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[Sponsors] |
Job Record #19046 | |
Title | Physics-informed machine-learning models for gas-solid flow |
Category | PostDoc Position |
Employer | Chalmers University of Technology |
Location | Sweden, Gothenburg |
International | Yes, international applications are welcome |
Closure Date | Monday, April 15, 2024 |
Description: | |
This is for a 2-year Post-Doc position. Missing or incorrectly modeled physics contributes to the lack of a widely accepted drag model formulation for universal use across all fluidization regimes and particle types. Overall fluidization simulation accuracy cannot improve without a mechanistic understanding of what physical interactions are not adequately captured in the current models. To address this, machine learning (ML) / artificial intelligence (AI) can spot profound and elucidate ambiguous relations that can be generalizable. Previous ML/AI studies for drag offer new formulations that outperform typical ones for targeted problems. What remains amiss is a widely accepted drag model that is generally applicable, or a set of drag models that work well for different parameter spaces. This motivates the current project. Other novel ideas along this line are welcomed. |
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Contact Information: | |
Please mention the CFD Jobs Database, record #19046 when responding to this ad. | |
Name | Jia Chew |
jia.chew@chalmers.se | |
Email Application | Yes |
Address | Avdelning Kemiteknik | Division of Chemical Engineering Chalmers tekniska högskola | Chalmers University of Technology SE-412 96 Göteborg, Sweden |
Record Data: | |
Last Modified | 13:34:09, Tuesday, March 12, 2024 |
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