LearnPIV: An Interactive, Web-Based Learning Tool for Particle Image Velocimetry Basics

Jack Elliott; Angela Minichiello; Kevin Roberts

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: Mechanical Engineering: Fluids, Heat Transfer
Page Count: 20
DOI: 10.18260/1-2--40474
Permanent URL: https://peer.asee.org/40474
Download Count: 593

Paper Authors

biography

Jack Elliott Utah State University

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Jack Elliott is a concurrent M.S. (Mechanical Engineering) and Ph.D. (Engineering Education) student at Utah State University. His M.S. research is in fluid dynamics including the application of PIV, and his Ph.D. work examines student collaboration in engineering education.

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Angela Minichiello Utah State University

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Angela (Angie) Minichiello, Ph.D., P. E., is an Assistant Professor of Engineering Education and Adjunct Faculty in Mechanical and Aerospace Engineering at Utah State University. Her research employs asset-based frameworks to improve access, participation, and inclusivity across all levels of engineering education. Angie engages with qualitative, mixed-method, and multi-method approaches to better understand student experience for the ultimate purpose of strengthening and diversifying the engineering workforce. Her most recent work explores the effects of mobile educational technology, online learning and distance education; metacognition and self-regulation, and contemporary engineering practice on engineering student learning and professional identity development. Angie graduated from the United State Military Academy at West Point with a bachelor's degree in mechanical engineering. She later earned a master's degree in mechanical engineering at the Georgia Institute of Technology, and a Ph.D. in engineering education at Utah State University. In 2021, Angie's research earned her a National Science Foundation CAREER Award to critically examine the professional formation of undergraduate student veterans and service members in engineering.

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Kevin Roberts

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Abstract

This paper introduces LearnPIV, a freely available, interactive, web-based simulation tool designed to aide students, instructors, and novice engineering professionals in learning how to accurately apply Particle Image Velocimetry (PIV) techniques. PIV is a non-invasive, state-of-the-art experimental approach used to measure the velocities of optically accessible fluid flows by taking two successive images of a flow in real time. Along with providing quantitative velocity information at each location in a flow field, PIV output is also used to visualize structures and patterns occurring within the flow. Because of this ability to visualize as well as measure fluid flow using PIV results, engineering educators have shown marked interest in bringing PIV into engineering classrooms.

Obtaining accurate—and educationally useful— PIV output, however, depends on flow image quality; poor image quality is likely to provide erroneous and even non-physical results. In addition, new PIV users are often unaware how to assess and improve PIV image quality. Therefore, to enhance and extend the instructional impact of PIV in engineering education generally, we present LearnPIV: a web-based simulation tool that introduces learners to the technical facets of PIV measurement (i.e., image cross correlation and experimental parameters). LearnPIV begins with visual overviews of basic PIV analytic techniques, including direct, and Fast Fourier Transform (FFT) cross correlation methods. The interactive website also provides descriptions and examples of essential experimental PIV image parameters (e.g., image noise level, particle image diameter, etc.) and indicators of output quality, including qualitative (e.g., correlation peak width, shape, etc.) and quantitative (i.e., primary peak ratio) measures. Last, LearnPIV demonstrates how critical imaging parameters (e.g., aperture, frame rate, and ISO,) dynamically affect PIV results.

To interactively engage PIV users with the interplay of experimental PIV parameters and demonstrate image pair statistical analysis (i.e., image pair cross-correlation), LearnPIV enables students to create fluid flow image pairs on a flow of choice via an embedded synthetic fluid flow image generator. LearnPIV then allows users to calculate and observe the correlation plane for the image pair they created. To level this activity and guide learning for novice learners, LearnPIV provides students with the capability to vary a single image (e.g., particle displacement, interrogation region size, shear) over a specified range of acceptable values. Keeping all other parameters fixed, LearnPIV users can visualize how each discrete parameter impacts the image pair, the resulting correlation plane, and the output vector. In this way, novices can develop skill and an intuitive sense for taking PIV measurements. To explore the complicated interactions of image parameters, LearnPIV also provides a free interaction mode wherein more advanced students and more experienced PIV users may vary multiple image parameters concurrently and observe the resulting image pair generated, correlation plane, and output vector. Finally, Learn PIV provides users the capability to save their experiment results for later viewing and comparison.

Citation
Format

Elliott, J., & Minichiello, A., & Roberts, K. (2022, August), LearnPIV: An Interactive, Web-Based Learning Tool for Particle Image Velocimetry Basics Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--40474

TY  - CPAPER
AB  - This paper introduces LearnPIV, a freely available, interactive, web-based simulation tool designed to aide students, instructors, and novice engineering professionals in learning how to accurately apply Particle Image Velocimetry (PIV) techniques. PIV is a non-invasive, state-of-the-art experimental approach used to measure the velocities of optically accessible fluid flows by taking two successive images of a flow in real time. Along with providing quantitative velocity information at each location in a flow field, PIV output is also used to visualize structures and patterns occurring within the flow. Because of this ability to visualize as well as measure fluid flow using PIV results, engineering educators have shown marked interest in bringing PIV into engineering classrooms.

Obtaining accurate—and educationally useful— PIV output, however, depends on flow image quality; poor image quality is likely to provide erroneous and even non-physical results. In addition, new PIV users are often unaware how to assess and improve PIV image quality. Therefore, to enhance and extend the instructional impact of PIV in engineering education generally, we present LearnPIV: a web-based simulation tool that introduces learners to the technical facets of PIV measurement (i.e., image cross correlation and experimental parameters). LearnPIV begins with visual overviews of basic PIV analytic techniques, including direct, and Fast Fourier Transform (FFT) cross correlation methods. The interactive website also provides descriptions and examples of essential experimental PIV image parameters (e.g., image noise level, particle image diameter, etc.) and indicators of output quality, including qualitative (e.g., correlation peak width, shape, etc.) and quantitative (i.e., primary peak ratio) measures. Last, LearnPIV demonstrates how critical imaging parameters (e.g., aperture, frame rate, and ISO,) dynamically affect PIV results.

To interactively engage PIV users with the interplay of experimental PIV parameters and demonstrate image pair statistical analysis (i.e., image pair cross-correlation), LearnPIV enables students to create fluid flow image pairs on a flow of choice via an embedded synthetic fluid flow image generator. LearnPIV then allows users to calculate and observe the correlation plane for the image pair they created. To level this activity and guide learning for novice learners, LearnPIV provides students with the capability to vary a single image (e.g., particle displacement, interrogation region size, shear) over a specified range of acceptable values. Keeping all other parameters fixed, LearnPIV users can visualize how each discrete parameter impacts the image pair, the resulting correlation plane, and the output vector. In this way, novices can develop skill and an intuitive sense for taking PIV measurements. To explore the complicated interactions of image parameters, LearnPIV also provides a free interaction mode wherein more advanced students and more experienced PIV users may vary multiple image parameters concurrently and observe the resulting image pair generated, correlation plane, and output vector. Finally, Learn PIV provides users the capability to save their experiment results for later viewing and comparison.
AU  - Jack Elliott
AU  - Angela Minichiello
AU  - Kevin Roberts
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - LearnPIV: An Interactive, Web-Based Learning Tool for Particle Image Velocimetry Basics
UR  - https://peer.asee.org/40474
DO  - 10.18260/1-2--40474
ER  -