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Fluids
Special Issues
Flow Visualization: Experiments and Techniques
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Flow Visualization: Experiments and Techniques

A special issue of Fluids (ISSN 2311-5521). This special issue belongs to the section "Mathematical and Computational Fluid Mechanics".

Deadline for manuscript submissions: 30 July 2024 | Viewed by 6727

Special Issue Editors

Dr. Guangjian Zhang

E-Mail Website
Guest Editor
Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, China
Interests: two-phase structure and dynamics of cavitation; flow visualization; particle image velocimetry (PIV); numerical simulation of turbulent cavitating flow
Dr. Mingming Ge

E-Mail Website
Guest Editor
Lab of AI for Fluids, Westlake University, Hangzhou, China
Interests: experimental fluid mechanics, cavitation and multiphase flow; aI for fluids; numerical simulation
Dr. Xin-Lei Zhang

E-Mail Website
Guest Editor
The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
Interests: turbulence modeling; data assimilation; machine learning

Special Issue Information

Dear Colleagues,

Flow visualization is a technique used to observe and analyze the patterns and characteristics of fluid flow. It is a valuable tool in fluid mechanics research and engineering applications, as it allows researchers to gain insights into the behavior of fluids and identify various flow phenomena.

Flow visualization methods can be broadly categorized into two types:

  1. Direct flow visualization: involves directly observing the flow using techniques such as dye injection, particle tracking, or smoke visualization;
  2. Indirect flow visualization: involves using instruments and sensors to measure flow properties such as pressure, velocity, and temperature. These data are then processed to visualize the flow patterns and characteristics.

This Special Issue aims to gather recent advancements in the field of flow visualization techniques and present new findings in fluid mechanics using flow visualization techniques.

Suggested topics include, but are not limited to:

  • Multiphase flow measurement and instrumentation;
  • PIV/MicroPIV/Tomo-PIV/LIF-PIV techniques;
  • High-speed photography;
  • Experimental fluid mechanics;
  • Measurements of a two-phase structure and dynamics of cavitation;
  • Traditional and synchrotron X-ray imaging;
  • Pressure-sensitive paint (PSP) technique;
  • Multi-sensor data fusion;
  • Pressure field reconstruction;
  • AI techniques applied in experimental fluid mechanics.

We look forward to receiving your contributions, and hope this Special Issue will provide a platform for researchers to share their work and exchange knowledge and ideas.

Dr. Guangjian Zhang
Dr. Mingming Ge
Dr. Xin-Lei Zhang
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. Fluids 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 1800 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

  • flow visualization
  • multiphase flow
  • cavitation
  • high-speed photography
  • PIV
  • X-ray imaging
  • pressure field reconstruction
  • data assimilation
  • machine learning

Published Papers (6 papers)

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Research

24 pages, 26677 KiB  
Article
Wind Tunnel Experiments on Parallel Blade–Vortex Interaction with Static and Oscillating Airfoil
by Andrea Colli, Alex Zanotti and Giuseppe Gibertini
Fluids 2024, 9(5), 111; https://doi.org/10.3390/fluids9050111 (registering DOI) - 10 May 2024
Viewed by 177
Abstract
This study aims to experimentally investigate the effects of parallel blade–vortex interaction (BVI) on the aerodynamic performances of an airfoil, in particular as a possible cause of blade stall, since similar effects have been observed in literature in the case of perpendicular BVI. [...] Read more.
This study aims to experimentally investigate the effects of parallel blade–vortex interaction (BVI) on the aerodynamic performances of an airfoil, in particular as a possible cause of blade stall, since similar effects have been observed in literature in the case of perpendicular BVI. A wind tunnel test campaign was conducted reproducing parallel BVI on a NACA 23012 blade model at a Reynolds number of 300,000. The vortex was generated by impulsively pitching a second airfoil model, placed upstream. Measurements of the aerodynamic loads acting on the blade were performed by means of unsteady Kulite pressure transducers, while particle image velocimetry (PIV) techniques were employed to study the flow field over the blade model. After a first phase of vortex characterisation, different test cases were investigated with the blade model both kept fixed at different incidences and oscillating sinusoidally in pitch, with the latter case, a novelty in available research on parallel BVI, representing the pitching motion of a helicopter main rotor blade. The results show that parallel BVI produces a thickening of the boundary layer and can induce local flow separation at incidences close to the stall condition of the airfoil. The aerodynamic loads, both lift and drag, suffer important impulsive variations, in agreement with literature on BVI, the effects of which are extended in time. In the case of the oscillating airfoil, BVI introduces hysteresis cycles in the loads, which are generally reduced. In conclusion, parallel BVI can have a detrimental impact on the aerodynamic performances of the blade and even cause flow separation, which, while not being as catastrophic as in the case of dynamic stall, has relatively long-lasting effects. Full article
(This article belongs to the Special Issue Flow Visualization: Experiments and Techniques)
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25 pages, 23569 KiB  
Article
Analyzing the Influence of Dean Number on an Accelerated Toroidal: Insights from Particle Imaging Velocimetry Gyroscope (PIVG)
by Ramy Elaswad, Naser El-Sheimy and Abdulmajeed Mohamad
Fluids 2024, 9(5), 103; https://doi.org/10.3390/fluids9050103 - 25 Apr 2024
Viewed by 403
Abstract
Computational Fluid Dynamics (CFD) simulations were utilized in this study to comprehensively explore the fluid dynamics within an accelerated toroidal vessel, specifically those central to Particle Imaging Velocimetry Gyroscope (PIVG) technology. To ensure the robustness of our simulations, we systematically conducted grid convergence [...] Read more.
Computational Fluid Dynamics (CFD) simulations were utilized in this study to comprehensively explore the fluid dynamics within an accelerated toroidal vessel, specifically those central to Particle Imaging Velocimetry Gyroscope (PIVG) technology. To ensure the robustness of our simulations, we systematically conducted grid convergence studies and quantified uncertainties, affirming the stability, accuracy, and reliability of our computational grid and results. Comprehensive validation against experimental data further confirmed our simulations’ fidelity, emphasizing the model’s fidelity. As the PIVG is set up to address the primary flow through the toroidal pipe, we focused on the interaction between the primary and secondary flows to provide insights into the relevant dynamics of the fluid. In our investigation covering Dean numbers (De) from 10 to 70, we analyzed diverse aspects, including primary flow, secondary flow patterns, pressure distribution, and the interrelation between primary and secondary flows within toroidal structures, offering a comprehensive view across this range. Our research indicated stability and fully developed fluid dynamics within the toroidal pipe under accelerated angular velocity, particularly for low De. Furthermore, we identified an optimal Dean number of 11, which corresponded to ideal dimensions for the toroidal geometry with a curvature radius of 25 mm and a cross-sectional diameter of 5 mm. This study enhances our understanding of toroidal fluid dynamics and highlights the pivotal role of CFD in optimizing toroidal vessel design for advanced navigation technologies. Full article
(This article belongs to the Special Issue Flow Visualization: Experiments and Techniques)
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30 pages, 6875 KiB  
Article
Application of a Combinatorial Vortex Detection Algorithm on 2 Component 2 Dimensional Particle Image Velocimetry Data to Characterize the Wake of an Oscillating Wing
by Mathew Bussière, Guilherme M. Bessa, Charles R. Koch and David S. Nobes
Fluids 2024, 9(3), 53; https://doi.org/10.3390/fluids9030053 - 22 Feb 2024
Viewed by 1235
Abstract
To investigate the vortical wake pattern generated by water flow past an oscillating symmetric airfoil, using experimental velocity fields from particle image velocimetry (PIV), a novel combinatorial vortex detection (CVD) algorithm is developed. The primary goal is to identify and characterize vortices within [...] Read more.
To investigate the vortical wake pattern generated by water flow past an oscillating symmetric airfoil, using experimental velocity fields from particle image velocimetry (PIV), a novel combinatorial vortex detection (CVD) algorithm is developed. The primary goal is to identify and characterize vortices within the wake. Experimental flows introduce complexities not present in numerical simulations, posing challenges for vortex detection. The proposed CVD approach offers a more robust alternative, excelling in both vortex detection and quantification of essential parameters, unlike widely-used methods such as Q-criterion, λ2-criterion, and Δ-criterion, which rely on subjective and arbitrary thresholds resulting in uncertainty. The CVD algorithm effectively characterizes the airfoil wake, identifying and analyzing vortices aligning with the Burgers model. This research enhances understanding of wake phenomena and showcases the algorithm’s potential as a valuable tool for vortex detection and characterization, particularly for experimental fluid dynamics. It provides a comprehensive, robust, and non-arbitrary approach, overcoming limitations of traditional methods and opening new avenues for studying complex flows. Full article
(This article belongs to the Special Issue Flow Visualization: Experiments and Techniques)
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15 pages, 6572 KiB  
Article
Quantitative Color Schlieren for an H2–O2 Exhaust Jet Developing in Air
by Emilia-Georgiana Prisăcariu and Tudor Prisecaru
Fluids 2024, 9(1), 19; https://doi.org/10.3390/fluids9010019 - 8 Jan 2024
Viewed by 1369
Abstract
Throughout many decades, the Schlieren visualization method has been mainly used as means to visualize transparent flows in a qualitative manner. The images recorded provide data regarding the existence of the flow, or illustrate predicted flow geometries and details. The colored Schlieren method [...] Read more.
Throughout many decades, the Schlieren visualization method has been mainly used as means to visualize transparent flows in a qualitative manner. The images recorded provide data regarding the existence of the flow, or illustrate predicted flow geometries and details. The colored Schlieren method has been developed in the late 1890s and has always had the intent to provide quantitative data rather than qualitative pictures of the studied phenomena. This paper centers on applying a quantitative color Schlieren method to help determine the gasodynamic parameters of an H2–O2 exhaust jet, developing in air. A comparison between the parameters obtained through calibrating the color filter for the Schlieren method and the results from a CFD simulation is performed to assess the range of the CS (color Schlieren) measurement. This paper’s findings address the issues of calibrated color filter Schlieren encounter during its implementation and discusses possible errors appearing when the method is applied to a 3D flow. While the qualitative Schlieren images are still impressive to observe, the quantitative Schlieren presents challenges and a low measurement accuracy (75%) when applied to 3D flows and compared to 2D cases found in the literature (97–98%). Full article
(This article belongs to the Special Issue Flow Visualization: Experiments and Techniques)
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14 pages, 3906 KiB  
Article
Volumetric Flow Field inside a Gas Stirred Cylindrical Water Tank
by Yasmeen Jojo-Cunningham, Xipeng Guo, Chenn Zhou and Yun Liu
Fluids 2024, 9(1), 11; https://doi.org/10.3390/fluids9010011 - 28 Dec 2023
Cited by 1 | Viewed by 1369
Abstract
Ladle metallurgy serves as a crucial component of the steelmaking industry, where it plays a pivotal role in manipulating the molten steel to exercise precise control over its composition and properties. Turbulence in ladle metallurgy influences various important aspects of the steelmaking process, [...] Read more.
Ladle metallurgy serves as a crucial component of the steelmaking industry, where it plays a pivotal role in manipulating the molten steel to exercise precise control over its composition and properties. Turbulence in ladle metallurgy influences various important aspects of the steelmaking process, including mixing and distribution of additives, alongside the transport and removal of inclusions within the ladle. Consequently, gaining a clear understanding of the stirred flow field holds the potential of optimizing ladle design, improving control strategies, and enhancing the overall efficiency and steel quality. In this project, an advanced Particle-Tracking-Velocimetry system known as “Shake-the-Box” is implemented on a cylindrical water ladle model while compressed air injections through two circular plugs positioned at the bottom of the model are employed to actively stir the flow. To mitigate the particle images distortion caused by the cylindrical plexi-glass walls, the method of refractive matching is utilized with an outer polygon tank filled with a sodium iodide solution. The volumetric flow measurement is achieved on a 6 × 6 × 2 cm domain between the two plugs inside the cylindrical container while the flow rate of gas injection is set from 0.1 to 0.4 L per minute. The volumetric flow field result suggests double gas injection at low flow rate (0.1 L per minute) produce the least disturbed flow while highly disturbed and turbulent flow can be created at higher flow rate of gas injection. Full article
(This article belongs to the Special Issue Flow Visualization: Experiments and Techniques)
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23 pages, 6256 KiB  
Article
An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units
by Ali Shirinzad, Khodr Jaber, Kecheng Xu and Pierre E. Sullivan
Fluids 2023, 8(11), 285; https://doi.org/10.3390/fluids8110285 - 27 Oct 2023
Viewed by 1686
Abstract
Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm [...] Read more.
Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV–Python software (Version 0.25.1, OpenPIV, Tel Aviv-Yafo, Israel) is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, momentary velocity fields, and vortices. Full article
(This article belongs to the Special Issue Flow Visualization: Experiments and Techniques)
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