Computational Fluid Dynamics (CFD) Optimization in Smart Factories: AI-Based Predictive Modelling
DOI:
https://doi.org/10.51903/jtie.v4i1.264Keywords:
Artificial Intelligence, Computational Fluid Dynamics (CFD), Smart FactoryAbstract
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
References
Abtahi Mehrjardi, S. A., Khademi, A., & Fazli, M. (2024). Optimization of a Thermal Energy Storage System Enhanced with Fins Using Generative Adversarial Networks Method. Thermal Science and Engineering Progress, 49, 102471. https://doi.org/10.1016/j.tsep.2024.102471
Anjaneya, G., S, S., N K, M., Santhosh, A., Patil, S. A., Prasad, C. D., Aden, A. A., & B G, G. (2024). Study of Impact of Nano Fluids on Performance of Microchannel Heat Exchangers Using CFD. International Journal of Thermofluids, 24, 100836. https://doi.org/10.1016/j.ijft.2024.100836
Bounds, C. P., Desai, S., & Uddin, M. (2024). Enhancing CFD Predictions with Explainable Machine Learning for Aerodynamic Characteristics of Idealized Ground Vehicles. Vehicles, 6(3), 1318–1344. https://doi.org/10.3390/vehicles6030063
Bournet, P. E., & Rojano, F. (2022). Advances of Computational Fluid Dynamics (CFD) Applications in Agricultural Building Modelling: Research, Applications and Challenges. Computers and Electronics in Agriculture, 201, 107277. https://doi.org/10.1016/j.compag.2022.107277
Filo, G., Lempa, P., & Wisowski, K. (2025). Review of Deterministic and AI-Based Methods for Fluid Motion Modelling and Sloshing Analysis. Energies, 18(5), 1263. https://doi.org/10.3390/en18051263
Hafsa, N., Rushd, S., & Yousuf, H. (2023). Comparative Performance of Machine-Learning and Deep-Learning Algorithms in Predicting Gas–Liquid Flow Regimes. Processes, 11(1), 117. https://doi.org/10.3390/pr11010177
Hussain, M., Lin, D., Waqas, H., & Al-mdallal, Q. M. (2025). Mechanics an Artificial Intelligence and Machine Learning-Driven CFD Simulation for Optimizing Thermal Performance of Blood-Integrated Ternary Nano-Fluid. Engineering Applications of Computational Fluid Mechanics, 19(1), 2459664. https://doi.org/10.1080/19942060.2025.2459664
Iman, M., Arabnia, H. R., & Rasheed, K. (2023). A Review of Deep Transfer Learning and Recent Advancements. Technologies, 11(2), 1–14. https://doi.org/10.3390/technologies11020040
Kabir, M. F., Chen, T., & Ludwig, S. A. (2023). A Performance Analysis of Dimensionality Reduction Algorithms in Machine Learning Models for Cancer Prediction. Healthcare Analytics, 3, 100125. https://doi.org/10.1016/j.health.2022.100125
Łach, Ł., & Svyetlichnyy, D. (2025). Advances in Numerical Modeling for Heat Transfer and Thermal Management: A Review of Computational Approaches and Environmental Impacts. Energies, 18(5), 1302. https://doi.org/10.3390/en18051302
Li, G., & Jung, J. J. (2023). Deep Learning for Anomaly Detection in Multivariate Time Series: Approaches, Applications, and Challenges. Information Fusion, 91, 93–102. https://doi.org/10.1016/j.inffus.2022.10.008
Liu, L., & Huang, Y. (2024). HVAC Design Optimization for Pharmaceutical Facilities with BIM and CFD. Buildings, 14(6), 1627. https://doi.org/10.3390/buildings14061627
Mira, D., Pérez-Sánchez, E. J., Borrell, R., & Houzeaux, G. (2023). HPC-Enabling Technologies for High-Fidelity Combustion Simulations. Proceedings of the Combustion Institute, 39(4), 5091–5125. https://doi.org/10.1016/j.proci.2022.07.222
Molinero-Hernández, D., Galván-González, S. R., Herrera-Sandoval, N. D., Guzman-Avalos, P., Pacheco-Ibarra, J. J., & Domínguez-Mota, F. J. (2024). Turbomachinery GPU Accelerated CFD: An Insight into Performance. Computation, 12(3), 1–20. https://doi.org/10.3390/computation12030057
Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine Intelligence and Autonomous Robotic Technologies in the Corporate Context of SMEs: Deep Learning and Virtual Simulation Algorithms, Cyber-Physical Production Networks, and Industry 4.0-Based Manufacturing Systems. Applied Sciences, 13(3), 1681. https://doi.org/10.3390/app13031681
Panchigar, D., Kar, K., Shukla, S., Mathew, R. M., Chadha, U., & Selvaraj, S. K. (2022). Machine Learning-Based CFD Simulations: A Review, Models, Open Threats, and Future Tactics. Neural Computing and Applications, 34(24), 21677–21700. https://doi.org/10.1007/s00521-022-07838-6
Peifeng Li, J. Z., & Krebs, P. (2022). Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach. Water, 14(6), 993. https://doi.org/10.3390/w14060993
Sharma, P., Chung, W. T., Akoush, B., & Ihme, M. (2023). A Review of Physics-Informed Machine Learning in Fluid Mechanics. Energies, 16(5), 1–21. https://doi.org/10.3390/en16052343
Shirzadi, M., Fukasawa, T., Fukui, K., & Ishigami, T. (2023). Application of Deep Learning Neural Networks for the Analysis of Fluid-Particle Dynamics in Fibrous Filters. Chemical Engineering Journal, 455, 140775. https://doi.org/10.1016/j.cej.2022.140775
Venier, C., Urrutia, R., Yao, J., & Ghasem, N. (2024). Combining CFD and AI/ML Modeling to Improve the Performance of Polypropylene Fluidized Bed Reactors. Fluids, 9(12), 298. https://doi.org/10.3390/fluids9120298
Wang, C. C., Kuo, P. H., & Chen, G. Y. (2022). Machine Learning Prediction of Turning Precision Using Optimized XGBoost Model. Applied Sciences, 12(15), 7739. https://doi.org/10.3390/app12157739
Wang, F. Z., Animasaun, I. L., Muhammad, T., & Okoya, S. S. (2024). Recent Advancements in Fluid Dynamics: Drag Reduction, Lift Generation, Computational Fluid Dynamics, Turbulence Modelling, and Multiphase Flow. Arabian Journal for Science and Engineering, 49(8), 10237–10249. https://doi.org/10.1007/s13369-024-08945-3
Ye, B., & Zhou, W. (2024). Efficiency Increment of CFD Modeling by Using ANFIS Artificial Intelligence for Thermal-Based Separation Modeling. Case Studies in Thermal Engineering, 60, 104820. https://doi.org/10.1016/j.csite.2024.104820
Ye, C. C., Zhang, P. J. Y., Wan, Z. H., Yan, R., & Sun, D. J. (2022). Accelerating CFD Simulation with High Order Finite Difference Method on Curvilinear Coordinates for Modern GPU Clusters. In Advances in Aerodynamics (Vol. 4, Issue 1). Advances in Aerodynamics. https://doi.org/10.1186/s42774-021-00098-3
Zhu, S., Lv, S., Wang, W., & Cui, M. (2024). Toward Optimal Design of a Factory Air Conditioning System Based on Energy Consumption Prediction. Processes, 12(12), 2615. https://doi.org/10.3390/pr12122615
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Technology Informatics and Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
