Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems

TL;DR

This paper develops and evaluates surrogate models to rapidly predict thrust forces in multi-fin flapping propulsion systems, balancing accuracy and computational efficiency for different fin geometries.

Contribution

It introduces surrogate modeling techniques tailored for tandem fin propulsion, improving prediction speed while maintaining acceptable accuracy across various geometries.

Findings

Surrogate models significantly reduce computation time compared to CFD.

Models accurately predict thrust for unseen fin geometries.

Tradeoffs between speed and accuracy are analyzed.

Abstract

The aim of this study is to develop surrogate models for quick, accurate prediction of thrust forces generated through flapping fin propulsion for given operating conditions and fin geometries. Different network architectures and configurations are explored to model the training data separately for the lead fin and rear fin of a tandem fin setup. We progressively improve the data representation of the input parameter space for model predictions. The models are tested on three unseen fin geometries and the predictions validated with computational fluid dynamics (CFD) data. Finally, the orders of magnitude gains in computational performance of these surrogate models, compared to experimental and CFD runs, vs their tradeoff with accuracy is discussed within the context of this tandem fin configuration.