Surrogate modeling for flow simulations using design variable-coded deep learning networks
Abstract In recent years, machine learning techniques have emerged as pivotal tools across scientific and engineering disciplines. One notable application is in computational fluid dynamics (CFD), where there is a growing demand for cost-effective alternatives to traditional, resource-intensive simu...
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
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| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-025-00634-8 |
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| Summary: | Abstract In recent years, machine learning techniques have emerged as pivotal tools across scientific and engineering disciplines. One notable application is in computational fluid dynamics (CFD), where there is a growing demand for cost-effective alternatives to traditional, resource-intensive simulations. This study explores a surrogate modeling approach known as Design Variable Coded Multilayer Perceptron (DV-MLP), aimed at predicting velocity on unseen geometries using CFD simulation data. The DV-MLP model integrates spatial and design variables directly, offering a mesh-independent, scalable solution for rapid flow prediction across various geometric configurations. This method seeks to replace CFD simulations for geometries resembling those used in training, thereby accelerating the design process. The dataset comprises CFD results from four distinct bump heights. The objective is to train the MLP using data from three bump heights and assess its predictive performance on a middle bump height. The results indicate that the MLP accurately predicts flow features for the middle bump height. This demonstrates its ability to provide precise predictions of near-wall flow behavior for similar geometries, making it a valuable tool for industries requiring swift and reliable CFD predictions for design refinement and optimization. The DV-MLP model is designed primarily for interpolation between geometries and flow conditions represented in the training data, providing accurate predictions for cases that fall within the range of the sampled design space. |
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| ISSN: | 1110-1903 2536-9512 |