Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System

Year : 2025 | Volume : 12 | Issue : 03 | Page : 1 9
    By

    G.V. Rajeswari,

  • Manas Kumar Yogi,

  1. Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence & Machine Learning), Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract

Convolutional 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.

Keywords: CNN, Pipe, Flow, Fluid Dynamics, Optimization, Prediction

[This article belongs to Recent Trends in Fluid Mechanics ]

How to cite this article:
G.V. Rajeswari, Manas Kumar Yogi. Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System. Recent Trends in Fluid Mechanics. 2025; 12(03):1-9.
How to cite this URL:
G.V. Rajeswari, Manas Kumar Yogi. Application of Convolutional Neural Networks in Design of Efficient Pipe Flow System. Recent Trends in Fluid Mechanics. 2025; 12(03):1-9. Available from: https://journals.stmjournals.com/rtfm/article=2025/view=225168


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Regular Issue Subscription Review Article
Volume 12
Issue 03
Received 31/05/2025
Accepted 12/08/2025
Published 30/08/2025
Publication Time 91 Days
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