour results in easy way of waste picking.
For the authorities, it is very difficult to acquire land for creating waste management
and dumping facilities so it is essential that the present dumpsites are reformed to collect
current and future wastes. Usually haphazard dumping of waste across the dumpsites has
been a routine practice results in pollution of the surrounding areas, so those dumpsites
need to be reformed and redesigned to recover space for future wastes. These reformation
measures should include actions to be taken pollutant migration. This dumping site shall be
designed in such a way to accept future wastes for a period of 20 years or more and well
planned operations, processes and maintenance.
The human activities create waste, and the way of practice that waste is handled,
stored, collected, and disposed of can create risks to the environment and to human health.
In urban areas like especially rapidly growing cities of the developing world, problems and
issues of municipal solid waste management (MSWM) are of immediate importance.[16]
Most of state governments have acknowledged the importance of MSWM; however, rapid
population growth and changing life style overcomes the capability of most municipal
establishments to provide the most basic services.
Typically one – to two – thirds of the solid waste that is generated is not collected in
major cities. The uncollected waste is dumped in the streets or in drains in open channel,
create problem like flooding, breeding of insect, rodent vectors and spreading of diseases.
Even solid waste that is collected is often disposed of in uncontrolled dumpsites or burned,
polluting water resources and the air. Proper implementation of MSWM practices benefits
directly to both human health and environmental quality.
MSW – A growing challenge
5
1.3 MSW – A growing challenge
Growing urbanization in India will result in a massive increase of waste over the next
two decades. The urban population is expected to represent 41% of the overall population
by the year 2021. During a study conducted by the CPCB on management of MSW in the
country, estimates that waste generation from the present 48 million tons (MT) per year is
expected to increase to 300 MT per year, by the year 2047 (490 g per capita to 945 g per
capita). The estimated requirement of land for disposal would be 169.6 square kilometer
(km2) in 2047 as against 20.2 square kilometer (km2) in 1997 (CPCB 2000a).[14]
Annually in India at present 48.0 MT of MSW is produced.
There is a increase in urban population by 3 – 3.5 % per annum.
In India per capita waste generation is increasing by 1.3 % per annum.
Yearly increase of waste generation in India is approximately 5%.
The urban local bodies are investing around35 – 50% of its available funds to deal
with the waste generated in urban areas. They are spending about Rs. 500 – 1500 per ton
on solid waste management. Hence there is a vital need to increase effectiveness for better
service delivery and optimization.
Land selection for the disposal of solid wastes has been very important criteria for
centuries. Agricultural, municipal, urban and industrial activities produce enormous
amounts of wastes, which require safe, stable and permanent secured disposal. For
extending financial resources, the Central Government has incorporated solid waste
management as one of the components in the Jawaharlal Nehru National Urban Renewal
Mission (JNNURM) programme for the growing challenge of solid waste management in the
country. Many municipal corporations and cities are getting benefit from this enormous
programme.
1.4 Transportation of Waste
The following rules apply to transport of solid waste:
• Transport vehicles used for waste collection and dispose are covered.
• While doing transport waste, it should not be visible to public and exposed to the
environment, thus preventing the strewing of waste on rod.
• For clearing of waste attend to storage facilities daily.
Introduction
6
• Regular empty bins or containers before they start overflowing.
• Waste transport vehicles are designed so that it can eliminate multiple handling of waste
before final disposal.
1.5 Transport of Waste in Open Vehicles
Waste handling in the big cities, ULBs and nagarpalikas is not normal practice.
Unluckily, service of waste management is performed very unproductively and in an
unhealthy method. Open tractor trailers and trucks are used to transport waste and those
are loaded manually. In rainy season the handling of these types of waste by local workers
of the village/town is most dangerous. This inefficient process results in decreasing labour
efficiency and increases the possibility of work‐related health risk to all workers as shown in
following photographs.
Fig.1.2 Waste transportation in open vehicles [16] Fig.1.3 Inappropriate secondary storage[16]
Fig.1.4 Manually waste loading in open Fig.1.5 Manual loading of waste in open tractors in small towns[16] trucks in large cities[16]
Major drawbacks of the SWM transport system
7
1.6 Major drawbacks of the SWM transport system:
Major drawbacks of the SWM transportation system are summarized as follows:
• While transporting the solid waste in open trucks and tractor trailer, it falls and thereby
causing nuisance.
• Manual handling of waste like loading or unloading from vehicles without use of any
protection is hazardous to the health of workers.
• The waste transportation system is not properly matched with the secondary storage
system. Problems faced even a transport taskforce is modernized, because waste at the
secondary storage station is still dumped on the ground. If the secondary storage system
is restructured without a proper number of recent vehicles, similar problems may occur.
• Multiple handling of waste results in lower down labour and equipment productivity.
• Over flowing secondary waste storage depots result in irregular and untimely transport of
waste.
• All the areas cannot be given service properly because of an inadequate number of
vehicles.
• Vehicles are not properly maintained because of inadequate workshop facilities and
lengthy maintenance procedures. This problem leads to frequent breakdowns resulting
vehicles shall be kept out of service for longer periods.
• Spare parts are not easily available, because of the procurement system which is
cumbersome and very slow.
• Vehicle movement is not observed properly in terms of quantity of waste carried, number
of trips made and optimum use of workers.
• Due to unplanned routing of vehicles, ineffectiveness transport logistics is found.
The process of MSWM is divided into majorly five stages like generation, collection,
transportation, processing and disposal. In the present study major focus is given on
collection and transportation stage. The drawbacks of the MSWM described above are more
or less related to the collection and transportation of solid waste. Hence in the present
study the focus is made on the modifications in design of CLD with lower capacity of
containers which can be used in small towns, ULBs and villages.
Introduction
8
1.7 Introduction to Container Lifting Device used for Solid Waste Management
For transport of solid waste the concerned authority authorities need to decide the
type of vehicles to be procured and the system of transportation to be adopted. If they use
a containerized system the vehicles needed may be calculated according to the number of
containers that will become full each day and the number of containers each container
lifting device will be able to take to the transfer station, treatment plant or disposal site.
Normally, one vehicle will be able to lift seven or eight containers if the distances to
be travelled are within 5 kilometers. The number of trips may be reduced to five or six if the
distance is between 5 and 10 kilometers, and it may be further reduced depending on the
distance travelled. In addition, 25 to 30 percent additional spare vehicles will be needed to
maintain reliability of service during breakdowns and during preventive maintenance of
vehicles.
Possibly vehicles are to be utilized in two shifts to ensure maximum benefits, better
results and optimum use of investment made.
Fig. 1.6 Truck mounted container lifting device[14]
In all mega cities and municipal corporations' hcv chassis are used for loading and
unloading the containers having size more than 5.5 cubic meters. But for all towns and
nagarpalikas these may not be preferred due to space limitations and narrow size roads. So
here tractor driven container lifting device may be used which will lift up to 4.5 cubic meter
containers. They can use containers of up to 4 to 4.5 cubic meters capacity, which will make
optimum use of the tractors. Even for more space limitations small containers mounted on
LCVs chassis can also be used.
Objectives of the study
9
1.8 Objectives of the study
The main Objective of this study is to design the container lifting device operated
with tractors or LCVs used for small towns.
To determine the feasibility of improving performance of container lifting device for
solid waste management.
To identify the problems/difficulties faced with the existing design/specifications of
the container lifting device.
To identify significant parameters affecting performance of the container lifting
device.
To review existing design/specifications of container lifting device.
To establish the improved design/specifications of the container lifting device that
will overcome most of the problems related the performance parameters.
1.9 Scope of the work
Feasibility assessment through survey based on questionnaires from end users and
manufacturers of container lifting device.
Problem identifications based on survey conducted.
Identification of critical parameters will be assessed based on literatures available,
technical talk with experts and self‐assessment of the actual operations cycle of the
existing systems.
Analysis tool will be used to finalize the most favorable design.
1.10 Description of the research work
The detailed survey work for the different container lifting device will be done in the
first phase. It covers study and analysis of existing container lifting device used for
the solid waste management.
In second phase problems during operations and maintenance will be identified from
the all data collected.
Introduction
10
Actual need base analysis is done according to the requirement of solid waste
management.
Based on the above analysis new improved optimum utilization and convenient
design will be suggested for easy operation and less maintenance.
The same design will be checked using software as well as it will be checked
experimentally under AMC, AUDA and GUDC.
1.11 Tools to be used
Iterative design procedure and calculation – MATLAB/SCILAB
CAD model – Solidworks
Design analysis – Hypermesh, Hyperworks, ANSYS
Design Optimization
Design testing and validation
11
CHAPTER 2
REVIEW OF LITERATURE
2.1 Introduction to literature review
Initially literature review had been done in the field of solid waste management.
There is always a scope of improvement possible at the every stage of SWM in our country.
It had been observed that mostly all municipal corporations are using 5.5 and 6 cubic meter
containers operated by a truck as the main source of waste collection and transportation.
But at ULBs, small towns and villages, it is very difficult to operate truck operated containers
because of following reasons:
1) Quantity of Solid waste to be handled is less as compare to Municipal Corporation.
2) Cost of truck operated container lifting device is also very high.
3) Narrow size of the roads and space limitations are also not allowing us to use such
solid waste handling systems.
After that focused in the design of small containers having capacity of 4.5 cubic
meter and it can be operated by tractor operated container lifting device. So the idea was to
design and develop new container lifting device to be used to handle 4.5 cubic meter
container and which is to be operated by tractor. If tractor is used to operate container
lifting device, in spare time the same tractor can be used to operate trolleys, water tanker
etc. at the Nagarpalikas. In short tractor can be used to perform multiple activities at ULB
level. This combination can be made at less than half of cost if it is compare with truck
operated container lifting device.
In second phase of literature review had been carried out for design, failure analysis,
vibration and FEA related papers of hydraulic cylinder, link chain etc. used in container
lifting device. After designing, FEA and vibration analysis of container lifting device its most
critical component i.e. hydraulic cylinders needs to be optimised. So in third phase of
literature review, optimizations of hydraulic cylinders papers were reviewed. Here main
focus was given to single and multi‐objective optimization of hydraulic cylinders.
2.2 Literature Review on Solid Waste Management
(1) Mufeed Sharholy & etc. at Elsevier (2008) present that Municipal solid waste
management (MSWM) was one of the major environmental problems of Indian cities.
Review of literature
12
Improper management of municipal solid waste (MSW) causes hazards to inhabitants.
Several studies reveal that about 90% of MSW was disposed of unscientifically in open
garbage dump and landfills, creating problems to public health and the environment.[58]
Generally transfer stations are not using the same vehicle, which collects refuse from
individual dustbins, precedes it to the processing or disposal site (Colon and Fawcett, 2006;
Khan, 1994). The MSW collected from the garbage bins at collection points are transported
to the processing or disposal sites using a variety of vehicles. In rural towns, bullock carts,
tractor‐trailers, tricycles, hand carts etc., are mainly used for the transportation of solid
waste. Light motor vehicles (LMV) and trucks (HCV) are generally used in big towns or cities
for transportation of MSW. The trucks used for transportation of MSW are generally of an
open body type and are usually kept uncovered; thus during transportation, the waste tends
to fall on the roads resulting in unhealthy environments. In some cities, modern hydraulic
vehicles are gradually being introduced (Bhide and Shekdar, 1998; Reddy and Galab,
1998).[58]
Author briefly describe present trend in transportation of MSWM in small and big
town. According to author Collection and transportation activities constitute almost 80–95%
of the total budget of MSWM. Hence, it forms a important component in determining the
economics of the entire MSWM system. Municipal agencies use their own vehicles for MSW
transportation although in some cities these may be hired from private service providers.
(2) Somnath Debnath & S.K.Bose at Elsevier (2013)[86]reflects the present stage of the
MSW services in India and explores full cost accounting (FCA) structure in its aptitude
towards generation of information on cost related aspects and supportable distribution of
capitals.
Authors also quoted the case study of SWM costs of Municipal Corporation of
Greater Mumbai. The findings from the study indicates that the feasibility of having FCA as a
temporary arrangement to use accounting data. It also generates the information that
would be relevant for improvements in decision making. The classification and gathering of
data is somewhat arbitrary in the selected example of MCGM – Municipal Corporation of
Greater Mumbai. So the purpose of this study is not to establish the precision of data or its
explanation.[86]
The aim of the study was to invite the consideration toward the transparency of FCA
framework. This will also support the municipal authorities with identification,
Literature Review on Solid Waste Management
13
ascertainment and cost control to steer towards strategies that should support optimal
distribution of resources.
(3) R. K. Kaushal & others at I.I.T., Delhi (2012) reviews the current practices of
assessing and projecting of MSW and highlights their limitations.
In the said paper, there was an effort has been made for the study of the changing
trends of quantity and characteristics of MSW. It also suggests finding its impact on the
performance and capacity planning of recovery – recycle, compost, incineration and landfill
facilities. The altering pattern of waste composition emphasises the importance of
segregation for successful operation of waste management facilities.[72]
Municipal authorities shall maintain the waste management services in such a
manner that unhygienic and unsanitary conditions will not be created. The altering need for
the suitable waste management technologies with respect to the changing pattern of the
waste generation shall be highlighted. This may help the urban local bodies accountable for
MSW management in preparing more efficient plans.[72]A new survey shall be carried out on
the generation and characterization of MSW in India. A large number of MSW samples are
to be collected and analysed to obtain more reliable results as MSW is heterogeneous in
nature.
2.3 Literature review on Design and FEA of CLD
(1) M Osman Abdalla & Others, Malasiyaat IJERA (2013)[50]suggested that the small
sized opening ports of the cylinders resist the flow of discharged oil and because of that the
piston motion is slowed down. This will lead to a lot of heat generation and energy loss
within the actuators. This study had investigated and analyzed the potential of reducing the
hydraulic resistance and rising efficiency of the hydraulic actuator.
Conventional hydraulic cylinders are simulated in FLUENT. The projected system is a
four ports hydraulic cylinder fitted with a novel flow control valve. Results show that the
small outlet ports are the sources of energy loss in hydraulic cylinders.[50]
. An innovative hydraulic system was projected as a solution to relieve the hydraulic
resistance in the actuators. The proposed system is of four ports cylinder and simulated.
Results show that the hydraulic resistance is extensively reduced and the proposed cylinders
run faster than the conventional cylinders. It gives better performance regarding the piston
Review of literature
14
speed and energy savings. This hydraulic system can be used in all engineering and portable
applications.
(2) P.J. Gamez – Montero& etc. at Elsevier (2009) presented an analytical method
based on misalignment to calculate the load capacity of an actuator by determining the
critical load (buckling) and limit load (yield stress). [26]
Results of maximum load capacity, imperfection angle and experimental work are also
presented for a specific unit of actuator.
From the result author found that Imperfection angle and a 5% of wear on the guide
ring reduce the cylinder load capacity by approximately 10% for the tested actuator.Results
of imperfection angle show that the effect of fluid compressibility reduces the imperfection
angle between rod and cylinder tube. [26] Moreover, wear of the guide rings has a major
influence on the imperfection angle than that of the radial deformation owing to the
internal fluid pressure.
(3) Mouleeswaran SENTHIL KUMAR & Sabapathy VIJAYARANGAN (2007)[57]
represented static and fatigue analysis of steel leaf spring – composite multiple leaf spring
made up of glass fibre reinforced polymer using life data analysis.
The dimensions of a present conventional steel leaf spring of a light commercial
vehicle are taken in to consideration and are verified by design calculations. Static analysis
of 2‐D model of conventional leaf spring is also performed using ANSYS 7.1 and matched
with the experimental results. [57]
Using E glass – Epoxy unidirectional laminates, same dimensions of conventional leaf
spring are used to manufacture a composite multiple leaf spring. The stiffness, load carrying
capacity and weight of composite leaf spring are compared with that of steel leaf spring
analytically and experimentally.
Stresses and deflections are the design constraints. Finite element analysis with full
bump load on 3‐D model of composite multi leaf spring is done using ANSYS 7.1.After that
the analytical results are matched with experimental results. Fatigue life of steel leaf spring
and composite leaf is also predicted.[57]
Literature review on Design and FEA of CLD
15
Fig.2.1 Variation of experimental stress of steel Fig.2.2 Load‐deflection curves for steel and composite leaf Springs [57] and composite leaf springs[57]
If composite leaf spring is compared with the conventional steel spring then, the
composite leaf spring is found to have 64.95 % higher stiffness, 67.35 % lesser stress, 126.98
% higher natural frequency and68.15 % weight reduction than that of existing steel leaf
spring. It is also concluded by the researcher that fatigue life of composite is more than that
of conventional steel leaf spring.
(4) Tae – GuKIM and others (2010) performed 3D modelling and FE analysis to classify
the correct and incorrect installation procedures of link chain. In the same context factors
are also identified which can lead to disastrous failures because of the incorrect installation.
This study utilized 3D modelling and finite element analysis of the chains which are used to
operate the crane. Based on the analysis, assessed the effect of different load cases on
structural stiffness and rigidity. Three dimensional interactive application programs (CATIA)
was used for 3D modelling of the chain and commercial finite element analysis software,
ANSYS was used for this analysis.[91]
(A) Correct installation: tensile load[91] (B) Incorrect installation: bending load[91]
Fig.2.3 The state of loading condition with respect to the installation of crane chain.
Review of literature
16
Using the ANSYS FEA software, the stress distribution was calculated under the condition of
axial tensile load acts on the chain. The stress distribution was also analyzed when the
bending load was applied to the chain. The results show that the maximum Von‐Mises stress
is 455 MPa from ANSYS under axial tension. While the bending moment is applied, the
stress is 1,236 MPa. Allowing for the case of 5 tons of tensile load, the analysis result
computed from FE analysis is approximately 460 MPa, which is lesser than that of the yield
strength of the material. On the contrary, when 5 tons of bending load is applied, the result
amounts to approximately 1,180 MPa, nearly reaching the ultimate strength (1,204 MPa).
Also the result shows that the stress magnitude occurred by a bending force is 2.5 times
greater than that of a plain tensile load. From the results, it was found that the magnitude
of stress is severely dependent on the stress state acting on the fracture surface.[91]
(5) Khaled Al – Fadhalahand others at Elsevier (2010) studied the failure of grade 80
alloy steel chain links at the weld during hauling operations of heavy – weight. Optical
emission spectrometry is the process of chemical analysis performed on the chain. The
results of this analysis indicate that some of the alloying elements such as Ni, Cr and Mo are
present with lesser concentrations than usually required by ASTM standard. Because of that
there was a decrease in the hardness and hardenability of the base metal and the weld, as it
was indicated by hardness test. The base metal and the weld were also inspected by tensile
testing, showing strength of nearly 800 MPa. Unlike the ductile base metal sample, the weld
sample failed in a brittle manner at a very low strain value of 0.05. The weld brittleness is
explained by metallographic examination, which indicated that welding defects are present
in the weld. It is also consisting of internal and external cracks, where the latter are found in
the weld fins near the weld interface. Weld inhomogeneity was confirmed by the presence
of two modes of final fracture; namely ductile fracture at the weld centre, and brittle
behaviour at the outer circumference of the weld specifying improper post welding heat
treatment.[38]
Literature review on Design and FEA of CLD
17
Fig.2.4 Tensile stress–strain behaviour of the Fig.2.5 Mating fracture of surfaces at Grade-80 steel chain[38] welded portion[38]
It is also found that daily – repetitive heavy towing loads had produced high cyclic
stresses in the chains. Fatigue primarily nucleated at the exterior cracks of the weld fins, and
later propagated to the inside of the links until sudden fracture occurred.[38] In addition to
the manufacturing defects of the weld, simple fatigue analysis and evidence of large
fracture area in the failed link indicate that failure by overloading was a major cause to the
short fatigue life. In general, the results provide a confirmation of lack of some significant
alloying elements, welding defects and improper post weld heat treatment of the chain
links. This leads to partial contentment of the ASTM standard requirements for Grade–80
steel chains.
(6) Vinkel Arora and others (2011): In the work presented, single leaf spring is
modelled using dedicated modelling software CATIA. Here the author has also considered
various eye design and the stresses induced in the leaf spring. As eye end plays active role
during application of leaf spring i.e. eyes have the critical areas where the most stresses
induced in a leaf spring. So by changing the eye design, stresses may be reduced. So two
different types of eye design for leaf spring analysis were considered. These two types eye
design are (1) Standard eye and (2) Casted eye.[98]
Review of literature
18
1) Standard eye 2) Casted eye
Fig.2.6 Different types of eye design used for leaf spring [98]
This work comprises of design and analysis of single leaf spring under static loading
conditions. The 3D model is prepared in CATIA and then CAE analysis is performed using
ANSYS – 11. From the results obtained from ANSYS, discussions have been made and it will
be concluded that:
Table.2.1 Parameter comparison between standard eye and casted eye[98]
Parameter Standard Eye Casted Eye Variation
Load 50 N 50 N Nil
Deflection 109.62 mm 115.61 mm 5.4%
Von Mises Stress 773.34MPa 750.08MPa 3.0%
Normal Stress 654.12MPa 783.51MPa 19.08%
Factor of Safety Maximum – 15 Maximum – 15 Nil
Minimum‐0.381 Minimum‐0.331 13.1%
1) As shown in above table 2.1, when similar load is applied to standard and casted leaf
spring equivalent stress reduction of 3.0% and increase in deflection of 5.4% is achieved. As
maximum stress induced in the spring material is below the yield stress therefore it is
concluded that casted eye is also safe under given loading conditions.
2) It is also observed that as the minimum factor of safety is reduced by 13.1% in case of
casted eye, the area under minimum factor of safety will fail earlier in case of casted eye.
Hence casted eye is not recommended for the use. [98]
Literature review on Optimization of hydraulic cylinders
19
2.4 Literature review on Optimization of hydraulic cylinders
(1) D. J. Wilde in the subject of “Monotonicity and dominance in optimal hydraulic
cylinder” at ASME (1975) stated that monotonicity and dominance were used to find
general principles for designing hydraulic cylinders optimal for a wide class of objective
functions and stress conditions.[102] The design method, although guaranteed to give the
optimum design, requires no knowledge of optimization theory by the designer. Optimal
cylinders should be designed for minimum force. Only two designs can be optimal—one
with maximum pressure and minimum wall thickness; the other with maximum stress.In the
former case, the design is retained if and only if the stress is less than allowable. Otherwise,
a one‐variable search in a restricted interval is needed. The results suggest the potential
importance of monotonicity and dominance in identifying the critical constraints in a design.
(2) Nestor F. Michelena and Alice M. Agogino in the subject of “Multiobjective
Hydraulic Cylinder” at ASME (1988) states that The limitations of the repeated calculations
in Pareto optimization have been countered by the complexity reductions possible by
application of monotonicity analysis and the Karush‐Kuhn‐Tucker optimality conditions. The
analysis applied to a multiobjective formulation of a hydraulic cylinder optimal design
problem allows parametric curves for tradeoffs between three objectives representing
material costs, lifetime and operating costs. The author had also stated that “If one wish to
minimize cross‐sectional area, a hydraulic cylinder designer should never design for a force
above the minimum required and should always design for the minimum allowable
thickness.”[62]
(3) D. J. Wilde in the subject of “THE MONOTONICITY TABLE IN OPTIMAL
ENGINEERING DESIGN” (2007) states that monotonicity analysis has been established helpful for
identifying which combinations of constraints might be active at an optimum. To make the
Monotonicity Table, detailed modeling is not required. The results are valid for any objective or
constraints having the same monotonicities even though coefficients and exponents may be
different. Consequent calculations have fewer degrees of freedom than it may be possible in
the original formulation. Since all local optima are generated, finding the global optimum is
guaranteed.[103]
In every example a simple design procedure involves evaluating at most two cases,
was found without information of the functions other than their monotonicities. Even when
the method does not solve the problem entirely, it can lead to simplifications significant
enough to justify its use on any optimal engineering design problem.
Review of literature
20
(4) M. BALACHANDRAN& J. S. GERO in the subject of “A COMPARISON OF THREE
METHODS FOR GENERATINGTHE PARETO OPTIMAL SET” (2007) describes and compares
three approaches for solving design optimization problems with multiple conflicting
objectives. The three techniques are described in detail and then applied to an example
which demonstrates how information is accumulated which leads to a logical and efficient
multi criteria optimal design. The techniques employed (weighting, Non inferior set
estimation and constraint methods) are compared to each other by considering their
computational efficiencies and their abilities to produce an approximation of the Pareto
optimal set.[9]
The constraint method shows a precise and appealing feature that it will always
create the shape of the whole Pareto set. By distinction, the weighting and NISE method will
extent only the rounded portion of the Pareto set.[9] Furthermore, the constraint method
provides absolute control of the spacing and exposure of the Pareto set.
(5) ALICE M. AGOGINO & ANN S. ALMGREN at Taylor & Francis (2015) states The
results of the qualitative and functional levels of computation in the *SYMON‐SYMFUNE
programs give strong insights into the mathematical structure of optimization problems and
can eliminate the need for further numerical analysis. The symbolic results provide useful
assistance at the numerical level in generating parametric curves for applications in multi‐
objective optimization, sensitivity analysis, and parametric, probabilistic or fuzzy design.
These powerful techniques for integrating qualitative reasoning and symbolic computation
in SYMON and SYMFUNE typically outperform human reasoning in reducing the complexity
of optimal design problems.[6]
The results of the qualitative and functional levels of computation in the SYMONSYMFUNE
programs give strong insights into the mathematical structure of optimization problems and
can eliminate the need for further numerical analysis. The symbolic results provide useful
assistance at the numerical level in generating parametric curves for applications in multi‐
objective optimization, sensitivity analysis and parametric, probabilistic or fuzzy design.
*SYMON – SymbolicMONotonicity Analysis,
SYMFUNE – SYMbolicFUNctionalEvaluation [6]
(6) P. PAPALAMBROS & D. J. Wilde at ASME publication (1979) states that
Monotonicity analysis can assist in both modeling correctly and solving for global optimality.
It can guard the designer against the pitfalls of numerical optimization methods and guide
Literature review on Optimization of hydraulic cylinders
21
the improvement of the original model. It leads to strikingly simple design procedures
guaranteed to give the global optimum without complicated iterative calculations. Since it
relies on the interplay of the engineering with the mathematical properties of a proposed
design, it is well suited to problems of engineering design.[67]
2.5 Performance Problem Identification through Survey
Gujarat Urban Development Corporation (GUDC) working under Government of
Gujarat had carried survey at different 161 ULBs across the state for smooth functioning and
improvement in different aspects of solid waste management system. This survey and
detailed analysis of reports were carried out by the team of technical experts. Format for
the survey of Container Lifting Device (CLD) is kept in ANNEXURE – II, it covers various
parameters which affects on the performance of CLD. It was prepared in consultation with
technical approval group formed by GUDC under Government of Gujarat. This survey
reports focused on various critical parameters like quality of hydraulic hoses used, type of
hydraulic cylinders assembly used, proper fittings of hydraulic oil pipe and joints fittings of
hydraulic lines etc. related to CLD.
After the detailed survey carried out for CLD with manufacturers, contractors,
operators, drivers, technicians, technical staff of ULBs, supervisors following problems are
found while using CLD.
While transportation of container in container lifting device due to immediate braking
and vibration due to road obstacle, there are chances to movement of container from
its position. This may damage the other parts of CLD.
During loading and unloading, container only supported by hoist chain. So it is free to
oscillate and chances to strike with hydraulic cylinder, hydraulic oil supply pipes etc. and
may damage the same.
Generally during operation of container lifting device there is no proper ground level at
storage depot station and dumping site. So there may be chances of unstable or
pitching of vehicle due to lack of supporting structure.
Hydraulic oil supply system from hydraulic motor to cylinder through pipe network is
not properly placed, fixed and positioned. Due to that in short running time it may be
damaged and leaked.
Due to improper design of CLD without proper side and front wall, waste from overfilled
container may fall on the road during travelling.
Review of literature
22
2.6 Research Gap
After studying many research papers regarding the work to be carried out, some
research gap found out and some of things were found out this has been less focused.
Mainly three types of research papers were studied like review papers regarding solid waste
management, design, vibration and FEA of different components used in CLD and
optimization of hydraulic cylinders as most critical part of CLD.
Solid waste management in India has every chance of improvement at all stages like
collection, transportation, processing and disposal. Out of these here collection and
transportation stage were selected.
There is also lots of scope of technical improvements required to be adopted for the
existing container lifting device used with tractor for SWM work.
Identified the problems/difficulties faced with the existing design / specifications of the
container lifting device.
Identified its important components and significant parameters affecting performance of
the container lifting device (CLD).
Design of critical engineering parts used in CLD is to be done & check i.e. hydraulic
cylinder assembly, cross‐rod with two arms, different pin joints, rear mechanical jack
assembly, leaf springs and chains.
Studied dynamic behaviour of moving parts and analyse its performance.
Establish the improved design or optimize the current design of the container lifting
device that will overcome most of the problems related to the performance parameters.
Identified the most critical engineering component of container lifting device i.e.
hydraulic cylinders.
Using FFT analyzer vibrations of hydraulic cylinder are to be measured during actual
working conditions.
Optimization hydraulic cylinder is to be carried out using both single and multi‐objective
optimizations based upon the requirements.
23
CHAPTER 3
DESIGN METHODOLOGY
3.1 Introduction
Present research work focused on analysis and design of Container lifting device (CLD) used
for lifting and lowering 4.5 cubic meter container operated by tractor. Following are the
components used in CLD.
1. Hydraulic cylinder assembly
2. Pin joints
3. Cross‐ rod
4. Rear mechanical jack
5. Welded joints
6. Locking pins
7. Leaf springs
8. Hoisting chains
This chapter describes the design philosophy of above mentioned components. It was
observed after analysis of survey reports and literature review that the modifications are
required in the design of CLD to make it compatible with tractor driven instead of truck
mounted. Hence the design procedure for various components is outline in this chapter.
3.2 Design of Hydraulic Cylinder
Hydraulic cylinders are actuation devices that convert the hydraulic energy of
pressurized fluids into the mechanical energy needed to control the movement of machine
linkages and attachments.
Hydraulic cylinders are used at elevated pressures and create large forces and
accurate movement. For this reason they are made of strong materials such as steel and
designed to withstand huge forces. The fluid pushes against the face of the piston and
produces a force.
Fig. 3.1 Forces acting on hydraulic cylinder
Design methodology
24
The force produced is given by the formula:
F = P A ………..Eq. (3.2.1)
Where, P is the pressure in N/m2
Let ‘A’ be the full area of the piston and ‘a’ be the cross sectional area of the rod. If
the pressure is acting on the rod side, then the area on which the pressure acts is as (A ‐ a).
3.2.1 Material used for Hydraulic Cylinder
To cope up with higher stress either wall thickness has to be increased or material
with higher allowable stress has to be selected. To reduce overall size mostly material with
higher allowable stress are selected such as plain carbon steel or low or medium alloy steel.
But these materials are difficult to weld. If working environment of hydraulic cylinder is of
corrosive in nature, then use brass or stainless steel seamless pipe. If there is no limitation
about bore size, and hydraulic fluid is also not corrosive, then for such condition seamless
pipe of low to medium carbon steel with carbon percentage in between 0.15 to 0.35% is
used. [48]
Mostly ground and hard‐chrome plated rod of C40, SAE 1045, DIN CK 45 and JIS S45C
grade material and in standard size is available in market and widely used for piston‐rod.
Piston‐rod can also be made from mild steel, alloy steel, stainless steel, cast‐iron (for larger
size and short length of piston‐rod under compressive load) etc. depends on application.
Now a days medium carbon steel SAE 1045 grade in ground and coated condition are also
available, which are much better than C40 grade piston rod. [49]
3.2.2 Theoretical Design of Cylinder Tube
Cylinder tubes are divided in to two categories.
1) Thin Cylinder Tube
2) Thick Cylinder Tube
When ratio of cylinder bore diameter to wall thickness is bigger than 10 then it is
called thin cylinder and when it is equal to or less than 10 then it is called thick cylinder.
Equations to calculate wall thickness, and assumption made to use them are different. But
in high pressure and load application only thick cylinder tube is used, which is able to sustain
high pressure.
Design of Hydraulic Cylinder
25
3.2.3 Design of Thick Cylinder
In case of thin cylinder, stress assumed to be uniformly distributed over the section
of wall, but In case of thick cylinder stress distribution are as follow.
Fig. 3.2 Thick cylindrical shell stress distribution over its thickness[48]
Maximum radial stresses are generally equal to the internal pressure, and it is
maximum at inner surface of cylinder.
σr (max) = ‐P
For calculating tangential stress following four equations are used.
1) Lame's equation
12
i t
t
d Pt
P
………………………Eq. (3.1.1)
,
t
where t thicknessof cylinder
Tensile stressof thecylinder material
Lame's equation is used for designing cylinder of brittle material and it depends on
maximum‐ stress theory of failure, and could be used for open as well as closed cylinder.
2) Brinies’ equation:
Brinies’ equation depends upon the maximum strain theory of failure. That is failure
will occur when the strain reaches a limiting value. According to this theory the wall
thickness of cylinder is.
1
12 1
ti
t
Pdt
P
……………...........Eq. (3.1.2)
Where, 'Poisson s ratio
This equation is generally used for open‐end cylinder made of ductile material, such as gun‐
barrels.
Design methodology
26
3) Clavarino's equation:
This equation is similar to Brinies’ equation, but applies to closed‐end cylinder made of
ductile material. According to this equation the thickness of a cylinder,
1 2
12 1
ti
t
Pdt
P
…………….…….Eq. (3.1.3)
4) Barlow's equation:
This equation is generally used for high pressure oil and gas pipes. According to this
equation the thickness of a cylinder,
2
i
t
Pdt
……………….………………….Eq. (3.1.4)
3.3 Theoretical Design of Piston and Piston Rod
Piston rod transfers the force developed at piston to work piece. This force may be pushing
or pulling. Piston rod is designed to transfer these forces along its central axis. It is not
designed or expected to take any side bending load. Then the cross‐section required to
transfer force within safe stress limit can be calculated using basic simple formula such as.
2
4 tFt
d ……………………………..…......Eq. (3.3.1)
And for piston design equation is given like…
2 2
4 t
i
FP
D d
……………….……….…Eq. (3.3.2)
3.4 Design of Cross‐Rod
In container lifting device (CLD) cross‐rod is used to support the load by container loading
and transmit motion from the cylinder to container for loading and unloading. In design of
cross‐rod, it is consider as a fixed beam supported at two ends with two point load at a
same distance from ends. Here beam is considered as a hollow pipe section.
Fig. 3.3 Cross‐rod fixed at both ends
W = Ap
C = Rati
L = Tota
Now, d
like belo
Flexure
Shear st
Maximu
3.5 Des
Mechan
load at
jack is u
contain
station
not onl
proper
simply
column
3.5.1 Lo
Centric
allowab
Eccentr
the bea
ply load, do
io of inner t
al length of
ue to applie
ow…
bending st
tress
um principa
sign of Rea
nical Jack is
some heig
used to sup
ner is not do
and dump
y support t
design of r
consider a
n is less than
oading Loca
loading: T
ble stress is
ric loading:
am load will
o = Outer D
to outer dia
Beam, a = D
ed load W a
ress
2 2
4
1
W
D C
al stress 1
r Mechanic
s a device
ght. In tract
pport the lo
one with pr
ing site. So
the load bu
rear jack is
as a short
n 50 with ec
ation
The load is
determined
The load is
proceed in
iameter, di
ameter, M =
Distance be
at distance ‘
3 4
32
1
M
D C
………
2 2
cal Jack of C
which can
tor driven c
ad. Genera
oper groun
o, at that ti
ut also stab
necessary.
column be
ccentric loa
s applied a
d from stre
s offset from
n the colum
27
i = Inner Dia
= Bending m
etween fixed
‘a’ from bot
……………
……………………
22
……
CLD Model
support an
container li
ally loading
nd level like
me mechan
ilize the ve
For design
ecause slen
ding condit
at the cent
ngth (P/A) o
m the cent
n. This offse
ameter
moment
d end and a
th end gene
……..Eq. (3.4
………Eq. (3.4
……..Eq. (3.4
nd also rais
ifting devic
and unload
at storage
nical jack is
hicle. That’
of rear jac
nderness ra
ion.
Fig.3.4 3D
troid of th
or buckling.
roid of the
et causes be
applied poin
eral stress g
4.1)
4.2)
4.3)
se the
e rear
ding of
depot
s used
’s why
k, it is
atio of
model of m
e cross sec
.
cross sectio
ending alon
Design of C
nt load
generated is
mechanical r
ction. The
on because
ng with axia
ross‐Rod
s given
rear jack
limiting
e of how
l stress.
Design methodology
28
Fig. 3.5 Eccentric loading of column
The eccentricity causes bending stresses by a moment of value equal to P x e. Within the
elastic range (linear stresses) it can be superposition or add up the normal and bending
stresses:
x a b
p Myf f f
A I ……………………Eq. (3.5.1)
(A)
The resulting stress distribution is still linear and the N.A. moves (if there is one).
(B)
Fig.3.6 Resulting stress distribution (A) without N.A (B) with N.A[49]
The value of e (or location of P) that causes the stress at an edge to become zero is at the
edge of the kern. As long as P stays within the kern, there will not be any tensile stress.
Limit Criteria
1
. .a b
a b
f f
F F F O S Interaction formula…………..Eq. (3.5.2)
A) Eccentric loading of column with eccentricity (e)
B) Eccentric load right with Tension in left
3.5.2 De
Two pa
body o
welded
require
followin
2L = len
3.5.3 De
Under
with do
d = diam
2
p
A
3.6 Des
wheele
semi el
steel of
tie hole
road ir
energy
energy
esign of we
arallel fillet
f CLD mod
portion is
d to supp
ng calculatio
ngth of weld
esign of loc
loading co
ouble shear
meter of pin
2
2 p
d
sign of Leaf
A leaf spri
d vehicles.
liptical leaf
f rectangula
es are provid
The leaf sp
regularities
is stored i
storage ca
elded joint
welds is p
del. In load
shear stre
ort and su
on.
d, S = weld
cking pin
ndition pin
area.
n
(∵A =πd
f Spring
ing is a sim
It is also on
f spring (SE
ar cross‐sec
ded at eithe
pring is be s
s by means
in spring a
apacity of
rovided to
ing conditio
ess. So, we
ustain the
size
τ = shear
0.707
2SL
n is subject
d /4)…….Eq
mple form
ne of the ol
LS) it takes
tion. The ce
er end for a
supposed to
s of variatio
s strain en
a leaf sprin
Desi
29
join the re
on stress p
lded size a
load is de
r stress,
7 p
L………….E
ted to shea
q. (3.5.4)
of spring,
dest forms
s the form
entre of the
ttaching to
o absorb th
ons in the
ergy and t
ng ensures
ign of Rear
ear jack to
produce in
and length
erive from
Fig.3.7 Colu
Eq. (3.5.3)
ar load
Fig
commonly
of springin
of a slende
e arc provid
the vehicle
he vertical v
spring def
then releas
s a more a
Mechanical
umn welded
g.3.8 Shear
y used for
ng. Sometim
er arc‐shape
des location
e body.
vibrations a
flection so
ed slowly.
amenable s
l Jack of CLD
d to body o
r of locking
the suspen
mes referred
ed length o
n for the axl
and impacts
that the p
So, increas
suspension
D Model
of CLD
pin
nsion in
d to as a
of spring
le, while
s due to
potential
sing the
system.
Design methodology
30
According to the results of the studies made the material with maximum strength and
minimum modulus of elasticity in the longitudinal direction is the most appropriate material
for a leaf spring. Fatigue failure is the predominant mode of in‐service failure of many
automobile components. This is due to the fact that the automobile components are
subjected to variety of fatigue loads like shocks caused due to road irregularities traced by
the road wheels, the sudden loads due to the wheel travelling over the bumps etc. The leaf
springs are more affected due to fatigue loads, as they are a part of the unspring mass of
the automobile.
The leaf does the following functions:
Supports the chassis weight.
Controls chassis roll more efficiently‐‐high rear moment centre and wide spring base.
Controls rear end wrap‐up.
Controls axle damping.
Controls lateral forces much the same way a hard bar does.
Controls braking forces.
Regulates wheelbase lengths (rear steers) under acceleration and braking.
Fig.3.9 Semi‐elliptical leaf spring [21]
fn = number of extra full‐length leaves
gn = number of graduated‐length leaves including master leaf
n= total number of leaves
b = width of each leaf (mm)
t = thickness of each leaf (mm)
12L = Length of span or overall length of the spring
Design of Leaf Spring
31
l = Width of band or distance between centres of U‐bolts. (It is the ineffective length of the
spring)
L = length of the cantilever or half the length of semi‐ elliptic leaf spring (mm)
F = force applied at the end of the leaf spring (N)
fF = portion of F taken by the extra full‐length leaves of the leaf spring (N)
gF = portion of F taken by the graduated‐length leaves of the leaf spring (N)
E = Modulus of Elasticity of Material
Maximum Bending Moment in the Centre (M) = FL
Effective length of the spring is…
2L = 12L ‐ l (When the band is used) ……………………….Eq. (3.6.1)
2L= 12L ‐ 2
3l (When U‐bolts are used) ……………………Eq. (3.6.2)
When there is only one full‐length leaf (i.e. master leaf only), then the number of leaves to
be cut will be n and when there are two full length leaves (including one master leaf), then
the number of leaves to be cut will be (n‐1) if a leaf spring has two full‐length leaves, then
the length of leaves is obtained as follows:
Length of smallest leaf of the leaf spring = effective length
1n + Ineffective Length …Eq. (3.6.3)
Length of next leaf of the leaf spring = effective length
1n 2 + Ineffective Length ..Eq. (3.6.4)
Similarly,
Length of (n‐1) leaf of the leaf spring = effective length
1n n 1 + Ineffective Length
…...Eq. (3.6.5)
The nth leaf will be the master leaf and it is of full length. Since the master leaf has eyes on
both sides, therefore
Length of Master Leaf of the leaf spring = 12L + 2d t …………………………………Eq. (3.6.6)
Where d = Inside diameter of eye
t = Thickness of master leaf
The relation between the radius of curvature(R) and the camber (y) of the spring is given by:
212y R y L
………………………………..Eq. (3.6.7)
Where L1 = Half span of the leaf spring.
Design methodology
32
The maximum deflection (δ) of the spring is equal to camber (y) of the spring.
We know,
fF + gF = F ……………………….......................Eq. (3.6.8)
Ff =f
f g
3n F
3n 2n …. (Force taken by no. of nf master leaf) ..............Eq. (3.6.9)
Fg= g
f g
2n F
3n 2n …. (Force taken by no. of ng graduated leaf) …….Eq. (3.6.10)
bg = 2
f g
12FL
bt 3n 2n ….(stress induce in ng graduated leaf) …….Eq. (3.6.11)
bf = 2
f g
18FL
bt 3n 2n ….(stress induce in nf master leaf) ………..Eq. (3.6.12)
y = 3
3f g
12FL
Ebt 3n 2n …. (Deflection at the end of the spring) ……Eq. (3.6.13)
Multi‐leaf springs are designed using load stress and load deflection equations. The
standard dimensions for the width and thickness of the leaf section are as follows:
Nominal thickness (mm): 3.2, 4.5, 5, 6, 6.5, 7, 7.5 8, 9, 10,11,12,14, and 16.
Nominal width (mm): 32, 40, 45, 50, 55, 60, 65, 70, 75, 80, 90, 100 and 125.
3.6.1 Materials for leaf spring
Plain carbon steel, Chromium vanadium steel, Chromium‐ Nickel‐ Molybdenum steel,
Silicon‐ manganese steel, are the typical materials that are used in the design of leaf springs.
Like steel conforming to 55si7, 60si7, 65si7, ASTM grade A689. Other spring steel having
similar hardenability, toughness and physical property are considered for manufacture of
leaf spring.
33
CHAPTER 4
DESIGN CALCULATIONS
4.1 Design calculations – Anchor pin for 4.5 cubic meter container
Selection of container to be used with tractor operated container lifting device is
based on volumetric capacity. Size of container is minimized based on operation and
performance criteria.
After detailed study and analysis 4.5 cubic meter container is selected for the
operation.
It is to be fabricate with Mild Steel sheets with four top openings and one rear
lockable tailgate with heavy‐duty hinges as per below mentioned technical
specifications and drawing.
The lifting hooks/anchor pins shall be integrated within the frame and be capable of
taking the specified load.
Design calculations
34
Fig. 4.1 Drawing of 4.5 cubic meter container used for CLD
Design calculations – Anchor pin for 4.5 cubic meter container
35
Fig. 4.2 Drawing of anchor pin used in 4.5 cubic meter Container
Bending moment equation in this case
,
b
Where
Bending stress
M Bending moment
Y Perpendicular distance between point of force and neutral axis
I Moment of inertia for circular cross section
2
250 /
2yt
d Diameter of pin
S Yield strength of material N mm
FOS Factor of safety
Since there are four hook in the container for lifting purpose, so we divide the
maximum load of 5 ton by four to get the maximum load at single hook of container as
1.25 ton (12500 N).
Thu
Usin
Wh
4.2 Des
used fo
anneale
machin
(BK+S),
tube en
and tra
mechan
hydraul
us total forc
ng equation
ere,
sign Calcula
ST52 is low
r Hydrau
ed or norma
ability and
with yield
nds capped
ST52 Hyd
nsport lifti
nical tools
lic pressure
e acting at s
n (4.1.1)
ations of Hy
w carbon ste
lic Cylind
alized. Hydr
weldability
strength ov
for protect
raulic Cylin
ing equipm
and equip
e, pneumati
single hook
ydraulic Cyl
eel grade, e
er Tubes
raulic Cylind
y property.
ver 520 MP
ion. [93]
der Tubes
ment, wast
pment, com
c cylinder, o
36
k = 12500 N
linder
xcellent we
. Normall
der Tubes S
Supplied
Pa. The insid
s, applicab
te disposal
mpressors
oil pump ba
= 1.25 Ton
eldable, the
ly supplie
ST52 and ST
seamless,
de diamete
ble to mac
transport,
and earth
arrel.
e most wide
ed stresse
T52.3 are fo
cold drawn
er is honed
chinery, h
food proc
moving e
Design calc
ely used ste
ed relieve
ound with e
n & stress
and oiled w
oists, auto
cessing equ
quipment
culations
el grade
ed and
excellent
relieved
with the
omotive
uipment,
etc. like
Design Calculations of Hydraulic Cylinder
37
2
2 2
2
Pr 160 200 /
200 9.81Pr , / 19.62 /
100, , Pr ,
5000 9.81 19.624
56.41 63
, 63 .
i
i
essure Range Considered to kg cm
essure p N mm N mm or MPa
Force F Area A essure p
d
d mm mm
So thediameter of piston rod is taken mm standard
The inside diameter is honed and oiled with the tube ends capped for protection. ISO
standard cylinder bore size is 32, 40, 50, 63, 80, and 100 mm.
93
.
Hydraulic Cylinder Tube ‐ ST52
Compressive/ Tensile yield strength=250MPa
Ultimate tensile strength, = 520MPa
520104
5
ult
ultt
ForHydraulic cylinder tubematerial is steel
MPaFOS
' : .(3.1.1)
63 104 19.621 1
2 2 104 19.62
6.63 8
,
, 1.75.
1.75 1.75 6
t ii
t i
O
i
O i
By Lame s equation asper Eq
pdt
p
t mm
By taking the ratio of externaldiameter of cylinder d to
the internaldiameter of cylinder d is
d d
3 110 .mm
The piston rod size available with cylinder bore 110 mm is 63 mm.
Design calculations
38
2
cr 2
cr
2
Buckling calculation for piston rod :
According to Euler 's equation,
n. .E.AP ........Eq.(4.2.1)
LK
where, P Critical load (N) 5 ton 5000kg 49050 N
n end fixitycoefficient
n 1 if both ends hinged
E 207000 N / mm
L 29
4
2
00mm
.dI d64K
.dA 44
By putting all these values, weget d 45mm,
but in our caseint ernaldiameter is63mm,hencesafein buckling also.
4.3 Design for pin joint
Cylindrical pin joints are used in container lifting device mainly at three interconnections
between CLD base to hydraulic cylinders, hydraulic piston rod to big link and big link to
trailer base. Cylindrical pin joints are used to connect different component of CLD and help
to transmit motion between them. To sustain load and for proper functioning of CLD model
it is necessary to design cylindrical pin joint.
(A) (B) (C)
Fig. 4.3 Cylindrical pin joints (A) base to hydraulic cylinders (B) hydraulic piston rod to big link (C) big link to trailer base.
For the design of the cylindrical pin, it can be fail in to double shear in loading condition.
Material selected for design of cylindrical pin is medium carbon steel.
Design for pin joint
39
P = 168000 N, y = 400 MPa, y = 400 * 0.58 = 232 MPa, F.O.S = 3
2
2
2
P P
A d
(∵A =πd /4)
2d = 2P
π y
FOS
d= 37.2 ≈ 40 mm
4.4 Design Calculation of Cross‐Rod
Fig. 4.4 Drawing of Cross‐rod used in CLD
3 4
,
,
32....
.. .(3.4
.1)1
bb
o
i
o
i
o
b
For hollow circular cross section
Where
Inside diameter of hollow shaft
Outside diameter of hollow shaft
Masper Eq
d C
dC
d
d
d
M Bending moment
24525 N
150 .
24525 150 24525 150 ( )
7357500 .
(
2
)
50
b
b
Assuming downward Load on pipe at a distance
of mm from fixed end from both side
We get force distance
M
N mm
Yield strength of mild steel MP and
M
a
...... .(4.4.1)
25083.3
,
33
3.3.1 4.4.1
0.8
115 =
ytb
i o
Eq
After solving equation and we get
Outer diameter of hollow circular shaft is
Inner diameter of hollow circular shaf
SMPa
FOS
d Ct dis
115 mm
92mm
Design calculations
40
For the design of cross‐rod material selected is structure steel St 30 with property as given
below...
y (Yield stress) = 170 N/mm2
W = 25000 N, L = 1830 mm, a = 115 mm
D = 115 mm, F.O.S = 3,
M = W a
Now critical point need to be consider for inner diameter calculation is fixe end, load point
and at centre point. But in this loading condition maximum bending moment is generated at
fixe end. By considering combine effect of bending stress and shear stress at fixe end,
maximum principal stress at fixe end should be less than yield stress.
As per Eq. (3.4.1), (3.4.2) and (3.4.3)
2 2
1 3 4 3 4 2 2
16 16 4
1 1 1
M M W
D C D C D C
(σ1= . .
y
f o s
)
By trial and error method final found value of ratio of diameter is…
C ≈ 0.8
So, inner diameter d = C D
= 0.8 115
≈ 90 mm
Therefore material thickness of cross‐rod is tc = 12 mm.
4.5 Design Calculation for Rear Mechanical Jack
Here, for design of the jack column structure steel plate of grade st 30 is selected with
following specification.
Ultimate strength = 340 MPa
Yield strength Fa or Fb = 170 MPa
p = 5 104 N
Design Calculation for Rear Mechanical Jack
41
Fig. 4.5 Cross‐section area of column section
Plate thickness t = 5 mm
Column Square size = a
Area of cross‐section A = 20a – 100
F.O.S = 3
Moment of Inertia, I = 44a a 10
12
By putting above value in Eq. (3.5.1) and eq. (3.5.2) we get following…
24
44
1 35.6 10
20 100 10
a
a a a
By trial and error method, we got value of a ≈ 100 mm.
For design of welded joint, size of weld ‘S’ is taken as 3 mm for plate thickness around 5 mm
and design stress is around 79 MPa for fillet welds from design data book. So, according to
eq. (3.5.3)
L = wel
0.707 p f .o.s
2τSη
= 280 mm
≈ 300 mm
By using eq. (3.5.4) diameter of locking key and swinging plate mounting pin is determine.
Here material for pin is medium carbon steel.
y 400 MPa
Design calculations
42
y 400 0.58 = 230 MPa
d 2 f.o.s
πτ
p= 20.38 mm
≈ 20 mm
4.6 Design Calculation of Leaf Spring for CLD Model
Here tractor driven lifting device may be used which will lift up to 4.5 cubic meter
containers. They can use containers of up to 4 to 4.5 cubic meters capacity, which will make
optimum use of the tractors. For the smooth and jerk free motion of CLD model while
transportation, it is necessary to design leaf spring. Here the weight of container when fully
loaded is around 2000kg and weight of CLD body is around 2000kg without considering
container.
So,
Total force on leaf spring = 4000*10 (take g= 10 N/mm2)
= 40,000 N
Because of road irregularity and bump instant jerk produces and stress induce at same load
is higher than normal and in this condition force acting on leaf spring is more than normal
so, while design we consider higher value of force is..
Total force on leaf spring = 40,000*1.5
= 60,000 N
Force at leaf spring eye F = 60,000/4
= 15,000 N
Material of the spring is 50 Cr 1 V 23 spring steel.
Yield stress y 180 kgf / mm2 (1800 MPa)
Young modulus E = 200 GPa
F.O.S = 2
Number of graduated‐length leaves including master leaf ng = 6
Number of extra full‐length leaves fn = 1
Total number of leaves n = 7
Width of each leaf b = 70 mm
Length of span or overall length of the spring 2L1 = 1000 mm
Distance between centres of U‐bolts l = 100 mm
Design Calculation of Leaf Spring for CLD Model
43
Effective length of semi‐ elliptic spring 2L = 933 mm [from Eq. (3.6.2)]
By using Eq. (3.6.12), thickness of leaf (t) is...
ft = f f g
18FL
bσ 3n 2n
yσσ
f .o.sf
= 11.55 ≈ 12 mm
By using eq. (3.6.11), thickness of leaf (t) is...
gt = g f g
12FL
bσ 3n 2n
yg
σσ
f .o.s
= 9.43 ≈ 10 mm
Here, tf > tg
Therefore t = tf = 12 mm
By using eq. (3.6.13), deflection of leaf spring is...
y = 3
3f g
12FL
Ebt 3n 2n
= 61.48 ≈ 62 mm
so, camber of leaf spring is taken more than deflection y because it prevent strike the upper
face of leaf to the vehicle chassis on over loading.
Camber δ = 80 mm
By using eq. (3.6.3, 3.6.4, 3.6.5), we can compute length of each leaf spring like…
Length of smallest leaf of the leaf spring L7 = (933/7) + 67
= 222 mm
Length of next leaf L6 = 378 mm
L5 = 538 mm
L4 = 690 mm
L3 = 844 mm
L2 = 1000 mm
By using eq. (3.6.6), Length of Master Leaf is…
= 1000 + π (20 + 12) × 2 (∵eye dia. d = 20 mm)
= 1201 mm
By using eq. (3.6.7), the radius of curvature(R) is…
R =
21L
δδ
2
= 1522 mm
Design calculations
44
4.7 Selection of Hoisting Chain Link for Container Lifting
The chain is one of the most familiar for hoist as well as one of the most useful of
mechanical device. It is made up of a series of links fastened through each other. Each link is
made of a rod of wire bent into an oval shape and welded at one or two points. The weld
ordinarily causes a slight bulge on the side or end of the link. The chain size refers to the
diameter in millimeter (mm) of the rod used to make the link. Simple terms used with chain
are given below.
Working Load Limit (WLL)
The "Working Load Limit" (rated capability) is the highest load that shall be applied in direct
tension to an undamaged straight length of chain.
Proof Test
The "Proof Test" (manufacturing test force) is a term designating the minimum tensile force
which shall be applied to a chain under a continuously increasing force in direct tension
during the manufacturing process. These loads are manufacturing integrity tests and shall
not be used as criterion for examination or design purpose.
Minimum Breaking Force
The "Minimum Breaking Force" is the least force at which the chain during the
manufacturing has been found by testing to break when a continuously rising force is
applied in direct tension. This test is a manufacturer's attribute acceptance test and shall
not be used as a criterion for service or design purposes.
Overhead Lifting
That process of lifting that would elevate a freely suspended load to such a position that
dropping the load would present a possibility of bodily injury or property damage.
Overload
Any static or dynamic load is in excess of "Working Load Limit."
Selection of Hoisting Chain Link for Container Lifting
45
Fig.4.6 Model of chain link
4.7.1 Materials for chain link
Carbon chain
The selection of the base steel is left to the verdict of the individual chain manufacturer
provided the steel meets the following criteria: Carbon, 0.35% max.; Phosphorous,
0.040% max.; and Sulphur, 0.050% max.
Alloy chain
The selection of chain and amounts of the alloying elements present in the steel are left
on the opinion of the individual chain manufacturer. Generally the steel used for chain
manufacturing meets the following composition: Carbon, 0.35% max.; Phosphorous,
0.035% max.; Sulphur, 0.040% max. Nickel must be present as an alloying amount (0.40%
min.), and at least one of the following elements must also be present as an alloying
amount: Chromium (0.40% min.) or Molybdenum (0.15% min.).
Stainless steel chain
The material used in manufacturing this chain shall be a 300 series austenitic stainless
steel.
Now, selection of an open‐link chain by using the following rule of thumb
SWC = 28 D
SWC = Safe working capacity in tons
D = chain link diameter/thickness in inches
Design calculations
46
For a 5/8 “diameter chain link by using above equation,
SWC = 8× (5/8)2 = 3.125 tons
Table 4.1 Grade 80 alloy chain standard by national association of chain manufacturer [100]
Now according to loading situation of container while operation, chain link size
should be selected within working load limit of 8200 kg. So, as per the standard of National
association of chain manufactures for welded steel chain
Inside length (max) L = 51 mm
Inside width (min) E = 24 mm
Material diameter D = 16 mm = 5/8”
Working load limit (max) = 8200 kg
Min breaking force = 322 kN = 32834.86 kg
So we select the chain link from above table 4.1 for grade 80 alloy materials with 16 mm
diameter, inside width 24 mm and inside length of 51.2 mm.
47
CHAPTER 5
MODELLING AND ANALYSIS OF CONTAINER LIFTING DEVICE
5.1 3D Modelling
The modelling has been performed on the Solid Works 2014 and then after the
analysis work has been performed on the ANSYS 15.0 version.
5.1.1 Salient Features of Modelling Software
Solid Works is a parametric and feature based solid modelling tool. It allows
converting the basic 2D drawing into a solid model using very easy modelling tools.
It creates a digital prototyping as opposite to the physical prototyping by integrating
2D drawings and 3D data into a single digital model.
It can quickly and easily create stunning renderings, animations and presentations,
which improve communication.
It can easily create and contribute production ready drawings for manufacturing
teams.
The automatic updating characteristic allows doing easy changes in model.
It also helps in improving visualization of the solid model and understanding its
drawing.
It also gives more flexibility in editing the solid model to imagine the effect of the
changes.
Define, group, and apply properties such as loads, constraints, and materials—
making it easier to accurately predict product performance.
5.2 Finite Element Analysis (FEA)
It is widely accepted method of accessing product performance without the need for
physical building and testing. It also shortens prototype development cycle times &
facilitates quicker product launch. FEA consists of a model of a material or design that is
loaded and analysed for specific results. It is used in new product design, and existing
product refinement.
Steps Required For Development of FEA Model
Steps required for development of finite element model are as under:
Modelling and Analysis of container lifting device
48
Assigning material and its properties to various parts.
Discretize and choose element types.
Choose a displacement function.
Derive the element stiffness matrix and equations.
Generate global or total equations from the element equations and introduce loads
and boundary conditions.
Solving for elemental strains and stresses and interpretation of the model
5.3 Transient Structural Analysis of Container Lifter Model in ANSYS
Transient structural analysis provides users with the ability to decide the dynamic
response of the system under any type of time‐varying loads. Unlike rigid dynamic analyses,
bodies can be either rigid or flexible. For flexible bodies, nonlinear materials can be
included, and stresses and strains can be output. Transient structural analysis is also known
as time‐history analysis or transient structural analysis. Transient structural analysis
encompasses static structural analysis and rigid dynamic analysis, and it allows for all types
of Connections, Loads, and Supports.
In a transient structural analysis, Workbench Mechanical solves the general equation
of motion:
[M]{Ẍ}+[C]{Ẋ} +[K]{X}={F(t)} ………Eq. (5.3.1)
Some points of interest:
Applied loads and joint conditions may be a function of time and space.
As seen above, inertial and damping effects are now included. Hence, the user
should include density and damping in the model.
Nonlinear effects, such as geometric, material, and/or contact nonlinearities, are
included by updating the stiffness matrix.
Transient structural analysis can be both linear and nonlinear. All types of
nonlinearities are allowed like large deformations, plasticity, contact, hyper elasticity and so
on. The transient structural analysis that specifically uses the ANSYS Mechanical APDL solver
also ANSYS Workbench offers an additional solution method of Mode Superposition to
perform linear transient structural analysis.
Transient Structural Analysis of Container Lifter Model in ANSYS
49
5.3.1 Steps for transient analysis in ANSYS Workbench
Create Analysis System: Select any dynamic analysis template like Transient Structural,
harmonic analysis etc. and drag it in to Project Schematic.
Define Engineering Data: According to types of material used in analysis, select material and
its property. Like linear material, isotropic material, temperature dependent material etc.
Attach Geometry: Virtual 3‐D model from any solid modelling software, for calling of this
solid model in ANSYS system it is need to convert in to IGES. OR STEP. File.
Define Part Behaviour: In a transient structural analysis, rigid parts are allow to use model
mechanisms that have particular motion, movement and relocation of loads between parts,
but stress and strain distribution is not of attention. The output of any rigid part is the
overall motion of that part and any force transferred via that part to the rest of the
structure. A point mass connected to the rest of the structure through joints is known as
“rigid” part. Hence in a transient structural analysis the only appropriate loads on a rigid
part are the acceleration and rotational velocity loads. Additional load may be applied to a
rigid part via joint loads.
Define Connections: All Contact, joints and springs are applicable in a transient structural
analysis. In a transient structural analysis, user can give number of joints and contacts
between parts according to application.
Apply Mesh Controls/Preview Mesh: An enough mesh concentration shall be provided on
contact surfaces to permit contact stresses to be distributed in a soft manner. Similarly,
adequate mesh density shall be provided for resolving stresses particularly at the areas
where stresses or strains are of concern. At these areas comparatively fine mesh are
required as compared to displacement or nonlinearity resolution. The mesh should be such
that it shall be able to capture the effects of the nonlinearities, if they are included. For
example, plasticity requires a sensible integration point density and consequently a fine
element mesh in areas with high plastic deformation gradients. To represent the peak mode
shape of interest in a dynamic analysis, the mesh shall be fine enough.
Modelling and Analysis of container lifting device
50
Establish Analysis Settings: For transient structural analyses, the basic controls are:
Usually slender structures are subjected to maximum deflection. If the transverse
displacement in a slender is more than 10% of the thickness that will be shown by the
maximum deflection.
Output controls allows specifying the time points at which results shall be available
for post processing. It is necessary to perform lots of solutions at midway time in a transient
nonlinear analysis. However as the size of all intermediate results file is so bulky and if all
the intermediate results are not required then it should be modified as per users’
requirement except for stress and strain.
Convergence criteria and other specific solution controls are modified using
nonlinear controls. There is no need to change the default values for using this control.
These can be modified as step basis.
Damping for the structure in a transient analysis shall be specified by damping
controls. The following damping controls are available for transient analysis:
1) Beta damping and
2) Numerical damping
In addition to these elements based damping for spring elements and material based
damping are also accessible for the transient structural analysis.
Data management setting allows saving specific solution files from the transient
structural analysis to be used in other analyses. The default behaviour is to keep the files
required for post processing. These controls can be used to keep all files created throughout
the solution or to create and save the Mechanical APDL application database (db file).
Define Initial Conditions:
A transient analysis always involves all the loads; those are the functions of the time.
The foremost step to apply transient loads for setting up initial conditions (i.e. the condition
at Time = 0).
The default first condition for a transient structural analysis is that the structure is
“at rest”, i.e. both initial displacement and velocity are zero. A transient structural analysis is
at rest, by default. The Initial Condition of the object allows specifying velocity.
Transient Structural Analysis of Container Lifter Model in ANSYS
51
In several analyses one or more parts will have an initial known velocity such as in a
drop test, metal forming analysis or kinematic analysis. If required constant velocity at initial
condition shall be specified in these types of analyses. This constant velocity shall be scoped
to one or more parts of the structure. The remaining parts of the structure which are not
part of the scoping will retain the “at rest” in the initial condition.
Initial condition using steps: Initial conditions are specified using step controls. It can
be processed by specifying multiple steps in a transient analysis. It will also control the time
integration effects along with activation or deactivation of loads. This will be very useful for
different parts of model which are having different initial velocities and more critical initial
conditions.
Apply Loads and Supports: All inertial, structural loads and structural supports joint loads
are used to drive the joints kinematically in transient structural analysis for applicable loads
or supports.
In the analysis, load’s magnitude shall be a stable or could fluctuate with time as defined in
a table or using the function. The particulars of applying tabular or function load are
described in specifying load values. In addition to this the apply loads and supports section
shows more information about ramped loads and time stepping.
It is also suggested that for the solver to converge, inclined joint load angles and positions
from zero to the actual original condition in only single step.
Solve: While performing nonlinear analysis, there will be convergence difficulties because of
number of reasons. In few examples initially open contact surfaces causing rigid body
motions, large load increments causing non‐convergence, material instabilities or large
deformations. This may cause mesh distortion and that will result in element shape errors.
Some tools are accessible under result information object detail view to recognize the
possible problem areas.
Solution Output: During the analysis it will continuously updates all listing output from the
solver. These will also provide precious information on the behavior of the structure. As
explained in the Solution Information section, all convergence data output is graphically
displayed in the printout.
Modelling and Analysis of container lifting device
52
Review Results: As a result of a transient structural analysis, all structural results are
available except frequencies. Solution Information can be used to track, check or analyze
problems that occur during the solution. After the accessibility of solution, the results can
be animated to review the response of the structure. In nonlinear static analysis, the
solutions are available at some time points. Here probe shall be used to show the variation
of a result as the load increases.
Loading and Boundary Conditions:
A load of 5 ton (50000 N) is equally shared by both hydraulic cylinder assemblies as dynamic
load condition described in Table 5.1 and transient structural analysis is carried out.
Table 5.1 Rigid dynamic analysis setup for CLD model
As base is rested on CLD bottom surface hence ground to base is taken as fixed boundary
conditions. Hydraulic cylinder assembly, piston to big link and big link to base are displaced
in planer angular direction with respect to base. Hence revolute boundary conditions are
considered. There is sliding motion of piston rod inside the hydraulic cylinder hence the
translation type joint is considered. Cross rod to big link is having rigid joint hence fixed
boundary condition is considered.
5.4 Dynamic Analysis of Container Lifting Device
Step 1) 3‐D model of existing container lifting device build and assemble in Solid Works 14.0
Analysis setup Object name Value
Acceleration All body 9.8 m/s2 in global C. S. +y axis
Remote force Cross‐ rod 25000 N in global C. S. –y axis
Joint velocity Cylinder‐ piston pair 20 mm/s in reference C. S. +x axis
Remote
displacement
Cylinder, piston, big link 1) x displacement is 0 mm
2) rotation in y and z axis is 00
Dynamic Analysis of Container Lifting Device
53
Fig. 5.1 Solid Works 3‐D model of container lifting device
Step 2) For perform ANSYS dynamic analysis convert SolidWorks assembly file .SLDASM in to
ANSYS .STEP file.
Step 3) Assign material property to each and every individual parts of container lifting
device.
Table 5.2 Material and its property for individual component of CLD
Material and it’s property
Component of CLD modal
Hydraulic
cylinder
Piston
rod
Cross‐rod Big link Base
Material ST 52 SAE 1045 S 355
Density (Kg/m3) 7800 7872 7800
Tensile Yield stress(Pa) 520 310 355
Compressive yield stress (Pa) 520 310 355
Ultimate stress (Pa) 900 565 470 ‐ 630
Young’s Modulus (GPa) 200 200 210
Poisson's Ratio 0.3 0.29 0.3
Bulk Modulus (GPa) 167 158 175
Shear Modulus (GPa) 77 77.5 80.7
Modelling and Analysis of container lifting device
54
Step 4) Generate geometry in ANSYS and give adequate connection between parts to
allowed motion like body to body or body to ground. In CLD model connection or joint
applied between parts are as follows…
Table 5.3 Joint applied to different pair of CLD component
Object name Type of joint Type of connection
Ground to base Fixed Body to ground
base to cylinder Revolute Body to body
cylinder to piston Translation Body to body
piston to big link Revolute Body to body
big link to base Revolute Body to body
cross rod to big link Fixed Body to body
Fig. 5.2 Joints between individual components of CLD model
5.4.1 Rigid dynamic analysis of CLD model
For rigid parts, the following conditions apply:
Line bodies cannot be set to rigid.
Multibody parts must have all bodies set to rigid.
Density is the only material property needed to calculate mass properties. All other
material specifications will be ignored.
Dynamic Analysis of Container Lifting Device
55
An “Inertial Coordinate System” will automatically be defined at the centroid of the
part
Rigid bodies are rigid, so no stresses, strains, or relative deformation is calculated.
Hence, no mesh is required
Step 5) In Analysis setting number of step taken is one with end time is 60s.
Table 5.4 Analysis setting in rigid dynamic analysis of CLD model
Number Of Steps 1
Current Step Number 1
Step End Time 60 s
Auto Time Stepping on
Initial Time Step 1.e‐002 s
Minimum Time Step 1.e‐007 s
Maximum Time Step 5.e‐002 s
Time Integration Type Runge‐Kutta 4
Relative Assembly Tolerance on
Store Results At All Time Points
Step 6) In Rigid dynamic analysis CLD model setup like load (Equally shared by both
hydraulic cylinder assembly, 25000 N), velocity, displacement etc. are given below.
Fig. 5.3 Remote force, constrain, joint velocity, acceleration shown in CLD model
Modelling and Analysis of container lifting device
56
Step7) Aim to perform rigid dynamic analysis is to find out force generate at different joint
probe and working behaviour of model in 3‐D space. After completion of solution result are
found are as follows.
Fig. 5.4 Force shown on translation joint probe
Fig. 5.5 Translation joint force change with time
‐2.00E+05
‐1.50E+05
‐1.00E+05
‐5.00E+04
0.00E+00
5.00E+04
1.00E+05
1.50E+05
0 10 20 30 40 50 60 70
Force (N)
Time (Sec.)
Dynamic Analysis of Container Lifting Device
57
Table 5.5 Value of translation joint force with respect to time
Second Force(N) Second Force(N) Second Force(N)
0 1.12 510 21 ‐7547.7 41 ‐60160
1 90991 22 ‐10081 42 ‐63323
2 76687 23 ‐12589 43 ‐66595
3 65865 24 ‐15079 44 ‐69992
4 57219 25 ‐17557 45 ‐73526
5 50034 26 ‐20029 46 ‐77218
6 43884 27 ‐22625 47 ‐81088
7 38497 28 ‐24978 48 ‐85161
8 33688 29 ‐27467 49 ‐89467
9 29330 30 ‐29971 50 ‐94043
10 25327 31 ‐32496 51 ‐98932
11 21611 32 ‐35047 52 ‐1.04 510
12 18128 33 ‐37630 53 ‐1.10 510
13 14837 34 ‐40250 54 ‐1.16 510
14 11704 35 ‐42913 55 ‐1.23 510
15 8703 36 ‐45624 56 ‐1.31 510
16 5811.6 37 ‐48391 57 ‐1.39 510
17 3011.7 38 ‐51220 58 ‐1.50 510
18 288.1 39 ‐54118 59 ‐1.61 510
19 ‐2372.4 40 ‐57095 60 ‐1.76 510
20 ‐4981
5.4.2 Transient dynamic analysis of CLD model
Step5) Meshing is to be done by
automatic generate mesh option with
element shape used was tetrahedral.
Total numbers of element is 33790
with numbers of node is 69041.
Fig. 5.6 Fine meshing of CLD model
Modelling and Analysis of container lifting device
58
Step 6) In Analysis setting number of step taken is one with end time is 60s.
Table 5.6 Transient dynamic analysis setting
Number Of Steps 1
Current Step Number 1
Step End Time 60. s
Auto Time Stepping on
Initial Time Step 0.1 s
Minimum Time Step 0.1 s
Maximum Time Step 0.1 s
Time Integration on
Newton Raphson option Program control
Store Results At All Time Points
Table 5.7 Transient dynamic analysis setup of CLD model
Step 7) In transient dynamic analysis CLD model setup like load, velocity, displacement etc.
are given below.
Analysis setup Object name Value
Acceleration All body 9.8 m/s2 in global C. S. +y
Remote force Cross‐ rod 25000 N in global C. S. ‐y
Joint velocity Cylinder‐ piston pair 20 mm/s in reference C. S. +x
Dynamic Analysis of Container Lifting Device
59
Fig. 5.7 Time varying force applied on piston
Fig. 5.8 Time varying reaction force applied on cylinder
Fig. 5.9 Remote force, constrain, joint velocity, acceleration shown in CLD model for
transient analysis
‐200000
‐150000
‐100000
‐50000
0
50000
100000
150000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61
‐150000
‐100000
‐50000
0
50000
100000
150000
200000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61
Modelling and Analysis of container lifting device
60
Step 8) Aim to perform transient dynamic analysis is to find out time‐history charts to
understand the transient response of the system. Like time varying stress, strain,
acceleration, joint force and moment etc. After completion of solution result are found are
as follows.
Fig. 5.10 Von‐Mises stress contour generated in CLD model
Fig. 5.11 Maximum Von‐Mises stress generate in CLD model
Dynamic Analysis of Container Lifting Device
61
Fig. 5.12 Maximum value of Von‐Mises stress change with time
Fig. 5.13 Equivalent elastic strain contour generated in CLD model
Fig. 5.14 Maximum value of Equivalent elastic strain change with time
0.00E+00
2.00E+07
4.00E+07
6.00E+07
8.00E+07
1.00E+08
1.20E+08
1.40E+08
1.60E+08
1.80E+08
0 10 20 30 40 50 60 70
VON‐MISES STR
ESS (PA)
TIME (SEC.)
0.00E+00
1.00E‐04
2.00E‐04
3.00E‐04
4.00E‐04
5.00E‐04
6.00E‐04
7.00E‐04
8.00E‐04
9.00E‐04
1.00E‐03
0 10 20 30 40 50 60 70
Von‐mises Strain (m/m
)
Time (Sec.)
Modelling and Analysis of container lifting device
62
Fig. 5.15 Total deformation of CLD model
Fig. 5.16 Total deformation of CLD model with respect to time
Fig. 5.17 Cylinder‐ base total revolute joint probe force
0.00E+00
5.00E‐01
1.00E+00
1.50E+00
2.00E+00
2.50E+00
3.00E+00
3.50E+00
4.00E+00
4.50E+00
0 10 20 30 40 50 60 70
DISPLA
CEM
ENT (M
)
TIME (SEC.)
Dynamic Analysis of Container Lifting Device
63
Fig. 5.18 Time varying total cylinder‐ base revolute joint force
Fig. 5.19 Big link‐ base total revolute joint probe force
Fig. 5.20 Time varying total big link‐ base revolute joint force
0.00E+00
2.00E+04
4.00E+04
6.00E+04
8.00E+04
1.00E+05
1.20E+05
1.40E+05
1.60E+05
1.80E+05
0 10 20 30 40 50 60 70
Total Force on Joint (N)
Time (Sec.)
0.00E+00
2.00E+04
4.00E+04
6.00E+04
8.00E+04
1.00E+05
1.20E+05
1.40E+05
1.60E+05
1.80E+05
0 10 20 30 40 50 60 70
Total Force on Joint (N)
Time (Sec.)
Modelling and Analysis of container lifting device
64
Fig. 5.21 Big link‐ piston rod total revolute joint probe force
Fig. 5.22 Time varying total big link‐ piston rod revolute joint force
Fig. 5.23 Safety factor contour generate in CLD model
0.00E+00
2.00E+04
4.00E+04
6.00E+04
8.00E+04
1.00E+05
1.20E+05
1.40E+05
1.60E+05
1.80E+05
0 10 20 30 40 50 60 70
Total Force on Joint (N)
Time (Sec.)
Dynamic Analysis of Container Lifting Device
65
Fig. 5.24 Minimum time varying safety factor
5.5 Static Analysis of Leaf Spring
Step 1) 3–D solid model of leaf spring with help of SolidWorks 14.0
Fig. 5.25 3‐D model of leaf spring
Step 2) convert SolidWorks .SLDPRT file in to .STEP file and import in to ANSYS workbench
15.0.
Step 3) Apply no separation contact between pair of each leaf.
Step 4) Generate mesh in leaf spring model
Step 5) Applied load and constrain to model
0
1
2
3
4
5
6
0 10 20 30 40 50 60 70
Saftey Factor
Time (Sec.)
Modelling and Analysis of container lifting device
66
Fig. 5.26 Load and constrain applied to model of leaf spring
Steps 6) after completion solution, result are found like below.
Fig. 5.27 Stress generated in leaf spring
Fig. 5.28 Strain generated in leaf spring
Static Analysis of Mechanical Jack
67
5.6 Static Analysis of Mechanical Jack
Step 1) 3–D solid model of mechanical jack with help of SolidWorks 14.0 Step 2) convert SolidWorks .SLDPRT file in to .STEP file and import in to ANSYS workbench
15.0. Step 3) Apply no separation contact to pair of box column and revolute joints at hole for
locking pin and inner column to swinging base.
Step 4) Generate the mesh in mechanical jack.
Step 5) Applied load and constrain to model
Fig. 5.29 Load and constrain applied to model of mechanical jack
Step 6) after completion solution, results are found like below.
Fig. 5.30 Stress generated in mechanical jack
Modelling and Analysis of container lifting device
68
Fig. 5.31 Strain generated in mechanical jack
5.7 FFT Analyzer used for vibration measurement of hydraulic cylinder
Hydraulic cylinders are actuating devices converting the hydraulic energy of the
pressurized fluids into the mechanical energy needed to control the movement of machine
linkages and attachments. Hydraulic cylinders are used at high pressures and create huge
forces and precise movements. For this reason they are constructed of strong materials
such as steel and designed to withstand heavy forces. The fluid pushes the face of the piston
and produces a force.
Hydraulic cylinders are one of the most important components of the hydraulic
systems used in a different industrial application. Design of a hydraulic cylinder consists of a
different loading and boundary conditions. Vibration of the cylinder during working
condition is one of the most crucial elements of the failure criterion. In the container lifting
device operated by tractor, the most critical part is two hydraulic cylinders of 5 ton capacity.
It would be very important to optimize the dynamic characteristics of hydraulic cylinders,
because on the case of they will have to carry out the operation with huge weight inertia.
During the analysis, the dynamic characteristics of hydraulic cylinders are obtained by using
the Time Capture Analysis and Real‐time FFT, which is equipped on the container lifting
device with tractor used for solid waste management.
In the CLD operated by a tractor, the diesel engine would be pointed out as the main
source of power that creates the noise and vibration problems. The major requirements of
hydraul
dynami
from th
Fi
cylinde
should
way to
cylinde
hydraul
firing co
from th
compon
underst
interpre
transfo
returns
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69
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Modelling and Analysis of container lifting device
70
spectrum that was recorded at that particular measurement point when the machine was
known to be in good condition.
The fast Fourier transform (FFT) is an efficient algorithm used to compute a discrete
Fourier transform (DFT). This Fourier transforms outputs vibration amplitude as a function
of frequency so that the analyzer can understand what is causing the vibration. The
frequency resolution in an FFT is directly proportional to the signal length and sample
rate. To improve the resolution the time of the recording must be extended; but be careful
of a changing vibration environment.
A spectrogram takes a series of FFTs and overlaps them to illustrate how the
spectrum (frequency domain) changes with time. If vibration analysis is being done on a
changing environment, a spectrogram can be a powerful tool to illustrate exactly how that
spectrum of the vibration changes.
A power spectral density (PSD) takes the amplitude of the FFT, multiplies it by its
complex conjugate and normalizes it to the frequency bin width. This allows for accurate
comparison of random vibration signals that have different signal lengths. For this reason,
PSDs are typically used to describe random vibration environments like those specified in
military and commercial test standards.
FFT Analyzer used for vibration measurement of hydraulic cylinder
71
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72
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OMNITREND software and its applications
73
or the predefined ISO alarms as per DU ISO 10316 can be easily added to keep track of
any machine condition. A large bearing fault frequency database comes along within
OMNITREND freely which helps to identify specific failures with their root cause.
With OMNITREND Web Report, a separate reporting module is also available. Because
of the same quickly visualization of failures reported. OMNITREND will also integrate
all readings, alarms and other important parameters in CMMS systems. That becomes
ideal for the users of SAP, Maximo or other CMMS programs.
5.9 VIBXPERT – FFT (Fast Fourier Transform) data collector and signal analyzer
• As the double acting hydraulic cylinder is most important element for container
lifting device.
• We had taken a trial run for measurement of vibrations for the same element at
different 14 stages of lifting and lowering the empty and loaded 4.5 cubic meter
container with container lifting device.
• Thus we had measured the vibrations of double acting hydraulic cylinder taking the
different 14 stages readings. Out of that four samples are shown as under:
1) Empty container lifting acceleration spectrum
Modelling and Analysis of container lifting device
74
2) Loaded container lifting position 3 acceleration spectrum
3) Loaded container lifting position 4 displacement spectrum
4) Loaded container emptying position 8 velocity spectrum
Fig. 5.35 FFT spectrum of Hydraulic cylinder
VIBXPERT – FFT (Fast Fourier Transform) data collector and signal analyzer
75
Table 5.8 Typical measurements of RMS values
RMS Values
Sr. No.
Description Displacement (µm)
Velocity (mm/sec)
Acceleration (m/sec2)
1 Empty container lowering rest 10.05 1.01 0.193
2 Empty container lowering Vertical
24.38 2.42 0.271
3 Empty container lowering down 54.74 5.86 0.581
4 Empty container lifting 31.31 3.18 0.298
5 Loaded container lifting 1 26.15 3.20 0.349
6 Loaded container lifting 2 34.25 3.72 0.399
7 Loaded container lifting 3 15.98 1.75 0.197
8 Loaded container lifting 4 21.75 2.23 0.268
9 Loaded Container Emptying 5 41.04 4.43 0.476
10 Loaded Container Emptying 6 18.80 1.93 0.198
11 Loaded Container Emptying 7 43.45 3.79 0.359
12 Loaded Container Emptying 8 11.37 1.12 0.152
13 Loaded Container Emptying 9 43.98 4.38 0.427
14 Loaded Container Emptying 10 14.35 1.15 0.174
During actual working on CLD with FFT analyzer, vibration analysis of the hydraulic
cylinder under variable loading application and different boundary conditions is obtained.
The main cause of the vibration in hydraulic cylinder is stick‐slip phenomenon between
piston and cylinder which is also responsible for the failure of a sealing material and reduces
the fatigue life as well the performance of the hydraulic system. Vibration analysis can be
done by static, dynamic and transient way. The Mode Superposition Method of a transient
dynamic analysis is one of the most important methods to predict mode shapes. All readings
taken are checked with the standard given and they are all falls within the limit hence it is
proved that hydraulic cylinders performance is safe in vibration.
Fig. 5.336 Photograaphs of actu
Mod
76
ual readings
delling and A
s taken of C
Analysis of co
CLD by FFT a
ontainer liftin
analyzer
ng device
77
CHAPTER 6
OPTIMIZATION OF HYDRAULIC CYLINDER
6.1 Introduction
The success of optimum design is naturally a gorgeous objective to the designer.
Apart from possible economic advantages, there is the fulfillment of producing the ‘best’
design possible confirming to a stated ‘objective’. In the past, considerable effort has been
devoted to the associated mathematical and computational backgrounds, and the methods
explored have been wide‐ranging and often mathematically complex. The concept of
‘gradient’ has been central and forms the basis of a class of methods suitably described as
‘hill‐climbing’. The gradient concept is not required in the genetic algorithm.
Many methods are in use for design optimization of various mechanical elements. All
use mathematical programming techniques to come into view at optimum solution. In most
of the practical problems in engineering design, the design variable is distinct. This is due to
the accessibility of components in standard sizes and constraints due to production and
manufacturing practices. A few algorithms have been developed to handle the distinct
nature of design variables. This issue is of the enormous importance in solving sensible
problems of design optimization. Lots of scientific linear and nonlinear programming
methods have been advanced for solving optimization problems during the last few years.
However, no single method has been found to be totally capable and strong for all different
kinds of engineering optimization problems. Some methods, such as the penalty‐function
method, conjugate gradient method and Lagrangian method, look for a local optimum in
which by moving in a way related to the local gradient. Some additional methods may apply
the first and second order with essential conditions to search for a local smallest by cracking
a set of nonlinear equations.
Usually these methods search for a solution in the region from the starting point.
Here the global optimum cannot be assured because the outcome will depend on the
selection of the initial point, if there is more than single local optimum existing in the
problem. Moreover gradient search becomes complicated and unsteady, when the objective
function and constraints have many sharp peaks.
Optimization of Hydraulic cylinder
78
6.2 Optimization Technique ‐ Genetic Algorithm
Over the last few years, genetic algorithms (GAs) have been widely used as a search
and optimization tools in various problem domains, including the sciences, commerce and
engineering. The main reasons for their success are their large applicability, ease of use and
global perspective.
The genetic algorithm is a typical model of machine learning, which develops its
behaviour from a symbol of the procedures of development in nature. The individual
characters in the population go through a process of evolution. According to Darwin, this is
made up of the principles of mutation and selection. However, the recent genetic evolution
theory also knows crossover and isolation mechanisms to increase the adaptiveness of the
living beings to their environments.
With genetic algorithms, elements are swapped between individuals as if by sexual
grouping and reproduction (crossover), others are altered at random (mutation). New
generations appear from clones of the current population, in proportion to their fitness. A
single objective functions of the parameters that return a numerical value, to distinguish
between good and bad solutions. Then Fitness is used to apply selection process to the
population in a ‘Darwin’ method (existence of the fittest).[17]
GAs combine the concept of artificial survival of the fittest with genetic operators
abstracted from nature to form a robust search mechanism. GAs differ from traditional
optimization algorithms in many ways.
GAs always searches from population of points, not from a single point.
GAs work with a coding of the parameter set, not the parameters themselves.
GAs use objective function information, not derivatives, calculus or other supporting
knowledge.
GAs use probabilistic alteration rules, not deterministic rules.
GAs use arbitrary based on the previous information to guide the search, instead of
gradient search, so that the derivative information and step size calculation are not
essential.
GAs must work must work in a restricted space for coding of the parameters. Genetic
Algorithms are not hill climbing algorithms. So called local hill climbing problems are
eliminated in these algorithms. Therefore, the probability of getting entrapped in a local
minimum is reduced.
Optimization Technique ‐ Genetic Algorithm
79
The genetic algorithm is a method for resolving both constrained and unconstrained
optimization problems that is based on usual selection. This process drives biological
evolution. The genetic algorithm frequently modifies a population of individual resolutions.
At each step, the genetic algorithm selects persons at random from the current population
to be parents and uses them to create the children for the following generation. Over the
consecutive generations, the population "evolves" toward an optimal solution. Anyone can
apply the genetic algorithm to solve a diversity of optimization problems that are not well
suited for standard optimization algorithms. This may include the problems in which the
objective function is intermittent, non‐differentiable, stochastic, or extremely nonlinear.
The genetic algorithm can be used for the problems of mixed integer programming, where
some components are restricted to be integer‐valued.[17]
During the optimization using Genetic Algorithm mainly depends on three types of
different rules. At the end of each step which helps to produce the next generation from
present population.
Assortment of rules with select the individuals, who called parents that, are
contributing to the population at the next generation.
Secondly, with the crossover rules which compress two parents to form children for the
subsequent generation.
Mutation rules are related with arbitrary changes to distinct parents to form children.
The genetic algorithm varies from a classical, derivative‐based, optimization
algorithm in two main ways, as summarized in the following table 6.1.
Table 6.1 Comparison of Classical and Genetic Algorithm
Classical Algorithm Genetic Algorithm
Generation of single point at every iteration.
The progression of points approaches to an
optimal solution.
Generation of population of points at every
iteration. The best point in the population
approaches to an optimal solution.
Selects the subsequent point in the
progression by a deterministic computation.
Selects the next population by calculation
which uses arbitrary number generators.
6.2.1 Outline of Genetic Algorithm
This section describes fractural analysis of outline of genetic algorithm. The following
stepwise description shows that the genetic algorithm continues:
Optimization of Hydraulic cylinder
80
(1) In the beginning, algorithm generating arbitrary initial population.
(2) Afterwards, algorithm creates a progression of novel populations. At every step, the
algorithm uses the individuals in the recent generation to create the next population.
To generate the novel population, the algorithm performs the following steps:
By computing the fitness value, it scores each member of the present population.
Scales the raw suitability marks to alter them into a more functional range of values.
Selection of members, which called parents, depends on their fitness measurement.
Some of the personalities in the present population that have lesser fitness which are
chosen as elite. Such elite individuals are conceded to the subsequent population.
Creation of the children is from the parents. Children are created either by framing
arbitrary deviations towards a single parent to mutation or by uniting the vector entries
of a pair of parents to crossover.
Change the present population with the children to form the subsequent generation.
The algorithm terminate when one of the ending criteria is met.
6.3 Optimization in Hydraulic Cylinder Design – A Case Study
Monotonicity and dominance were used to find general principles for designing
hydraulic cylinders optimal for a wide class of objective functions and stress conditions. The
design method, although guaranteed to give the optimum design.
Optimal cylinders should be designed for minimum force. Only two designs can be
optimal—one with maximum pressure and minimum wall thickness; the other with
maximum stress.
In the former case, the design is retained if and only if the stress is less than
allowable. Otherwise, a one‐variable search in a restricted interval is needed. The results
suggest the potential importance of monotonicity and dominance in identifying the critical
constraints in a design.
Optimization in Hydraulic Cylinder Design – A Case Study
81
Here we have taken five design variables,
(1) Inside diameter, d x(1) (2) Wall thickness, t x(2) (3) Material Stress, s (4) Force, f (5) Oil Pressure, p x(3) First Optimum design will be with maximum pressure and minimum wall thickness, second with maximum stress. Subject to
2 2
, 7
, 5 5000 49050
, 200 / 19.62 /
Wall thickness t mm
Force f ton so kg N
Pressure p kg cm say N mm
There are three physical relations: First relates force, pressure and area.
2 ....... .(6.3.1)4
f d p Eq
The second gives the wall stress,
......... .(6.3.2)2
p ds Eq
t
Also to find Cross‐sectional area of hydraulic cylinder:
Cross‐sectional area,
2
2
. . .
. ........ .(6.3.3)
A d t t
A d t t Eq
Genetic algorithm is most suitably used for multi modal functions because it always
searches from population of points instead of a single point, which is usually followed in
classical optimization techniques.
In design of hydraulic cylinder there are five variables taken namely internal diameter,
thickness of cylinder, material stress, internal pressure and force on the piston. Objectives
are maximization of stress, minimization of force, minimization of cross sectional area of
cylinder. All these objectives are conflicting in nature hence the problem can be solved
using multi‐objective optimization techniques. Classical optimization techniques are not
much suitable for multi‐objective optimization problem so heuristic method such as genetic
algorithm is used for solving such problems.
Optimization of Hydraulic cylinder
82
6.4 Single Objective Optimization Problem – Nonlinear Constrained Minimization
Optimization Toolbox provides widely used algorithms for standard and large‐scale
optimization. These algorithms solve constrained and unconstrained continuous and
discrete problems. The toolbox includes functions for linear programming, quadratic
programming, binary integer programming, nonlinear optimization, nonlinear least squares,
systems of nonlinear equations, and multi‐objective optimization. We can use them to find
optimal solutions, perform tradeoff analyses, balance multiple design alternatives, and
incorporate optimization methods into algorithms and models.
Using MATLAB 2012 following eight examples were created and solved related to
optimization and design hydraulic cylinder to be used for container lifting device, out of
these results for two examples are shown below:
Example 6.1 app2 ‐‐ Minimize the force, f
The iteration table in the command window shows how MATLAB searched for the minimum
value of force function in the unit disk. This table is the same whether to be used as
Optimization Tool or the command line. MATLAB reports the value of three variables (i.e.
internal diameter (d), cylinder wall thickness (t), pressure (p) and minimization of force, (f)
as below:
Output
x = 57.0000 7.0000 19.6200
fval = 5.0040e+04
Fig.6.1 Optimization using MATLAB for the function : Minimization of force value (f)
exerted on piston
Single Objective Optimization Problem – Nonlinear Constrained Minimization
83
Example 6.2
app16 – Minimization of cross‐sectional area
Again the iteration table in the command window shows how MATLAB searched for
the minimum value of cross‐sectional area function in the unit disk. This table is the same
whether to be used as Optimization Tool or the command line. MATLAB reports the value of
three variables (i.e. internal diameter (d), cylinder wall thickness (t), pressure (p) and
minimization of cross‐sectional area, (A) as below:
Output
x = 50.0000 7.0000 16.0707
fval = 1.2529e+03
Fig.6.2 Optimization using MATLAB for the function: Minimization of Cross‐sectional area (A)
of the Hydraulic Cylinder
6.5 Multi Objective Optimization using Genetic Algorithm
A multi‐objective optimization problem (MOOP) tool deals with more than one
objective function. There are fundamental differences between the working principles of
single and multi‐objective optimization algorithms. However, in a single objective
optimization, the task is to find one solution which optimizes the sole objective function.
A multi objective optimization problem has a number of objective functions which
are to be maximized or minimized. As in the single – objective optimization problem, here
also the problem usually has a number of constraints which may have any feasible solution
(including the optimal solution).
Optimization of Hydraulic cylinder
84
The general form of Multi‐objective optimization problem (MOOP) is stated as follows:
[ ] [ ]
/ , 1, 2,......., ;
0, 1,2,........, ;
0, 1,2,........, ;
, 1, 2,........., .
m
j
k
L Ui i i
Minimize Maximize f x m M
subject to g x j J
h x k K
x x x i n
……….6.5.1
A solution x is a vector of number of decision variables (n): 1 2, ,.........,T
nx x x x .
The last sets of constraints are called variable bounds, restricting each decision variable ix to
take a value within a lower [ ]Lix (Lower bound) and upper [ ]U
ix (Upper bound) bound. These
confines to establish a decision variable space (D).
In many engineering disciplines we need to find solutions in the presence of
conflicting objectives. In such cases, solutions are chosen such that there are reasonable
trade‐offs among different objectives. In certain problems, it may not be obvious that the
objectives are not conflicting to each other. In such combinations of objectives, the resulting
Pareto‐optimal set will contain only one optimal solution. Pareto search is an approach for
handling such situations. Instead of generating a single optimal solution, many solutions are
generated that satisfy Pareto Optimality Criterion. According to this criterion, a solution
point P is accepted only if there are no solutions better than P with respect to all the
objectives. For example, even if P is worse than another solution P1 with respect to one
objective, P is accepted provided that it is better than P1 in at least one objective. Thus each
Pareto optimal solution is good in some respect. The set of all Pareto optimal solutions form
a surface known as a Pareto front. The Pareto front helps engineers understand the nature
of trade‐offs that need to be made in order to select good solutions. Visualizing the front
helps engineers make good decisions.
6.6 Difference with Single‐Objective Optimization:
Besides having multiple objectives there are number of fundamental differences between
single‐objective and multi‐objective optimization, as follows:[33]
two goals instead of one;
dealing with two search spaces;
no artificial fix‐ups.
Difference with Single‐Objective Optimization
85
6.6.1 Two goals Instead of one:
In a single‐objective optimization, there is one goal – the search for an optimum
solution. In the case multi‐modal optimization, the goal is to find a number of local and
global optimal solutions, instead of finding one optimum solution. However, most single‐
objective optimization algorithms target to find one optimum solution, even though number
of optimal solutions exists.
However, in multi‐objective optimization, there are clearly two goals. Progressing
towards the Pareto‐optimal front is certainly an important goal. However, maintaining a
diverse set of solutions in the non‐dominated front is also essential. An algorithm that finds
a closely packed set of solutions on the Pareto‐optimal front satisfies the first goal of
convergence to the Pareto‐optimal front, but does not satisfy maintenance of a diverse set
of solutions. Since all objectives are important in a multi – objective optimization, a diverse
set of obtained results near to the Pareto‐optimal front provides a variety of optimal results,
trading objectives differently. A multi‐objective optimization algorithm that cannot find a
diverse set of solutions in a problem is as good as a single‐objective optimization algorithm.
Since both goals are important, an efficient multi‐objective optimization algorithm must
work on satisfying both of them. Looking to these dual tasks, multi – objective optimization
is more challenging than single – objective optimization. [33]
6.6.2 Dealing with two search spaces
Another difficulty is that multi‐objective optimization involves two search spaces,
instead of one. In a single‐objective optimization, there is only one search space – the
decision variable space. An algorithm works in this space by accepting and rejecting
solutions based on their objective function values. Here in addition to the decision variable
space, there is also the objective function space. When this occurs, the measures in both
spaces must be synchronized in such a way that the formation of new results in the decision
variable space is complimentary to the variety needed in the objective space. This by no
means, is an easy task and more importantly is dependent on the mapping between the
decision variables and objective function values. [33]
Optimization of Hydraulic cylinder
86
6.6.3 No Artificial Fix‐Ups
The most real world optimization problems are naturally posed as a multi – objective
optimization problem. Multi‐objective optimization for finding multiple Pareto‐optimal
solutions eliminates all such fix‐ups and can, in principle, find a set of optimal solutions
corresponding to different weight and e‐vectors. It is true that in general a multi – objective
optimization is more complex than a single‐objective optimization. But the avoidance of
multiple simulation runs, no artificial fix‐ups and availability of effective population based
optimization algorithms, and above all, the conception of dominance helps to overcome
some of the difficulties and give a user the real means to handle many objectives, a matter
which was not possible to achieve in the past. [33]
6.7 Multi‐objective Optimization
Multi – objective optimization is apprehensive with the minimization of a vector for
objectives F(x) that can be the matter of a number of constraints or bounds:
min ,
0, 1,....., ; 0, 1,....., ; .
n
i e i e
F x subjected tox R
G x i k G x i k k l x u
Note that because F(x) is a vector, if any of the constituents of F(x) are contending,
there is no unique solution to this problem. So instead of that the concept of non –
inferiority (also known as Pareto optimality) must be used to describe the objectives. A non
– inferior solution is one in which an improvement in one objective requires a degradation
of another. To define this concept more precisely, consider a feasible region, , in the
parameter space. X is an element of the n‐dimensional real numbers nx R that satisfies all
the constraints, i.e.
nx R ,
Subject to
0, 1,....., ,
0, 1,....., ,
i e
i e
G x i k
G x i k k
l x u
This permits description of the corresponding possible region for the objective
function space :
: , .my R y F x x
The presentation vector F(x) maps parameter space into objective function space, as
represented in two dimensions in the Figure 6.3.
Multi‐objective Optimization
87
Fig.6.3 Mapping from Parameter Space into Objective Function Space
A non – inferior solution point can now be defined.
Definition: Point x is a non – inferior solution if for some neighborhood of x there
does not exist a x such that x x and
, 1,...., ,
.
i i
j j
F x x F x i m and
F x x F x for at least one j
In the two – dimensional representation of Figure 6.4 the set of non – inferior
solutions lies on the curve between C and D. Points A and B represent specific non – inferior
points.
Fig.6.4 Set of Non – inferior Solutions
A and B are obviously non – inferior solution points because an enhancement in one
objective, F1, requires a degradation in the other objective, F2, i.e. , 1 1 2 2, .B A B AF F F F
Optimization of Hydraulic cylinder
88
Since any point in that is an inferior point in which improvement can be attained in all
the objectives, it is clear that such a point is of no value.
Multi‐objective optimization is, therefore, concerned with the generation and selection of
non – inferior solution points.
Non – inferior results are also called Pareto optima. An overall objective of multi‐
objective optimization is making the Pareto optima.
Using MATLAB 2012 ten different examples were created and solved related to
multi‐objective optimization and design hydraulic cylinder to be used for container lifting
device, out of these results for following three examples are shown.
Example 6.3 app9 ‐‐ Multi objective Optimization, Pareto Optimization, Maximize the stress, s & Minimization of force, f linked with mymulti4.m. [d = x(1), t = x(2) and p = x(3)] App9 options = gaoptimset('PopulationSize',60,... 'ParetoFraction',0.7,'PlotFcns',@gaplotpareto); [xfval flag output population] = gamultiobj(@mymulti4,3,... [],[],[],[],[55,7,15.696],[70,15,19.62],options) mymulti4.m function f = mymulti4(x) f(1) = -x(3)*x(1)/(2.0*x(2)); f(2) = 0.785*x(1)^2*x(3); Table 6.2 Value of each variable Internal diameter (d), Thickness of Cylinder (t) and Internal
Pressure (p) after each iteration
Sr. No.
Internal Diameter, d, x(1) in mm
Thickness of
Cylinder, t, x(2) in mm
Internal Pressure, p, x(3) in N/mm2
Sr. No.
Internal Diameter, d, x(1) in mm
Thickness of Cylinder, t, x(2) in mm
Internal Pressure, p, x(3) in N/mm2
1 55.0000 7.0000 15.6960 31 55.0000 7.0000 15.6960
2 66.2678 7.0059 19.5723 32 58.3710 7.0075 19.4684
3 55.0000 7.0000 15.6960 33 55.8222 7.0116 19.4376
4 55.0000 7.0000 15.6960 34 64.7664 7.0126 19.4935
5 62.4450 7.0580 19.4929 35 62.5167 7.0069 19.5045
6 60.4970 7.0069 19.5850 36 55.7202 7.0073 18.6119
7 60.1910 7.0055 19.4753 37 63.0392 7.0155 19.5742
8 64.0234 7.0124 19.5256 38 65.2111 7.0204 19.5760
9 55.0613 7.0047 18.4560 39 64.4622 7.0227 19.5644
10 55.1285 7.0795 16.5295 40 59.3725 7.0063 19.4064
11 55.1080 7.0361 16.1410 41 66.3172 7.0970 19.4580
12 61.5908 7.0063 19.4276 42 60.1721 7.0188 19.1510
13 55.5851 7.0042 17.1733 43 56.3911 7.0056 19.4793
14 58.2347 7.0028 19.5183 44 59.6663 7.0058 19.0352
15 55.1813 7.0154 18.4295 45 55.1911 7.0041 16.6825
16 57.9182 7.0066 19.5054 46 61.1807 7.0069 19.5383
17 64.9375 7.0077 19.5621 47 61.8250 7.0066 19.5524
Multi‐objective Optimization
89
18 66.0774 7.0193 19.4926 48 65.9791 7.0154 19.5238
19 65.4093 7.0061 19.5791 49 56.3966 7.0052 19.5736
20 55.3754 7.0041 18.4095 50 55.0618 7.0129 18.2643
21 58.5363 7.0245 19.5747 51 55.4177 7.0050 19.1041
22 62.7189 7.0064 19.5771 52 57.7819 7.0059 19.4920
23 57.4923 7.0119 19.4306 53 55.0702 7.0042 16.4860
24 63.2921 7.0069 19.4777 54 66.2678 7.0059 19.5723
25 55.7036 7.0036 16.5999 55 55.1476 7.0629 17.2362
26 56.2273 7.0055 19.4126 56 55.1774 7.0132 17.7655
27 61.5269 7.0458 19.3891 57 55.0156 7.0000 15.6960
28 55.7098 7.0088 19.3550 58 55.0963 7.0109 19.4399
29 63.4252 7.0314 19.5727 59 61.5894 7.1083 19.5141
30 55.3446 7.0098 17.8110 60 60.5595 7.1319 19.5850
Fig.6.5 Pareto optimization using Genetic Algorithm plot of Stress generated (N/mm2) v/s
Force on Piston (N)
Example 6.4
app6 ‐‐ Multiobjective Optimization, Pareto Optimization, Maximize the force, f& Minimization of thickness, t linked with mymulti1.m. [d = x(1), s = x(2) and p = x(3)]
App6
options = gaoptimset('PopulationSize',60,... 'ParetoFraction',0.7,'PlotFcns',@gaplotpareto);
Optimization of Hydraulic cylinder
90
[xfval flag output population] = gamultiobj(@mymulti1,3,... [],[],[],[],[55,80,15],[80,92,19.62],options) mymulti1.m
function f = mymulti1(x) f(1) = -0.785*x(1)^2*x(3); f(2) = x(3)*x(1)/(2.0*x(2));
Table 6.3 Value of each variable Internal diameter (d), Stress of Cylinder (s) and Internal
Pressure (p) after each iteration
Sr. No.
Internal Diameter, d, x(1) in mm
Stress of Cylinder, s, x(2) in N/mm2
Internal Pressure, p, x(3) in N/mm2
Sr. No.
Internal Diameter, d, x(1) in mm
Stress of Cylinder, s, x(2) in N/mm2
Internal Pressure, p, x(3) in N/mm2
1 55.0078 91.6168 15.0018 31 69.1143 91.7195 15.1666
2 79.9919 91.7703 19.6176 32 59.2841 91.8107 15.5315
3 79.9919 91.7547 19.6176 33 79.7091 91.7845 16.0754
4 79.2017 91.7932 16.5609 34 79.8247 91.7922 17.2598
5 55.0078 91.6480 15.0018 35 65.0814 91.7806 15.2036
6 55.0078 91.6793 15.0018 36 79.6472 91.7693 16.2253
7 79.9694 91.8333 19.3448 37 57.2698 91.7542 15.1802
8 79.8608 91.7727 15.8229 38 79.6972 91.7660 18.5507
9 66.3763 91.7202 15.1005 39 61.4632 91.7095 15.1355
10 76.0792 91.7928 15.0875 40 62.8751 91.6910 15.1317
11 79.4116 91.7394 17.0860 41 55.0078 91.6793 15.0018
12 79.8643 91.8274 19.0110 42 57.5984 91.7088 15.0473
13 63.2734 91.7015 15.0096 43 78.8803 91.7897 15.8159
14 79.8288 91.7790 17.8853 44 68.5697 91.7179 15.2937
15 65.0628 91.7493 15.2036 45 79.1919 91.7576 17.0069
16 64.9417 91.6681 15.0416 46 59.2251 91.6635 15.1759
17 77.6260 91.7956 15.2363 47 78.6092 91.7721 17.4948
18 67.0868 91.7160 15.3411 48 73.1853 91.7286 15.5022
19 70.5506 91.6568 15.0636 49 76.8068 91.7216 15.4737
20 75.3182 91.7606 15.3205 50 75.4550 91.8006 15.3853
21 67.3269 91.7229 15.0905 51 68.5267 91.7862 15.0148
22 79.3568 91.7997 16.7358 52 78.1924 91.7886 15.5452
23 79.6428 91.7405 18.5703 53 79.9295 91.7737 19.3981
24 79.9240 91.8879 17.5826 54 72.4331 91.7462 15.4561
25 79.9256 91.8502 18.3170 55 79.7663 91.7790 17.8853
26 77.3328 91.8284 15.1115 56 70.4219 91.7718 15.4908
27 72.7814 91.7847 15.0560 57 79.9240 91.8254 17.5826
28 79.8332 91.8116 18.7950 58 63.2734 91.6390 15.0643
29 59.9086 91.7026 15.4638 59 55.0586 91.7418 15.0018
30 56.6810 91.7107 15.0201 60 79.5994 91.7578 15.6826
Multi‐objective Optimization
91
Fig.6.6 Pareto optimization using Genetic Algorithm plot of Force (N) v/s Thickness of
Cylinder (mm)
Example 6.5
app17 ‐‐ Multiobjective Optimization, Pareto Optimization, Maximize the force, f & Minimization of cross‐sectional area, A linked with mymulti17.m. [d = x(1), t = x(2) and p = x(3)] options = gaoptimset('PopulationSize',60,... 'ParetoFraction',0.7,'PlotFcns',@gaplotpareto); [xfval flag output population] = gamultiobj(@mymulti17,3,... [],[],[],[],[55,07,15.696],[70,16,19.62],options) mymulti17.m
function f = mymulti17(x) f(1) = -0.785*x(1)^2*x(3); f(2) = 3.14*((x(1)*x(2)+x(2)^2));
Optimization of Hydraulic cylinder
92
Table 6.4 Value of each variable internal diameter (d), Thickness of Cylinder (t) and Internal
Pressure (p) after each iteration
Sr. No.
Internal Diameter, d, x(1) in mm
Thickness of
Cylinder, t, x(2) in mm
Internal Pressure, p, x(3) in N/mm2
Sr. No.
Internal Diameter, d, x(1) in mm
Thickness of Cylinder, t, x(2) in mm
Internal Pressure, p, x(3) in N/mm2
1 55.0000 7.0000 16.0085 31 55.4372 7.0396 19.2665
2 69.9987 7.0283 19.6200 32 65.5574 7.0109 19.6028
3 69.9987 7.0127 19.6200 33 55.9352 7.0152 19.5074
4 57.6986 7.0018 19.5913 34 55.3623 7.0012 18.9455
5 55.0031 7.0002 18.8725 35 63.8330 7.0108 19.3648
6 63.5562 7.0312 19.5854 36 69.6822 7.0121 19.6186
7 55.0000 7.0000 17.5416 37 64.9928 7.0043 19.5858
8 55.0000 7.0000 16.0085 38 65.8195 7.0137 19.6042
9 55.0000 7.0000 16.0241 39 60.3892 7.0092 19.5858
10 55.0000 7.0000 16.9024 40 59.5785 7.0034 19.2825
11 55.0031 7.0012 18.8725 41 61.5670 7.0146 19.6042
12 56.7591 7.0100 19.6194 42 57.3518 7.0055 19.4248
13 69.2413 7.0112 19.6099 43 65.6511 7.1359 19.6028
14 68.5541 7.0106 19.5913 44 55.1262 7.0001 18.3711
15 56.3311 7.0160 19.5552 45 59.4147 7.0100 19.4629
16 55.0000 7.0000 16.3537 46 58.3674 7.0041 19.2737
17 69.9987 7.0225 19.6200 47 55.2397 7.0133 19.3382
18 61.9767 7.0049 19.6109 48 62.2097 7.0236 19.4095
19 69.9987 7.0127 19.6200 49 63.2301 7.0185 19.6084
20 66.5631 7.0163 19.6021 50 60.8089 7.0126 19.6011
21 59.7798 7.0104 19.6011 51 57.7745 7.0087 19.3398
22 55.0012 7.0001 18.3711 52 59.3529 7.0099 19.3541
23 62.3718 7.0077 19.4629 53 68.9767 7.0144 19.5854
24 63.9051 7.0162 19.6023 54 56.8451 7.0144 19.5399
25 67.5167 7.0110 19.4657 55 67.1126 7.0062 19.5764
26 60.9584 7.0099 19.3632 56 67.1009 7.0032 19.5319
27 55.0000 7.0000 17.0923 57 55.7167 7.0280 19.4999
28 67.5035 7.0199 19.6129 58 62.8502 7.0158 19.6097
29 68.0485 7.0104 19.6050 59 57.6018 7.0055 19.4248
30 58.8005 7.0098 19.2952 60 66.5631 7.0163 19.3521
Multi‐objective Optimization
93
Fig. 6.7 Pareto optimization using Genetic Algorithm plot of Force (N) v/s Cross‐sectional
area of Cylinder (mm2)
6.8 Conclusion of optimization
Most real‐world search and optimization problems are naturally posed as multi‐
objective optimization problems. However, due to the complexities involved in solving
multi‐objective optimization problems and due to the lack of suitable and efficient solution
techniques, they have been transformed and solved as single objective optimization
problems. Moreover, because of the presence of conflicting multiple objectives, a multi‐
objective optimization problem results in a number of optimal solutions, known as Pareto‐
optimal solutions. One major drawback of developing Pareto‐optimal plots is the extensive
calculation required to obtain the complete curve. Hence MATLAB is used to overcome such
problems.
Pareto optimization is a methodology for solving multi criteria decision problems.
This methodology provides a systematic approach towards design problems with multiple
conflicting objectives. In Pareto optimal design situations, the designer has more than one
performance found to be measure of interest. An optimal solution is generally defined as
the finest solution. However, with multi criteria problems, the "best" is often dependent
upon a designer's inclinations. The Pareto optimization methodology generally generates a
huge number of alternatives of which the designer evaluates in order to arrive at his best
solution frequently termed the best compromise solution.
94
CHAPTER 7
CONCLUSION AND FUTURE SCOPE OF RESEARCH
7.1 Conclusion of Research Work
Container lifting device for maximum load carrying capacity about 5000 Kg and that
can handle container about 4 to 4.5 m3 is designed. To find its behavior in dynamic
environment rigid and transient structure dynamic analysis has been carried out. Results of
analysis are discussed and suggested modifications in CLD to eliminate operational issues
and improving its performance. From the above work final conclusions are derived as under:
1) As per the dynamic analysis of CLD model, results of Von – Mises stress and strain, safety
factor etc. are found much below then allowable values. Comparisons are given below.
Table 7.1 Comparison of results with allowable value
Component Result Maximum value
generated
Allowable
value
Safety factor
during design
Safety factor
in ANSYS result
Piston rod Stress 64.5 MPa 310 MPa 3 4.7
Cross‐ rod Stress 52 MPa 355 MPa 3 6.8
Big link Stress 169 MPa 355 MPa ‐ 2.2
Base Stress 129 MPa 355 MPa ‐ 2.8
Piston Stress 30 MPa 310 MPa 3 10
All body Strain 0.00086 0.002
So, from the table it is found that maximum stress and strain generated during operation
of CLD model at maximum load is within prescribed limit.
2) As per the static analysis of leaf spring, simulated in ANSYS 15, at design load maximum
stress generated is found 813 MPa, which is less than 1800 MPa (yield stress) with safety
factor of 2 considered in design. That indicate design is safe under working condition.
3) As per the static analysis of mechanical jack, simulated in ANSYS 15, at design load
maximum stress generated is found nearly 90 MPa which is less than 170 MPa (yield
stress) with safety factor of 3 considered in design. That indicate design is safe under
working condition.
4) In single objective optimization problem, objective function is minimizing the force or
cross‐sectional area or thickness of cylinder, in which inside diameter (d) is the only
variable. The objective function is monotonic with respect to its variable (i.e. inside
Conclusion of Research Work
95
diameter, d). Hence single objective optimization is not much suitable in this case, in
addition to the variable inside diameter (d), pressure (p) and thickness (t) are also active,
so multi‐objective optimization is carried out.
Multi‐objective optimization problem results in a number of optimal solutions, known as
Pareto‐optimal solutions. The Pareto‐optimal curves of (a) Maximization of stress &
minimization of force (variables are d, t, p) (b) Maximization of force & minimization of
thickness (design variables are d, s, p) and (c) maximization of force & minimization of
cross sectional area of cylinder (variables are d, t, p) are obtained. These Pareto‐optimal
curves are useful to obtain different values of design variables for different cross
sectional area of cylinder, force on piston and stress generated.
5) After review of existing design/specifications of container lifting device, significant
parameters affecting performance of the container lifting device were identified.
Following modifications are also suggested on existing device which can eliminate major
difficulties faced during operation.
Two front support added to container lifting device for proper positioning and
placing of container and remains at its position while transportation.
Four side supports is provided to prevent the damage of hydraulic cylinder and oil
pipes.
Two rear mechanical jacks added to container lifting device for proper support and
stabilized the vehicle.
Side wall added to container lifting device to prevent the fall of waste directly from
container to ground.
Proper positioning and placing of hydraulic pipes.
7.2 Future scope of research work
After doing this research work, there are also some future work may possible for
improvement in performance of container lifting device.
Further performance investigation of CLD model by changing different hydraulic fluid
during operation.
Computational fluid dynamic (CFD) analysis of double acting hydraulic cylinder to
analyse the performance of CLD model at different hydraulic fluid.
Conclusion and future scope of Research Work
96
Effects of Vibration on fatigue life of hydraulic cylinders and seals can be evaluated.
Optimization of Cycle time by providing external hydraulic power pack may be
evaluated.
97
ANNEXURE I Definitions – MSW (Management and Handling) Rules 2000
Definitions: MSW (Management and Handling) rules 2000 i. "Anaerobic digestion" means a controlled process involving microbial decomposition of
organic matter in the absence of oxygen;
ii. "Authorization" means the consent given by the Board or Committee to the "operator of
a facility”;
iii. "Biodegradable substance" means a substance that can be degraded by microorganisms;
iv. "Biomethanation" means a process which entails enzymatic decomposition of the
organic matter by microbial action to produce methane rich biogas;
v. "Collection" means lifting and removal of solid wastes from collection points or any other
location;
vi. "Composting" means a controlled process involving microbial decomposition of organic
matter;
vii. "Demolition and construction waste" means wastes from building materials debris and
rubble resulting from construction, re‐modeling, repair and demolition operation;
viii. "Disposal" means final disposal of municipal solid wastes in terms of the specified
measures to prevent contamination of ground‐water, surface water and ambient air
quality;
ix."Form" means a Form appended to these rules;
x. "Generator of wastes" means persons or establishments generating municipal solid
wastes;
xi. "Land filling" means disposal of residual solid wastes on land in a facility designed with
protective measures against pollution of ground water, surface water and air fugitive
dust, wind‐blown litter, bad odour, fire hazard, bird menace, pests or rodents,
greenhouse gas emissions, slope instability and erosion;
xii. "Leachate" means liquid that seeps through solid wastes or other medium and has
extracts of dissolved or suspended material from it;
Annexure ‐ I
98
xiii. "Lysimeter" is a device used to measure rate of movement of water through or from a
soil layer or is used to collect percolated water for quality analysis;
xiv. "Municipal authority" means Municipal Corporation, Municipality, Nagar Palika, Nagar
Nigam, Nagar Panchayat, Municipal Council including notified area committee(NAC) or
any other local body constituted under the relevant statutes and, where the
management and handling of municipal solid waste is entrusted to such agency;
xv. "Municipal solid waste" includes commercial and residential wastes generated in a
municipal or notified areas in either solid or semi‐solid form excluding industrial
hazardous wastes but including treated bio‐medical wastes;
xvi. "Operator of a facility" means a person who owns or operates a facility for collection,
segregation, storage, transportation, processing and disposal of municipal solid wastes
and also includes any other agency appointed as such by the municipal authority for
the management and handling of municipal solid wastes in the respective areas;
xvii. "Pelletisation" means a process whereby pellets are prepared which are small cubes or
a cylindrical piece made out of solid wastes and includes fuel pellets which are also
referred as refuse derived fuel;
xviii. "Processing" means the process by which solid wastes are transformed into new or
recycled products;
xix. "Recycling" means the process of transforming segregated solid wastes into raw
materials for producing new products, which may or may not be similar to the original
products;
xx. "Schedule" means a Schedule appended to these rules;
xxi. "Segregation" means to separate the municipal solid wastes into the groups of organic,
inorganic, recyclables and hazardous wastes;
xxii. "State Board or the Committee" means the State Pollution Control Board of a State, or
as the case may be, the Pollution Control Committee of a Union territory;
xxiii. "Storage" means the temporary containment of municipal solid wastes in a manner so
as to prevent littering, attraction to vectors, stray animals and excessive foul odour;
Annexure ‐ I
99
xxiv. "Transportation " means conveyance of municipal solid wastes from place to place
hygienically through specially designed transport system so as to prevent foul odour,
littering, unsightly conditions and accessibility to vectors;
xxv. "Vadose water" water which occurs between the ground, surface and the water table
that is the unsaturated zone;
xxvi. "Vermicomposting" is a process of using earthworms for conversion of biodegradable
wastes into compost.
100
ANNEXURE II
Survey Report of Equipments at ULBs
Client: GUJARAT URBAN DEVELOPMENT COMPANY LIMITED
Name of Project: Municipal Solid Waste Management Project
Tender/Contract No.: ________________________________________________
Name of Municipality: _________________________________________________
Name of District: _________________________________________________
Name of Consultant: _________________________________________________
Name of Supplier: _________________________________________________
Sr. No.
List of Check Point Remarks & Suggestions
A Container Lifting Device
B Total Number Required By ULB
C Coding/Punching on Equipment
1 CLD Chassis No.
2 CLD RTO Registration No.
3 Overall dimension
4 Rear axle size (75 x 75)
5 Toe hook size (30 mm)
6 No. of bottom supports
7 Lifting arm plate thickness
8 Cross shaft (boom) diameter
9 Hydraulic cylinder make
10 Hydraulic cylinder dimension (63 x110)
Annexure II
101
11 Lift chain thickness (15 mm)
12 Parallel lift function
13 Tyre (make and size)
14 DC valve make and working
15 Stabilizer jack working
16 Hydraulic hoses (make and quality)
17 Any leakages observed in hydraulic system?
18 Whether tool kit is provided
19 Any transit damage found?
20 Lifter operating performance.
21 Whether brake provided and working properly?
22 Whether greasing/lubrication facility is provided?
23 Problems raised by ULB, if any.
Sign Sign
S. I. (Consultant)
Name: Name:
Seal
102
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44 Leone Corradi and Lelio Luzzi,“Collapse of Thick Cylinders Under Radial Pressure and Axial Load” Department of Nuclear Engineering, Italy, ASME , Vol. 72, JULY 2005.
45 Lim M. Full cost accounting in solid waste management: the gap in the literature on newly industrialised countries. Journal of Applied Management Accounting Research 2011;9(1):21–36.
46 Limin Yang, Torgeir Moan, “Dynamic analysis of wave energy converter by incorporating the effect of hydraulic transmission lines”, Ocean Engineering 38 (2011) 1849–1860
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48 Machine Design Book by Robert Norton, Pearson Education Publication.
49 Mechanical Engineering Design Book by Joseph Edward Shigley, McGraw‐Hill Book Company.
50 M Osman Abdalla & Others "Analysis of Innovative Design of Energy Efficient Hydraulic Actuators" year 2012
51 M. O. Abdalla, T. Nagarajan, and M. H. Fakhruldin, "Numerical study of flow field and energy loss in hydraulic proportional control valve," in National Postgraduate conference NPC2011., UTP, Seri Iskandar, Perak, Malaysia., 2011.
52 M. STOSIAK, “Vibration insulation of hydraulic system control components”, Archives of civil and mechanical engineering 2011, Vol. XI, No.1
53 Mahadevia D, Wolfe JM. Solid waste management in indian cities: status and emerging practices. New Delhi: Concept Publishing Company; 2008.
54 Malviya, R., Chaudhary, R., Buddhi, D., 2002. Study on solid waste assessment and management – Indore city. Indian Journal of Environmental Protection 22 (8), 841–846.
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60 N.Upendra, P.Moulali, K.Ajay Kumar Reddy, “Static and modal analysis of laminated composite Hydraulic Cylinder”, International Journal of Engineering Research ,ISSN:2319‐6890) (online), 2347‐5013(print), Volume No.3 ,Issue No: Special 1, pp: 140‐143 22nd March 2014.
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87 Somwrita Sarkar, Andy Dong and John S. Gero "A LEARNING AND INFERENCE MECHANISM FOR DESIGN OPTIMIZATION PROBLEM (RE)_FORMULATION USING SINGULAR VALUE DECOMPOSITION"
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LIST OF PUBLICATIONS
1 Prof. U. V. Shah, Himanshu Raiyani, Vijay Mistry "Municipal solid waste management in Indian cities – A review" in 2nd national conference on emerging trends in engineering technology & management at Indus institute of technology & engineering
2 Prof. U. V. Shah, Himanshu Raiyani, Vijay Mistry "Scenario of Municipal Solid Waste Management in India – A Review" in National Conference on ‘Transportation and Water resource Engineering” (NCTWE – 2015) at L. D. College of Engineering, Ahmedabad
3 Prof. U. V. Shah, Himanshu D. Raiyani, Prof. G. H. Upadhyay "Design and Dynamic analysis of existing container lifting device used for Solid Waste Management" in International Journal for Scientific Research and Development (IJSRD),Vol. 3, Issue 03, 2015, ISSN: 2321‐0613
4 Prof. U. V. Shah, Dr. G. H. Upadhyay, "Optimization of Hydraulic Cylinder Design used for container lifting device using Genetic Algorithm" in International Journal of Engineering and Techniques (IJET), ISSN: 2395‐1303, Volume 4 Issue 1 – 2018.