research-article

MVE-Net: : An Automatic 3-D Structured Mesh Validity Evaluation Framework Using Deep Neural Networks

Authors:

Bo ChenAuthors Info & Claims

Volume 141, Issue C

https://doi.org/10.1016/j.cad.2021.103104

Published: 01 December 2021 Publication History

Abstract

An important objective of quality control in CFD pre-processing is the facility to indicate to the engineer the validity of the generated mesh. Existing quality measures mainly focus on the subjective evaluation of the shape information of mesh elements, such as aspect ratio, skewness, and shape regularity, and often ignore mesh distribution details. In order to ensure a precise evaluation result, these measures usually work with knowledge-based manual re-evaluation, which heavily increases the meshing cost and hampers automation of the meshing process. In this work, we propose an automatic 3-D structured mesh validity evaluation framework, MVE-Net. It takes mesh files as input, employs deep neural networks to study the role of mesh point distribution on numerical accuracy, and finally outputs the overall validity of the mesh for the simulation. For training the network, we introduce the first 3-D mesh benchmark dataset containing 24576 labeled structured meshes with different models and sizes. The experimental results on the dataset demonstrate the potential of deep neural networks in 3-D mesh validity evaluation and the effectiveness of MVE-Net. The well-trained MVE-Net can be a useful and helpful tool in the fully automatic pre-processing procedure.

Highlights

•

A large-scale labeled benchmark dataset with 3-D structured mesh samples.

•

A novel deep neural network to learn CFD mesh quality features from point-based input.

•

An automatic mesh validity evaluation framework for meshes with different sizes and models.

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Cited By

Jiang QWang QChi LMa WLi FLiu JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)DeepEnhancer: Temporally Consistent Focal Transformer for Comprehensive Video EnhancementProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658031(969-977)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658031
Liu XLin HLiu XQian JCai SFan HGao Q(2024)LAFlowNetEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108896136:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108896
Lock CHassan OSevilla RJones J(2023)Meshing using neural networks for improving the efficiency of computer modellingEngineering with Computers10.1007/s00366-023-01812-z39:6(3791-3820)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00366-023-01812-z
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Index Terms

MVE-Net: An Automatic 3-D Structured Mesh Validity Evaluation Framework Using Deep Neural Networks
1. Applied computing
  1. Physical sciences and engineering
2. Computing methodologies

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Computer-Aided Design

Computer-Aided Design Volume 141, Issue C

Dec 2021

195 pages

ISSN:0010-4485

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Butterworth-Heinemann

United States

Publication History

Published: 01 December 2021

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Cited By

Jiang QWang QChi LMa WLi FLiu JGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)DeepEnhancer: Temporally Consistent Focal Transformer for Comprehensive Video EnhancementProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658031(969-977)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658031
Liu XLin HLiu XQian JCai SFan HGao Q(2024)LAFlowNetEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108896136:PAOnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108896
Lock CHassan OSevilla RJones J(2023)Meshing using neural networks for improving the efficiency of computer modellingEngineering with Computers10.1007/s00366-023-01812-z39:6(3791-3820)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00366-023-01812-z
Chen XGong CLiu JPang YDeng LChi LLi K(2022)A novel neural network approach for airfoil mesh quality evaluationJournal of Parallel and Distributed Computing10.1016/j.jpdc.2022.03.006164:C(123-132)Online publication date: 18-May-2022
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Wang ZChen XLi TGong CPang YLiu J(2022)Evaluating mesh quality with graph neural networksEngineering with Computers10.1007/s00366-022-01720-838:5(4663-4673)Online publication date: 1-Oct-2022
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Chen XLi TWan QHe XGong CPang YLiu J(2022)MGNet: a novel differential mesh generation method based on unsupervised neural networksEngineering with Computers10.1007/s00366-022-01632-738:5(4409-4421)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s00366-022-01632-7

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