Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE

Inverse Uncertainty Quantification (UQ) is a process to quantify the uncertainties in random input parameters while achieving consistency between code simulations and physical observations. In this paper, we performed inverse UQ using an improved modular Bayesian approach based on Gaussian Process (GP) for TRACE physical model parameters using the BWR Full-size Fine-Mesh Bundle Tests (BFBT) benchmark steady-state void fraction data. The model discrepancy is described with a GP emulator. Numerical tests have demonstrated that such treatment of model discrepancy can avoid over-fitting. Furthermore, we constructed a fast-running and accurate GP emulator to replace TRACE full model during Markov Chain Monte Carlo (MCMC) sampling. The computational cost was demonstrated to be reduced by several orders of magnitude. A sequential approach was also developed for efficient test source allocation (TSA) for inverse UQ and validation. This sequential TSA methodology first selects experimental tests for validation that has a full coverage of the test domain to avoid extrapolation of model discrepancy term when evaluated at input setting of tests for inverse UQ. Then it selects tests that tend to reside in the unfilled zones of the test domain for inverse UQ, so that one can extract the most information for posterior probability distributions of calibration parameters using only a relatively small number of tests. This research addresses the "lack of input uncertainty information" issue for TRACE physical input parameters, which was usually ignored or described using expert opinion or user self-assessment in previous work. The resulting posterior probability distributions of TRACE parameters can be used in future uncertainty, sensitivity and validation studies of TRACE code for nuclear reactor system design and safety analysis

MODELING DEFORMATION BEHAVIOR AND STRENGTH CHARACTERISTICS OF SAND-SILT MIXTURES: A MICROMECHANICAL APPROACH

This dissertation is comprised of six chapters. In the first chapter the motivation of this research, which was modeling the deformation behavior and strength characteristics of soils under internal erosion, is briefly explained. In the second chapter a micromechanis-based stress-strain model developed for prediction of sand-silt mixtures behavior is presented. The components of the micromechanics-based model are described and undrained behavior of six different types of sand-silt mixtures is predicted for several samples with different fines contents. The need for a more comprehensive compression model for sand-silt mixtures is identified at the end of this chapter. This desired compression model should be able to explicitly consider the fines content of the mixture and incorporates particle crushing effects as well. In the third chapter a new hypothesis of active and inactive void ratios in granular material and its application in modeling compressibility is examined and a compression model for sands is proposed. In the fourth chapter the concept of inactive void ratio is extended to sand-silt mixtures and a new model is developed for compression of these mixtures that can explicitly consider the fines content in its formulation. After the validity of the new hypothesis of active and inactive voids in granular material is verified in chapters 3 and 4, the model is further developed in chapter 5 to incorporate the effects of particle crushing on the compressibility of granular material. The sixth chapter is conclusion of this work and recommendations for future investigations

Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram

A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case. The framework aims at capturing the distribution of patches in wavelet scalogram of time series using single image generative models and producing realistic wavelet coefficients for the generation of synthetic time series. It is demonstrated that the framework is effective with respect to fidelity and diversity for time series with insignificant to no trends. Also, the performance is more promising for generating samples with the same duration (reshuffling) rather than longer ones (retargeting).Comment: 8 pages, 3 figures, 2 table