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All Journal Dedikasi: Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Khidmatuna: Journal of Research and Community Service Journal of Technology Informatics and Engineering
Najmi, M. Ikhwan
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Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Physics Social Sciences Other

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Journal : Journal of Technology Informatics and Engineering

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Computational Fluid Dynamics (CFD) Optimization in Smart Factories: AI-Based Predictive Modelling Ibrahim, Said Maulana; Najmi, M. Ikhwan
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.264

Abstract

In the era of Industry 4.0, optimizing fluid flow systems in smart factories is essential to improve energy efficiency and operational stability. Traditional Computational Fluid Dynamics (CFD) simulations provide accurate fluid flow analysis but require extensive computational resources and long processing times, making real-time applications challenging. To address this limitation, this study aims to develop an AI-based predictive model for CFD simulations, utilizing Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) to accelerate the estimation of fluid flow characteristics in industrial environments. The research methodology involves generating CFD simulation datasets, preprocessing data, and training AI models to predict key fluid parameters such as pressure, velocity, and temperature. The evaluation results show that CNN achieves a Mean Squared Error (MSE) of 0.0025 and a Root Mean Squared Error (RMSE) of 0.05, outperforming XGBoost, which records an MSE of 0.0030 and an RMSE of 0.055. Moreover, CNN predicts fluid dynamics in just 15.2 seconds, while XGBoost achieves results in 10.5 seconds, compared to the 1200.5 seconds required by traditional CFD simulations. These findings highlight the potential of AI in reducing computation time by over 98%, making real-time fluid flow analysis feasible in industrial settings. This study contributes to the advancement of AI-integrated CFD modeling, demonstrating that AI can significantly enhance the efficiency of fluid dynamics analysis without compromising accuracy. Future research should focus on expanding AI models to handle more complex flow conditions and integrating AI with smart factory design tools for real-time optimization
Co-Authors Ahmad Taufiq Ahmad Taufiq Aripriharta - Asyiah, Nor Chusna, Nadiya Miftachul Futri Yuliana Hendra Susanto Ibrahim, Said Maulana Markus Diantoro Maya Safitri Maya, Safitri Muhaemina, Maulanie MUHAMMAD NUGROHO, MUHAMMAD Nadiya Miftachul Chusna Nandang Mufti Ninis Hadi Haryanti, Ninis Hadi Oktafiansyah, Rahmad Sunaryono Sunaryono Sunaryono, Sunaryono Totok Wianto Yuliana, Futri