Optimization of an Advanced Combustion Strategy Towards 55% BTE for the Volvo SuperTruck Program

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Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
This paper describes a novel design and verification process for analytical methods used in the development of advanced combustion strategies in internal combustion engines (ICE). The objective was to improve brake thermal efficiency (BTE) as part of the US Department of Energy SuperTruck program. The tools and methods herein discussed consider spray formation and injection schedule along with piston bowl design to optimize combustion efficiency, air utilization, heat transfer, emission, and BTE. The methodology uses a suite of tools to optimize engine performance, including 1D engine simulation, high-fidelity CFD, and lab-scale fluid mechanic experiments. First, a wide range of engine operating conditions are analyzed using 1-D engine simulations in GT Power to thoroughly define a baseline for the chosen advanced engine concept; secondly, an optimization and down-select step is completed where further improvements in engine geometries and spray configurations are considered. Next, simultaneous high-fidelity simulation using StarCD and OpenFOAM as well as lab-scale unsteady jet mixing experiments are used to understand the interplay between fuel injection and piston bowl designs. A key to the success of this design and analysis process is the accuracy of both the 1-D and high-fidelity simulations; these tools have been significantly improved and benchmarked against a range of engine operating conditions throughout the program. We conclude by outlining future directions for this methodology.
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DOI
https://doi.org/10.4271/2017-01-0723
Pages
10
Citation
O'Connor, J., Borz, M., Ruth, D., Han, J. et al., "Optimization of an Advanced Combustion Strategy Towards 55% BTE for the Volvo SuperTruck Program," SAE Int. J. Engines 10(3):1217-1227, 2017, https://doi.org/10.4271/2017-01-0723.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0723
Content Type
Journal Article
Language
English