Development of micro-scale CFD model to predict wind environment on complex terrain In-bok Lee

Transcription

1 Development of micro-scale CFD model to predict wind environment on complex terrain In-bok Lee S N U

2 CONTENTS

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4 Mountain meteorological observation system Over 65% of South Korea s territory is forestland. Importance of accurate information of mountain weather Construction plan of total 200 mountain meteorological observation system

5 Guidelines for construction Standards for installation of meteorological observation system (World Meteorological Organization, National Rural Fire Authority, Canadian Forest Service, National Wildfire Coordinating Group) Insufficiency of mountain meteorological observation system - 10 times larger clear-cut size than maximum height of trees or 10 m higher than maximum height of trees World Meteorological Organization Canadian Forest Service National Rural Fire Authority National Wildfire Coordinating Group

6 Construction of observation system No standard for installation of mountain meteorological observation system in South Korea 10 m height of observation system in 10m X 10m size of clear-cut?

7 Field experiment Numerical modeling Difficulties in acquiring results - unstable and unpredictable environment conditions Difficulties in changing the experimental conditions Time and labor consuming Theory Possibility of changing experimental and environmental conditions Visualization of invisible air flow Low cost for result analysis according to various experimental conditions Predictability of results To overcome limitations of field experiment, numerical modeling was used based on theory To secure reliability of numerical modeling, validation was conducted through field experiment

8 Synoptic or meso scale modeling Micro scale modeling Seong et al., 2013; Srinivas et al., 2013; Olatinwo et al., 2011;2010; etc. Blocken et al., 2015a;2015b; Park et al., 2014; Meronet et al., 2012; etc.

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10 To design micro-scale CFD model for estimation of wind environment of mountainous terrain

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12 Flow chart Location of experimental region Digital Elevation Model Land use map Design of CFD simulation model No Set-up of boundary condition Validation of CFD simulation model Yes Development of prediction model for micro-climate at mountainous terrain [ On-going ] Plant air-resistance modules - Conifer tree - Deciduous tree Step.1 Coefficient of air-resistance (previous research) Step.2 Coefficient of air-resistance (field experiment) Determination of proper installation method of mountain meteorological observation system - Location of system - Height of system - Size of clear-cut - etc.

13 Experimental region for CFD validation Gwangreung forest - Location : Pocheon-si, Gyeonggi-do, Korea - 2 Flux towers : GDK (Gwangneung Deciduous Korea) tower 37º 45' N, 127º 09' E : GCK (Gwangneung Conifer Korea) tower GDK Tower 37º 44' 54.7'' N, 127º 09' 46.6'' E 6 km Weather analysis period GCK Tower - Jan. 1, 2010 ~ Dec. 31, km

14 Experimental region for CFD validation Height measurement of conifer trees GCK Tower Measuring point Type of tree : Abies holophylla Measurement : 13 trees Average height : 30.2 m (SD : 2.0) Average height of trunk : 13.4 m (SD : 4.2)

15 Experimental region for CFD validation Height measurement of deciduous trees GDK Tower Measuring point Type of tree : Quercus serrata Measurement : 11 trees Average height : 17.0 m (SD : 3.9) Average height of trunk : 6.5 m (SD : 2.4)

16 air resistance of trees Source term of momentum conservation equation - Darcy-Forchheimer equation p = μ α v + C ρv2 p = C ρv2 Wilson(1985) equation p = L AD C D ρv 2 C 2 = 2 L AD C D p =pressure drop μ = coefficient of viscosity α = coefficient of penetration ρ = density of air v = velocity of air C D = Drag coefficient of tree L AD = Leaf area density C 2 = coefficient of inertial resistance of tree

17 air resistance of trees (previous research) Conifer trees - Wind tunnel experiment or field experiment Drag coefficient (C D ) : 0.1 ~ 1.1 Leaf area index of conifer tree from Gwangneung arboretum : 4 ~ 8 Calculated coefficient of inertial resistance : 0.08 ~ 1.76 Deciduous trees (Raymer, 1962; Mayhead, 1973; Rudnicki et al., 2004; Yoo et al., 2010; Bitog et al., 2011; etc.) - Wind tunnel experiment or field experiment Drag coefficient (C D ) : ~ 1.06 Leaf area index of conifer tree from Gwangneung arboretum : 1 ~ 6 Calculated coefficient of inertial resistance : 0.03 ~ 1.27

18 air resistance of trees (field experiment) Experimental trees - Conifer tree(abies holophylla) and Deciduous tree(quercus serrata) Side view Top view Ultra sonic anemometer (DeltaOHM, Italia) Multi channel hot-wire anemometer (Kanomax Inc., Japan)

19 Computational Fluid Dynamics Recently widely used to analyze turbulent flow, multiphase flow, ventilation of agricultural facility, etc. Solving non-linear Navier-stokes equation ANSYS, CFX, Star-CCM+, etc. Continuity eq. ρ t + ρu x j = 0 i Momentum eq. t ρu i + x i ρu i u j = x j pδ ij + μ u i x j + u j x i + ρg i Energy eq. t ρc at + x i ρu j C a T x j λ T x j = S T

20 CFD simulation model Ground surface classification Land use map Extraction of classified data Land use classification Grassland Target area Conifer tree Deciduous tree

21 CFD simulation model Characteristics of CFD model Designed domain Mesh size Mesh type Diameter: 5.0km, Height: 2.0km Horizontal size : 20m Height of nearby surface : 0.3m Growth rate of nearby surface : 1.1 Tetra / Prism Number of meshes 8,439,864 Wind conditions CO 2 sources Turbulence model UDF modules Wind-speed : 1.0 m/s Wind-direction: WNW, W C3 plant, conifer tree, deciduous tree Standard k-ε turbulence model Wind profile, plant respiration module, soil respiration module Atmospheric stability etc.

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23 Weather analysis Prevailing wind analysis GDK tower GCK tower 0.5 m/s 0.5 ~ 1.5 m/s 1.5 ~ 2.5 m/s 2.5 ~ 3.5 m/s 3.5 ~ 4.5 m/s 4.5 ~ 5.5 m/s > 5.5 m/s

24 Air resistance coefficient Field experiment for air resistance of conifer trees

25 Air resistance coefficient Field experiment for air resistance of deciduous trees

26 Air resistance coefficient Determination of air resistance Measurement of wind speed reduction through tree - Determination of wind direction using ultra sonic anemometer -> Determination of windward sensor and leeward sensor -> tendency of wind speed vs. reduction of wind speed Conifer tree 1 Conifer tree 2 Deciduous tree 1 Deciduous tree 2

27 Coefficient of inertial resistance (C 2 ) Air resistance coefficient Determination of air resistance 1) Reduction of wind speed according to coefficient of inertial resistance Windward wind speed (m/s) C 2 V ref V V ref V V ref V V ref V V ref V ) Tendency of coefficient of inertial resistance y = x x R² = Constant a

28 Coefficient of inertial resistance (C 2 ) Air resistance coefficient Determination of coefficient of inertial resistance using field experiment data y = x x R² = Coefficient of inertial resistance (C 2 ) 1 st Exp. 2 nd Exp. Average 0.6 C 2 = Conifer tree Conifer tree Constant a Conifer 1 st Experiment a = Deciduous tree Deciduous tree

29 CFD simulation Qualitative analysis Valley direction Valley direction

30 CFD model validation Validation of air flow (boundary wind condition : W, 1.0 m/s) 95% confidence interval of measured wind environment data Standard Lower limit of Upper limit of Average deviation confidence interval confidence interval Wind speed (m/s) GCK Wind direction (degree) GDK Wind speed (m/s) Wind direction (degree) Simulated results according to turbulence model Standard k-ε RNG k-ε Realizable k-ε Standard k-ω SST k-ω GCK GDK Wind speed (m/s) Wind direction (degree) Wind speed (m/s) Wind direction (degree)

31 CFD model validation Validation of air flow (boundary wind condition : E, 1.0 m/s) 95% confidence interval of measured wind environment data Standard Lower limit of Upper limit of Average deviation confidence interval confidence interval Wind speed (m/s) GCK Wind direction (degree) GDK Wind speed (m/s) Wind direction (degree) Simulated results according to turbulence model Standard k-ε RNG k-ε Realizable k-ε Standard k-ω SST k-ω GCK GDK Wind speed (m/s) Wind direction (degree) Wind speed (m/s) Wind direction (degree)

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33 Conclusions Design of CFD model considering land use classification and complex topography Realization of air resistance of tree according to type of tree and land use classification Suitable to simulate wind environment using developed 3D CFD model with standard k-ε turbulence model

34 THANK YOU A3EL Homepage: a3el.snu.ac.kr