Aerodynamic pressure field reconstruction from sparse points using data assimilation method

HUANG Jun; GUO Yuxin; JI Jingjing; HUANG Yong'an

doi:10.11729/syltlx20230021

Volume 37 Issue 5

Oct. 2023

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Article Navigation > Journal of Experiments in Fluid Mechanics > 2023 > 37(5): 9-17

HUANG J, GUO Y X, JI J J, et al. Aerodynamic pressure field reconstruction from sparse points using data assimilation method[J]. Journal of Experiments in Fluid Mechanics, 2023, 37(5): 9-17 doi: 10.11729/syltlx20230021

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Aerodynamic pressure field reconstruction from sparse points using data assimilation method

doi: 10.11729/syltlx20230021

School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan　430074, China

Received Date: 2023-02-24
Accepted Date: 2023-04-10
Rev Recd Date: 2023-03-22

Available Online: 2023-06-02

Publish Date: 2023-10-30

Abstract

Abstract

In wind tunnel experiments, the high-precision pressure distribution of models is highly required, but existing measurement methods still have certain shortcomings. In order to obtain the global pressure distribution of the wind tunnel model, this paper assimilated the sparse measured data and numerical calculation data of the wind tunnel experiment by Ensemble Transform Kalman Filter (ETKF), and realized the high-precision reconstruction of the full-space flow field based on the finite measurement points of the model surface. Two-dimensional airfoil RAE 2822 and NACA 0012 were used for experimental verification. Sparse reconstruction of pressure results of RAE 2822 is more consistent with the measured results than the linear theory correction. This effect is especially evident at the shock wave position, and the prediction error of the pressure coefficient is reduced by about 3%. The lift coefficient and moment coefficient of the wing calculated by using the modified ETKF set mean of attack angle and Mach number are less than 1% error from the experimental values. Experiment of NACA 0012 is oriented to the full-field sensing application of wind tunnel experiments and explore the feasibility of pressure reconstruction based on a small number of measurement points. The experimental results show that the relative error of pressure coefficients reconstructed using six measurement points on the wing object surface can be reduced to 2.42%, and the comparison results show that the assimilation effect is highly dependent on the data point locations.
- ensemble transform Kalman filter,
- data assimilation,
- sparse reconstruction of pressure,
- wind tunnel measurement

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References

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