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Design and Optimization of Fluid Machinery, 3rd Edition

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".

Deadline for manuscript submissions: 25 July 2025 | Viewed by 383

Special Issue Editors

National Research Center for Pumps and Systems, Jiangsu University, Zhenjiang, China
Interests: Computational Fluid Dynamics (CFD); fluid machinery; unsteady flow; hydraulic machinery; cavitation; multiphase flow; optimization of pump
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, USA
Interests: computational fluid dynamics (CFD); computational magnetohydrodynamics (MHD); electromagnetics; computational aeroacoustics; multidisciplinary design and optimization; rarefied gas dynamics and hypersonic flows; bio-fluid dynamics; flow and flight control
Special Issues, Collections and Topics in MDPI journals
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
Interests: hydraulic model; computational fluid dynamics (CFD); electric submersible pump (ESP); unstable flow; pressure pulsation; energy characteristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fluid machinery refers to fluid as the working medium for energy conversion machinery, including turbines, pumps, and compressors. Due to the wide application range, diverse applicability, and complex structure of fluid machinery, it is difficult to meet the requirements of changeable operating conditions using a fixed structure. Therefore, to maximize the structural performance of fluid machinery, it is necessary to optimize its structural parameters to fully understand its internal flow law; this will enable us to meet the development requirements of a wide range, high efficiency, and energy savings in the current fluid machinery industry.

In recent years, with the emergence of artificial intelligence, machine learning, and various advanced optimization algorithms, the design and optimization of fluid machinery has er-emerged as a topic of interest. In particular, with the help of CFD technology, researchers can observe the abnormal flow phenomenon in fluid machinery more intuitively and achieve the rapid design and automatic optimization of fluid machinery structures by setting different optimization objectives.

This Special Issue seeks high-quality original research and review articles focusing on the latest novel advances in the design and optimization of fluid machinery.

Potential topics include but are not limited to the following:

  • The design and optimization of fluid machinery;
  • Cavitation performance and its control;
  • The numerical simulation of transient flow and instabilities;
  • Flow-induced vibration in fluid machinery;
  • Advanced optimization algorithms;
  • The application of artificial intelligence and machine learning in optimization;
  • Innovative technologies for flow control;
  • The suppression of unsteady flow.

Dr. Leilei Ji
Prof. Dr. Ramesh Agarwal
Dr. Yang Yang
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. Water 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 2600 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

  • CFD
  • fluid machinery
  • design and optimization
  • shock and vibration
  • unsteady flow
  • cavitation
  • rotor dynamics

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Published Papers (1 paper)

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Research

20 pages, 10537 KiB  
Article
Research on Performance Prediction of Elbow Inline Pump Based on MSCSO-BP Neural Network
by Chao Wang, Zhenhua Shen, Yin Luo, Xin Wu, Guoyou Wen and Shijun Qiu
Water 2025, 17(8), 1213; https://doi.org/10.3390/w17081213 - 18 Apr 2025
Viewed by 141
Abstract
The vertical inline pump, a single-stage centrifugal pump with a bent elbow inlet, is widely used in marine engineering. The unique water inlet passage combined with uneven inflow at the impeller inlet tends to form an inlet vortex and secondary flow area, which [...] Read more.
The vertical inline pump, a single-stage centrifugal pump with a bent elbow inlet, is widely used in marine engineering. The unique water inlet passage combined with uneven inflow at the impeller inlet tends to form an inlet vortex and secondary flow area, which reduces performance and causes vibration. To predict the performance of the elbow inline pump, this study uses spline curve fitting for the centerline and cross-sectional shape of the elbow passage. With four elbow inlet variables from experimental design as the input layer and targeting efficiency under pump operating conditions, a pump performance prediction model based on an improved sand cat swarm optimization algorithm combined with a BP neural network (MSCSO-BP) is proposed. Six test functions are used to effectively test the improved sand cat swarm optimization algorithm. The results show that compared to the unimproved algorithm, the improved algorithm has significantly faster convergence speed, shorter parameter optimization time, and higher accuracy. For more demanding multidimensional test functions, the improved optimization algorithm can more accurately find the optimal solution, enhancing the prediction accuracy and generalization ability of inline pump performance. This provides a more effective engineering solution for the design and optimization of inline pumps. Full article
(This article belongs to the Special Issue Design and Optimization of Fluid Machinery, 3rd Edition)
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