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Machine-learning-based surrogate modeling of aerodynamic flow around distributed structures
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Zhang, Jincheng and Zhao, Xiaowei (2021) Machine-learning-based surrogate modeling of aerodynamic flow around distributed structures. AIAA Journal, 59 (3). pp. 868-879. doi:10.2514/1.J059877 ISSN 0001-1452.
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WRAP-Machine-learning-based-surrogate-modeling-aerodynamic-flow-structures-Zhang-2020.pdf - Accepted Version - Requires a PDF viewer. Download (6Mb) | Preview |
Official URL: https://doi.org/10.2514/1.J059877
Abstract
A machine-learning-based surrogate modeling method for distributed fluid systems is proposed in this paper, where a dimensionality reduction technique is used to reduce the flowfield dimension and a regression model is used to predict the reduced coefficients from the input parameters. The surrogate modeling method is specifically designed to tackle the fluid systems involving distributed aerodynamic structures, and its performance is illustrated by the application on the wake flow around wind turbine arrays in an atmospheric boundary layer. The main idea is to first decompose the whole fluid domain into subdomains, then carry out surrogate modeling for each subdomain by treating both the boundary information and the distributed flow parameters as the input parameters, and finally obtain the whole flowfield by combining the flowfield of each subdomain with the consideration of the matching condition at the subdomain interface. The proposed surrogate modeling method is applied to two test cases: a one-dimensional Poisson equation and a high-fidelity wind farm wake model. The results demonstrate the great efficiency and accuracy of the surrogate model and its excellent scalability to distributed systems of different scales.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Machine learning, Aerodynamics -- Mathematics, Aerodynamics -- Data processing, Computational fluid dynamics | ||||||||
Journal or Publication Title: | AIAA Journal | ||||||||
Publisher: | American Institute of Aeronautical and Astronautics | ||||||||
ISSN: | 0001-1452 | ||||||||
Official Date: | March 2021 | ||||||||
Dates: |
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Volume: | 59 | ||||||||
Number: | 3 | ||||||||
Page Range: | pp. 868-879 | ||||||||
DOI: | 10.2514/1.J059877 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | Copyright © 2020 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 16 November 2020 | ||||||||
Date of first compliant Open Access: | 1 February 2021 | ||||||||
RIOXX Funder/Project Grant: |
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