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Meeting	MS&T24: Materials Science & Technology
Symposium	Uncertainty Quantification Applications in Materials and Engineering
Presentation Title	Bayesian Calibration of Cladding Creep Model Coefficients in the PAD5 Fuel Performance Code Using the Dakota Toolkit
Author(s)	Aiden Ochoa, Cole Horan, Yun Long, Wenzhong Zhou, Martin Nieto-Perez
On-Site Speaker (Planned)	Aiden Ochoa
Abstract Scope	Calibration of model coefficients in Westinghouse fuel performance code PAD5 has traditionally relied on optimizing the model coefficients to minimize the difference between the prediction and measurement with little consideration of input or measurement uncertainties. Recently, however, a statistical method of characterizing calibration parameters known as Bayesian calibration has gained increasing support in the nuclear industry due to its robustness and ability to efficiently identify sources of uncertainty. This work focuses on a simple coupling of Sandia National Labs' Dakota toolkit to PAD5 for the application Bayesian calibration to the cladding-creep correlation coefficients using real creep measurements from a variety of sources. Preliminary results indicate interesting difference between traditionally calibrated values, and those obtained from Bayesian methods. Future work involves the expansion of the Bayesian calibration and UQ method to other models and include additional sources of contributing uncertainties.

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A Parametric Study of Optical Floating-Zone Crystal-Growth Furnace Through Modeling of Heat Transfer: Effect of Sample Properties and Environment Gas Pressure

Automating Engineering Design with UQ-Aware Scientific Learning

Bayesian Calibration of Cladding Creep Model Coefficients in the PAD5 Fuel Performance Code Using the Dakota Toolkit

Bayesian Protocols for High-Throughput Optimization of Kinematic Hardening Models Using Cyclic Microindentation Experiments

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