design modelling

Design Methods – Prof. Stein Ove Erikstad

Design ModellingTMR4115

Thursday, August 28 2006

Prof. Stein Ove ErikstadReality

Decision Models


2

Agenda

• Recapturing last week– characteristics marine systems design

• Modelling the design process– basic building blocks

• Modelling in design – basic model elements – modelling examples


3

Models and Methods in Design

Reality

Decision models (and their respective

method)

• understand the problem

• select the correct model

• focus on what is important

• simplify

• understand• improve• DECIDE


4

The Design Paradox

Postpone decisions

Increase knowledge


5

Syntax vs Semantics

(hull form & propeller) (required SHP)(hull form) (seakeeping behaviour)(hull form, propeller, machinery) (ship speed)(all ship systems) (total cost)


6

Activities and Events

Clarification of task

Conceptual design

Embodiment design

Designinitiation

Problem description(Statement of need)

Outline specification (Tender invitation)

Contractspecification

Project development Class design

Engineering design

Fabrication engineering

Procurement

Materials management

Fabrication

Tendering/sales Build project

Outline SpecificationRequest for tender

Concept definition


7

The basic design processReference model

GENERATE

ANALYSE EVALUATE

DECIDE

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


8

Design vocabularyV

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


9

Example Vocabulary Elements

Symbol Name Unit

B Transv. dist. columns [m]

Bd Breadth of deck [m]

BM Distance between vertical centre [m]

Bp Breadth of pontoons [m]

dc Diameter of columns [m]

Dp Depth of pontoon [m]

KB Distance keel and VCB [m]

KG Distance keel K and VCG [m]

Ld Length of deck [t]

Lp Length of pontoon [m]

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


10

Semisub example

Column height hc Top

Tsur

Ttr

hag

Bp

dc

dc

B

Lp

Lp

Ld Bd

Dp


11

Syntactic Knowledge Ks

• Design alternatives – materials– components– variants

• Design ranges• Logical constraints

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


12

Syntactic Knowledge KsSemisub sample

• Design alternatives – 4, 6, 8 columns– circular versus quadratic columns

• Design ranges– column diameter 8-15 m– Pontoon length 80-120m

• Logical constraints– Bp < B– Bc < Bp

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


13

Design description SemiSub

*INITIAL POINT**Current Point: Values of design variables: x( 1)= 80.000000 x( 2)= 12.000000 x( 3)= 10.000000 x( 4)= 10.000000 x( 5)= 25.000000 x( 6)= 50.000000 x( 7)= 70.000000 x( 8)= 60.000000 x( 9)= 40.000000 x(10)= 5.0000000

Decision space

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


14

Other examples Design Descriptions

• Contract specification• CAD model• Equipment data sheets• P/ID diagrams


15

What is then the next step?

• We have now described what the design look likes

• We now need to describe what it can do

• i.e. from FORM to FUNCTION


16

Design Interpretation SemiSub

BpDp

h

B

Dc

Steel weight of the pontoons: 307.17 Steel weight of the columns: 380.26 Steel weight deck structure: 961.80 Total steel weight: 2099.23 Displacement, oper. cond.: 9251.03 Heave period operation: 20.32

Variable Deck load 1200 t

W

t

VDL

Analysis

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


17

Analysis knowledge

• Knowledge that relates FORM and FUNCTION

• Examples– functional relations– finite element analysis– comutational fluid

dynamics– systems simulations

• Both the relations themselves, and how to derive such relations

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


18

Semisub example

Column height hc Top

Tsur

Ttr

hag

Bp

dc

dc

B

Lp

Lp

Ld Bd

Dp


19

Whats next?

• We now have derived some measure of the performance of our design. How can we use this to make decisions?

• -> We need to evaluate this against our design goals and requirements

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


20

Design interpretationsI

• Intended interpreations– goals, objectives, constraints– I.e. what we want to achieve, requirements

• Inferred interpretations– I.e. the actual performance of the current

design based on analysis


21

Semisub example• • High Variable Deck Load (VDL): The main purpose of the

vessel is to provide a working area for drilling operations. To perform the drilling operation a number of consumables are needed, e.g. drill pipes, drilling mud, fuel, water supplies. A high VDL is advantageous because the time interval for supplies can be longer.

• • Large deck area: A large deck area will provide space for storage of consumables and equipment, and working space. Alternatively, the deck area can be handled as a user requirement, and hence be modeled as a constraint.

• • Low construction costs: the construction costs of the vessel is difficult to model. A common approach is to approximate these costs as a linear function of the vessels Light Weight (LW) (steel + machinery + outfit weight). Since the Machinery and outfit weights are assumed fixed, the cost will be a function of the steel weight only.

• • High availability: in order to carry out the drilling operation, the motion of the deck area must be within certain range. Within this range, the motions of the platform can be compensated for by the use of a heave compensator. Outside this range, the drilling operation must be stopped, and eventually the vessel must be de-ballasted to survival draft.

• The two main governing factors of the availability will be the motion characteristics of the vessel, and the airgap between the water surface and the platform deck.


22

ExampleSemisub goals and reqs

min f wVDL 1 VDL

VDLtar

wSW

WS p WSc WSd

WS tar 1

22LpBpDp cm 1 dc

2 Top Dp

9.81 dc2 treq 0

Maximize deck load and minimize building cost

Requirement for max heave period


23

Representational spaces

• the design space, capturing the description of the design object. The ultimate goal of the design process

• the performance space, holding the functional interpretation of the design object, that is, “objective” measures of performance usually derived from design analysis

• the goal space, representing the intentions and requirements of the designer, and gives the design process a direction towards purposeful solutions. The goal space contains “modal design relations“ instead of factual statements (e.g. Ship has length 50 m) , the relation is “modified” to express statements about requirements and expectations about the design object (e.g. Ship should have capacity 3000 TEU, Ship must have B < 32.2 m)

• the value space, holding an interpretation of the design object with respect to “subjective” measures of performance, based on the synthesis of the functional interpretation and the design goals

designspace

performance space

goal space

value space


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DECIDE

• Based on the evaluation of the design performance vs. goals, requirements...

• ... the current design solution is either selected, or a new iteration is started

VGENERATE

GEN

Kgen

DANALYSE

ANA

Kana

Ifunc

EVALUATE

EVAL

Keval

Igoal IevalDECIDE

DECD


25

SummaryClassification building blocks

Object level knowledge

Design Object Entities

Domain Knowledge Entities

V KgenD KanaIfunc KevalIgoal Ieval

ShipX Knowledge Entities

Control level knowledge

GEN ANA

EVALDEC


26

Why?

• The PURPOSE has been to identify the main BUILDING BLOCKS in the design PROCESS

• These are found in most design processes, though their sequence and relations may vary

• Thil will be the subject throughout this semester


Basic inference processes


28

Inference processes in design: Deduction

• E.g. design analysis

I = 1(Ki, D) (2.1)

Ki: x DW(x) V(x) Resistance(x) = f(DW(x), V(x))D: DW(ShipA) = 200.000 V(ShipA) = 15I: Resistance(ShipA) = 1200


29

Inference processes in design: Generating designs

• E.g. systematic parameter variation

D = 3(Ks,V) (2.3)


30

Inference processes in design: Deriving design descriptions

• E.g. linear optimisation, simplex – from outside the system

D = 2(Ki, I) (2.2)

Ki: x DW(x) V(x) Resistance(x) = f(DW(x), V(x))I: Resistance(ShipA) = 1200D: DW(ShipA) = 200.000 V(ShipA) = 15


31

Inference processes in design:Acquiring design knowledge

• E.g. towing tank

Ki = 5({D1, I1}, {D2, I2}, ...)

D1, I1: DW(ShipA) = 200.000 V(ShipA) = 15, Resistance(ShipA) = 1200

D2, I2: DW(ShipB) = 212.000 V(ShipB) = 14.8, Resistance(ShipB) = 1240

D3, I3: …

Ki: x DW(x) V(x) Resistance(x) = f(DW(x), V(x))

D1: V(ShipA) = 15.0 IsType(ShipA, TypeX)

D2: V(ShipB) = 14.8 IsType(ShipB, TypeX)

D3: …

Ks: x IsType(x, TypeX) V(x) isTypically around 15 knots

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