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Unscented Kalman Filter (ukf)-based Nonlinear Parameter Estimation For A Turbulent Boundary Layer

Title: Unscented Kalman Filter (ukf)-based Nonlinear Parameter Estimation For A Turbulent Boundary Layer: A Data Assimilation Framework.
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Name(s): Pan, Zhao, author
Zhang, Yang, author
Gustavsson, Jonas P. R., author
Hickey, Jean-Pierre, author
Cattafesta, Louis N., author
Type of Resource: text
Genre: Journal Article
Text
Journal Article
Date Issued: 2020-09
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: A turbulent boundary layer is a ubiquitous element of fundamental and applied fluid mechanics. Unfortunately, accurate measurements of turbulent boundary layer parameters (e.g. friction velocityu tau tau(w)) are challenging, especially for high-speed flows (Smitset al2011). Many direct and/or indirect diagnostic techniques have been developed to measure wall shear stress (Vinuesaet al2017). However, based on various principles, these techniques generally give different results with varying uncertainties. The current study introduces a nonlinear data assimilation framework based on the unscented Kalman filter (UKF) that can fuse information from (i) noisy and discretized measurements from stereo particle image velocimetry (SPIV), a Preston tube, and a MEMS shear stress sensor, as well as (ii) the uncertainties of the measurements to estimate the parameters of a turbulent boundary layer. A direct numerical simulation of a fully developed turbulent channel flow is used first to validate the data assimilation algorithm. The algorithm is then applied to experimental boundary layer data at Mach 0.3 obtained in a blowdown wind tunnel facility. Drag coefficients from control volume analysis of the SPIV and wall pressure data and laser interferometer skin friction measurements are used for independent cross-validation. The UKF-based data assimilation algorithm is robust to the uncertain and discretized experimental data and is able to provide accurate estimates of turbulent boundary layer parameters with quantified uncertainty.
Identifier: FSU_libsubv1_wos_000553859700001 (IID), 10.1088/1361-6501/ab8904 (DOI)
Keywords: data assimilation, evolution, Kalman filter, law, piv, reynolds, skin-friction, stereo-PIV, turbulent boundary layer, uncertainty quantification, unscented Kalman filter
Publication Note: The publisher's version of record is availible at https://doi.org/10.1088/1361-6501/ab8904
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000553859700001
Host Institution: FSU
Is Part Of: Measurement Science and Technology.
0957-0233
Issue: iss. 9, vol. 31

Choose the citation style.
Pan, Z., Zhang, Y., Gustavsson, J. P. R., Hickey, J. -P., & Cattafesta, L. N. (2020). Unscented Kalman Filter (ukf)-based Nonlinear Parameter Estimation For A Turbulent Boundary Layer: A Data Assimilation Framework. Measurement Science And Technology. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000553859700001