Uncertainty quantification in safety codes using a Bayesian approach with data from separate and integral effect tests

Uncertainty quantification in safety codes using a Bayesian approach with data from separate and integral effect tests

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2014.

Bibliographic Details
Main Author:	Yurko, Joseph Paul
Other Authors:	Jacopo Buongiorno.
Format:	Thesis
Language:	eng
Published:	Massachusetts Institute of Technology 2014
Subjects:	Nuclear Science and Engineering.
Online Access:	http://hdl.handle.net/1721.1/92095

Similar Items

Bayesian Calibration of Safety Codes Using Data from Separate-and Integral Effects Tests
by: Youngblood, Robert, et al.
Published: (2017)

Quantitative Phenomena Identification and Ranking Table (QPIRT) for Bayesian Uncertainty Quantification
by: Buongiorno, Jacopo, et al.
Published: (2014)

Uncertainty quantification and calibration in nuclear safety codes using Gaussian process active learning
by: Fugleberg, Eric N. (Eric Nels)
Published: (2017)

Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation
by: Buongiorno, Jacopo, et al.
Published: (2015)

Scalable Inverse Uncertainty Quantification by Hierarchical Bayesian Modeling and Variational Inference
by: Chen Wang, et al.
Published: (2023-11-01)

Coupled differential and integral data analysis for improved uncertainty quantification of the ⁶³,⁶⁵Cu cross section evaluations
by: Sobes, Vladimir
Published: (2014)

Computational fluid dynamics and turbulence model uncertainty quantification for nuclear reactor safety applications
by: Acton, Michael (Michael John)
Published: (2021)

Bayesian uncertainty quantification for data-driven equation learning
by: Martina-Perez, S, et al.
Published: (2021)

Effect of helium injection on diffusion dominated air ingress accidents in pebble bed reactors
by: Yurko, Joseph Paul
Published: (2011)

On the quantification of safety margins
by: Pagani, Lorenzo P
Published: (2006)

Nuclear Data Uncertainty Quantification in Criticality Safety Evaluations for Spent Nuclear Fuel Geological Disposal
by: Matthias Frankl, et al.
Published: (2021-07-01)

Nuclear data uncertainty quantification on PWR spent nuclear fuel as a function of burnup
by: Federico Grimaldi, et al.
Published: (2023-04-01)

Objectified quantification of uncertainties in Bayesian atmospheric inversions
by: A. Berchet, et al.
Published: (2015-05-01)

Uncertainty-optimized predictive testing for a nuclear waste container
by: Wolford, Andrew Joseph
Published: (2005)

Reliability quantification of nuclear safety-related software
by: Zhang, Yi
Published: (2005)

Sparsifying priors for Bayesian uncertainty quantification in model discovery
by: Seth M. Hirsh, et al.
Published: (2022-02-01)

Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
by: Wang, Jian, et al.
Published: (2021)

Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
by: Wang, Jian, et al.
Published: (2021)

Bayesian calibration, process modeling and uncertainty quantification in biotechnology.
by: Laura Marie Helleckes, et al.
Published: (2022-03-01)

Editorial: Uncertainty quantification in nuclear physics
by: Maria Piarulli, et al.
Published: (2023-08-01)

Code of safety for nuclear merchant ships
by: International Maritime Organization
Published: (1982)

Uncertainty Quantification for MLP-Mixer Using Bayesian Deep Learning
by: Abdullah A. Abdullah, et al.
Published: (2023-04-01)

Improved Uncertainty Quantification for Neural Networks With Bayesian Last Layer
by: Felix Fiedler, et al.
Published: (2023-01-01)

Bayesian model uncertainty quantification for hyperelastic soft tissue models
by: Milad Zeraatpisheh, et al.
Published: (2021-01-01)

Best estimate safety analysis for nuclear power plants : uncertainty evaluation
by: International Atomic Energy Agency 7924
Published: (2008)

A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty
by: Christopher E. Gillies, et al.
Published: (2020-08-01)

Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models
by: Hugo K.H. Olierook, et al.
Published: (2021-01-01)

Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection
by: Sascha Ranftl, et al.
Published: (2019-12-01)

Uncertainty and sensitivity analysis for long-running computer codes : a critical review
by: Langewisch, Dustin R
Published: (2010)

Design for safety of nuclear power plants : a code of practice
by: 7924 International Atomic Energy Agency
Published: (1978)

Quantum Approximate Bayesian Optimization Algorithms With Two Mixers and Uncertainty Quantification
by: Jungin E. Kim, et al.
Published: (2023-01-01)

Bayesian Uncertainty Quantification for Channelized Reservoirs via Reduced Dimensional Parameterization
by: Anirban Mondal, et al.
Published: (2021-05-01)

On off-line and on-line Bayesian filtering for uncertainty quantification of structural deterioration
by: Antonios Kamariotis, et al.
Published: (2023-01-01)

Bayesian chemical reaction neural network for autonomous kinetic uncertainty quantification
by: Li, Qiaofeng, et al.
Published: (2023)

Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
by: Blanchard, Antoine, et al.
Published: (2022)

Assessing the applicability of the ASME V&V20 standard for uncertainty quantification of CFD in nuclear systems fluid modeling
by: Alvarez, Andres Felipe, S.B. Massachusetts Institute of Technology
Published: (2017)

An integrated formal approach for developing reliable software of safety-critical system
by: Ouyang, Meng
Published: (2005)

Testing the EPRI reactivity depletion decrement uncertainty methods
by: Sykora, Elliot M
Published: (2016)

Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data
by: Frederik Aerts, et al.
Published: (2023-08-01)

Data-driven quantification of public–private partnership experience levels under uncertainty with Bayesian hierarchical model
by: Wang, Yongqi, et al.
Published: (2022)