Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
2.8
Journals
Processes
Special Issues
Multiphase Flow and Optimal Design in Fluid Machinery
share announcement

Multiphase Flow and Optimal Design in Fluid Machinery

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 1539

Special Issue Editor

Dr. Ning Qiu

E-Mail Website
Guest Editor
National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China
Interests: cavitation erosion; reliability of centrifugal pump systems

Special Issue Information

Dear Colleagues,

Multiphase flows, which involve the simultaneous movement of different phases such as gases, liquids, and solids, are integral to many natural and industrial processes. These flows exhibit complex interactions and behaviors that pose significant challenges in understanding, predicting, and controlling multiphase flow. Multiphase interactions can lead to phenomena such as phase separation, flow pattern transitions, and instabilities, directly affecting the performance and durability of fluid machinery. Optimizing the design of fluid machinery operating in multiphase environments helps to improve efficiency, reliability, and performance. By integrating advanced computational methods, experimental techniques, and theoretical models, engineers can develop machinery that better accommodates these complex flow behaviors. This integration allows for the precise prediction of flow patterns, pressure drops, and phase distributions, which are essential in designing machinery that minimizes energy loss, reducing wear and enhancing the overall system efficiency.

This Special Issue will bring together cutting-edge research and practical insights to drive the development of fluid machinery design, ultimately contributing to more efficient, durable, and environmentally friendly industrial systems. By leveraging a multidisciplinary approach that combines computational fluid dynamics, experimental fluid mechanics, and material science, we can develop innovative solutions to address the challenges posed by multiphase flows, ensuring better performance and sustainability in various industrial applications.

Dr. Ning Qiu
Guest Editor

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. Processes is an international peer-reviewed open access monthly 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

  • multiphase flow
  • optimal design
  • fluid machinery
  • power engineering
  • computational method
  • experimental techniques
  • theoretical model
  • ocean engineering

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 7130 KiB  
Article
Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network
by Maolin Dai and Zhiqiang Huang
Processes 2024, 12(9), 1929; https://doi.org/10.3390/pr12091929 - 8 Sep 2024
Viewed by 706
Abstract
Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method [...] Read more.
Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method based on time-frequency analysis and convolutional neural networks. Firstly, continuous wavelet transform (CWT) is used to convert the collected vibration signals into time-frequency diagrams, providing a comprehensive database for fault diagnosis. Next, a SqueezeNet-based fault diagnosis model is developed to identify faults. To validate the effectiveness of the proposed method, fault signals from the fluid end were collected, and fault diagnosis experiments were conducted. The experimental results demonstrated that the proposed method achieved an accuracy of 97.77% in diagnosing nine types of faults at the fluid end, effectively enabling precise fault diagnosis, which is higher than the accuracy of a 1D convolutional neural network by 14.55%. This study offers valuable insights into the fault diagnosis of drilling pumps and other complex equipment. Full article
(This article belongs to the Special Issue Multiphase Flow and Optimal Design in Fluid Machinery)
►▼ Show Figures

Figure 1

19 pages, 9035 KiB  
Article
Experimental Research on Prediction of Remaining Using Life of Solar DC Centrifugal Pumps Based on ARIMA Model
by Qin Hu, Jianbao Wang, Jing Xiong, Meng Zhang, Hua Fu, Ji Pei and Wenjie Wang
Processes 2024, 12(9), 1857; https://doi.org/10.3390/pr12091857 - 30 Aug 2024
Viewed by 526
Abstract
In order to improve the stability and reliability of the solar DC centrifugal pump real-time operation and prevent the centrifugal pump failure caused by the unexpected shutdown of the system, a set of accurate and efficient centrifugal pump condition monitoring systems was built. [...] Read more.
In order to improve the stability and reliability of the solar DC centrifugal pump real-time operation and prevent the centrifugal pump failure caused by the unexpected shutdown of the system, a set of accurate and efficient centrifugal pump condition monitoring systems was built. A time series-based strategy for predicting the remaining using life (RUL) of centrifugal pumps was proposed. The time series of head and efficiency of centrifugal pumps at specific flow conditions were measured, the corresponding failure thresholds were set, and different differential autoregressive integrated moving average (ARIMA) models were developed to predict the remaining useful life of the pumps. The results show that the maximum prediction error of the head ARIMA model established under the design conditions of the pump was 0.040%, and the head time series reaches the failure threshold of 8 m at the 653rd data point; the maximum prediction error of the efficiency ARIMA model was 0.042%, and the efficiency time series reaches the failure threshold of 16% at the 672nd data point. According to the proposed prediction strategy, the RUL of the centrifugal pump under the design condition was 53 h. The head time series of the pump at high flow conditions reaches a failure threshold of 5 m at the 640th data point; the efficiency time series will reach a failure threshold of 12.5% at the 578th data point, and the RUL of the centrifugal pump at high flow conditions was 78 h. The established ARIMA model has a high prediction accuracy and can effectively predict the RUL of centrifugal pumps. Full article
(This article belongs to the Special Issue Multiphase Flow and Optimal Design in Fluid Machinery)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop