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    A Surrogate Model for Shallow Water Equations Solvers with Deep Learning

    Source: Journal of Hydraulic Engineering:;2023:;Volume ( 149 ):;issue: 011::page 04023045-1
    Author:
    Yalan Song
    ,
    Chaopeng Shen
    ,
    Xiaofeng Liu
    DOI: 10.1061/JHEND8.HYENG-13190
    Publisher: ASCE
    Abstract: Physics-based models (PBMs), such as shallow water equations (SWEs) solvers, have been widely used in flood simulation and river hydraulics analysis. However, they are usually computationally expensive and unsuitable for parameter optimizations that need many runs. An alternative is the machine learning (ML) method, which can be used to construct computationally efficient surrogates for PBMs that can approximate their input-output dynamics. Among many ML techniques, convolutional neural network (CNN) is a prevalent method for image-to-image regressions on structured or regular meshes (e.g., mapping from the boundary conditions to flow solutions of SWEs). However, CNN-based methods have significant limitations because of their raster-image nature. Such methods cannot precisely capture the boundary geometry of obstacles and near-field flow features, which are of paramount importance to fluid dynamics. We introduced an efficient, accurate, and flexible neural network (NN) surrogate model [which is based on deep learning and can make point-to-point (p2p) predictions on unstructured meshes] called NN-p2p. The new method was evaluated and compared against CNN-based methods. NN-p2p improves the accuracy of the near-field flow prediction with a mean relative error of 0.56% for the velocity magnitude around piers with unseen length/width ratios. It also respects conservation laws more strictly than the CNN-based models and performs reasonably well for spatial extrapolation. The surrogate reduces computing time by almost 3-orders of magnitude in comparison with its corresponding PBM. Moreover, as a demonstration of the NN-p2p model’s practical applicability, we calculated drag coefficient using NN-p2p for piers of varying length-to-width ratios and obtained a novel linear relationship between the drag coefficient and the logarithmic transformation of the pier’s geometry.
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      A Surrogate Model for Shallow Water Equations Solvers with Deep Learning

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    contributor authorYalan Song
    contributor authorChaopeng Shen
    contributor authorXiaofeng Liu
    date accessioned2024-04-27T20:49:52Z
    date available2024-04-27T20:49:52Z
    date issued2023/11/01
    identifier other10.1061-JHEND8.HYENG-13190.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296055
    description abstractPhysics-based models (PBMs), such as shallow water equations (SWEs) solvers, have been widely used in flood simulation and river hydraulics analysis. However, they are usually computationally expensive and unsuitable for parameter optimizations that need many runs. An alternative is the machine learning (ML) method, which can be used to construct computationally efficient surrogates for PBMs that can approximate their input-output dynamics. Among many ML techniques, convolutional neural network (CNN) is a prevalent method for image-to-image regressions on structured or regular meshes (e.g., mapping from the boundary conditions to flow solutions of SWEs). However, CNN-based methods have significant limitations because of their raster-image nature. Such methods cannot precisely capture the boundary geometry of obstacles and near-field flow features, which are of paramount importance to fluid dynamics. We introduced an efficient, accurate, and flexible neural network (NN) surrogate model [which is based on deep learning and can make point-to-point (p2p) predictions on unstructured meshes] called NN-p2p. The new method was evaluated and compared against CNN-based methods. NN-p2p improves the accuracy of the near-field flow prediction with a mean relative error of 0.56% for the velocity magnitude around piers with unseen length/width ratios. It also respects conservation laws more strictly than the CNN-based models and performs reasonably well for spatial extrapolation. The surrogate reduces computing time by almost 3-orders of magnitude in comparison with its corresponding PBM. Moreover, as a demonstration of the NN-p2p model’s practical applicability, we calculated drag coefficient using NN-p2p for piers of varying length-to-width ratios and obtained a novel linear relationship between the drag coefficient and the logarithmic transformation of the pier’s geometry.
    publisherASCE
    titleA Surrogate Model for Shallow Water Equations Solvers with Deep Learning
    typeJournal Article
    journal volume149
    journal issue11
    journal titleJournal of Hydraulic Engineering
    identifier doi10.1061/JHEND8.HYENG-13190
    journal fristpage04023045-1
    journal lastpage04023045-15
    page15
    treeJournal of Hydraulic Engineering:;2023:;Volume ( 149 ):;issue: 011
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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