Advancements to the non-destructive evaluation of strategic components

Date
2021-12-02
Authors
Thompson, Cole Joseph
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Abstract

This dissertation studies the nexus of nuclear engineering, machine learning, and computer vision. This is realized in two ways. The first is through the design of a neutron multiplicity counter and the application of shallow machine learning methods to the multiplicity counter data collected to estimate different parameters with high accuracy. As an extension of this work, other shallow machine learning methods are used to improve the estimation of item leakage multiplication, yielding doubles rate estimates approximately three times better than with traditional methods. The second way is through the application of deep learning models in the form of convolutional neural nets and transformers to the pixel-wise segmentation of welding defects from radiographic images of welds. The results from this application show that a novel transformer network proposed in this work surpasses the performance of other models when compared using a standard candle by at least one percent. All together this work represents a contribution towards leveraging the vast computing and data capabilities of machine learning within nuclear engineering.

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