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Applied Sciences
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Application and Simulation of Fluid Dynamics in Pipeline Systems
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Application and Simulation of Fluid Dynamics in Pipeline Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Fluid Science and Technology".

Deadline for manuscript submissions: 20 August 2025 | Viewed by 2971

Special Issue Editors

Dr. Michal Kubrak

E-Mail Website
Guest Editor
Faculty of Building Services, Hydro and Environmental Engineering, Warsaw University of Technology, 00-653 Warsaw, Poland
Interests: water hammer; hydraulic transients; unsteady pipe flow; experimental hydraulics; numerical modeling
Dr. Mohsen Besharat

E-Mail Website
Guest Editor
School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: hydraulic transients; two-phase unsteady flows; experimental hydraulics; numerical hydraulics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Pipeline systems are crucial in numerous industries, including oil and gas, water distribution, and chemical processing. Simulating various phenomena related to fluid flow in these pipes is essential for optimizing performance, ensuring safety, and preventing issues such as leaks or blockages. This Special Issue, entitled “Application and Simulation of Fluid Dynamics in Pipeline Systems”, welcomes submissions of recent research works which explore advances in fluid dynamics within pipeline systems, emphasizing innovative computational techniques and practical applications. Potential topics include, but are not limited to, the following:

  • Unsteady pipe flow modeling;
  • Pressure surges and water hammer effects;
  • Multiphase pipe flow;
  • Fluid–structure interaction in pipeline dynamics;
  • Leak detection and prevention strategies;
  • Heat transfer and thermal effects in fluid transport;
  • Flow optimization in complex pipeline networks;
  • Energy efiiciency in pipeline systems;
  • AI-driven techniques in fluid dynamics.

By addressing these critical areas, this Special Issue aims to bridge the gap between theoretical research and practical implementations. While original work showcasing experimental research and theoretical and numerical developments is encouraged, review papers and comparative studies are also welcome to be submitted to this Special Issue.

Dr. Michal Kubrak
Dr. Mohsen Besharat
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pipeline systems
  • pipe flow
  • water hammer
  • hydraulic transients
  • fluid–structure interaction
  • leak detection
  • pipe networks
  • pumping systems
  • computational fluid dynamics
  • numerical modeling
  • experimental measurements

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Published Papers (2 papers)

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Research

19 pages, 4687 KiB  
Article
Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
by Faisal Saleem, Zahoor Ahmad and Jong-Myon Kim
Appl. Sci. 2025, 15(1), 185; https://doi.org/10.3390/app15010185 - 28 Dec 2024
Viewed by 901
Abstract
This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian [...] Read more.
This study introduces an advanced deep-learning framework for the real-time detection of pipeline leaks in smart city infrastructure. The methodology transforms acoustic emission (AE) signals from the time domain into scalogram images using continuous wavelet transform (CWT) to enhance leak-related features. A Gaussian filter minimizes background noise and clarifies these features further. The core of the framework combines convolutional neural networks (CNNs) with long short-term memory (LSTM), ensuring a comprehensive examination of both spatial and temporal features of AE signals. A genetic algorithm (GA) optimizes the neural network by isolating the most important features for leak detection. The final classification stage uses a fully connected neural network to categorize pipeline health conditions as either ‘leak’ or ‘non-leak’. Experimental validation on real-world pipeline data demonstrated the framework’s efficacy, achieving accuracy rates of 99.69%. This approach significantly advances smart city capabilities in pipeline monitoring and maintenance, offering a durable and scalable solution for proactive infrastructure management. Full article
(This article belongs to the Special Issue Application and Simulation of Fluid Dynamics in Pipeline Systems)
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18 pages, 5533 KiB  
Article
Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach
by Saif Ullah, Niamat Ullah, Muhammad Farooq Siddique, Zahoor Ahmad and Jong-Myon Kim
Appl. Sci. 2024, 14(22), 10339; https://doi.org/10.3390/app142210339 - 10 Nov 2024
Viewed by 1662
Abstract
Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid [...] Read more.
Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid convolutional neural network–long short-term memory (CNN-LSTM) model for pipeline leak detection that uses acoustic emission signals was designed. In this model, acoustic emission signals are initially preprocessed using a Savitzky–Golay filter to reduce noise. The filtered signals are input into the hybrid model, where spatial features are extracted using a CNN. The features are then passed to an LSTM network, which extracts temporal features from the signals. Based on these features, the presence or absence of a leakage is determined. The performance of the proposed model was compared with two alternative approaches: a method that employs combined features from the time domain and LSTM and a bidirectional gated recurrent unit model. The proposed approach demonstrated superior performance, as evidenced by lower validation loss, higher validation accuracy, enhanced confusion matrices, and improved t-distributed stochastic neighbor embedding plots compared to the other models when tested on industrial data. The findings indicate that the proposed model is more effective in accurately detecting pipeline leaks, offering a promising solution for enhancing smart cities and industrial safety. Full article
(This article belongs to the Special Issue Application and Simulation of Fluid Dynamics in Pipeline Systems)
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