Class
Article
College
College of Science
Department
English Department
Faculty Mentor
Jack Elliott
Presentation Type
Poster Presentation
Abstract
Particle Image Velocimetry (PIV) is an experimental measurement technique used for quantitative and visual analysis of the velocity distribution in a flow field. Researchers conduct PIV by using lasers to illuminate particles in a flow field of interest, digital cameras to record images of these illuminated particles, and PIV software to analyze the recorded images, producing a velocity vector field. Applying proper techniques to gather useful images and identifying PIV processing algorithm parameters to the collected images is a challenging task for novice PIV users who are unaware of the inner workings of PIV algorithms. Recognizing this problem, we created Learnpiv.org a freely available, online learning tool intended to teach novice PIV users about PIV algorithms and techniques.This presentation describes the creation of our Learnpiv.org website, designed to educate novice PIV users on proper imaging and processing methods. We created Learnpiv.org with Django, a high-level Python web framework which allows fast and easy application development. After completing an original version of Learnpiv.org, we chose to deploy the website to Heroku, a cloud platform that enables developers to build, update, and deliver Django applications. Through our Django application, we provide users of Learnpiv.org with pages of PIV content containing PIV basics and information specific to the interaction aspects of Learnpiv.org. Specifically, the interactive experimentation pages allow users to input variables and generate synthetic PIV images according to the assigned variable values. In the Experiment with a Single Variable page, users can see the difference in images and analysis results from changing one variable (such as Region Size, Particle Diameter, Noise Mean, etc.) while the rest of the variables remain static. In the Experiment with Multiple Variables page, more advanced users may change all image and processing variables. Further, users can save these experiments for later observation. Future Learnpiv.org improvements include reducing synthetic image generation runtime and adding user capabilities. Additional user capabilities include Forums where users can discuss PIV content or experimentation results. Other future research in Learnpiv.org involves website sustainability and database handling. As new versions of software (such as Python, Django, Heroku, etc.) are released, we need to ensure these versions are used by Learnpiv.org and compatible with each other. Current research involves developing algorithms to clear database storage of inactive users over a certain time period. In addition to these improvements, feedback from our current users helps us best develop a better learning experience for future novice PIV users.
Location
Logan, UT
Start Date
4-6-2022 12:00 AM
Included in
Development of Particle Image Velocimetry Learning Tool: Learnpiv.org
Logan, UT
Particle Image Velocimetry (PIV) is an experimental measurement technique used for quantitative and visual analysis of the velocity distribution in a flow field. Researchers conduct PIV by using lasers to illuminate particles in a flow field of interest, digital cameras to record images of these illuminated particles, and PIV software to analyze the recorded images, producing a velocity vector field. Applying proper techniques to gather useful images and identifying PIV processing algorithm parameters to the collected images is a challenging task for novice PIV users who are unaware of the inner workings of PIV algorithms. Recognizing this problem, we created Learnpiv.org a freely available, online learning tool intended to teach novice PIV users about PIV algorithms and techniques.This presentation describes the creation of our Learnpiv.org website, designed to educate novice PIV users on proper imaging and processing methods. We created Learnpiv.org with Django, a high-level Python web framework which allows fast and easy application development. After completing an original version of Learnpiv.org, we chose to deploy the website to Heroku, a cloud platform that enables developers to build, update, and deliver Django applications. Through our Django application, we provide users of Learnpiv.org with pages of PIV content containing PIV basics and information specific to the interaction aspects of Learnpiv.org. Specifically, the interactive experimentation pages allow users to input variables and generate synthetic PIV images according to the assigned variable values. In the Experiment with a Single Variable page, users can see the difference in images and analysis results from changing one variable (such as Region Size, Particle Diameter, Noise Mean, etc.) while the rest of the variables remain static. In the Experiment with Multiple Variables page, more advanced users may change all image and processing variables. Further, users can save these experiments for later observation. Future Learnpiv.org improvements include reducing synthetic image generation runtime and adding user capabilities. Additional user capabilities include Forums where users can discuss PIV content or experimentation results. Other future research in Learnpiv.org involves website sustainability and database handling. As new versions of software (such as Python, Django, Heroku, etc.) are released, we need to ensure these versions are used by Learnpiv.org and compatible with each other. Current research involves developing algorithms to clear database storage of inactive users over a certain time period. In addition to these improvements, feedback from our current users helps us best develop a better learning experience for future novice PIV users.