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A stochastic topology optimization algorithm for improved fluid dynamics systems

Published online by Cambridge University Press:  03 January 2023

Fox Furrokh Open the ORCID record for Fox Furrokh [Opens in a new window]  and
Nic Zhang
Fox Furrokh*
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK
Nic Zhang
Affiliation:
Department of Mechanical Engineering, University of Bath, Bath BA2 7AY, UK
*
Author for correspondence: Fox Furrokh, E-mail: fox-f@outlook.com
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Abstract

The use of topology optimization in the design of fluid dynamics systems is still in its infancy. With the decreasing cost of additive manufacture, the application of topology optimization in the design of structural components has begun to increase. This paper provides a method for using topology optimization to reduce the power dissipation of fluid dynamics systems, with the novelty of it being the first application of stochastic mechanisms in the design of 3D fluid–solid geometrical interfaces. The optimization algorithm uses the continuous adjoint method for sensitivity analysis and is optimized against an objective function for fluid power dissipation. The paper details the methodology behind a vanilla gradient descent approach before introducing stochastic behavior through a minibatch-based system. Both algorithms are then applied to a novel case study for an internal combustion engine's piston cooling gallery before the performance of each algorithm's resulting geometry is analyzed and compared. The vanilla gradient descent algorithm achieves an 8.9% improvement in pressure loss through the case study, and this is surpassed by the stochastic descent algorithm which achieved a 9.9% improvement, however this improvement came with a large time cost. Both approaches produced similarly unintuitive geometry solutions to successfully improve the performance of the cooling gallery.

Keywords

Adjoint methodalgorithmscomputational fluid dynamicsmachine learningstochastic optimization
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e35
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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