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Computational framework for efficient high-fidelity optimization of bio-inspired propulsion and its application to accelerating swimmers

https://doi.org/10.1016/j.jcp.2023.112038

Abouhussein, Ahmed ; Peet, Yulia T. ( June 2023 , Journal of Computational Physics)

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Optimized hydrodynamic interactions in phalanx school arrays of accelerated thunniform swimmers

https://doi.org/10.1088/1402-4896/acb859

Abouhussein, Ahmed ; Peet, Yulia T ( February 2023 , Physica Scripta)

Abstract
Optimal fish array hydrodynamics in accelerating phalanx schools are investigated through a computational framework which combines high fidelity Computational Fluid Dynamics (CFD) simulations with a gradient free surrogate-based optimization algorithm. Critical parameters relevant to a phalanx fish school, such as midline kinematics, separation distance and phase synchronization, are investigated in light of efficient propulsion during an accelerating fish motion. Results show that the optimal midline kinematics in accelerating phalanx schools resemble those of accelerating solitary swimmers. The optimal separation distance in a phalanx school for thunniform biologically-inspired swimmers is shown to be around 2L(whereLis the swimmer’s total length). Furthermore, separation distance is shown to have a stronger effect,ceteris paribus, on the propulsion efficiency of a school when compared to phase synchronization.

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Constraint Enforcement to Guarantee Strictly Feasible Solutions in a Surrogate Based Optimizer

https://doi.org/10.2514/6.2022-1611

Abouhussein, Ahmed ; Islam, Nusrat ; Peet, Yulia ( December 2021 , AIAA paper)

Surrogate based optimization (SBO) methods have gained popularity in the field of constrained optimization of expensive black-box functions. However, constraint handling methods do not usually guarantee strictly feasible candidates during optimization. This can become an issue in applied engineering problems where design variables must remain feasible for simulations to not fail. We propose a simple constraint-handling method for computationally inexpensive constraint functions which guarantees strictly feasible candidates when using a surrogate-based optimizer. We compare our method to other SBO algorithms and an EA on five analytical test functions, and an applied fully-resolved Computational Fluid Dynamics (CFD) problem concerned with optimization of an undulatory swimming of a fish-like body, and show that the proposed algorithm shows favorable results while guaranteeing feasible candidates.
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