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研究生：	蘇家錦 Sutopo, Yulianto Suteja
論文名稱：	卡車之計算模擬與流場分析 Computational Simulation and Flow-Field Analysis of Heavy Duty Truck Model
指導教授：	陳世雄 Chen, Shih-Hsiung
學位類別：	碩士 Master
系所名稱：	工學院 - 航空太空工程學系 Department of Aeronautics & Astronautics
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	108
外文關鍵詞：	Computational Fluid Dynamics (CFD), Class-8 tractor Trailer, Drag Coefficient, RNG k–s
相關次數：	點閱：76 下載：1
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僅以2009年觀之，人類所生產的能源中便有20%使用在交通運輸之上，若單純以液態燃料而言，則此一比例更高達50%，且此一消耗在未來只增不減。在各樣陸運交通模式當中，尤以大型卡車對貨運最為重要，而其中50%的燃料消耗皆肇因於空氣動力阻力的克服。故減低重型貨卡的氣動阻力便可大幅減少其燃油消耗。相關的研究自1950年代便頗受重視，其間確認了貨櫃尾端低壓區乃是氣壓阻力的主要成因之一。本研究以全三維計算流體力學(CFD) 模擬計算一Class-8級通用常規模型(GCM)之卡車於雷諾數四百萬、馬赫數0.15下的狀況，並與NASA以12呎風洞測試之結果比對以作為計算之驗證，其結果將可作為未來改良設計之計算基礎。其模型之建立與計算過程採用非結構性網格、穩態分析、以及RNG k–epsilon 紊流模型，同時不同大小之格點亦用來驗證結果之正確性。結果確認三區塊為主要關鍵；車頭與貨櫃間縫隙、貨櫃尾端、與貨櫃底盤。若以總阻力觀之，則貨車車頭約占總阻力之39%，而貨櫃約占37%。希冀此一結果可協助未來減組機構之設計與分析。

In 2009, transportation sector consumed almost 20% of the world’s total delivered energy. More than 50% of world’s liquid fuel is consumed by the transportation alone, and the consumption is predicted to keep increasing for several decades in near future. Among the transportation modes, heavy trucks have been the most dominant in commercial freight industry. Fifty percent of the energy consumption of ground vehicles is used to overcome the aerodynamic drag. These facts have triggered the idea of reducing the drag of heavy duty vehicles in order to reduce the fuel consumption. Efforts to reduce the aerodynamic drag of heavy vehicles have been seriously performed since 1950s. It is learned that the trailer back is the region with high total pressure loss contributing to the total drag. Fully three-dimensional Computational Fluid Dynamics (CFD) simulations were then proposed to evaluate this phenomenon. The research performed simulations of a Class-8 truck model, known as the Generic Conventional Model (GCM). The GCM has also been tested experimentally by the NASA using the 12-Ft Pressure Wind Tunnel in order to provide baseline measurements for further computational researches in the truck drag. The CFD simulations of the GCM were conducted at Reynolds number of 4 million, and Mach number of 0.15. The unstructured grid, false time approach, and the RNG k–epsilon turbulence model were used in the simulations. Mesh size was varied to ensure the mesh independency of the results. After successful computations, it is found that there are three critical regions which contribute to the truck drag, the tractor-trailer gap, the trailer back, and the trailer underbody. The tractor dominated the drag with 39% of the total drag, followed by the trailer with 37% of the total drag. As a final point, it is hoped that this research have brought and given birth to new ideas of truck drag reduction device potential.

ABSTRACT	ii
ACKNOWLEDGEMENTS	iv
CONTENTS	vi
LIST OF TABLES	ix
LIST OF FIGURES	x
NOMENCLATURE	xv
INTRODUCTION	1
1. Research Background	1
2. Previously Conducted Research	4
2.1. Experimental Research	5
2.2. Computational Research	8
3. Research Objective	14
4. Research Scope	14
5. Research Methodology	15
6. Research Content Preview	16
PROBLEM DESCRIPTION	27
1. Generic Conventional Model	27
2. Experimental Setup	28
2.1. NASA Ames 7-Ft × 10-Ft Wind Tunnel	28
2.2. NASA Ames 12-Ft Pressure Wind Tunnel	30
3. Experimental Results	31
3.1. Surface Pressures	31
3.2. Drag, Lift, and Side Force Measurements	32
3.3. Particle Image Velocimetry (PIV)	33
SIMULATION APPROACH	39
1. Governing Equations	39
1.1. Introduction to Reynolds Averaged Navier-Stokes	41
1.2. Renormalization Group (RNG) k– Turbulence Model	43
1.3. Advanced Wall Functions	45
2. Numerical Discretization Technique	48
2.1. Shape Functions and Discretization of Governing Equations	48
2.2. Solution Schemes	51
2.3. Solution Strategy	53
COMPUTATIONAL MODELING	58
1. Grid Construction Strategy	58
2. Pre-Simulation Setup	61
2.1. Physical Modeling	62
2.2. Boundary Condition Modeling	63
2.3. Solver Control Modeling	66
RESULTS AND DISCUSSION	74
1. Computation’s Residual History	74
2. Drag Coefficient	76
3. Pressure Coefficient	78
4. Contour and Streamlines	79
4.1. Velocity Contour	79
4.2. Pressure Contour	80
4.3. Velocity Streamline and Vector	80
CONCLUSION AND FUTURE RESEARCH	104
1. Concluding Remark	104
2. Future Research	105
References	106
                                    

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