Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2024-03-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0187783 |
| _version_ | 1797228310186426368 |
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| author | Yuri Koide Arjun J. Kaithakkal Matthias Schniewind Bradley P. Ladewig Alexander Stroh Pascal Friederich |
| author_facet | Yuri Koide Arjun J. Kaithakkal Matthias Schniewind Bradley P. Ladewig Alexander Stroh Pascal Friederich |
| author_sort | Yuri Koide |
| collection | DOAJ |
| description | Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels as well as machine learning models trained on simulated data to predict the drag coefficient and Stanton number. We show that convolutional neural networks (CNNs) can accurately predict target properties at a fraction of the computational cost of numerical simulations. We use CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data augmentation techniques are incorporated to enforce physical invariances toward shifting and flipping, contributing to precise prediction for fluid flow and heat transfer characteristics. Moreover, we approach the interpretation of the trained model to better understand relevant channel structures and their influence on heat transfer. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering. |
| first_indexed | 2024-04-24T14:54:40Z |
| format | Article |
| id | doaj.art-7c60e784d03448f0b59cf5a83b24491c |
| institution | Directory Open Access Journal |
| issn | 2770-9019 |
| language | English |
| last_indexed | 2024-04-24T14:54:40Z |
| publishDate | 2024-03-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Machine Learning |
| spelling | doaj.art-7c60e784d03448f0b59cf5a83b24491c2024-04-02T19:46:06ZengAIP Publishing LLCAPL Machine Learning2770-90192024-03-0121016108016108-1210.1063/5.0187783Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transferYuri Koide0Arjun J. Kaithakkal1Matthias Schniewind2Bradley P. Ladewig3Alexander Stroh4Pascal Friederich5Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, GermanyInstitute of Fluid Mechanics, Karlsruhe Institute of Technology, Kaiserstr. 10, 76131 Karlsruhe, GermanyInstitute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, GermanyInstitute for Micro Process Engineering, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Karlsruhe, GermanyInstitute of Fluid Mechanics, Karlsruhe Institute of Technology, Kaiserstr. 10, 76131 Karlsruhe, GermanyInstitute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131 Karlsruhe, GermanyNumerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in simple flat channels is a relatively easy task for various simulation methods. However, once the channel geometry becomes more complex, numerical simulations become a bottleneck in optimizing wall geometries. We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels as well as machine learning models trained on simulated data to predict the drag coefficient and Stanton number. We show that convolutional neural networks (CNNs) can accurately predict target properties at a fraction of the computational cost of numerical simulations. We use CNN models in a virtual high-throughput screening approach to explore a large number of possible, randomly generated wall architectures. Data augmentation techniques are incorporated to enforce physical invariances toward shifting and flipping, contributing to precise prediction for fluid flow and heat transfer characteristics. Moreover, we approach the interpretation of the trained model to better understand relevant channel structures and their influence on heat transfer. The general approach is not only applicable to simple flow setups as presented here but can be extended to more complex tasks, such as multiphase or even reactive unit operations in chemical engineering.http://dx.doi.org/10.1063/5.0187783 |
| spellingShingle | Yuri Koide Arjun J. Kaithakkal Matthias Schniewind Bradley P. Ladewig Alexander Stroh Pascal Friederich Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer APL Machine Learning |
| title | Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer |
| title_full | Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer |
| title_fullStr | Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer |
| title_full_unstemmed | Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer |
| title_short | Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer |
| title_sort | machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer |
| url | http://dx.doi.org/10.1063/5.0187783 |
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