Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “UQ”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

L3:RTM.MCH.P19.06: Neutronics UQ

This report summarizes some of the work performed in the last year on the nuclear data uncertainty tasks using the multipole representation. This approach was proposed to provide a simple and integrated way of evaluating the nuclear data uncertainty in a Monte Carlo simulation and evaluating their impact on multigroup cross sections. As previously described, the windowed multipole format allows for a resonance based representation of the cross sections with direct Doppler broadening. This representation also lends itself nicely to the evaluation of sensitivities of resonance parameters and the direct perturbation of resonance parameters in the simulation environment. Both of these approaches rely on the need of conversion of the covariance data (File 32 of the ENDF library) into a pole and residues form: i.e. into a multipole covariance. Section 5 details the current pathways that have been used so far to achieve this. In order to verify the conversion process of the covariance data, the sensitivies to the resonance parameters and the sampling from these parameters, an analytical benchmark of the slowing process was developed. The benchmark provides a closed-form solution for the flux in the presence of Hydrogen and multiple resonant nuclides. Section 2 details the analytical benchmark and provides the closed-form solutions for the flux and its sensitivity to resonance parameters. A verification of the proposed methodology is performed and compared with the analytic solution to demonstrate the accuracy of the benchmark and the applicability of our embedded resonance perturbation approach in a small Monte Carlo code. Papers are currently being drafted documenting the benchmark and the generation of sensitivities. As paths to best generate multipole covariance matrices are being compared, we also explored new methods to propagate nuclear data uncertainty across the Monte Carlo neutron transport calculation. Section 4 studies the use of multipole covariance matrices to perform sensitivity analysis. Section 3.2 documents the results of our investigations into a new way of propagating nuclear data uncertainty across Monte Carlo transport simulations by sampling new epistemic cross sections for each new neutron.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation

The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. When comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that GLLS predictions fail to replicate computed response distributions for nonlinear applications, while MOCABA shows near agreement, and IUQ uses computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

FOS: Computer and information sciences↗