张伟伟, 寇家庆, 刘溢浪
CJA
收稿日期:2020-09-01
修回日期:2020-09-25
发布日期:2020-12-14
通讯作者:
张伟伟
E-mail:aeroelastic@nwpu.edu.cn
基金资助:ZHANG Weiwei, KOU Jiaqing, LIU Yilang
Received:2020-09-01
Revised:2020-09-25
Published:2020-12-14
Supported by:摘要： 人工智能（AI）是21世纪的前沿科技，流体力学如何在智能化时代焕发青春是值得本领域研究者思考的话题。从智能赋能流体力学角度，就其研究内涵、研究内容、近期研究及难点进行了总结，并对智能流体力学未来的发展进行了展望。研究指出，流体力学计算或试验中所产生的数据是天生的大数据，如何通过深度神经网络、随机森林、强化学习等机器学习方法来利用这些数据，缓解甚至替代理论和方法层面对人脑的依赖，挖掘新的知识，成为一种新的研究范式；相关研究将涵盖流动控制方程的机器学习、湍流模型的机器学习、物理量纲分析与标度的智能化以及数值模拟方法的智能化；借助人工智能技术，发展流动信息特征提取与多源数据融合的智能化是流体力学发展的迫切需求；研究内容应至少涵盖海量数据挖掘方法以及多源气动数据的智能融合；发展数据驱动的流体力学多学科、多物理场耦合建模与控制是工程应用的迫切需求，相关工作涉及多场耦合建模、气动外形智能优化设计以及流动智能自适应控制等方面。
中图分类号:
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): 524689-524689.
ZHANG Weiwei, KOU Jiaqing, LIU Yilang. Prospect of artificial intelligence empowered fluid mechanics[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 524689-524689.
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