Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization
Abstract A computationally efficient dehomogenization technique was developed based on a bioinspired diffusion-based pattern generation algorithm to convert an orientation field into explicit large-scale fluid flow channel structures. Due to the transient nature of diffusion and reaction, most diffu...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-41316-w |
| _version_ | 1827634377467625472 |
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| author | Sarah N. Hankins Yuqing Zhou Danny J. Lohan Ercan M. Dede |
| author_facet | Sarah N. Hankins Yuqing Zhou Danny J. Lohan Ercan M. Dede |
| author_sort | Sarah N. Hankins |
| collection | DOAJ |
| description | Abstract A computationally efficient dehomogenization technique was developed based on a bioinspired diffusion-based pattern generation algorithm to convert an orientation field into explicit large-scale fluid flow channel structures. Due to the transient nature of diffusion and reaction, most diffusion-based pattern generation models were solved in both time and space. In this work, we remove the temporal dependency and directly solve a steady-state equation. The steady-state Swift-Hohenberg model was selected due to its simplistic form as a single variable equation and intuitive parameter setting for pattern geometry control. Through comparison studies, we demonstrated that the steady-state model can produce statistically equivalent solutions to the transient model with potential computational speedup. This work marks an early foray into the use of steady-state pattern generation models for rapid dehomogenization in multiphysics engineering design applications. To highlight the benefits of this approach, the steady-state model was used to dehomogenize optimized orientation fields for the design of microreactor flow structures involving hundreds of microchannels in combination with a porous gas diffusion layer. A homogenization-based multi-objective optimization routine was used to produce a multi-objective Pareto set that explored the trade-offs between flow resistance and reactant distribution variability. In total, the diffusion-based dehomogenization method enabled the generation of 200 unique and distinctly different microreactor flow channel designs. The proposed dehomogenization approach permits comprehensive exploration of numerous bioinspired solutions capturing the full complexity of the optimization and Swift-Hohenberg design space. |
| first_indexed | 2024-03-09T15:12:32Z |
| format | Article |
| id | doaj.art-2f8db1e9a7db4248bb240376e6ec52ea |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:12:32Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2f8db1e9a7db4248bb240376e6ec52ea2023-11-26T13:16:45ZengNature PortfolioScientific Reports2045-23222023-09-0113111410.1038/s41598-023-41316-wGenerative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenizationSarah N. Hankins0Yuqing Zhou1Danny J. Lohan2Ercan M. Dede3Electronics Research Department, Toyota Research Institute of North AmericaElectronics Research Department, Toyota Research Institute of North AmericaElectronics Research Department, Toyota Research Institute of North AmericaElectronics Research Department, Toyota Research Institute of North AmericaAbstract A computationally efficient dehomogenization technique was developed based on a bioinspired diffusion-based pattern generation algorithm to convert an orientation field into explicit large-scale fluid flow channel structures. Due to the transient nature of diffusion and reaction, most diffusion-based pattern generation models were solved in both time and space. In this work, we remove the temporal dependency and directly solve a steady-state equation. The steady-state Swift-Hohenberg model was selected due to its simplistic form as a single variable equation and intuitive parameter setting for pattern geometry control. Through comparison studies, we demonstrated that the steady-state model can produce statistically equivalent solutions to the transient model with potential computational speedup. This work marks an early foray into the use of steady-state pattern generation models for rapid dehomogenization in multiphysics engineering design applications. To highlight the benefits of this approach, the steady-state model was used to dehomogenize optimized orientation fields for the design of microreactor flow structures involving hundreds of microchannels in combination with a porous gas diffusion layer. A homogenization-based multi-objective optimization routine was used to produce a multi-objective Pareto set that explored the trade-offs between flow resistance and reactant distribution variability. In total, the diffusion-based dehomogenization method enabled the generation of 200 unique and distinctly different microreactor flow channel designs. The proposed dehomogenization approach permits comprehensive exploration of numerous bioinspired solutions capturing the full complexity of the optimization and Swift-Hohenberg design space.https://doi.org/10.1038/s41598-023-41316-w |
| spellingShingle | Sarah N. Hankins Yuqing Zhou Danny J. Lohan Ercan M. Dede Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization Scientific Reports |
| title | Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization |
| title_full | Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization |
| title_fullStr | Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization |
| title_full_unstemmed | Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization |
| title_short | Generative design of large-scale fluid flow structures via steady-state diffusion-based dehomogenization |
| title_sort | generative design of large scale fluid flow structures via steady state diffusion based dehomogenization |
| url | https://doi.org/10.1038/s41598-023-41316-w |
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