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An efficient goal-based reduced order model approach for targeted adaptive observations

F. Fang, C. C. Pain, Ionel M. Navon, D. Xiao, Dunhui Xiao Orcid Logo

International Journal for Numerical Methods in Fluids, Volume: 83, Issue: 3, Pages: 263 - 275

Swansea University Author: Dunhui Xiao Orcid Logo

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DOI (Published version): 10.1002/fld.4265

Abstract

An efficient adjoint sensitivity technique for optimally collecting targeted observations is presented. The targeting technique incorporates dynamical information from the numerical model predictions to identify when, where and what types of observations would provide the greatest improvement to spe...

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Published in: International Journal for Numerical Methods in Fluids
ISSN: 0271-2091
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa46452
first_indexed 2018-12-06T20:27:45Z
last_indexed 2023-01-11T14:23:16Z
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spelling 2022-09-27T17:04:20.7243415 v2 46452 2018-12-06 An efficient goal-based reduced order model approach for targeted adaptive observations 62c69b98cbcdc9142622d4f398fdab97 0000-0003-2461-523X Dunhui Xiao Dunhui Xiao true false 2018-12-06 ACEM An efficient adjoint sensitivity technique for optimally collecting targeted observations is presented. The targeting technique incorporates dynamical information from the numerical model predictions to identify when, where and what types of observations would provide the greatest improvement to specific model forecasts at a future time. A functional (goal) is defined to measure what is considered important in modelling problems. The adjoint sensitivity technique is used to identify the impact of observations on the predictive accuracy of the functional, then placing the sensors at the locations with high impacts. The adaptive (goal) observation technique developed here has the following features: (i) over existing targeted observation techniques, its novelty lies in that the interpolation error of numerical results is introduced to the functional (goal), which ensures the measurements are a distance apart; (ii) the use of proper orthogonal decomposition (POD) and reduced order modelling for both the forward and backward simulations, thus reducing the computational cost; and (iii) the use of unstructured meshes.The targeted adaptive observation technique is developed here within an unstructured mesh finite element model (Fluidity). In this work, a POD reduced order modelling is used to form the reduced order forward model by projecting the original complex model from a high dimensional space onto a reduced order space. The reduced order adjoint model is then constructed directly from the reduced order forward model. This efficient adaptive observation technique has been validated with two test cases: a model of an ocean gyre and a model of 2D urban street canyon flows. Journal Article International Journal for Numerical Methods in Fluids 83 3 263 275 0271-2091 30 1 2017 2017-01-30 10.1002/fld.4265 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2022-09-27T17:04:20.7243415 2018-12-06T14:52:08.2151700 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Aerospace Engineering F. Fang 1 C. C. Pain 2 Ionel M. Navon 3 D. Xiao 4 Dunhui Xiao 0000-0003-2461-523X 5
title An efficient goal-based reduced order model approach for targeted adaptive observations
spellingShingle An efficient goal-based reduced order model approach for targeted adaptive observations
Dunhui Xiao
title_short An efficient goal-based reduced order model approach for targeted adaptive observations
title_full An efficient goal-based reduced order model approach for targeted adaptive observations
title_fullStr An efficient goal-based reduced order model approach for targeted adaptive observations
title_full_unstemmed An efficient goal-based reduced order model approach for targeted adaptive observations
title_sort An efficient goal-based reduced order model approach for targeted adaptive observations
author_id_str_mv 62c69b98cbcdc9142622d4f398fdab97
author_id_fullname_str_mv 62c69b98cbcdc9142622d4f398fdab97_***_Dunhui Xiao
author Dunhui Xiao
author2 F. Fang
C. C. Pain
Ionel M. Navon
D. Xiao
Dunhui Xiao
format Journal article
container_title International Journal for Numerical Methods in Fluids
container_volume 83
container_issue 3
container_start_page 263
publishDate 2017
institution Swansea University
issn 0271-2091
doi_str_mv 10.1002/fld.4265
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 0
active_str 0
description An efficient adjoint sensitivity technique for optimally collecting targeted observations is presented. The targeting technique incorporates dynamical information from the numerical model predictions to identify when, where and what types of observations would provide the greatest improvement to specific model forecasts at a future time. A functional (goal) is defined to measure what is considered important in modelling problems. The adjoint sensitivity technique is used to identify the impact of observations on the predictive accuracy of the functional, then placing the sensors at the locations with high impacts. The adaptive (goal) observation technique developed here has the following features: (i) over existing targeted observation techniques, its novelty lies in that the interpolation error of numerical results is introduced to the functional (goal), which ensures the measurements are a distance apart; (ii) the use of proper orthogonal decomposition (POD) and reduced order modelling for both the forward and backward simulations, thus reducing the computational cost; and (iii) the use of unstructured meshes.The targeted adaptive observation technique is developed here within an unstructured mesh finite element model (Fluidity). In this work, a POD reduced order modelling is used to form the reduced order forward model by projecting the original complex model from a high dimensional space onto a reduced order space. The reduced order adjoint model is then constructed directly from the reduced order forward model. This efficient adaptive observation technique has been validated with two test cases: a model of an ocean gyre and a model of 2D urban street canyon flows.
published_date 2017-01-30T01:55:24Z
_version_ 1822093448964997120
score 11.048302

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