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研究生:蘇家錦
研究生(外文):Yulianto SutejaSutopo
論文名稱:卡車之計算模擬與流場分析
論文名稱(外文):Computational Simulation and Flow-Field Analysis of Heavy Duty Truck Model
指導教授:陳世雄陳世雄引用關係
指導教授(外文):Shih-Hsiung Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:航空太空工程學系碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
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 TrailerDrag CoefficientRNG k–s
相關次數:
  • 被引用被引用:3
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  • 下載下載:31
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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
1.1. Research Background 1
1.2. Previously Conducted Research 4
1.2.1. Experimental Research 5
1.2.2. Computational Research 8
1.3. Research Objective 14
1.4. Research Scope 14
1.5. Research Methodology 15
1.6. Research Content Preview 16
PROBLEM DESCRIPTION 27
2.1. Generic Conventional Model 27
2.2. Experimental Setup 28
2.2.1. NASA Ames 7-Ft × 10-Ft Wind Tunnel 28
2.2.2. NASA Ames 12-Ft Pressure Wind Tunnel 30
2.3. Experimental Results 31
2.3.1. Surface Pressures 31
2.3.2. Drag, Lift, and Side Force Measurements 32
2.3.3. Particle Image Velocimetry (PIV) 33
SIMULATION APPROACH 39
3.1. Governing Equations 39
3.1.1. Introduction to Reynolds Averaged Navier-Stokes 41
3.1.2. Renormalization Group (RNG) k– Turbulence Model 43
3.1.3. Advanced Wall Functions 45
3.2. Numerical Discretization Technique 48
3.2.1. Shape Functions and Discretization of Governing Equations 48
3.2.2. Solution Schemes 51
3.2.3. Solution Strategy 53
COMPUTATIONAL MODELING 58
4.1. Grid Construction Strategy 58
4.2. Pre-Simulation Setup 61
4.2.1. Physical Modeling 62
4.2.2. Boundary Condition Modeling 63
4.2.3. Solver Control Modeling 66
RESULTS AND DISCUSSION 74
5.1. Computation’s Residual History 74
5.2. Drag Coefficient 76
5.3. Pressure Coefficient 78
5.4. Contour and Streamlines 79
5.4.1. Velocity Contour 79
5.4.2. Pressure Contour 80
5.4.3. Velocity Streamline and Vector 80
CONCLUSION AND FUTURE RESEARCH 104
6.1. Concluding Remark 104
6.2. Future Research 105
References 106
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