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Structural Uncertainty Analysis of Nuclear Reactor Core Load Pads
Title
Structural Uncertainty Analysis of Nuclear Reactor Core Load Pads
Creator
Wozniak, Nicholas
Date
2019
Description
In fast spectrum nuclear reactors, reactivity is directly related to the capability of the reactor to sustain a fission chain reaction for...
Show moreIn fast spectrum nuclear reactors, reactivity is directly related to the capability of the reactor to sustain a fission chain reaction for power production. Historically, mechanical/structural analysis and design have been driven primarily by deterministic methods. However, reactivity is extremely sensitive to the location of the fuel within the reactor; which is subject to uncertainties. This makes deterministic models unstable and can allow manufacturing errors to contribute to uncertainties in analysis, resulting in potential safety concerns and incorrect reactor lifetime prediction. One potential means to address this challenge is the use of stochastic analysis. A framework is presented which introduces uncertainty analysis through the use of Monte Carlo Simulation. Latin Hypercube Sampling is used to reduce the number of sample runs and the computational effort and storage space requirements for the results. Geometric parameters such as the gaps at the load pad contact points, the location of the above core load pad (ACLP), and even temperature gradient profiles, that are important to the design of nuclear reactors are varied, and their effects on the overall performance are studied through sensitivity analysis. The main focus was to quantify the effects of the variation of these parameters directly on the variation of the contact forces and deformations of the fuel assemblies which house and control the movement of the fuel. Based on the results of the sensitivity study, this study found that the ACLP location has the largest effect on contact forces. And as such, any uncertainty in this parameter results in a rather large variation in the intensity of the contact force. Furthermore, specific recommendations are given to help control these variations as well as for further investigations on other parameters that may be significant to the design of fuel assemblies.
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Machine Learning for NDE Imaging Applications
Title
Machine Learning for NDE Imaging Applications
Creator
Zhang, Xin
Date
2023
Description
Infrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges...
Show moreInfrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges to be analyzed and characterized due to the nature of complex signal patterns and poor signal-to-noise ratios (SNR). Industries such as nuclear energy, are constructed with components produced using high-strength superalloys. These metallic components face challenges for wide deployment because material defects and mechanical conditions need to be non-destructively evaluated to identify potential danger before they enter service. Low NDE performance and lack of automation, particularly considering the complex environment in the in-situation NDE and nuclear power plant, present a major challenge to implement conventional NDE. This study solves the problems of using the advantages of machine learning as signal processing methods for Infrared Thermography and Ultrasonic NDE imaging applications. In Pulsed Infrared Thermography (PIT), for quality control of metal additive manufacturing, we proposed an intelligent PIT NDE system and developed innovative unsupervised learning models and thermal tomography 3D imaging algorithms to detect calibrated internal defects (pores) of various sizes and depths for different nuclear-grade metallic structures. Unsupervised learning aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Difficulties to detect defects by using PIT are thermal imaging noise patterns; uneven heating of the specimen; defects of micron-level size with overly weak temperature signals and so on. The unsupervised learning methods overcome these barriers and achieve the high defect detection accuracies (F-score) of 0.96 to detect large defects and 0.89 to detect microscopic defects, and can successfully detect defects with diameter of only 0.101-mm. In addition, we researched and developed innovative unsupervised learning models to compress high-resolution PIT imaging data and achieve the average high compression ratio >30 and a highest compression of 46 with reconstruction accuracy peak signal-to-noise ratio (PSNR) >73dB while preserving weak thermal features corresponding to microscopic defects. In ultrasonic NDE imaging, for structural health monitoring of materials, we built a high-performance ultrasonic computational system to inspect the integrity of high-strength metallic materials which are used in high-temperature corrosive environments of nuclear reactors. For system automation, we have been developing neural networks with various architectures for grain size estimation by characterizing the ultrasonic backscattered signals with high accuracy and data-efficiency. In addition, we introduce a response-based teacher-student knowledge distillation training framework to train neural networks and achieve 99.27% characterization accuracy with a high image processed throughput of 192 images/second on testing. Furthermore, we introduce a reinforcement learning based neural architecture search framework to automatically model the optimal neural networks design for ultrasonic flaws detection. At last, we comprehensively researched the performance of using unsupervised learning methods to compress 3D ultrasonic data and achieve high compression performance using only 4.25% of the acquired experimental data.
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