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Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation

D. Xiao, J. Du, F. Fang, C.C. Pain, J. Li, Dunhui Xiao Orcid Logo

Computers & Fluids, Volume: 177, Pages: 69 - 77

Swansea University Author: Dunhui Xiao Orcid Logo

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DOI (Published version): 10.1016/j.compfluid.2018.10.006

Abstract

This paper presents a novel Ensemble Kalman Filter (EnKF) data assimilation method based on a parameterised non-intrusive reduced order model (P-NIROM) which is independent of the original computational code. EnKF techniques involve the expensive calculations of ensembles. In this work, the recently...

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Published in: Computers & Fluids
ISSN: 0045-7930
Published: 2018
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URI: https://cronfa.swan.ac.uk/Record/cronfa46448
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spelling 2022-09-27T17:12:15.4636094 v2 46448 2018-12-06 Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2018-12-06 AERO This paper presents a novel Ensemble Kalman Filter (EnKF) data assimilation method based on a parameterised non-intrusive reduced order model (P-NIROM) which is independent of the original computational code. EnKF techniques involve the expensive calculations of ensembles. In this work, the recently developed P-NIROM Xiao et al. [40] is incorporated into EnKF to speed up the ensemble simulations. A reduced order flow dynamical model is generated from the solution snapshots, which are obtained from a number of the high fidelity full simulations over the specific parametric space RP. The varying parameter is the background error covariance σ ∈ RP. Using the Smolyak sparse grid method, a set of parameters in the Gaussian probability density function is selected as the training points. The proposed method uses a two-level interpolation method for constructing the P-NIROM using a Radial Basis Function (RBF) interpolation method. The first level interpolation approach is used for generating the solution snapshots and POD basis functions for any given background error covariance while the second level interpolation approach for forming a set of hyper-surfaces representing the reduced system.The EnKF in combination with P-NIROM (P-NIROM-EnKF) has been implemented within an unstructured mesh finite element ocean model and applied to a three dimensional wind driven circulation gyre case. The numerical results show that the accuracy of ensembles and updated solutions using the P-NIROM-EnKF is maintained while the computational cost is significantly reduced by several orders of magnitude in comparison to the full-EnKF. Journal Article Computers &amp; Fluids 177 69 77 0045-7930 Parameterised NIROM, RBF, POD, Enkf 30 11 2018 2018-11-30 10.1016/j.compfluid.2018.10.006 COLLEGE NANME Aerospace Engineering COLLEGE CODE AERO Swansea University 2022-09-27T17:12:15.4636094 2018-12-06T14:51:55.0472391 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering D. Xiao 1 J. Du 2 F. Fang 3 C.C. Pain 4 J. Li 5 Dunhui Xiao 0000-0003-2461-523X 6 0046448-12122018113539.pdf xiao2018(2).pdf 2018-12-12T11:35:39.4570000 Output 2062614 application/pdf Version of Record true 2018-12-12T00:00:00.0000000 false eng
title Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
spellingShingle Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
Dunhui Xiao
title_short Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
title_full Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
title_fullStr Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
title_full_unstemmed Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
title_sort Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
author_id_str_mv 62c69b98cbcdc9142622d4f398fdab97
author_id_fullname_str_mv 62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao
author Dunhui Xiao
author2 D. Xiao
J. Du
F. Fang
C.C. Pain
J. Li
Dunhui Xiao
format Journal article
container_title Computers &amp; Fluids
container_volume 177
container_start_page 69
publishDate 2018
institution Swansea University
issn 0045-7930
doi_str_mv 10.1016/j.compfluid.2018.10.006
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering
document_store_str 1
active_str 0
description This paper presents a novel Ensemble Kalman Filter (EnKF) data assimilation method based on a parameterised non-intrusive reduced order model (P-NIROM) which is independent of the original computational code. EnKF techniques involve the expensive calculations of ensembles. In this work, the recently developed P-NIROM Xiao et al. [40] is incorporated into EnKF to speed up the ensemble simulations. A reduced order flow dynamical model is generated from the solution snapshots, which are obtained from a number of the high fidelity full simulations over the specific parametric space RP. The varying parameter is the background error covariance σ ∈ RP. Using the Smolyak sparse grid method, a set of parameters in the Gaussian probability density function is selected as the training points. The proposed method uses a two-level interpolation method for constructing the P-NIROM using a Radial Basis Function (RBF) interpolation method. The first level interpolation approach is used for generating the solution snapshots and POD basis functions for any given background error covariance while the second level interpolation approach for forming a set of hyper-surfaces representing the reduced system.The EnKF in combination with P-NIROM (P-NIROM-EnKF) has been implemented within an unstructured mesh finite element ocean model and applied to a three dimensional wind driven circulation gyre case. The numerical results show that the accuracy of ensembles and updated solutions using the P-NIROM-EnKF is maintained while the computational cost is significantly reduced by several orders of magnitude in comparison to the full-EnKF.
published_date 2018-11-30T03:58:01Z
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score 11.036706

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