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Unscented Kalman filter (UKF) based nonlinear parameter estimation for a turbulent boundary layer: DNS data
| Description: | This repository contains the raw data (9 snapshots) of a turbulent channel flow at a bulk Mach number of 0.3 and a Re_tau of about 750. Postprocessing scripts are available in GitLab at https://gitlab.com/mpilab_waterloo/dns-post-processing. This data was used for the analysis of the following paper: Pan, Z., Zhang, Y., Gustavsson, J.P.R., Hickey, J.-P. and Cattafesta III, L. N. Unscented Kalman filter (UKF) based nonlinear parameter estimation for a turbulent boundary layer: a data assimilation framework, Meas. Sci. and Technol. 2020. |
| Authors: | Pan, Zhao; Florida A&M University - Florida State University College of Engineering; University of Waterloo Zhang, Yang; Florida A&M University - Florida State University College of Engineering Gustavsson, Jonas P.R.; Florida A&M University - Florida State University College of Engineering Hickey, Jean-Pierre; University of Waterloo Cattafesta III, Louis N.; Florida A&M University - Florida State University College of Engineering |
| Keywords: | DNS turbulent channel flow direct numerical simulation |
| Field of Research: | Mathematics and statistics > Applied mathematics > Computational fluid mechanics
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| Publication Date: | 2020-03-26 |
| Publisher: | Federated Research Data Repository / dépôt fédéré de données de recherche |
| Funder: | Compute Canada |
| URI: | https://doi.org/10.20383/101.0222 |
| Related Identifiers: |
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Access to this dataset is subject to the following terms:
Creative Commons Public Domain Dedication (CC0 1.0) https://creativecommons.org/publicdomain/zero/1.0/
Citation
Pan, Z., Zhang, Y., Gustavsson, J., Hickey, J., Cattafesta III, L. (2020). Unscented Kalman filter (UKF) based nonlinear parameter estimation for a turbulent boundary layer: DNS data. Federated Research Data Repository. https://doi.org/10.20383/101.0222
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