耦合粒子图像测速误差的连续伴随数据同化技术

doi:10.7527/S1000-6893.2021.25305

流体力学与飞行力学

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耦合粒子图像测速误差的连续伴随数据同化技术

邓志文^1,2, 何创新^1,2, 刘应征^1,2

1. 上海交通大学机械与动力工程学院动力机械与工程教育部重点实验室, 上海 200240;
2. 上海交通大学燃气轮机研究院, 上海 200240

收稿日期:2021-01-22 修回日期:2021-02-23 发布日期:2021-03-18
通讯作者: 刘应征 E-mail:yzliu@sjtu.edu.cn
基金资助:
国家自然科学基金（12002208，11725209）

Continuous-adjoint based data assimilation technique coupled with particle image velocimetry error

DENG Zhiwen^1,2, HE Chuangxin^1,2, LIU Yingzheng^1,2

1. Key Laboratory of Ministry of Education for Power Machinery and Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Gas Turbine Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2021-01-22 Revised:2021-02-23 Published:2021-03-18
Supported by:
National Natural Science Foundation of China (12002208,11725209)

摘要/Abstract

摘要： 提出了一种耦合粒子图像测速（PIV）实验误差的连续伴随数据同化算法，通过优化目标损失函数，增强算法在不同误差场景下的鲁棒性。为了验证该算法的有效性，先对已知PIV流场植入合成误差进行同化对比测试，继而对PIV互相关算法不同参数设置所获得的流场进行同化研究。结果表明：相比于原连续伴随数据同化，耦合PIV实验误差的同化算法能够对实验观测数据去伪存真，抗误差干扰能力明显提升，鲁棒性更强，能够对高误差场景下的流动数据进行更好地同化，准确地预测流场的真实分布规律，还原流场细节。

关键词: PIV, 数据同化, 连续伴随算法, PIV实验误差, 平板绕流

Abstract: A continuous-adjoint based data assimilation technique coupled with Particle Image Velocimetry (PIV) error was proposed to optimize the objective loss function, thereby enhancing the robustness of the technique in different error scenarios. For verification, a given PIV flow field implanted with synthetic errors was selected as a preliminary test, and a further data assimilation test was implemented in the flow fields obtained with different parameter settings of the PIV cross-correlation algorithm. The results indicated that the continuous-adjoint algorithm coupled with the PIV error can discard the false experimental observations and improve the anti-interference ability and robustness, compared with its original counterpart. The high-fidelity flow fields can be well obtained using this data assimilation technique even in large error scenarios.

Key words: PIV, data assimilation, continuous-adjoint algorithm, PIV error, flat plate flow

中图分类号:

邓志文, 何创新, 刘应征. 耦合粒子图像测速误差的连续伴随数据同化技术[J]. 航空学报, 2022, 43(5): 125305-125305.

DENG Zhiwen, HE Chuangxin, LIU Yingzheng. Continuous-adjoint based data assimilation technique coupled with particle image velocimetry error[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(5): 125305-125305.

参考文献

[1] 何创新, 邓志文, 刘应征. 湍流数据同化技术及应用[J]. 航空学报, 2021, 42(4):524704. HE C X, DENG Z W, LIU Y Z.Turbulent flow data assimilation and its applications[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4):524704(in Chinese).
[2] CORRSIN S. Extendedapplications of the hot-wire anemometer[J]. Review of Scientific Instruments, 1947, 18(7):469-471.
[3] ADRIAN L, ADRIAN R J, WESTERWEEL J. Particle image velocimetry[M]. Cambridge:Cambridge University Press, 2011:203-214.
[4] MOIN P, MAHESH K. Direct numerical simulation:A tool in turbulence research[J]. Annual Review of Fluid Mechanics, 1998, 30:539-578.
[5] SAGAUT P. Large eddy simulation for incompressible flows:An introduction[J]. Heidelberg:Springer, 2006:9-15.
[6] ALFONSI G. Reynolds-averaged Navier-Stokes equations for turbulence modeling[J]. Applied Mechanics Reviews, 2009, 62(4):040802.
[7] BOUTTIER F, COURTIER P. Data assimilation concepts and methods March 1999[R]. 2002.
[8] SYMON S, DOVETTA N, MCKEON B J, et al. Data assimilation of mean velocity from 2D PIV measurements of flow over an idealized airfoil[J]. Experiments in Fluids, 2017, 58(5):61.
[9] LEMKE M. Adjoint based data assimilation in compressible flows with application to pressure determination from PIV data[D]. Berlin:Technische Universität Berlin, 2015.
[10] LE DIMET F X, TALAGRAND O. Variational algorithms for analysis and assimilation of meteorological observations:theoretical aspects[J]. Tellus A:Dynamic Meteorology and Oceanography, 1986, 38(2):97-110.
[11] FOURES D P G, DOVETTA N, SIPP D, et al. A data-assimilation method for Reynolds-averaged Navier-Stokes-driven mean flow reconstruction[J]. Journal of Fluid Mechanics, 2014, 759:404-431.
[12] MONS V, MARGHERI L, CHASSAING J C, et al. Data assimilation-based reconstruction of urban pollutant release characteristics[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2017, 169:232-250.
[13] KATO H, YOSHIZAWA A, UENO G, et al. A data assimilation methodology for reconstructing turbulent flows around aircraft[J]. Journal of Computational Physics, 2015, 283:559-581.
[14] KATO H, OBAYASHI S. Dataassimilation for turbulent flows[C]//16th AIAA Non-Deterministic Approaches Conference. Reston:AIAA, 2014.
[15] COVEY W. Weather prediction by numerical process[M]//The Emergence of Numerical Weather Prediction:Richardson's Dream. Cambridge:Cambridge University Press, 2006:1-27.
[16] EDWARDS C A, MOORE A M, HOTEIT I, et al. Regionalocean data assimilation[J]. Annual Review of Marine Science, 2015, 7:21-42.
[17] 王文, 寇小华. 水文数据同化方法及遥感数据在水文数据同化中的应用进展[J]. 河海大学学报(自然科学版), 2009, 37(5):556-562. WANG W, KOU X H. Methods for hydrological data assimilation and advances of assimilating remotely sensed datainto rainfall-runoff models[J]. Journal of Hohai University (Natural Sciences), 2009, 37(5):556-562(in Chinese).
[18] 秦耀军, 周晓勇, 杨亚宾, 等. 基于数据同化技术的地质参数反演分析研究[J]. 水科学与工程技术, 2017(6):78-82. QIN Y J, ZHOU X Y, YANG Y B, et al. Back analysis of geological parameters based on data assimilation[J]. Water Sciences and Engineering Technology, 2017(6):78-82(in Chinese).
[19] WELCH G, BISHOP G.An introduction to the Kalman filter:TR 95-041[R]. Chapel Hill:University of North Carolina at Chapel Hill, 2006.
[20] EVENSEN G. Data assimilation:The ensemble Kalman filter[M]. Heidelberg:Springer, 2009.
[21] BARKER D M, HUANG W, GUO Y R, et al. A three-dimensional variational data assimilation system for MM5:Implementation and initial results[J]. Monthly Weather Review, 2004, 132(4):897-914.
[22] COURTIER P, THÉPAUT J N, HOLLINGSWORTH A. A strategy for operational implementation of 4D-Var, using an incremental approach[J]. Quarterly Journal of the Royal Meteorological Society, 1994, 120(519):1367-1387.
[23] DENG Z W, HE C X,WEN X, et al. Recovering turbulent flow field from local quantity measurement:Turbulence modeling using ensemble-Kalman-filter-based data assimilation[J]. Journal of Visualization, 2018, 21(6):1043-1063.
[24] HE C X, LIU Y Z, GAN L. A data assimilation model for turbulent flows using continuous adjoint formulation[J]. Physics of Fluids, 2018, 30(10):105108.
[25] SYMON S, SIPP D, SCHMID P J, et al. Mean andunsteady flow reconstruction using data-assimilation and resolvent analysis[J]. AIAA Journal, 2019, 58(2):575-588.
[26] HE C X, LIU Y Z, GAN L. Instantaneous pressure determination from unsteady velocity fields using adjoint-based sequential data assimilation[J]. Physics of Fluids, 2020, 32(3):035101.
[27] WIENEKE B. PIV uncertainty quantification from correlation statistics[J]. Measurement Science and Technology, 2015, 26(7):074002.
[28] ZHANG Q S, LIU Y Z. Influence of incident vortex street on separated flow around a finite blunt plate:PIV measurement and POD analysis[J]. Journal of Fluids and Structures, 2015, 55:463-483.
[29] WANG H P, HE G W, WANG S Z. Globally optimized cross-correlation for particle image velocimetry[J]. Experiments in Fluids, 2020, 61(11):1-17.

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耦合粒子图像测速误差的连续伴随数据同化技术

Continuous-adjoint based data assimilation technique coupled with particle image velocimetry error

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