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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Atmaja, Sigit Tri
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Computer Science & IT Engineering

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Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio Fahmi, Faisal; Fajar, Rizqon; Atmaja, Sigit Tri; Erwandi, Erwandi; Rahuna, Daif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1969-1979

Abstract

Developing an engineering design is resource-intensive and time-consuming, particularly for the floats of a floatplane design, due to its complexity and limited testing facilities. Intelligent-based computational design (IBCD) techniques, which integrate computational design techniques and machine learning (ML) algorithms, offer a solution to reduce required testing by providing predictions. This paper proposes a deep learning (DL)-based IBCD method for modeling floats' lift-to-drag coefficient ratio (CL/CD), where DL is one of the most powerful ML. The proposed method consists of two phases: hyper-parameter optimization and DL model training and evaluation. A genetic algorithm (GA) is employed in the first phase to explore complex hyper-parameter combinations efficiently. Evaluation of the predicted CL/CD of the floats using the DL model resulted in a satisfactory R-squared of 0.9329 and the lowest mean squared error (MSE) of 0,001536. These results demonstrate the ability of DL model to predict the float's performance accurately and can facilitate further design optimization. Thus, the proposed method can offer a time-efficient and cost-effective solution for predicting float performance, aiding in optimizing floatplane designs and enhancing their functionalities.
Predicting water resistance and pitching angle during take-off: an artificial neural network approach Fajar, Muhammad; Atmaja, Sigit Tri; Pinindriya, Sinung Tirtha; Soemaryanto, Arifin Rasyadi; Hidayat, Kurnia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp142-150

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.
Co-Authors Erwandi Erwandi Fahmi, Faisal Hidayat, Kurnia Maarif, Muhammad Samsul Muhammad Fajar Pinindriya, Sinung Tirtha Rahuna, Daif Rizqon Fajar Soemaryanto, Arifin Rasyadi Wahidin, Agus