Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"Model-form uncertainty"”.
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Power Electronics Design Methodologies with Parametric and Model-Form Uncertainty Quantification
Modeling and simulation have become fully ingrained into the set of design and development tools that are broadly used in the field of power electronics. To state simply, they represent the fastest and safest way to study a circuit or system, thus aiding in the research, design, diagnosis, and …
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Physics-Informed, Data-Driven Framework for Model-Form Uncertainty Estimation and Reduction in RANS Simulations
… Navier-Stokes (RANS) equations based models are still the dominant tools for industrial applications. However, the predictive capability of RANS models is limited by potential inaccuracies driven by hypotheses in the Reynolds stress closure. With the ever-increasing use of RANS …
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Advanced framework for assessment and reduction of model form uncertainty of the closure laws in thermal-hydraulics codes
Accurate modeling of the two-phase flow phenomena is important for the safety analysis of light water reactors. The modeling approach must balance model resolution with computational feasibility. The direct implementation of local instant formulation is not practical for most engineering …
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Aerodynamic Uncertainty Quantification and Estimation of Uncertainty Quantified Performance of Unmanned Aircraft Using Non-Deterministic Simulations
This dissertation addresses model form uncertainty quantification, non-deterministic simulations, and sensitivity analysis of the results of these simulations, with a focus on application to analysis of unmanned aircraft systems. The model form uncertainty quantification utilizes equation error to …
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Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning
… simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or …
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Framework for Estimating Performance and Associated Uncertainty of Modified Aircraft Configurations
… A framework is introduced to predict the performance in the special case of a modification to an existing, previously certified aircraft. This framework uses a combination of existing flight test or high fidelity data of the original aircraft as well as lower fidelity data of the original and …
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Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty
Model validation is the process of determining the degree to which a model is an accurate representation of the true value in the real world. The results of a model validation study can be used to either quantify the model form uncertainty or to improve/calibrate the model. However, the model …
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Development and Use of a Spatially Accurate Polynomial Chaos Method for Aerospace Applications
Uncertainty is prevalent throughout the design, analysis, and optimization of aerospace products. When scientific computing is used to support these tasks, sources of uncertainty may include the freestream flight conditions of a vehicle, physical modeling parameters, geometric fidelity, numerical …
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A novel framework for data-driven modeling, uncertainty quantification, and deep learning of nuclear reactor simulations
… presents a novel and modern method for reactor modeling, simulation, and uncertainty characterization through an integrated framework developed under the terminology of combining four fundamental principles in scientific modeling and computing: Physics, Models, Data, and UQ (Uncertainty …