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|Title:||Aerodynamic drag analysis of an autonomous battery-electric truck||Authors:||Anirood, Kameel||Keywords:||Trucks -- Aerodynamics;Drag (Aerodynamics);Computational fluid dynamics||Issue Date:||2021||Publisher:||Cape Peninsula University of Technology||Abstract:||This research applies an aerodynamic drag analysis of an autonomous battery electric truck by means of using computational fluid dynamics (CFD) as a simulation tool. Aerodynamic drag on a conventional truck at highways speeds accounts for roughly 65% of the total energy demand of the truck. This results in increased fuel usage and greenhouse gas (GHG) emissions compared to other land freight options. Battery electric trucks (BETs) are seen as a viable technology path towards reducing global GHG emissions from heavy truck-trailers. Autonomous BETs present an opportunity to further increase aerodynamic efficiency of heavy trucks, as the exterior design and smoother driving profile of such a vehicle can be more streamlined compared to conventional trucks. The CFD simulation utilises the Reynolds-averaged Navier-Stokes (RANS) equations with a realizable k-𝜀 turbulence model and non-equilibrium wall functions to model the near-wall region of the domain. The simulation also considers the effect of a moving ground plane on aerodynamic drag. The simulation accuracy is validated against empirical results for the aerodynamic drag on the conventional generic model (GCM) truck, as tested in a wind tunnel. It was found that an autonomous BET can reduce aerodynamic drag by approximately 18% without any modification to existing trailers, and by approximately 35.5% with the addition of low cost commercial trailer drag reduction devices. The main conclusion of this research is that autonomous BETs can greatly reduce the overall aerodynamic drag of a truck, thereby reducing energy consumption and GHG emissions for the land freight sector. Further improvements can be made in refining the geometry of both the tractor and the trailer, as well as considering platoon formation driving for greater reductions in aerodynamic drag.||Description:||Thesis (MEng (Mechanical Engineering))--Cape Peninsula University of Technology, 2021||URI:||http://hdl.handle.net/20.500.11838/3531|
|Appears in Collections:||Mechanical Engineering - Master's Degree|
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