TY - JOUR
T1 - 智能赋能流体力学展望
AU - Zhang, Weiwei
AU - Kou, Jiaqing
AU - Liu, Yilang
N1 - Publisher Copyright:
© 2021, Beihang University Aerospace Knowledge Press. All right reserved.
PY - 2021/4/25
Y1 - 2021/4/25
N2 - Artificial Intelligence (AI) is an advanced technology in the 21 st century. For researchers in related fields, rejuvenation of fluid mechanics in the age of intelligence is worth consideration. This paper proposes intelligence empowered fluid mechanics, explaining and summarizing its meaning, important topics, research progress, and research difficulties. The future development of intelligent fluid mechanics is also discussed. The research points out that the data generated in computational fluid dynamics or experiments are inherently big data, and how to use these data through machine learning methods, like the deep neural network, random forest, and reinforcement learning, to alleviate or even replace the dependence on human brain is a new research paradigm; at the theoretical and methodological level, main research topics cover machine learning of the governing equations and turbulence modeling, the intellectualization of dimensional and scaling analysis, as well as numerical simulation; there is also an urge to develop the intellectualization of flow feature extraction and data fusion from multiple sources through AI; in this branch, data mining of flow dynamics and intelligent fusion of multi-source aerodynamic data are mainly included; moreover, the development of multidisciplinary and multiphysics modeling for fluid mechanics is in urgent need in many engineering applications, involving modeling of multi-field coupling problems, multi-disciplinary intelligent optimization design and adaptive flow control.
AB - Artificial Intelligence (AI) is an advanced technology in the 21 st century. For researchers in related fields, rejuvenation of fluid mechanics in the age of intelligence is worth consideration. This paper proposes intelligence empowered fluid mechanics, explaining and summarizing its meaning, important topics, research progress, and research difficulties. The future development of intelligent fluid mechanics is also discussed. The research points out that the data generated in computational fluid dynamics or experiments are inherently big data, and how to use these data through machine learning methods, like the deep neural network, random forest, and reinforcement learning, to alleviate or even replace the dependence on human brain is a new research paradigm; at the theoretical and methodological level, main research topics cover machine learning of the governing equations and turbulence modeling, the intellectualization of dimensional and scaling analysis, as well as numerical simulation; there is also an urge to develop the intellectualization of flow feature extraction and data fusion from multiple sources through AI; in this branch, data mining of flow dynamics and intelligent fusion of multi-source aerodynamic data are mainly included; moreover, the development of multidisciplinary and multiphysics modeling for fluid mechanics is in urgent need in many engineering applications, involving modeling of multi-field coupling problems, multi-disciplinary intelligent optimization design and adaptive flow control.
KW - Artificial Intelligence (AI)
KW - Data fusion
KW - Feature extraction
KW - Flow control
KW - Turbulence modeling
UR - http://www.scopus.com/inward/record.url?scp=85105723821&partnerID=8YFLogxK
U2 - 10.7527/S1000-6893.2020.24689
DO - 10.7527/S1000-6893.2020.24689
M3 - 文献综述
AN - SCOPUS:85105723821
SN - 1000-6893
VL - 42
JO - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
JF - Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica
IS - 4
M1 - 524689
ER -