Functional optimization of fluidic devices with differentiable stokes flow
© 2020 Owner/Author. We present a method for performance-driven optimization of fluidic devices. In our approach, engineers provide a high-level specification of a device using parametric surfaces for the fluid-solid boundaries. They also specify desired flow properties for inlets and outlets of the...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery (ACM)
2021
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| Online Access: | https://hdl.handle.net/1721.1/134003 |
| _version_ | 1826191990792912896 |
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| author | Du, Tao Wu, Kui Spielberg, Andrew Matusik, Wojciech Zhu, Bo Sifakis, Eftychios |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Du, Tao Wu, Kui Spielberg, Andrew Matusik, Wojciech Zhu, Bo Sifakis, Eftychios |
| author_sort | Du, Tao |
| collection | MIT |
| description | © 2020 Owner/Author. We present a method for performance-driven optimization of fluidic devices. In our approach, engineers provide a high-level specification of a device using parametric surfaces for the fluid-solid boundaries. They also specify desired flow properties for inlets and outlets of the device. Our computational approach optimizes the boundary of the fluidic device such that its steady-state flow matches desired flow at outlets. In order to deal with computational challenges of this task, we propose an efficient, differentiable Stokes flow solver. Our solver provides explicit access to gradients of performance metrics with respect to the parametric boundary representation. This key feature allows us to couple the solver with efficient gradient-based optimization methods. We demonstrate the efficacy of this approach on designs of five complex 3D fluidic systems. Our approach makes an important step towards practical computational design tools for high-performance fluidic devices. |
| first_indexed | 2024-09-23T09:04:29Z |
| format | Article |
| id | mit-1721.1/134003 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:04:29Z |
| publishDate | 2021 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1340032023-09-28T20:27:42Z Functional optimization of fluidic devices with differentiable stokes flow Du, Tao Wu, Kui Spielberg, Andrew Matusik, Wojciech Zhu, Bo Sifakis, Eftychios Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 2020 Owner/Author. We present a method for performance-driven optimization of fluidic devices. In our approach, engineers provide a high-level specification of a device using parametric surfaces for the fluid-solid boundaries. They also specify desired flow properties for inlets and outlets of the device. Our computational approach optimizes the boundary of the fluidic device such that its steady-state flow matches desired flow at outlets. In order to deal with computational challenges of this task, we propose an efficient, differentiable Stokes flow solver. Our solver provides explicit access to gradients of performance metrics with respect to the parametric boundary representation. This key feature allows us to couple the solver with efficient gradient-based optimization methods. We demonstrate the efficacy of this approach on designs of five complex 3D fluidic systems. Our approach makes an important step towards practical computational design tools for high-performance fluidic devices. 2021-10-27T19:57:35Z 2021-10-27T19:57:35Z 2020 2021-01-29T19:50:39Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/134003 en 10.1145/3414685.3417795 ACM Transactions on Graphics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) ACM |
| spellingShingle | Du, Tao Wu, Kui Spielberg, Andrew Matusik, Wojciech Zhu, Bo Sifakis, Eftychios Functional optimization of fluidic devices with differentiable stokes flow |
| title | Functional optimization of fluidic devices with differentiable stokes flow |
| title_full | Functional optimization of fluidic devices with differentiable stokes flow |
| title_fullStr | Functional optimization of fluidic devices with differentiable stokes flow |
| title_full_unstemmed | Functional optimization of fluidic devices with differentiable stokes flow |
| title_short | Functional optimization of fluidic devices with differentiable stokes flow |
| title_sort | functional optimization of fluidic devices with differentiable stokes flow |
| url | https://hdl.handle.net/1721.1/134003 |
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