Research on data assimilation strategy of turbulent separated flow over airfoil

Ying ZHANG, Lin DU, Weiwei ZHANG, Zichen DENG

doi:10.1007/s10483-022-2827-7

Applied Mathematics and Mechanics >

2022 , Vol. 43 >Issue 4: 571 - 586

DOI: https://doi.org/10.1007/s10483-022-2827-7

Articles

Research on data assimilation strategy of turbulent separated flow over airfoil

Expand

1. National Key Laboratory of Science and Technology on Aerodynamic Design and Research, School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Complex System Dynamics and Control Academic Exchange Center, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710119, China;
3. Rotor Aerodynamics Key Laboratory, China Aerodynamics Research and Development Center, Mianyang 621000, Sichuan Province, China

Received date: 2021-09-10

Revised date: 2021-12-20

Online published: 2022-03-29

Supported by

the Foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research of China (No. 614220119040101) and the National Natural Science Foundation of China (No.91852115)

Fold

Abstract

In order to increase the accuracy of turbulence field reconstruction, this paper combines experimental observation and numerical simulation to develop and establish a data assimilation framework, and apply it to the study of S809 low-speed and high-angle airfoil flow. The method is based on the ensemble transform Kalman filter (ETKF) algorithm, which improves the disturbance strategy of the ensemble members and enhances the richness of the initial members by screening high flow field sensitivity constants, increasing the constant disturbance dimensions and designing a fine disturbance interval. The results show that the pressure distribution on the airfoil surface after assimilation is closer to the experimental value than that of the standard Spalart-Allmaras (S-A) model. The separated vortex estimated by filtering is fuller, and the eddy viscosity field information is more abundant, which is physically consistent with the observation information. Therefore, the data assimilation method based on the improved ensemble strategy can more accurately and effectively describe complex turbulence phenomena.

Key words： turbulence; data assimilation; ensemble disturbance strategy; large angle of attack separation; ensemble transform Kalman filter (ETKF)

Cite this article

Ying ZHANG, Lin DU, Weiwei ZHANG, Zichen DENG . Research on data assimilation strategy of turbulent separated flow over airfoil[J]. Applied Mathematics and Mechanics, 2022 , 43(4) : 571 -586 . DOI: 10.1007/s10483-022-2827-7

References

[1] POUQUET, A. Turbulence, Statistics and Structures: an Introduction, Springer, Berlin, 163-212 (2007)
[2] RAFFEL, M., WILLERT, C., WERELEY, S., and KOMPENHANS, J. Experimental Fluid Mechanics, Springer, Berlin, 259-388(2007)
[3] XIAO, H. and CINNELLA, P. Quantification of model uncertainty in RANS simulations: a review. Progress in Aerospace Sciences, 108, 1-31(2019)
[4] EVENSEN, G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5), 10143-10162(1994)
[5] LAW, K., STUART, A. M., and ZYGALAKIS, K. C. Data assimilation: a mathematical introduction. arXiv, 150607825(2015)
[6] KATO, H., YOSHIZAWA, A., UENO, G., and OBAYASHI, S. A data assimilation methodology for reconstructing turbulent flows around aircraft. Journal of Computational Physics, 283, 559-581(2015)
[7] OLIVER, T. A. and MOSER, R. D. Bayesian uncertainty quantification applied to RANS turbulence models. Journal of Physics: Conference Series, 318(4), 042032(2011)
[8] ZHANG, X. L., MICHELÉN-STRÖFER, C., and XIAO, H. Regularized ensemble Kalman methods for inverse problems. Journal of Computational Physics, 416, 109517(2020)
[9] KATO, H. and OBAYASHI, S. Statistical approach for determining parameters of a turbulence model. Information Fusion; 201215th International Conference, Villach, 2452-2457(2012)
[10] MONS, V., CHASSAING, J. C., GOMEZ, T., and SAGAUT, P. Reconstruction of unsteady viscous flows using data assimilation schemes. Journal of Computational Physics, 316, 255-280 (2016)
[11] HE, C. G., LIU, Y. Z., and GAN, L. Instantaneous pressure determination from unsteady velocity fields using adjoint-based sequential data assimilation. Physics of Fluids, 32(3), 035101(2020)
[12] DENG, Z. W., HE, C. G., WEN, X., and LIU, Y. Z. Recovering turbulent flow field from local quantity measurement: turbulence modeling using ensemble-Kalman-filter-based data assimilation. Journal of Visualization, 21(6), 1043-1063(2018)
[13] SPALART, P. and ALLMARAS, S. A one-equation turbulence model for aerodynamic flows. 30th Aerospace Sciences Meeting and Exhibit, Reno, 439(1992)
[14] BISHOP, C. H., ETHERTON, B. J., and MAJUMDAR, S. J. Adaptive sampling with the ensemble transform Kalman filter, part I: theoretical aspects. Monthly Weather Review, 129(3), 420-436 (2001)
[15] BISHOP, C. H., ETHERTON, B. J., and MAJUMDAR, S. J. Adaptive sampling with the ensemble transform Kalman filter, part II: field program implementation. Monthly Weather Review, 130(5), 1356-1369(2002)
[16] SZUNYOGH, I., KOSTELICH, E. J., GYARMATI, G., KALNAY, E., HUNT, B. R., OTT, E., SATTERFIELD, E., and YORKE, J. A local ensemble transform Kalman filter data assimilation system for the NCEP global model. Tellus A: Dynamic Meteorology and Oceanography, 60(1), 113-130(2008)
[17] BARATA, J. C. A. and HUSSEIN, M. S. The Moore-Penrose pseudoinverse: a tutorial review of the theory. Brazilian Journal of Physics, 42(1-2), 146-165(2012)
[18] ANDERSON, J. D. Fundamentals of aerodynamics. AIAA Journal, 48(12), 2983-2983(2010)
[19] SOMERS, D. M. Design and experimental results for the S809 airfoil. O–ce of Scientific& Technical Information Technical Reports, 1-97(1997)
[20] GHARALI, K. and JOHNSON, D. A. Numerical modeling of an S809 airfoil under dynamic stall, erosion and high reduced frequencies. Applied Energy, 93, 45-52(2012)
[21] YANG, M. and XIAO, Z. Parameter uncertainty quantification for a four-equation transition model using a data assimilation approach. Renewable Energy, 158, 215-226(2020)
[22] SINGH, A. P. and DURAISAMY, K. Using field inversion to quantify functional errors in turbulence closures. Physics of Fluids, 28(4), 045110(2016)
[23] SINGH, A. P., MEDIDA, S., and DURAISAMY, K. Machine learning-augmented predictive modeling of turbulent separated flows over airfoils. AIAA Journal, 55(7), 1-13(2016)

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References