Predicting water resistance and pitching angle during take-off: an artificial neural network approach

Abstract

This research addresses the challenges faced by seaplanes and amphibious aircraft during takeoff and landing on water, emphasizing the limitations and costs associated with traditional towing tank tests and computational fluid dynamics (CFD) simulations. The study proposes an innovative approach that employs artificial neural networks (ANN) to predict water resistance and pitching angle during amphibious aircraft take-off, minimizing the reliance on expensive towing tank tests. The ANN models are developed and optimized using Bayesian optimization, showcasing improved accuracy in predicting water resistance and pitching angle. The research demonstrates the potential of machine learning, specifically ANNs, to significantly reduce the need for costly experimental tests, providing an efficient alternative for designing amphibious aircraft. The results indicate high accuracy in predicting water resistance and pitching angle, offering substantial time and resource savings during the experimental phase. However, the study highlights the need for model adaptation for different designs and test variations to enhance overall applicability.