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Jurnal
SINTA 1
Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio
IAES International Journal of Artificial Intelligence
ISSN 2252-8938, 2089-4872
Terbit 2024-06-01
Penerbit Institute of Advanced Engineering and Science
Bahasa eng
Abstrak
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.
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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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<dc:title xml:lang="en-US">Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio</dc:title>
<dc:creator>Fahmi, Faisal</dc:creator>
<dc:creator>Fajar, Rizqon</dc:creator>
<dc:creator>Atmaja, Sigit Tri</dc:creator>
<dc:creator>Erwandi, Erwandi</dc:creator>
<dc:creator>Rahuna, Daif</dc:creator>
<dc:subject xml:lang="en-US">Deep Learning; Floatplane; Genetic Algorithm</dc:subject>
<dc:subject xml:lang="en-US">Computational design; Deep learning; Floatplane; Genetic algorithm; Hydrodynamic performance;</dc:subject>
<dc:description xml:lang="en-US">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.</dc:description>
<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
<dc:contributor xml:lang="en-US"></dc:contributor>
<dc:date>2024-06-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US"></dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23780</dc:identifier>
<dc:identifier>10.11591/ijai.v13.i2.pp1969-1979</dc:identifier>
<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 2: June 2024; 1969-1979</dc:source>
<dc:source>2252-8938</dc:source>
<dc:source>2089-4872</dc:source>
<dc:source>10.11591/ijai.v13.i2</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23780/14000</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
Sitasi dan pengindeksan
Jumlah menurut tiap indeks pada 13 Nov 2026; setiap indeks menghitung sitasi dengan cara berbeda.
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APA 7
Fahmi, F., Fajar, R., Atmaja, S. T., Erwandi, E., & Rahuna, D. (2024). Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio. IAES International Journal of Artificial Intelligence, 13(2), 1969–1979. https://doi.org/10.11591/ijai.v13.i2.pp1969-1979
Salin
IEEE
F. Fahmi, R. Fajar, S. T. Atmaja, E. Erwandi, and D. Rahuna, “Deep learning-based prediction of float model performance in floatplanes: A case study on lift-to-drag coefficient ratio,” IAES International Journal of Artificial Intelligence, vol. 13, no. 2, pp. 1969–1979, 2024, doi: 10.11591/ijai.v13.i2.pp1969-1979.
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