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Machine learning-assisted sparse observation assimilation for real-time aerodynamic field perception

Science China(Technological Sciences) 2024年第5期67卷 1458-1469页

作者： ZHAO QingYu HUANG Jun GUO YuXin PAN YuXuan JI JingJing HUANG YongAn State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Accurate aerodynamic distribution perception and real-time flight state evaluation are crucial for flight safety,e.g.,stall ***,the observations are usually sparse due to limitations in sensor mounting space and cost,and a reconstruction technology is urgently ***,a machine learning-assisted assimilation method based on sparse observations has been *** from the traditional reconstruction methods focusing on boundary condition correction,the proposed method formulates the flow field pressure distribution as a linear superposition of flow field modes,thereby forming a real-time reconstruction pattern that combines offline modal extraction using computational fluid dynamics(CFD)with real-time determination of modal weights using a neural *** this study,CFD simulations were conducted under 800different operating conditions for common modal extraction and model *** weights of these modes were determined online based on merely five observations for reconstructing the full pressure field.A pressure reconstruction with a relative error of 6.1%and a mean square error of 0.003 was achieved within the prescribed condition *** computational cost was just2 ms for each reconstruction run,significantly faster than the 20 min required by the classical reconstruction ensemble transform Kalman *** also showed that the method maintains almost the same accuracy amidst 1.5%measurement *** practical examples,shock waves and the change of lift coefficient were analyzed using the proposed method,providing remarkable evidence for the capability of the method in supporting stall *** validate the method’s effectiveness and explore its potential in real-time and accurate monitoring of an aircraft.

关键词： aerodynamic force sparse observation neural networks pressure field reconstruction

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Theoretical and Applied Mechanics Letters

引用

Theoretical & Applied Mechanics Letters 2021年第2期11卷 63-69页

作者： Xuan Zhao Lin Du Xuhao Peng Zichen Deng Weiwei Zhang College of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China National Key Laboratory Science and Technology on Aerodynamic Design and Research Northwestern Polytechnical UniversityXi’an 710072China School of Mathematics and Statistics Northwestern Polytechnical UniversityXi’an 710129China MIITKey Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical UniversityXi’an 710072China School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an 710129China

The placement of pressure taps on the surface of the wind tunnel test model is an important means toobtain the surface pressure ***,limited by space location and experimental cost,it isdifficult to arrange enough pressure measuring taps on the surface of complex models to obtain completepressure distribution information,thus it is impossible to obtain accurate lift and moment characteristicsthrough *** paper proposes a refined reconstruction method of airfoil surface pressure basedon compressed sensing,which can reconstruct the pressure distribution with high precision with lesspressure measurement *** on typical airfoil subsonic flow around flow show that the accuracyof lift and moment after the pressure integration reconstructed by 4-8 measuring points can meet therequirements of the national military *** algorithm is robust to noise,and provides a new ideafor obtaining accurate force data from sparse surface pressure tests in engineering.

关键词： pressure test Compressed sensing Proper orthogonal decomposition pressure field reconstruction

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