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Open Access
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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n
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G.V. Rajeswari, Manas Kumar Yogi,
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- Assistant Professor, Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning), Pragati Engineering College (A), Surampalem, Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning), Pragati Engineering College (A), Surampalem, Andhra Pradesh, Andhra Pradesh, India, India
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Abstract
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nConvolutional Neural Networks exhibit remarkable capabilities in flow pattern recognition, pressure drop prediction, leak detection, and system optimization through their ability to process complex spatial and temporal data patterns. The study examines CNN architectures specifically adapted for fluid dynamics applications, including data preprocessing techniques, feature extraction methods, and performance optimization strategies. Key applications include real-time flow monitoring, predictive maintenance, design parameter optimization, and anomaly detection in pipe networks. Comparative analysis reveals that CNN-based approaches achieve 85-95% accuracy in flow prediction tasks and reduce computational time by up to 70% compared to traditional computational fluid dynamics methods. The integration of CNNs with physics-informed models shows promising results in maintaining physical consistency while achieving superior performance in complex pipe flow scenarios.nn
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Keywords: CNN, Pipe, Flow, Fluid Dynamics, Optimization, Prediction
n[if 424 equals=”Regular Issue”][This article belongs to Recent Trends in Fluid Mechanics ]
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nG.V. Rajeswari, Manas Kumar Yogi. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System[/if 2584]. Recent Trends in Fluid Mechanics. 30/08/2025; 12(03):-.
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nG.V. Rajeswari, Manas Kumar Yogi. [if 2584 equals=”][226 striphtml=1][else]Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System[/if 2584]. Recent Trends in Fluid Mechanics. 30/08/2025; 12(03):-. Available from: https://journals.stmjournals.com/rtfm/article=30/08/2025/view=0
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References n
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- Anderson K, et al. Deep learning applications in computational fluid dynamics for engineering design. J Comput Phys. 2019;445:110–25.
- Brown M, Johnson S. Convolutional neural networks for flow pattern recognition in multiphase pipe systems. Int J Heat Mass Transf. 2020;162:120–35.
- Chen L, et al. Physics-informed neural networks for pipe flow optimization and design. Comput Methods Appl Mech Eng. 2021;378:113–28.
- Davis R, Wilson P. Machine learning enhancement of computational fluid dynamics in industrial applications. Flow Turbul Combust. 2022;108:445–62.
- Evans T, et al. Real-time leak detection in pipeline networks using deep convolutional neural networks. J Pipeline Syst Eng Pract. 2022;13(2):04–18.
- Foster A, Thompson K. Multi-modal sensor fusion for pipeline monitoring using CNN architectures. Sens Actuators A Phys. 2022;334:113–27.
- Garcia M, et al. Adaptive control systems for pipe networks using convolutional neural networks. Control Eng Pract. 2022;124:105–18.
- Harris J, Martinez L. Spatial analysis and leak localization in water distribution networks using deep learning. Water Study. 2022;215:118–32.
- Ibrahim S, et al. Multi-objective optimization of pipe system design using CNN-based surrogate models. Eng Appl Artif Intell. 2023;116:105–20.
- Jackson D, Lee R. Hierarchical design optimization for complex pipe networks using multi-fidelity CNN models. Comput Fluids. 2023;251:105–19.
- Kumar V, et al. Graph neural networks and CNN integration for system-level pipe network analysis. Neural Netw. 2023;158:234–48.
- Lopez C, Singh A. Software integration frameworks for CNN-based engineering design tools. Comput Aided Des. 2023;155:103–17.
- Miller P, et al. Model predictive control enhancement using convolutional neural networks for pipe flow systems. J Process Control. 2023;124:87–102.
- Nelson B, Clark M. Online learning approaches for adaptive CNN-based pipe system control. Automatica. 2023;149:110–25.
- Patel R, et al. Uncertainty quantification in CNN-based pressure drop predictions for engineering design. Int J Heat Fluid Flow. 2023;99:109–23.
- Rodriguez F, White K. Distributed control architectures for large-scale pipe networks using CNN technologies. IEEE Trans Control Syst Technol. 2023;31(4):1456–71.
- Smith T, et al. Transfer learning applications for CNN models in diverse pipe flow environments. Appl Soft Comput. 2023;138:110–24.
- Taylor G, Brown J. Explainable AI techniques for CNN-based pipe flow analysis and design. Artif Intell Eng. 2024;67:205–20.
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| Volume | 12 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 03 | |
| Received | 31/05/2025 | |
| Accepted | 12/08/2025 | |
| Published | 30/08/2025 | |
| Retracted | ||
| Publication Time | 91 Days |
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