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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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Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
Title
Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
Creator
Di Russo, Miriam
Date
2023
Description
For conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their...
Show moreFor conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their energy performance exist. However, the regulations do not include requirements on the confidence value to associate with the results. As vehicles become more and more efficient to meet the stricter regulations mandates on emissions, fuel and energy consumption, traditional testing methods may become insufficient to validate these improvements, and may need revision. Without information about the accuracy associated with the results of those procedures however, adjustments and improvements are not possible, since no frame of reference exists. For connected and automated vehicles, there are no standard testing procedures, and researchers are still in the process of determining if current evaluation methods can be extended to test intelligent technologies and which metrics best represent their performance. For these vehicles is even more important to determine the uncertainty associated with these experimental methods and how they propagate to the final results. The work presented in this dissertation focuses on the development of a systematic framework for the evaluation of the uncertainty associated with the energy performance of conventional, electric and automated vehicles. The framework is based on a known statistical method, to determine the uncertainty associated with the different stages and processes involved in the experimental testing, and to evaluate how the accuracy of each parameter involved impacts the final results. The results demonstrate that the framework can be successfully applied to existing testing methods and provides a trustworthy value of accuracy to associate with the energy performance results, and can be easily extended to connected-automated vehicle testing to evaluate how novel experimental methods impact the accuracy and the confidence of the outputs. The framework can be easily be implemented into an existing laboratory environment to incorporate the uncertainty evaluation among the current results analyzed at the end of each test, and provide a reference for researchers to evaluate the actual benefits of new algorithms and optimization methods and understand margins for improvements, and by regulators to assess which parameters to enforce to ensure compliance and ensure projected benefits.
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