Abstract
In the present study, the performance of the metal heater-based freeze prevention system was predicted with respect to the major design variables of outdoor fire piping, including the fire pipe diameter, outer temperature, and insulation thickness. To this end, CFD analysis was conducted to obtain water center temperature data along the pipe-length direction with respect to the major design variables. Subsequently, four AI algorithms, including the deep neural network, decision tree, random forest, and support vector machine, were trained with the collected data, and their prediction performance was compared. Further, each algorithm, once trained, was tested for its ability to make reasonable predictions for the conditions that it had not been trained with. Overall, the deep neural network model exhibited the best prediction performance for both interpolation and extrapolation data. As a result, the model was determined to be the most suitable for the prediction of the water temperature.
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Acknowledgments
This study has been conducted with the support of the Korea Institute of Industrial Technology as “Development of intelligent root technology with add-on modules” (KITECH EO-23-0007).
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Hojoon Cho is an Executive Director of Afiliated Research Institute, NATM Co., Cheonan, Korea, since 2020. He received the Ph.D. in Mechanical Engineering from Inha university in 2008. His research interests include CAE and NVH of automotive system, high pressure vessel, and program development for prediction using AI in mechanical field.
Seongsik Lim is a Principal Researcher of molding and metal forming R&D Department, Korea Institute of Industrial Technology. He received his Ph.D. in Mechanical Engineering from Inha University. His research interests include material properties modeling and process optimization for metal forming process using CAE and AI.
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Cho, H., Seo, S., Heo, C. et al. Performance estimation of freeze protection system for outdoor fire piping by using AI algorithm. J Mech Sci Technol 37, 5093–5101 (2023). https://doi.org/10.1007/s12206-023-0914-7
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DOI: https://doi.org/10.1007/s12206-023-0914-7