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State-of-the-art methods for inverse design of an enclosed environment

The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective....

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Detalles Bibliográficos
Autores principales: Liu, Wei, Zhang, Tengfei, Xue, Yu, Zhai, Zhiqiang (John), Wang, Jihong, Wei, Yun, Chen, Qingyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127361/
https://www.ncbi.nlm.nih.gov/pubmed/32288031
http://dx.doi.org/10.1016/j.buildenv.2015.02.041
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author Liu, Wei
Zhang, Tengfei
Xue, Yu
Zhai, Zhiqiang (John)
Wang, Jihong
Wei, Yun
Chen, Qingyan
author_facet Liu, Wei
Zhang, Tengfei
Xue, Yu
Zhai, Zhiqiang (John)
Wang, Jihong
Wei, Yun
Chen, Qingyan
author_sort Liu, Wei
collection PubMed
description The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. This paper discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, pseudo-reversibility method, and regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the CFD-based adjoint method, CFD-based genetic algorithm method, and proper orthogonal decomposition method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The CFD-based adjoint method is accurate and can handle many design parameters without increasing computing costs, but the method may find a locally optimal design that could meet the design objective with constrains. The CFD-based genetic algorithm method, on the other hand, can provide the global optimal design that can meet the design objective without constraints, but the computing cost can increase dramatically with the number of design parameters. The proper orthogonal decomposition method is a reduced-order method that can significantly lower computing costs, but at the expense of reduced accuracy. This paper also discusses the possibility to reduce the computing costs of CFD-based design methods.
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spelling pubmed-71273612020-04-08 State-of-the-art methods for inverse design of an enclosed environment Liu, Wei Zhang, Tengfei Xue, Yu Zhai, Zhiqiang (John) Wang, Jihong Wei, Yun Chen, Qingyan Build Environ Article The conventional design of enclosed environments uses a trial-and-error approach that is time consuming and may not meet the design objective. Inverse design concept uses the desired enclosed environment as the design objective and inversely determines the systems required to achieve the objective. This paper discusses a number of backward and forward methods for inverse design. Backward methods, such as the quasi-reversibility method, pseudo-reversibility method, and regularized inverse matrix method, can be used to identify contaminant sources in an enclosed environment. However, these methods cannot be used to inversely design a desired indoor environment. Forward methods, such as the CFD-based adjoint method, CFD-based genetic algorithm method, and proper orthogonal decomposition method, show the promise in the inverse design of airflow and heat transfer in an enclosed environment. The CFD-based adjoint method is accurate and can handle many design parameters without increasing computing costs, but the method may find a locally optimal design that could meet the design objective with constrains. The CFD-based genetic algorithm method, on the other hand, can provide the global optimal design that can meet the design objective without constraints, but the computing cost can increase dramatically with the number of design parameters. The proper orthogonal decomposition method is a reduced-order method that can significantly lower computing costs, but at the expense of reduced accuracy. This paper also discusses the possibility to reduce the computing costs of CFD-based design methods. Elsevier Ltd. 2015-09 2015-03-17 /pmc/articles/PMC7127361/ /pubmed/32288031 http://dx.doi.org/10.1016/j.buildenv.2015.02.041 Text en Copyright © 2015 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Liu, Wei
Zhang, Tengfei
Xue, Yu
Zhai, Zhiqiang (John)
Wang, Jihong
Wei, Yun
Chen, Qingyan
State-of-the-art methods for inverse design of an enclosed environment
title State-of-the-art methods for inverse design of an enclosed environment
title_full State-of-the-art methods for inverse design of an enclosed environment
title_fullStr State-of-the-art methods for inverse design of an enclosed environment
title_full_unstemmed State-of-the-art methods for inverse design of an enclosed environment
title_short State-of-the-art methods for inverse design of an enclosed environment
title_sort state-of-the-art methods for inverse design of an enclosed environment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7127361/
https://www.ncbi.nlm.nih.gov/pubmed/32288031
http://dx.doi.org/10.1016/j.buildenv.2015.02.041
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