Virtual Optimization of Race Engines Through an Extended Quasi Steady State Lap Time Simulation Approach 2018-01-0587
Minimizing the lap time for a given race track is the main target in racecar development. In order to achieve the highest possible performance of the vehicle configuration the mutual interaction at the level of assemblies and components requires a balance between the advantages and disadvantages for each design decision. Especially the major shift in the focus of racecar powerunit development to high efficiency powertrains is driving a development of lean boosted and rightsized engines. In terms of dynamic engine behavior the time delay from requested to provided torque could influence the lap time performance. Therefore, solely maximizing the full load behavior objective is insufficient to achieve minimal lap time.
By means of continuous predictive virtual methods throughout the whole development process, the influence on lap time by dynamic power lags, e.g. caused by the boost system, can be recognized efficiently even in the early concept phase. As a first step, this paper presents a novel method that combines detailed 1D (one dimensional) gas dynamic engine models with the quasi steady state (QSS) lap time method. This allows for a predictive comprehension of lap time influence for different engine design parameters with the possibility to operate in an environment of detailed description of vehicle dynamics. Moreover, the direct application of 1D-CFD (computational fluid dynamics) engine models also increases the efficiency of the used virtual engineering tools.
In a second step, this paper gives an insight into a model supported development process of a lean boosted 4-cylinder race engine. An evaluation of the model’s predictive capabilities and a sensitivity study of basic boost system parameters are also part of this publication.
Citation: Malcher, S., Bargende, M., Grill, M., Baretzky, U. et al., "Virtual Optimization of Race Engines Through an Extended Quasi Steady State Lap Time Simulation Approach," SAE Technical Paper 2018-01-0587, 2018, https://doi.org/10.4271/2018-01-0587. Download Citation
Simon Malcher, Michael Bargende, Michael Grill, Ulrich Baretzky, Hartmut Diel, Sebastian Wohlgemuth
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