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Job Record #19046
TitlePhysics-informed machine-learning models for gas-solid flow
CategoryPostDoc Position
EmployerChalmers University of Technology
LocationSweden, Gothenburg
InternationalYes, international applications are welcome
Closure DateMonday, April 15, 2024
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.
Contact Information:
Please mention the CFD Jobs Database, record #19046 when responding to this ad.
NameJia Chew
Email ApplicationYes
AddressAvdelning Kemiteknik | Division of Chemical Engineering
Chalmers tekniska högskola | Chalmers University of Technology
SE-412 96 Göteborg, Sweden
Record Data:
Last Modified13:34:09, Tuesday, March 12, 2024

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