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数据驱动的气动热建模预测方法总结与展望

王泽 宋述芳 王旭 张伟伟

王泽, 宋述芳, 王旭, 张伟伟. 数据驱动的气动热建模预测方法总结与展望[J]. 气体物理. doi: 10.19527/j.cnki.2096-1642.1068
引用本文: 王泽, 宋述芳, 王旭, 张伟伟. 数据驱动的气动热建模预测方法总结与展望[J]. 气体物理. doi: 10.19527/j.cnki.2096-1642.1068
WANG Ze, SONG Shufang, WANG Xu, ZHANG Weiwei. Summary and Prospect of Data-Driven Aerothermal Modeling Prediction Methods[J]. PHYSICS OF GASES. doi: 10.19527/j.cnki.2096-1642.1068
Citation: WANG Ze, SONG Shufang, WANG Xu, ZHANG Weiwei. Summary and Prospect of Data-Driven Aerothermal Modeling Prediction Methods[J]. PHYSICS OF GASES. doi: 10.19527/j.cnki.2096-1642.1068

数据驱动的气动热建模预测方法总结与展望

doi: 10.19527/j.cnki.2096-1642.1068
基金项目: 

国家自然科学基金(92152301, 12072282)

详细信息
    作者简介:

    王泽(1998-)男,博士,主要研究气动热建模预测。E-mail:bdqywz123@163.com

    通讯作者:

    张伟伟(1979-)男,教授,主要研究智能流体力学和气动弹性力学。E-mail:aeroelastic@nwpu.edu.cn

  • 中图分类号: V211.47

Summary and Prospect of Data-Driven Aerothermal Modeling Prediction Methods

  • 摘要: 气动热的准确预测是指导高超声速飞行器设计的基础。在经典气动热预测方法愈发难以满足工程中高效准确的气动热预测需求的背景下,近年来蓬勃发展的数据驱动气动热建模预测方法逐渐成为气动热预测的新范式。对此,首先阐述了数据驱动气动热建模预测方法和经典气动热预测方法的相互关系。然后,从建模思路上将数据驱动气动热建模预测方法归纳为3类,即气动热特征空间降维建模预测、气动热逐点建模预测和气动热物理信息嵌入建模预测,并对这3类方法进行了详细介绍和分析总结。数据驱动气动热建模预测方法不仅比工程算法准确,而且和采样方法结合后,还能够有效降低实验测量和数值计算的工作量,给出的模型也更加高效简洁。最后,对数据驱动气动热建模预测方法的发展趋势进行了展望,指出数据驱动技术与经典气动热预测方法的深度结合、气动热物理信息嵌入建模预测方法和气动热预测大模型将会是未来研究的要点。

     

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出版历程
  • 收稿日期:  2023-06-23
  • 修回日期:  2023-08-16
  • 网络出版日期:  2024-03-13

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