3d-cfd method driven with the dynamic

RESEARCH

ARTIC

LE

Copyright copy 2011 American Scientific PublishersAll rights reservedPrinted in the United States of America

SENSOR LETTERSVol 9 947ndash957 2011

3D-CFD Method Driven with the Dynamic DataUsing Real-Time Online Monitoring for Temperature

Simulation of Greenhouse

Yanzheng Liu1 Jing Chen2 Guanghui Teng4lowast Tingwu Xu3Tijiang Xiaokai4 Yazhou Lv1 and Yunkai Li4lowast

1College of Mechanical and Electrical Engineering Beijing Vocational College of Agriculture Beijing 102442 China2Department of Information Engineering Yantai Vocational College Yantai 264670 China

3International College at Beijing China Agricultural University Beijing 100083 China4College of Water Conservancy and Civil Engineering China Agricultural University Beijing 100083 China

(Received 28 August 2010 Accepted 11 November 2010)

The whole information of greenhouse micro-climate environment could be fully explained with Com-putational Fluid Dynamics (CFD) numerical analysis whose advantages were shown by previ-ous studies while many input parameters of the current CFD simulation model were fixed andunchanged along early experimental results It will be created a significant error with invariant inputparameters values to predict the real-time dynamic system which hinder seriously the simulationand prediction for the complex dynamic greenhouse environment In this paper the principle ofDynamic Data Driven Application System (DDDAS) was introduced exploringly The CFD simulationmodel driven with the dynamic data using real-time online monitoring the greenhouse thermal envi-ronment was established And taken the North China type multi-span greenhouse covered doublepolyethylene for a case it was used to simulate the airflow and temperature characteristics of green-house under many conditions The simulation results had the high accuracy (error area is 40ndash97)but lowered the measured value The results show that the airflow field in the planting area waspresented a wave-type distribution Under shading net of greenhouse opened the temperatureswere increased gradually form the center line between the wet curtain and fan to the around alongwith the direction of airflow movement which presented a parabola type While under inner shadingnet of greenhouse opened with a small slit at the edge the temperatures under shading net wereuniform but the local high temperature region was presented at the top of mid-span greenhousefor the existence of two vortexes in opposite direction at the top of both sides span And the CFDsimulation results were applied to establish the scheme of optimal sensor placement

Keywords Greenhouse Microclimate Environment Computational Fluid Dynamics DynamicData Driven Real-Time Online

1 INTRODUCTION

The key elements of micro-climate environment in green-house presented obvious spatial variability which effectedby the external environment and regulating method suchas large-scale multi-span greenhouse with maximum tem-perature difference above 10 C12 With the more demandfor the high additional value plant such as special flowersmore greenhouses need to be achieved the full information

lowastCorresponding authors E-mails futongcaueducnliyunkai126com

of airflow and temperature fields So as to achieving thevisualization for the environment elements of full fieldshas been became the key issue for design the environ-mental monitoring and control system of modern green-house Sensors monitoring is the most direct method forachieving the environmental information of greenhouseand the developed sensor technologies has mad it possi-ble But the excess monitoring sensors would cause a sig-nificant increase in cost as well as data redundancy andinterfere with the normal temperature and airflow field Infact environment controlling is a complex process for heatand mass transfer in greenhouse and achieving the visual

Sensor Lett 2011 Vol 9 No 3 1546-198X20119947011 doi101166sl20111358 947

RESEARCH

ARTIC

LE

3D-CFD Method Driven with the Dynamic Data Using Real-Time Online Monitoring Liu et al

information with the fluid mechanics methods has becomethe hot and difficult problems for environment simulationof greenhouseIn recent years with the changing quickly computer

technology and developing rapidly simulation for complexfluid Paying attention to Computational Fluid Dynam-ics algorithm and its applications has been become moreand more high A lot of Commercial computing soft-ware had been come out successfully such as ldquoFLUENTrdquoldquoANSYSrdquo ldquoCFXrdquo etc It had been applied to many fieldsfrom aviation to environment industry The CFD methodcould realize movement prediction numerical experimen-tation movement diagnosis etc The CFD method canhelp designers to select the most rapid and economicalapproach to conveniently optimizing various alternativesthus significantly reducing the physical work during theexperiment With the complex prototype test and the dif-ficult test for full field information of environmental ele-ments in greenhouse the CFD method has become anindispensable component of modern regulating principle ofgreenhouse micro-climate environment The CFD methodwas used to simulate the greenhouse environment morethan twenty years It was applied to study greenhouse envi-ronment under naturally ventilation at first time3 althoughtheir simulation results was existed a larger deviation tothe experimental data it offered a new idea to study onthe greenhouse environment system Boe et al4 used theCFD method for simulating the airflow field in two-spangreenhouse which the simulated value and measured datawith acoustic velocimeter matched well And it marked atruly application of the CFD method for greenhouse envi-ronment simulation In recent years the CFD method wasuse widely to study on the ventilation effect and structuraloptimization of greenhouse5ndash15 And the appliction f CFDmethod to simulating for greenhouse microclimate envi-ronmental information such as temperature and humiditywas reported a little16ndash19 While overall many input param-eters of the current CFD simulation model were fixed andunchanged along early experimental results while thoseparameters often changed with time under real condition19

It will be created a significant error with invariant inputparameters values to predict the real-time dynamic systemeven for causing the failure of prediction and control ingreenhouse This same value of the input parameters topredict changes in real-time dynamic system of the tradi-tional simulation methods usually have a significant erroror even the failure to predict and control The defects thatthe model running and input data were not simultaneouslyharmonize had been hindered seriously the simulationand prediction for the complex dynamic system In 2000United States National Science Foundation pointed outthe Dynamic Data Driven Application System (DDDAS)which had solved the problem of current CFD method withthe dynamic operation mode and integrating with real-timesimulation real-time monitoring automatic feedback con-trol management etc20 While the system is too complex to

apply for greenhouse environment simulating it offered anew way to realize the dynamic simulation for greenhouseIn this paper the principle of DDDAS was introduced

based on analyzing for formation mechanism of green-house environment The 3D-CFD simulation model drivenwith the dynamic data using real-time online monitoringand its solution algorithm were established And taken theNorth China type multi-span greenhouse covered doublepolyethylene for a case the simulated value was checkedwith the measured data Based on this the CFD simulationresults were applied to establish the scheme of optimalsensor placement of greenhouse

2 CONSTRUCTION OF THE CFDSIMULATION MODEL AND ITS SOLUTION

21 Construction of Simulation Model

In the greenhouse the air flow can be regarded as steady-state viscous incompressible turbulent flow The basicmathematical and physical model for greenhouse temper-ature environment were composed with the quality equa-tion momentum equation and energy equation posed21

Continuity equation

u

x+ v

y+ w

z= 0 (1)

Navier-Stokes equations

u

t+ middotuU =minusP

x+ 2u+fx (2a)

v

t+ middotvU =minusP

y+ 2v+fy (2b)

w

t+ middotwU =minusP

z+ 2w+fz (2c)

31

2

Fig 1 Calculating region for greenhouse thermal environmentsimulation

948 Sensor Letters 9 947ndash957 2011

RESEARCH

ARTIC

LE

Liu et al 3D-CFD Method Driven with the Dynamic Data Using Real-Time Online Monitoring

Fig 2 Grid distribution

Energy equation

t

[

(e+ U 2

2

)]+ middot

[

(e+ U 2

2

)U

]

= q+

x

(a

T

x

)+

y

(a

T

y

)+

z

(a

T

z

)

minus up

xminus vp

yminus wp

z+ uxx

x+ uyx

y

+ uzx

z+ vxx

x+ vyx

y+ vzx

z

+ wxx

x+ wyx

y+ wzx

z+f middotU (3)

Table I Input parameters of the CFD model

Name Numerical Units Remarks

Double polyethylene 3192 K Measurementtemperature

Wall temperature on 2978 K Measurementthe eastern side

Wall temperature on 2972 K Measurementthe west side

Wall temperature on 2982 K Measurementthe south side

Wall temperature on 2969 K Measurementthe north side

Soil temperature 2956 K MeasurementOutdoor air 3020 K Measurement

temperatureAir density 1225 kgm3 Fluent manualAir viscosity 00242 kg(m middot s) Fluent manualIndoor air heat 100643 wmminus1kminus1 Fluent manual

transfer coefficientIndoor air speed 179E-05 ms Fluent manualIndoor water 290 kgmol Fluent manual

vapor contentAcceleration due 981 ms2 Fluent manual

to gravityAtmospheric pressure 101324 Pa Fluent manualImport air temperature 3010 K Measurement

form the wet curtainImport air speed 107 ms Measurement

from the wet curtainOutlet temperature 2902 K Measurement

from the fan

Table II Physical parameters of the materials

Specific Thermal AbsorptionMaterial Density heat conductivity rate of solarname kgm3 Jkg middotK Wm middotk radiation

Soil 1975 2120 244 092Double polyethylene 100 1380 0047 mdash

Standard kminus Model

tk+

xikU

=

xi

[(+ t

k

k

xi

)]+Gk +GbminusminusYM (4)

t+

xiU

=

xi

[(+ t

xi

)]+C1

kGk +CGb

minusC22

kminusR (5)

GK = ut

2[(

u

x

)2

+(v

y

)2

+(w

z

)2]

+(u

y+ v

x

)2

+(u

z+ w

x

)2

+(v

z+ w

y

)2

(6)

where U denotes fluid velocity U = ui+ v j +wk (m middotsminus1 u v w denoting the velocities on axes of x y and zxi denote the direction of coordinate (kg middotmminus3 and

(Pa middot s) denote water density and dynamic viscous coeffi-cient p (Pa) denotes fluid pressure fx fy and fz denote themass forces on 3 axes when gravity is the only mass forcefx = fy = 0 fz = minusg (kg middotmminus3 and (Pa middot s) denoterespectively the fluid density and dynamic viscosity coef-ficient P (Pa) denotes the fluid pressure e (J) denotes theunit mass of fluid which it is to be able to T (K) denotesair temperature a (W middotmminus1 middotKminus1 denotes the air thermalconductivity q (J) denotes per unit volume of fluid to theheat increment (Pa) denotes the stress tensor k(m2 middotsminus2

denotes turbulent kinetic energy (m2 middot sminus2 denotes tur-bulent dissipation rate t(m

2 middot s) denotes turbulent viscos-ity Gk(kg middotmminus1 middotsminus2 and Gb(kg middotmminus1 middotsminus2 denote averagevelocity gradient and turbulent pulse kinetic energy causedby buoyancy YM (kg middotmminus1 middotsminus2 denote contribution of fluc-tuating dilatation in compressible turbulence to the overalldissipation rate YM = 0 R(J middot kgmolminus1 middotKminus1 denote gas-law constant 831447times103 C1 = 11344 C2 = 11392 C =01309 k = 1130 = 1133

Sensor Letters 9 947ndash957 2011 949

RESEARCH

ARTIC

LE


Fig 3 Input module of temperature field parameter

Discrete Ordinates radiation using radiation (DO)model

middot Ir ss+ a+sIr s

= an2T2

+ s

4

middotint 4

0Ir sIs sprimedprime

intIr d

=Nsumi=1

iIr i (7a)

Nsumi=1

i = 4 (7b)

q =int4

Id=Nsumi=1

iiIi (7c)

Fig 4 Numerical simulation process

where s denotes the discrete space angle ii =12 13 13 13 N denotes the discrete direction i denotes theweighted sphere volume surface area Ir i (W middotmminus2

Kminus1 denotes the radiation intensity

22 Solution Process

The whole greenhouse internal space was selected for thecalculation domain In order to avoiding reflex stabilitycalculation and avoiding turbulence caused by boundaryconditions the wet curtain at the entrance was extended inthe computational domain as shown in Figure 1Within the computational region the hexahedron grid-

ding unit is used for CFD analysis The discrete approachis the method of finite volume GAMBIT was used forgridding generation By the frequentative gridding gen-eration it was found that the simulation precision was


RESEARCH

ARTIC

LE


Fig 5 Sampling points

increased none with decreasing the mesh size when thegrid size was lower than 200 mm The length of each gridwas 200 mm and the total meshes was 025 million Thegrid was shown as Figure 2 The grid quality was veryhigh which the skew rate was mainly in 04 or less notmore than 07The numerical calculation method is the non-coupling

implicit algorithm with definite constants The first-orderwindward pattern is used for the item of pressure etcThe SIMPLEC (Semi-Implicit Method for Pressure-LikedEquations Consistent) algorithm is selected for couplingpressure and velocity21 The accuracy of convergence ischosen to be 00001 FLUENT613 software was used forsolving the mathematical model

23 Boundary Conditions and Its Real-TimeCollection

The air flow was the research object of CFD simula-tion The cover material enclosure structure and soil wereset as the boundary condition and it were set to no-slip

Fig 6 The comparison between simulated and measured value

boundary as ldquoWallrdquo type The internal shading net wasset as the ldquoWallrdquo boundary which was zero thickness withtransmittance of 35 The input parameters of the CFDmodel was shown in Table I and a part of the thermalproperties parameters of the material as shown in Table IIThe boundary function method was used for low velocityregion near the boundaryThe CFD simulation software and greenhouse acquisi-

tion system was linked and the temperature of air formthe wet curtain air from the fan outdoor air wall soilandinleted air speed from the wet curtain were real-timecollected through the acquisition module And it was readas the parameter setting path of CFD calculation Theprogram was designed with LabVIEW 80 and the inputmodule and procedures chart were shown respectively inFigures 3 and 4

3 MODEL CALIBRATION AND SELECTION

31 Experimental Design

The North China type multi-span greenhouse covered dou-ble polyethylene was taken as the simulated object whichwas designed by the Key Laboratory of Agricultural Engi-neering in Structure and Environment Ministry of Agri-culture It is located in Haidian District (North attitude399 East longitude 1163) Beijing The specific char-acteristics of the greenhouse were shown as(1) Geometric characteristics the greenhouse was northsouth trend 3-span and each span 8 m 6-room and eachroom wide 3 m gutter high 3 m and ridge high 49 m(2) Enclosure structure material the covering materialwas the long double polyethylene The South wall mate-rial was PC plate and six fans (Type 9FJ125) were seton it The North wall material was sandwich steel plateand wet curtain was installed on it

In the center of covering double polyethylene surround-ing walls and soil the temperature sensors was arrangedFour temperature sensors were laid respectively 03 m out-side the wet curtain and fan from the greenhouse and itsaverage was the inlet air temperature from the wet cur-tain and the outlet air temperature from the fan Basedon assuming the air flow was imported horizontally witha uniform velocity the field within the wet curtain was

Table III Working conditions of CFD simulation

Working 3 fans 6 fans Innerconditions opened opened shading net Note

Tm1 1 0 0 (1) ldquo1rdquo denotes in workingand ldquo0rdquo denotes in restTm2 1 0 1

Tm3 0 1 1 (2) Shading net openedwith a silt of 5 cmTm4 0 1 0

Note Open the three fans for every one


RESEARCH

ARTIC

LE


(a) X=10 m (b) Y=14 m

Fig 7 Airflow distribution under the working condition of Tm1

(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m



RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m

Fig 11 Temperature distribution under the working condition of Tm1

(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m

(c) Y=35 m (d) Z =90 m (airflow field)



RESEARCH

ARTIC

LE


divided into four areas and one heat bulb (Beijing Detec-tion Instrument Factory ZRQF smart anemometer) was setto testing the outlet air speed in each area center (Theaverage value was the import parameters) Four tempera-ture sensors were arranged around 15ndash2 m outside to thegreenhouse and its average was regarded as the outdoorair temperature The inner shading net heat transferringwerenrsquot considered The measurement value required forCFD model were shown in Table I

32 Model Checking

With the CFD method the temperature distribution wassimulated under the six fans opened and the inner shadingnet closed which the calculation accuracy of the param-eters was 1 times 10minus4 Eight points from the simulationresults were elected at the center vertical cross-section ofthe greenhouse as shown in Figure 5 and the compari-son between the simulation and experimental results wasshown in Figure 6 The result showed that the simulationaccuracy was very high which correlation coefficient (R2are above 094 and the error is 40ndash97 While overallthe simulated values were lower than the measured oneswhich were created that the long wave radiation and con-vection of all internal appurtenances in greenhouse Thesimulation accuracy of lower five points were higher thanothers the error was 65 It was mean that the CFDmethod constructed in this paper could be used to analyzethe thermal environment

4 THERMAL ENVIRONMENT SIMULATIONUNDER MECHANICAL VENTILATION

41 Characteristics of Thermal Environment

The wet curtain-fan cooling system was usually used forcooling large-scale greenhouse in summer which effectedsignificantly the thermal environment A few of researcheshad shown that the environment should be the most uneven

(a) Y=14 m (b) X =40 100 180 m


distribution under this mechanical ventilation So it is nec-essary to research on the temperature distribution The air-flow and temperature fields had been simulated with CFDmethods under the four working conditions with mechani-cal ventilation which was shown in Table III The resultsof airflow were shown in Figures 7ndash10 and the results oftemperature field were shown in Figures 11ndash14From Figures 7ndash10 the results showed that

(1) the air velocity had gone through a process of slowto fast which the position of fan was fastest The airflowfield in the planting area was presented a wave-type dis-tribution(2) The air flow speeds under inner shading net closedwere higher than opened when only three fans opened butsix fans none(3) When the inner shading net was opened with a slitat the edge there were two vortexes in opposite directionbetween the inner shading net and covering materials butclosed none

From Figures 11ndash14 the results showed that(1) temperature distribution was layered significantly frombottom to top under four working conditions and it isuniform in even layer with the deviation of 2(2) Under shading net of greenhouse opened the tem-peratures of three cross-section of X = 4130 m 100 mand 180 m were increased gradually form the centerline between the wet curtain and fan to the around alongwith the direction of airflow movement which presenteda parabola type It was created with absorbing the longwave radiation released from soil and wall when the airwas moving(3) When under inner shading net of greenhouse openedwith a small slit at the edge (taken Tm3 for example) thelocal high temperature region was presented at the top ofmid-span greenhouse (Fig 13(c)) which was created withthe existence of two vortexes in opposite direction at thetop of other two sides span (Fig 13(d))(4) When the inner shading net was closed the tempera-ture under shading net was uniform and the error was lessthan 1 while there are local high-temperature center


RESEARCH

ARTIC

LE


42 Design Scheme of Optimal Sensor Placement ofGreenhouse

The data extraction of CFD simulation under four work-ing condition was made and the sampling points wereselected as Figure 15 There were four-dimensional tem-perature characteristics in every point and a total of276 data points was extracted Based on this the K-meansclustering algorithm was used to analysis in order todesigning the scheme of optimal sensor placement ofgreenhouseFrom Figure 10ndash13 the layered distribution of temper-

ature can be found obviously We can hypothesis that it

(a) vertical

(d) Horizontal level

Fig 15 Distribution of sampling points

would be regard as one layer when the temperature differ-ence of layers was lower than 4 C Three sensors must bearranged in order to knowing the full greenhouse environ-mental information so the extract data would be classifiedinto three classes with K-means algorithm and the resultswas shown in Table IV The sensor placement was deter-mined with the distance between every points and class-center The point with shortest distance was the optimalsensor placement and the second shortest distance was thesuboptimal one From the Table IV the optimal and subop-timal sensor placements were located in the two circle ofthe edge when it was projected into the horizontal plane


RESEARCH

ARTIC

LE


Table IV The results of the cluster analysis

Temperature of Average distance Optimal Suboptimalclass centerC between centers placem placem

Classes Numbers of samples Tm1 Tm2 Tm3 Tm4 1 2 3 X Y Z X Y Z

1 192 26131 26131 25137 25133 mdash 20134 9130 13135 2131 16135 1135 1135 161352 48 29138 29139 41133 37135 20134 mdash 12132 19135 4 13135 19135 4 161353 36 26138 26138 34130 28137 9130 12132 mdash 1135 2137 7135 22135 2137 13135

which was created with the radiation and convection ofenclosure structure In the vertical distribution the classwith most points was located in 15ndash21 m which was cre-ated with soil radiation and wet curtain-fan cooling systemAfter analysis the optimal position of three sensors placedat the points which were (X Y Z)= (15 15 165) (XY Z)= (15 27 75) (X Y Z)= (195 4 135) In orderto convenient for managing that the sensors was placed onthe point (X Z)= (15 165) at the vertical 15 m 27 mand 40 m were able to meet the requirements

5 CONCLUSIONS

(1) The 3D-CFD method driven with the dynamicdata using real-time online monitoring could be appliedto simulate the airflow and temperature of North Chinatype multi-span greenhouse covered double polyethyleneWhile the simulated values were lower than the measuredones which were created that the long wave radiation andconvection of all internal appurtenances in greenhouseAnd it could be referenced by other greenhouse(2) The air velocity driven with wet curtain-fan cooling

system had gone through a process of slow to fast and theairflow field in the planting area was presented a wave-type distribution When the inner shading net was openedwith a slit at the edge there were two low speed vortexesin opposite direction between the inner shading net andcovering materials(3) The temperature distribution was layered signifi-

cantly from bottom to top and it is uniform in even layerwith the deviation of 2 C Under shading net of green-house opened the temperatures were increased graduallyform the center line between the wet curtain and fan tothe around along with the direction of airflow movementwhich presented a parabola type Under inner shadingnet opened with a small slit at the edge the local hightemperature region was presented at the top of mid-spangreenhouse which was created with the existence of twovortexes in opposite direction at the top of other two sidesspan(4) Three sensors must be arranged in order to knowing

the full greenhouse environmental information With theK-means clustering algorithm analyzing the optimal andsuboptimal sensor placements were located in the two cir-cle of the edge when it was projected into the horizontalplane which the optimal position were (X Y Z) = (15

15 165) (X Y Z) = (15 27 75) (X Y Z) = (1954 135) In order to convenient for managing the sensorswas placed on the point (X Z)= (15 165) at the vertical15 m 27 m and 40 mThe CFD method was applied to simulate the micro-

climate environment only twenty years while it was a newthoughts and methods In this paper the steady-state theoryis only approximate to the environment whose error willbe larger In fact it is unsteady that the forced coolingprocess was executed with wet curtain-fan system and theunsteady simulation will be need

Acknowledgments We are grateful for financial sup-port from the National High Technology Develop-ment Plan Key Project (863) (No 2006AA100208-3)Project for City Agricultural Subject Groups of Bei-jing Education Committee (No XK100190553) Projectfor Transformation of Scientific and TechnologicalAchievements and Industrialization of Beijing Munici-pal Commission of Education and the Young ResearchFund from Beijing Vocational College of Agriculture(No XY-QN-09-27)

References and Notes

1 M Bambang and S Gajendra Proceedings of the World Congressof Computers in Agriculture and Natural Resources edited by F SZazueta and J Xin ASAE Publication Number 701P0301 (2002)

2 Y X Li B M Li Z Li and T Ding J Chin Agric Univ 9 87(2004)

3 L Okushima S Sase and M A Nara Acta Hortic 284 129 (1989)4 G P A Bot T Boulard and A Mistriotis Agric Eng Madrid

96B 1 (1996)5 A Mistriotis C Arcidiacono P Picuno G P A Bot and

G Scarascia-Mugnozza Agr Forest Meteorol 88 121 (1997a)6 A Mistriotis G P A Bot P Picuno and G Scarascia Mugnozza

Agr Forest Meteorol 85 217 (1997b)7 A Mistriotis T Jong M J M Wagemans G P A Bot and T De

Jong Neth J Agr Sci 45 81 (1997c)8 A Mistriotis P Picuno and G P A Bot Computational study of the

natural ventilation driven by buoyancy forces Proceeding of the 3rdInternational Workshop on Mathematical and Control Applicationsin Agriculture and Horticulture (1997d) Vol 110 pp 67ndash72

9 I Al-Helal A Computational Fluid Dynamics Study of Natural Ven-tilation in Arid Region Greenhouses The Ohio State UniversityOhio USA (1998)

10 T Boulard R Haxaire and M A Lamrani J Agri Eng Res74 135 (1999)

11 J I Montero and A Antoacuten J Agri Eng Res 79 213 (2001)12 R Haxaire T Boulard and M Mermier Acta Hortic 534 31

(2000)


Delivered by Publishing Technology to ingenta internal live 410IP 6115334204 On Fri 06 Sep 2013 191331

Copyright American Scientific Publishers

RESEARCH

ARTIC

LE


13 I Lee and T H Short Trans ASAE 43 745 (2000)14 I Lee L Okushima A Ikegushi S Sase and S H Short

Prediction of natural ventilation of multi-span greenhouses usingCFD techniques and its verification with wind tunnel test 93rdAnnual International Meeting of ASAE edited by S M JosephASAE Milwaukee Wisconsin USA (2000)

15 T Bartzanas T Boulard and C Kittas Comput Electron Agr34 207 (2002)

16 A Shklyar and A Arbel Greenhouse turbulence flow numericalsimulation Proceedings of the International Conference and British-Israeli Workshop on Greenhouse Techniques Towards the 3rd Mil-lennium (2000) Vol 534 pp 49ndash55

17 S Reichrath F Ferioli and T W Davies A simple computa-tional fluid dynamics model of a tomato glasshouse Proceedingsof the International Conference and British-Israeli Workshop

on Greenhouse Techniques Towards the 3rd Millennium (2000)Vol 534 pp 197ndash204

18 S Reichrath and T W Davies Computational fluid dynamics sim-ulations and validation of the pressure distribution on the roof ofa commercial multi-span Venlo-type glasshouse Proceedings of the1st Workshop on Management Identification and Control of Agri-culture Buildings MICAB UTAD Vila Real Portugal January(2001)

19 T Norton D Sun J Grant R Fallon and V Dodd A review Biore-source Technol 98 2386 (2007)

20 United States National Science Foundation DDDAS Dynamic datadriven applications systems Application examples [EBOL] (2006)http wwwnsfgovcisecnsdddasDDDAS_Appen-dix Jsp

21 F J Wang Theory and Its Applications of CFD Software TsinghuaPublicaion Beijing (2004) pp 123ndash129


RESEARCH

ARTIC

LE


information with the fluid mechanics methods has becomethe hot and difficult problems for environment simulationof greenhouseIn recent years with the changing quickly computer

technology and developing rapidly simulation for complexfluid Paying attention to Computational Fluid Dynam-ics algorithm and its applications has been become moreand more high A lot of Commercial computing soft-ware had been come out successfully such as ldquoFLUENTrdquoldquoANSYSrdquo ldquoCFXrdquo etc It had been applied to many fieldsfrom aviation to environment industry The CFD methodcould realize movement prediction numerical experimen-tation movement diagnosis etc The CFD method canhelp designers to select the most rapid and economicalapproach to conveniently optimizing various alternativesthus significantly reducing the physical work during theexperiment With the complex prototype test and the dif-ficult test for full field information of environmental ele-ments in greenhouse the CFD method has become anindispensable component of modern regulating principle ofgreenhouse micro-climate environment The CFD methodwas used to simulate the greenhouse environment morethan twenty years It was applied to study greenhouse envi-ronment under naturally ventilation at first time3 althoughtheir simulation results was existed a larger deviation tothe experimental data it offered a new idea to study onthe greenhouse environment system Boe et al4 used theCFD method for simulating the airflow field in two-spangreenhouse which the simulated value and measured datawith acoustic velocimeter matched well And it marked atruly application of the CFD method for greenhouse envi-ronment simulation In recent years the CFD method wasuse widely to study on the ventilation effect and structuraloptimization of greenhouse5ndash15 And the appliction f CFDmethod to simulating for greenhouse microclimate envi-ronmental information such as temperature and humiditywas reported a little16ndash19 While overall many input param-eters of the current CFD simulation model were fixed andunchanged along early experimental results while thoseparameters often changed with time under real condition19

It will be created a significant error with invariant inputparameters values to predict the real-time dynamic systemeven for causing the failure of prediction and control ingreenhouse This same value of the input parameters topredict changes in real-time dynamic system of the tradi-tional simulation methods usually have a significant erroror even the failure to predict and control The defects thatthe model running and input data were not simultaneouslyharmonize had been hindered seriously the simulationand prediction for the complex dynamic system In 2000United States National Science Foundation pointed outthe Dynamic Data Driven Application System (DDDAS)which had solved the problem of current CFD method withthe dynamic operation mode and integrating with real-timesimulation real-time monitoring automatic feedback con-trol management etc20 While the system is too complex to

apply for greenhouse environment simulating it offered anew way to realize the dynamic simulation for greenhouseIn this paper the principle of DDDAS was introduced

based on analyzing for formation mechanism of green-house environment The 3D-CFD simulation model drivenwith the dynamic data using real-time online monitoringand its solution algorithm were established And taken theNorth China type multi-span greenhouse covered doublepolyethylene for a case the simulated value was checkedwith the measured data Based on this the CFD simulationresults were applied to establish the scheme of optimalsensor placement of greenhouse

2 CONSTRUCTION OF THE CFDSIMULATION MODEL AND ITS SOLUTION

21 Construction of Simulation Model

In the greenhouse the air flow can be regarded as steady-state viscous incompressible turbulent flow The basicmathematical and physical model for greenhouse temper-ature environment were composed with the quality equa-tion momentum equation and energy equation posed21

Continuity equation

u

x+ v

y+ w

z= 0 (1)

Navier-Stokes equations

u

t+ middotuU =minusP

x+ 2u+fx (2a)

v

t+ middotvU =minusP

y+ 2v+fy (2b)

w

t+ middotwU =minusP

z+ 2w+fz (2c)

31

2

Fig 1 Calculating region for greenhouse thermal environmentsimulation


RESEARCH

ARTIC

LE



Energy equation

t

[

(e+ U 2

2

)]+ middot

[

(e+ U 2

2

)U

]

= q+

x

(a

T

x

)+

y

(a

T

y

)+

z

(a

T

z

)

minus up

xminus vp

yminus wp

z+ uxx

x+ uyx

y

+ uzx

z+ vxx

x+ vyx

y+ vzx

z

+ wxx

x+ wyx

y+ wzx

z+f middotU (3)















from the fan





tk+

xikU

=

xi

[(+ t

k

k

xi


t+

xiU

=

xi

[(+ t

xi

)]+C1

kGk +CGb

minusC22

kminusR (5)

GK = ut

2[(

u

x

)2

+(v

y

)2

+(w

z

)2]

+(u

y+ v

x

)2

+(u

z+ w

x

)2

+(v

z+ w

y

)2

(6)






RESEARCH

ARTIC

LE





= an2T2

+ s

4

middotint 4


intIr d

=Nsumi=1

iIr i (7a)

Nsumi=1

i = 4 (7b)

q =int4

Id=Nsumi=1

iiIi (7c)




22 Solution Process




RESEARCH

ARTIC

LE




















RESEARCH

ARTIC

LE


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m



RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m


(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m




RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE



Energy equation

t

[

(e+ U 2

2

)]+ middot

[

(e+ U 2

2

)U

]

= q+

x

(a

T

x

)+

y

(a

T

y

)+

z

(a

T

z

)

minus up

xminus vp

yminus wp

z+ uxx

x+ uyx

y

+ uzx

z+ vxx

x+ vyx

y+ vzx

z

+ wxx

x+ wyx

y+ wzx

z+f middotU (3)















from the fan





tk+

xikU

=

xi

[(+ t

k

k

xi


t+

xiU

=

xi

[(+ t

xi

)]+C1

kGk +CGb

minusC22

kminusR (5)

GK = ut

2[(

u

x

)2

+(v

y

)2

+(w

z

)2]

+(u

y+ v

x

)2

+(u

z+ w

x

)2

+(v

z+ w

y

)2

(6)






RESEARCH

ARTIC

LE





= an2T2

+ s

4

middotint 4


intIr d

=Nsumi=1

iIr i (7a)

Nsumi=1

i = 4 (7b)

q =int4

Id=Nsumi=1

iiIi (7c)




22 Solution Process




RESEARCH

ARTIC

LE




















RESEARCH

ARTIC

LE


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m



RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m


(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m




RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE





= an2T2

+ s

4

middotint 4


intIr d

=Nsumi=1

iIr i (7a)

Nsumi=1

i = 4 (7b)

q =int4

Id=Nsumi=1

iiIi (7c)




22 Solution Process




RESEARCH

ARTIC

LE




















RESEARCH

ARTIC

LE


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m



RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m


(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m




RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE




















RESEARCH

ARTIC

LE


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m



RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m


(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m




RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m


(a) X=10 m (b) Y=14 m



RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m


(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m




RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE


(a) Y=14 m (b) X=40 100 180 m


(a) Y=14 m (b) X =40 100 180 m


(a) Y=14 m (b) X =40 100 180 m




RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE



32 Model Checking





(a) Y=14 m (b) X =40 100 180 m






RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE





(a) vertical





RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE













RESEARCH

ARTIC

LE







5 CONCLUSIONS




















(2000)




RESEARCH

ARTIC

LE















RESEARCH

ARTIC

LE













3d-cfd method driven with the dynamic

Documents

shading net of greenhouse

inner shadingnet of

realtime dynamic system

external environment

microclimate environment

realtime online monitoring

dynamic datausing

cfd simulationmodel