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An Adaptive PIV Software for Pedagogy‐Oriented Practice and a Closed‐Loop Framework for Flow Experiments.

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Title: An Adaptive PIV Software for Pedagogy‐Oriented Practice and a Closed‐Loop Framework for Flow Experiments.
Authors: Qin, Jiasheng1 (AUTHOR), Fu, Xiaoli1 (AUTHOR) xlfu@tongji.edu.cn, Zhang, Zhen1 (AUTHOR), Shen, Chao1 (AUTHOR)
Source: Computer Applications in Engineering Education. May2026, Vol. 34 Issue 3, p1-16. 16p.
Subjects: Particle image velocimetry, Self-adaptive software, Scientific apparatus & instruments, Image recognition (Computer vision), Fluid mechanics, Flow simulations, Educational technology
Abstract: The integration of particle image velocimetry (PIV) into undergraduate fluid mechanics laboratories is often hindered by high equipment costs, steep learning curves for analysis software, and limited classroom hours. To address these barriers, this study introduces a specialized, adaptive PIV software built upon the OpenPIV kernel, alongside a "Observation–Analysis–Verification" closed‐loop teaching framework. Designed for low‐cost setups utilizing smartphone imaging and continuous lasers, the software features an innovative image feature recognition algorithm. This algorithm automates the configuration of critical parameters—such as interrogation window size, overlap ratio, and signal‐to‐noise ratio (SNR) thresholds—thereby significantly reducing the technical expertise required of students. Validation through flow experiments over a cylinder and a vertical flat plate demonstrates that students can successfully capture vector fields across various Reynolds numbers. The system allows for quantitative verification of the Strouhal number–vortex shedding frequency relationship and visualization of flow evolution, vortex structures, and turbulence dissipation. Educational assessment confirms that this framework effectively shifts the pedagogical focus from tedious software debugging to the exploration of fundamental fluid physics. The proposed solution offers a cost‐effective and efficient pathway for the digital transformation of experimental fluid mechanics education. [ABSTRACT FROM AUTHOR]
Copyright of Computer Applications in Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="DE" term="%22Particle+image+velocimetry%22">Particle image velocimetry</searchLink><br /><searchLink fieldCode="DE" term="%22Self-adaptive+software%22">Self-adaptive software</searchLink><br /><searchLink fieldCode="DE" term="%22Scientific+apparatus+%26+instruments%22">Scientific apparatus & instruments</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Fluid+mechanics%22">Fluid mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Flow+simulations%22">Flow simulations</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink>
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  Data: The integration of particle image velocimetry (PIV) into undergraduate fluid mechanics laboratories is often hindered by high equipment costs, steep learning curves for analysis software, and limited classroom hours. To address these barriers, this study introduces a specialized, adaptive PIV software built upon the OpenPIV kernel, alongside a "Observation–Analysis–Verification" closed‐loop teaching framework. Designed for low‐cost setups utilizing smartphone imaging and continuous lasers, the software features an innovative image feature recognition algorithm. This algorithm automates the configuration of critical parameters—such as interrogation window size, overlap ratio, and signal‐to‐noise ratio (SNR) thresholds—thereby significantly reducing the technical expertise required of students. Validation through flow experiments over a cylinder and a vertical flat plate demonstrates that students can successfully capture vector fields across various Reynolds numbers. The system allows for quantitative verification of the Strouhal number–vortex shedding frequency relationship and visualization of flow evolution, vortex structures, and turbulence dissipation. Educational assessment confirms that this framework effectively shifts the pedagogical focus from tedious software debugging to the exploration of fundamental fluid physics. The proposed solution offers a cost‐effective and efficient pathway for the digital transformation of experimental fluid mechanics education. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Computer Applications in Engineering Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/cae.70190
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      – Code: eng
        Text: English
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        PageCount: 16
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      – SubjectFull: Particle image velocimetry
        Type: general
      – SubjectFull: Self-adaptive software
        Type: general
      – SubjectFull: Scientific apparatus & instruments
        Type: general
      – SubjectFull: Image recognition (Computer vision)
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      – SubjectFull: Fluid mechanics
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      – SubjectFull: Flow simulations
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      – SubjectFull: Educational technology
        Type: general
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      – TitleFull: An Adaptive PIV Software for Pedagogy‐Oriented Practice and a Closed‐Loop Framework for Flow Experiments.
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            NameFull: Qin, Jiasheng
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            NameFull: Fu, Xiaoli
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            NameFull: Zhang, Zhen
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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