Wind Pressures on Structures by Proper Orthogonal Decomposition

Proper orthogonal decomposition （POD） is an effective statistical technique for data reduction and feature extraction of the random field including the wind field. This paper introduces the theory of the POD and illustrates engineering of structures. Using the POD technique, it is shown that wind pressure data can be accurately reconstructed with a limited number of modes using the wind pressure data from wind tunnel test. Comparing the reconstructed values by POD with the original measured values from the wind tunnel test both in the time and frequency domains, it is concluded that the proper orthogonal decomposition（POD） is an efficient and practical technique for deriving the random wind pressure field from limited known data as shown in the pitched roof example in this paper.

Theoretical and Applied Mechanics Letters

The placement of pressure taps on the surface of the wind tunnel test model is an important means toobtain the surface pressure distribution.However,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 integration.The 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 data.Tests 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 standard.The algorithm is robust to noise,and provides a new ideafor obtaining accurate force data from sparse surface pressure tests in engineering.

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

Accurate aerodynamic distribution perception and real-time flight state evaluation are crucial for flight safety,e.g.,stall detection.However,the observations are usually sparse due to limitations in sensor mounting space and cost,and a reconstruction technology is urgently required.Herein,a machine learning-assisted assimilation method based on sparse observations has been proposed.Different 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 network.In this study,CFD simulations were conducted under 800different operating conditions for common modal extraction and model training.The 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 range.The computational cost was just2 ms for each reconstruction run,significantly faster than the 20 min required by the classical reconstruction ensemble transform Kalman filter.It also showed that the method maintains almost the same accuracy amidst 1.5%measurement noise.As 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 detection.These validate the method’s effectiveness and explore its potential in real-time and accurate monitoring of an aircraft.