||Application of numerical simulation of wind field in offshore domain
||Department of Aeronautics & Astronautics
||Thanh Nhat Trieu Nguyen
Weather Research and Forecasting (WRF)
Large eddy simulation (LES)
Detached eddy simulation (DES)
Computational Fluid Dynamics (CFD)
Wind resource assessment is the foundation of wind farm development. The wind farms depend on meteorological conditions, especially the magnitude of the wind speed. Thus, the wind energy is sensitive to the wind speed, to the requirement, a good quality anemometer as wind mast (tower). Unfortunately, there are some errors may occur in the measurements even with proper calibration caused by tower shadow, the nearby obstacles, or even the anemometer tower itself, may cause vibration of the instrument, resulting in measurement error. Previous studies showed that numerical simulation using a mesoscale meteorological model to verify the measurement data. However, the meteorological model has the inherent limitation as the simulation relies on National Centers for Environmental Prediction (NCEP) reanalysis data with relatively coarse resolution, and no data assimilation technique was adopted to improve the accuracy of the simulation. To couple the meteorological model as Weather Research and Forecasting (WRF) with a commercial computational fluid dynamics (CFD) software FLUENT, this study aims to verify the accuracy of measurement data from a wind mast erected offshore near the west coast of Taiwan. The WRF is used to provide velocity profile inlet for unsteady boundary conditions for FLUENT. The results show that FLUENT data output with smooth terrain is closer to measurement data than WRF data. Fluent with higher resolution and strong techniques such as Computer Aided Design (CAD), Finite Volume Method, and many turbulent modeling as Large eddy simulation (LES) and Detached eddy simulation (DES) combined with the appropriate boundary condition, it can provide wind field simulation results in more accuracy. In addition, this study also examined the physical characteristics of a turbulent boundary layer with the CFD methods employed. Particularly, the region near the wall where the viscous effect dominates can affect the results and fidelity of numerical solutions. However, the numerical results indicated that the affected region is rather insignificant in comparison with the entire thickness of the boundary layer.
TABLE OF CONTENTS III
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER 1. THE ATMOSPHERIC BOUNDARY LAYER AND LITERATURE REVIEW 1
1.1 The Atmospheric Boundary Layer characteristics 1
1.1.1 What is the Atmospheric Boundary Layer (ABL)? 1
1.1.2 Wind and Flow 1
1.1.3 A classification scheme for meteorological phenomena 3
1.1.4 Significance of the Atmospheric Boundary Layer  4
1.1.5 Wind filed in ABL 4
1.2 Literature review 5
1.2.1. Atmospheric Turbulent Flow Solutions Coupled with a Mesoscale Weather Prediction Model. 5
1.2.2 Study on the Micro-scale simulation of wind field over complex terrain by RAMS/FLUENT modeling system.  6
1.2.3 Application of FLUENT on fine-scale simulation of wind field over complex terrain.  7
1.2.4. An application of the RAMS/FLUENT system on the multi-scale numerical simulation of the urban surface layer—A preliminary study.  8
CHAPTER 2. THEORIES AND METHODOLOGY 9
2.1 The Weather Research & Forecasting Model (WRF) 9
2.2 Tower mast 9
2.3 The approaches to describe physical phenomena of fluid 11
2.3.1 Experimental fluid dynamics (EFD) 11
2.3.2 Analytical fluid dynamics (AFD) 12
2.3.3 Computational fluid dynamics (CFD) 12
2.4 Characterizing Turbulence 13
2.4.1 Mean and turbulent part 13
2.4.2 Space and time series 13
2.5 Turbulence modeling 16
2.5.1 Large Eddy Simulation (LES) 17
220.127.116.11 Governing equation 18
18.104.22.168 Subgrid-Scale Model 19
22.214.171.124 Limitation of LES 20
2.5.2 Detached eddy simulation (DES) 21
126.96.36.199 DES with the Spalart-Allmaras (SA) model 21
2.6 Numerical method in FLUENT 22
2.6.1 Finite Volume Method 23
2.6.2 Interpolation methods 24
188.8.131.52 Linear interpolation 24
184.108.40.206 Bilinear interpolation 25
2.6.3 User Defined Function 25
2.6.4 Boundary conditions 26
220.127.116.11 Inlet conditions: 26
18.104.22.168 Outlet conditions: 27
22.214.171.124 Other boundary conditions: 27
CHAPTER 3. GRID GENERATION 29
3.1 Basic of grid generation 29
3.2 Classification of Grid: 30
3.3 Grid quality 31
3.3.1 Skewness 31
3.3.2 Smoothness 33
3.3.3 Aspect ratio 33
3.4 The concept y plus (y+) 34
3.4.1 Boundary layer theory: 34
3.4.2 Inner layer details: The law of the wall from F.White  35
3.4.3 Outer layer 36
3.4.4 Wall function approach 37
CHAPTER 4. GENERAL DESCRIBE THE PROBLEM AND THE IMPLEMENTATION PROCESS 41
4.1 General description of the problem 41
4.1.1 Description of WRF/FLUENT simulation 41
4.1.2 Description of WRF simulation 42
4.1.3 Description of FLUENT simulation 43
4.2 Description of grid generation 44
4.2.1 Depending on the number of grid points of WRF. 44
4.2.2 Based on roughness length classification table. 45
4.3 Investigation for the fully developed boundary layer turbulence 45
4.4 Implementation 46
Processing data 47
CHAPTER 5. RESULTS ANALYSIS 48
5.1 Verification accuracy of wind mast (tower) data 48
Testing the boundary condition and time step, wind mast (tower) data at 86 m and 50 m. 48
5.1.1 WRF data 2015-11-02 at 86 m height 48
5.1.2 WRF 2015-11-02 at 50 m height 58
5.1.3 WRF data 2016-02-05 at 86 m height 59
5.1.4 WRF data 2016-02-05 at 50 m height 61
5.2 Characterizing of the atmospheric boundary layer (ABL) or turbulent boundary layer 62
5.2.1 Consideration effect of boundary layer on simulation results. 62
126.96.36.199 Wall y plus 63
188.8.131.52 Y plus alongside vertical height 66
184.108.40.206 The effect of DES model to the results in 3D 68
220.127.116.11 Determination the RANS thickness region in DES 70
18.104.22.168 Friction velocity u_τ 71
5.2.2 Characterizing turbulence 78
22.214.171.124 Spatial series 78
126.96.36.199 Turbulence kinetic energy (TKE) 80
188.8.131.52 Time series 81
CHAPTER 6. CONCLUSION AND FUTURE WORK 85
6.1 Conclusion 85
6.2 Future work 86
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