Computational Simulation and Flow-Field Analysis of Heavy Duty Truck Model

碩士 === 國立成功大學 === 航空太空工程學系碩博士班 === 100 === 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. A...

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Bibliographic Details
Main Authors: Yulianto SutejaSutopo, 蘇家錦
Other Authors: Shih-Hsiung Chen
Format: Others
Language:en_US
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/61968311640751165332
Description
Summary:碩士 === 國立成功大學 === 航空太空工程學系碩博士班 === 100 === 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.