Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems
This work addresses computational efficiency challenges in designing flapping propulsion systems, though it appears incremental as it applies existing surrogate modeling techniques to a specific domain.
The study developed surrogate models to predict thrust forces for multi-fin flapping propulsion systems, achieving orders of magnitude computational speed gains compared to CFD and experimental methods while maintaining accuracy on unseen fin geometries.
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.