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Optimization of the Aerodynamic Lift and Drag of LYNK&CO 03+ with Simulation and Wind Tunnel Test

Dassault Systemes(Shanghai) Information Technology Co.-Weiliang Xie, Bo Li, Xiaowei Zhao
Geely Automobile Research Institute-Qian Feng, Biaoneng Luo, Huixiang Zhang, Hong Peng, Zhenying Zhu, Zhi Ding, Ling Zhu
  • Technical Paper
  • 2020-01-0672
To be published on 2020-04-14 by SAE International in United States
Based on the first sedan of the LYNK&CO brand from Geely, a high performance configuration with the additional aerodynamic package was developed. The aerodynamic package including the front wheel deflector, the front lip, the side skirt, the rear spoiler and the rear diffuser, were upgraded to generate enough aerodynamic downforce for better handing stability, without too much compromising of the aerodynamic drag of the vehicle to keep a low fuel consumption. Simulation approach with PowerFLOW, combined with the design space exploration method were used to optimize both of the aerodynamic lift and drag. Wind tunnel test was also used to firstly calibrate the simulation results and finally to validate the optimized design. The results turn out to be appropriate trade-off between the lift and the drag to meet the aerodynamics requirement, and a consistently good matching between the simulation and test.
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Engine Calibration Using Global Optimization Methods with Customization

Ford Motor Company-Ling Zhu, Yan Wang
Michigan State University-Anuj Pal, Guoming Zhu
  • Technical Paper
  • 2020-01-0270
To be published on 2020-04-14 by SAE International in United States
The automotive industry is subject to stringent regulations in emissions and growing customer demands for better fuel consumption and vehicle performance. Engine calibration, a process that optimizes engine performance by tuning engine controls (actuators), becomes challenging nowadays due to significant increase of complexity of modern engines. The traditional sweep-based engine calibration method is no longer sustainable. To tackle the challenge, this work considers two powerful global optimization methods: genetic algorithm (GA) and Bayesian optimization. In real engine testing platform, only the limited number of function evaluations (less than 400) is available. We customized GA to cope with limited resource. Another challenge of engine calibration is that, in real engine testing platform, some solutions cannot even run completely due to the engine hardware limitations. These solutions, called non-operational solutions, are part of infeasible solutions and do not have any information about either objectives or constraints. A constraint repair algorithm is applied to handle non-operational solutions. The experimental study on high-fidelity engine models demonstrated that both GA and Bayesian optimization effectively find solutions very close to global…