Predicting water resistance and pitching angle during take-off: an artificial neural network approach
Year: 2024
Type: article
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.
Authors: Muhammad Fajar, Sigit Tri Atmaja, Sinung Tirtha Pinindriya, Arifin Rasyadi Soemaryanto, Kurnia Hidayat
Institution: National Research and Innovation Agency
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Badan Riset dan Inovasi Nasional
OpenAlex ID: W4404852913