Author Information

Katherine HoffsetzFollow

Is this project an undergraduate, graduate, or faculty project?

Undergraduate

Project Type

individual

Campus

Daytona Beach

Authors' Class Standing

Katherine Hoffsetz, Senior

Lead Presenter's Name

Katherine Hoffsetz

Lead Presenter's College

DB College of Arts and Sciences

Faculty Mentor Name

Mihhail Berezovski

Abstract

This project explores advanced image analysis techniques to assess the microstructure of depleted uranium fuel compacts before and after undergoing deformation in a rolling mill. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract key quantitative metrics, including fuel plate thickness, particle size distribution, and material composition from scanning electron microscope (SEM) images. Additionally, uncertainty quantification methods will be applied to evaluate measurement accuracy. By automating feature extraction and material classification, this study contributes to enhancing the precision of SEM-based material characterization, which is critical for nuclear fuel research and reactor conversion programs.

Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?

No

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Quantitative Analysis for Scanning Electron Microscopy Images of DU-Silicide

This project explores advanced image analysis techniques to assess the microstructure of depleted uranium fuel compacts before and after undergoing deformation in a rolling mill. Utilizing Python imaging libraries such as scikit-image and OpenCV, we aim to extract key quantitative metrics, including fuel plate thickness, particle size distribution, and material composition from scanning electron microscope (SEM) images. Additionally, uncertainty quantification methods will be applied to evaluate measurement accuracy. By automating feature extraction and material classification, this study contributes to enhancing the precision of SEM-based material characterization, which is critical for nuclear fuel research and reactor conversion programs.

 

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