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Full-Text Articles in Engineering
Thermochemical And Continuum Modeling To Understand The Chemical Composition Of Pwr Fuel Crud, Jason T. Rizk
Thermochemical And Continuum Modeling To Understand The Chemical Composition Of Pwr Fuel Crud, Jason T. Rizk
Doctoral Dissertations
Computational modeling of Chalk River Undesirable Deposits (CRUD) allows for the prediction of associated phenomena that impact nuclear power plant performance, reliability, and safety. It also provides insight into the physical mechanisms by which CRUD forms and affects plant performance. A major concern in pressurized water reactors (PWRs) is Axial Offset Anomaly (AOA) which is caused by CRUD’s proficiency at trapping boron within the reactor core. The ability to predict AOA and other phenomena requires a detailed explanation of the chemical composition of CRUD. By pairing computational models that can simulate the structure and species trapping with detailed thermochemical models, …
Bayesian Calibration Tool, Sveinn Palsson, Martin Hunt, Alejandro Strachan
Bayesian Calibration Tool, Sveinn Palsson, Martin Hunt, Alejandro Strachan
The Summer Undergraduate Research Fellowship (SURF) Symposium
Fitting a model to data is common practice in many fields of science. The models may contain unknown parameters and often, the goal is to obtain good estimates of them. A variety of methods have been developed for this purpose. They often differ in complexity, efficiency and accuracy and some may have very limited applications. Bayesian inference methods have recently become popular for the purpose of calibrating model's parameters. The way they treat unknown quantities is completely different from any classical methods. Even though the unknown quantity is a constant, it is treated as a random variable and the desired …
Resource Allocation Framework: Validation Of Numerical Models Of Complex Engineering Systems Against Physical Experiments, Joshua Hegenderfer
Resource Allocation Framework: Validation Of Numerical Models Of Complex Engineering Systems Against Physical Experiments, Joshua Hegenderfer
All Dissertations
An increasing reliance on complex numerical simulations for high consequence decision making is the motivation for experiment-based validation and uncertainty quantification to assess, and when needed, to improve the predictive capabilities of numerical models. Uncertainties and biases in model predictions can be reduced by taking two distinct actions: (i) increasing the number of experiments in the model calibration process, and/or (ii) improving the physics sophistication of the numerical model. Therefore, decision makers must select between further code development and experimentation while allocating the finite amount of available resources. This dissertation presents a novel framework to assist in this selection between …