Nuclear Computations under Uncertainty New methods to infer and propagate nuclear data uncertainty across Monte Carlo simulations

This thesis introduces new methods to efficiently infer and propagate nuclear data uncertainty across Monte Carlo simulations of nuclear technologies. The main contributions come in two areas: 1. novel statistical methods and machine learning algorithms (Embedded Monte Carlo); 2. new mathematical parametrizations of the quantum physics models of nuclear interactions and their uncertainties (Stochastic Windowed Multipole Cross Sections). 1. Embedded Monte Carlo infers the uncertainty in nuclear codes inputs (reactor geometry, nuclear data, etc.) from samples of noisy outputs (e.g. experimental observations), and in turn propagates this uncertainty back to the simulation outputs(reactor power, reaction rates, flux, multiplication factor, etc.), without ever converging any single Monte Carlo reactor simulation. Such embedding of the uncertainty within the Nested Monte Carlo computations vastly outperforms previous methods(10–100 times less runs), and is achieved by approximating the input parametersBayesian posterior via variational inference, and reconstructing the outputs distribution via moments estimators. We validate the Embedded Monte Carlo method on anew analytic benchmark for neutron slowdown we derived. 2. Stochastic Windowed Multipole Cross Sections is an alternative way to parametrize nuclear interactions and their uncertainties (equivalent to R-matrix theory), whereby one can sample on-the-fly uncertain nuclear cross sections and analytically compute their thermal Doppler broadening. This drastically reduces the memory footprint of nuclear data (at least 1,000-fold), without incurring additional computational costs. These contributions are documented in nine peer-reviewed journal articles (eight published and one under review) and seven conference articles (six published and one under review), constituting the core of this thesis.Ph.D

Les projats at mesures d'interet fiscal conceraant les formes et les modeles de trausaisbion des valeurs mojilieras 1920-1930.

LEIDSSTELSELThese. R. Paris.OPLADEN-RUG0

La nouvelle règlementation des compléments alimentaires et ses conséquences pratiques (exemple sur une gamme de produits)

CHATENAY M.-PARIS 11-BU Pharma. (920192101) / SudocSudocFranceF

Converting point-wise nuclear cross sections to pole representation using regularized vector fitting

© 2017 Elsevier B.V. Direct Doppler broadening of nuclear cross sections in Monte Carlo codes has been widely sought for coupled reactor simulations. One recent approach proposed analytical broadening using a pole representation of the commonly used resonance models and the introduction of a local windowing scheme to improve performance (Hwang, 1987; Forget et al., 2014; Josey et al., 2015, 2016). This pole representation has been achieved in the past by converting resonance parameters in the evaluation nuclear data library into poles and residues. However, cross sections of some isotopes are only provided as point-wise data in ENDF/B-VII.1 library. To convert these isotopes to pole representation, a recent approach has been proposed using the relaxed vector fitting (RVF) algorithm (Gustavsen and Semlyen, 1999; Gustavsen, 2006; Liu et al., 2018). This approach however needs to specify ahead of time the number of poles. This article addresses this issue by adding a poles and residues filtering step to the RVF procedure. This regularized VF (ReV-Fit) algorithm is shown to efficiently converge the poles close to the physical ones, eliminating most of the superfluous poles, and thus enabling the conversion of point-wise nuclear cross sections