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Optimal Design Of Validation Experiments For Calibration And Validation Of Complex Numerical Models, Matthew Egeberg
Optimal Design Of Validation Experiments For Calibration And Validation Of Complex Numerical Models, Matthew Egeberg
All Theses
As prediction of the performance and behavior of complex engineering systems shifts from a primarily empirical-based approach to the use of complex physics-based numerical models, the role of experimentation is evolving to calibrate, validate, and quantify uncertainty of the numerical models. Oftentimes, these experiments are expensive, placing importance on selecting experimental settings to efficiently calibrate the numerical model with a limited number of experiments. The aim of this thesis is to reduce the experimental resources required to reach predictive maturity in complex numerical models by (i) aiding experimenters in determining the optimal settings for experiments, and (ii) aiding the model …
Predictive Maturity Of Inexact And Uncertain Strongly Coupled Numerical Models, Ismail Farajpour
Predictive Maturity Of Inexact And Uncertain Strongly Coupled Numerical Models, Ismail Farajpour
All Dissertations
The Computer simulations are commonly used to predict the response of complex systems in many branches of engineering and science. These computer simulations involve the theoretical foundation, numerical modeling and supporting experimental data, all of which contain their associated errors. Furthermore, real-world problems are generally complex in nature, in which each phenomenon is described by the respective constituent models representing different physics and/or scales. The interactions between such constituents are typically complex in nature, such that the outputs of a particular constituent may be the inputs for one or more constituents. Thus, the natural question then arises concerning the validity …
Evaluating The Predictive Capability Of Numerical Models Considering Robustness To Non-Probabilistic Uncertianty In The Input Parameters, Parker Shields
Evaluating The Predictive Capability Of Numerical Models Considering Robustness To Non-Probabilistic Uncertianty In The Input Parameters, Parker Shields
All Theses
The paradigm of model evaluation is challenged by compensations between various forms of errors and uncertainties that are inherent to the model development process due to, for instance, imprecise model input parameters, scarcity of experimental data and lack of knowledge regarding an accurate mathematical representation of the system. When calibrating model input parameters based on fidelity to experiments, such compensations lead to non-unique solutions. In turn, the existence of non-unique solutions makes the selection and use of one `best' numerical model risky. Therefore, it becomes necessary to evaluate model performance based not only on the fidelity of the predictions to …