基于高低阶多项式的风洞收缩段智能设计方法
Intelligent Wind Tunnel Contraction Design Based on Coupled High and Low Order Polynomials
DOI: 10.12677/app.2024.146052, PDF,   
作者: 朱喻成, 张惠林, 赵 峰, 李松锜*:比亚迪汽车工业有限公司,广东 深圳
关键词: 风洞人工智能优化空气动力学Wind Tunnel Artificial Intelligence Optimization Aerodynamics
摘要: 汽车风洞的收缩段对于风洞内的流场品质有着重要影响。在计算机技术快速发展的背景下,高精度数值仿真和智能优化算法的结合成为优化设计的趋势。针对收缩段气动轮廓的设计问题,本文创新地提出了一种基于高低阶多项式的智能设计方法,该方法结合高精度仿真与智能优化技术,可以显著提升收缩段的气动性能。以典型汽车风洞收缩段设计为例,该方法可以打破在传统设计方法的基础上实现更进一步的性能提升,实现更高的流场均匀性和平直度。该方法可以推广到车辆空气动力学优化等重点工程领域,为车辆低风阻开发、能源管理等应用场景提供有效的优化设计方法。
Abstract: In the context of rapidly advancing computer technology, synergizing high-fidelity numerical simulations and intelligent optimization algorithms holds significance for the design of the wind tunnel contraction section, which plays a crucial role in flow field quality. This paper introduces an innovative, intelligent design method, which integrates high- and low-order polynomial profiles. With numerical simulations and intelligent optimization, this novel method can enhance the aerodynamic performance of the contraction geometry. As demonstrated with a standard automotive wind tunnel design, this method surpasses traditional approaches, enhancing flow uniformity and straightness. This technique can be extended to key engineering problems such as vehicle aerodynamics optimization, facilitating effective design strategies for low drag development and energy-efficient management in automotive applications.
文章引用:朱喻成, 张惠林, 赵峰, 李松锜. 基于高低阶多项式的风洞收缩段智能设计方法[J]. 应用物理, 2024, 14(6): 470-480. https://doi.org/10.12677/app.2024.146052

参考文献

[1] Schuetz, T.C. (2015) Aerodynamics of Road Vehicles. 5th Edition, SAE International.
[2] 贾青, 杨志刚, 李启良. 汽车风洞试验段流场的试验研究[J]. 实验流体力学, 2011, 25(6): 5.
[3] 李国文, 徐让书. 风洞收缩段曲线气动性能研究[J]. 实验流体力学, 2009, 23(4): 73-76.
[4] Barlow, J.B., Pope, A., Harper, J.J., et al. (1999) Low-Speed Wind Tunnel Testing. 3rd Edition, John Wiley & Sons.
[5] 苏耀西, 林超强, 洪流. 三维收缩段设计问题[J]. 航空学报, 1992(2): 7-13.
[6] 李启良, 杨志刚. 计算流体力学在气动——声学风洞设计中的应用[J]. 空气动力学学报, 2009, 27(3): 373-377.
[7] 张伟伟, 寇家庆, 刘溢浪. 智能赋能流体力学展望[J]. 航空学报, 2021, 42(4): 26-71.
[8] Waudby-Smith, P., Bender, T., Sooriyakumaran, C., et al. (2024) The New China Automotive Technology and Research Center Aerodynamic-Acoustic and Climatic Wind Tunnels. SAE Technical Paper.
[9] 高丽敏, 刘哲, 蔡明, 等. 四种风洞收缩段流场特性对比[J]. 航空动力学报, 2020, 35(8): 1695-1705.