arXiv subjects

Philippe Humbert

Publications and source records attributed to Philippe Humbert.

6 recordsLinked to original sources

arXiv2024

Simulation and analysis of list mode measurements on SILENE reactor

Neutron time correlation measurements are used to characterize fissile systems. For this purpose, detailed information are obtained using list mode measurements where the neutron detection times are recorded in a file called the time list file. In this article, we present the simulation and analysis of list mode measurements performed on SILENE reactor. Analogue Monte Carlo simulations are performed using MCNPX code and the synthetic time list file is obtained by processing the MCNPX-PTRAC coincidence capture file. Both measured and computed time list files are processed using the same postprocessor that we have developed. This software is used to calculate counting statistics data like counting number probability distribution, Rossi alpha and Feynman functions for time gates of increasing duration. The parameter uncertainties are evaluated using a block bootstrap method.

physics.ins-det↗

arXiv2024

Neutron Counting Statistics calculations Using Deterministic Transport

For a number of applications like low-source reactor start-up or neutron coincidence counting it is necessary to take into account the stochastic nature of neutron transport and go beyond the average neutron density, which is solution of a linear Boltzmann equation. In this work, we are particularly interested in calculating the moments and probabilities of the number of neutrons detected during a time window. It is known that in case of a single initial neutron these quantities are solution of a system of coupled adjoint transport equations and that a neutron source can be taken into account in a second time using summations with the source strength. The purpose of the present work is first to present the derivation of these equations in a form where they can be solved up an arbitrary order and where the fission terms are expanded according to the moments of the fission multiplicity. For simplicity, the derivation is first presented in a lumped also called point model approximation before considering the full phase-space case. Thereafter, we describe the implementation of the solution algorithm in the deterministic, discrete ordinates code PANDA and finally, we present some numerical results and inter-code comparisons for verification purposes and to illustrate the applicability of the method.

physics.comp-ph↗

arXiv2024

Uncertainty quantification in neutron and gamma time correlation measurements

Neutron noise analysis is a predominant technique for fissile matter identification with passive methods. Quantifying the uncertainties associated with the estimated nuclear parameters is crucial for decision-making. A conservative uncertainty quantification procedure is possible by solving a Bayesian inverse problem with the help of statistical surrogate models but generally leads to large uncertainties due to the surrogate models' errors. In this work, we develop two methods for robust uncertainty quantification in neutron and gamma noise analysis based on the resolution of Bayesian inverse problems. We show that the uncertainties can be reduced by including information on gamma correlations. The investigation of a joint analysis of the neutron and gamma observations is also conducted with the help of active learning strategies to fine-tune surrogate models. We test our methods on a model of the SILENE reactor core, using simulated and real-world measurements.

stat.AP↗

arXiv2024

Neutron multiplicity counting distribution reconstruction from moments using Meixner polynomial expansion and N-forked branching approximations

Methods used to infer nuclear parameters from neutron count statistics fall into two categories depending on whether they use moments or count number probabilities. As probabilities are in general more difficult to calculate, we are interested here in the reconstruction of distributions from their first moments. For this, we explore two approaches. The first one relies on a generalization of the 2-forked branching correlation (quadratic) approximation used in the PMZBB and Poisson radical distributions, the second one is based on the expansion of the distribution on a basis of Meixner discrete orthogonal polynomials.

nucl-th↗

arXiv2024

Sequential design for surrogate modeling in Bayesian inverse problems

Sequential design is a highly active field of research in active learning which provides a general framework for designing computer experiments with limited computational budgets. It aims to create efficient surrogate models to replace complex computer codes. Some sequential design strategies can be understood within the Stepwise Uncertainty Reduction (SUR) framework. In the SUR framework, each new design point is chosen by minimizing the expectation of a metric of uncertainty with respect to the yet unknown new data point. These methods offer an accessible framework for sequential experiment design, including almost sure convergence for common uncertainty functionals. This paper introduces two strategies. The first one, entitled Constraint Set Query (CSQ) is adapted from D-optimal designs where the search space is constrained in a ball for the Mahalanobis distance around the maximum a posteriori. The second, known as the IP-SUR (Inverse Problem SUR) strategy, uses a weighted-integrated mean squared prediction error as the uncertainty metric and is derived from SUR methods. It is tractable for Gaussian process surrogates with continuous sample paths. It comes with a theoretical guarantee for the almost sure convergence of the uncertainty functional. The premises of this work are highlighted in various test cases, in which these two strategies are compared to other sequential designs.

stat.ME↗

arXiv2022

Multi-output Gaussian processes for inverse uncertainty quantification in neutron noise analysis

In a fissile material, the inherent multiplicity of neutrons born through induced fissions leads to correlations in their detection statistics. The correlations between neutrons can be used to trace back some characteristics of the fissile material. This technique known as neutron noise analysis has applications in nuclear safeguards or waste identification. It provides a non-destructive examination method for an unknown fissile material. This is an example of an inverse problem where the cause is inferred from observations of the consequences. However, neutron correlation measurements are often noisy because of the stochastic nature of the underlying processes. This makes the resolution of the inverse problem more complex since the measurements are strongly dependent on the material characteristics. A minor change in the material properties can lead to very different outputs. Such an inverse problem is said to be ill-posed. For an ill-posed inverse problem the inverse uncertainty quantification is crucial. Indeed, seemingly low noise in the data can lead to strong uncertainties in the estimation of the material properties. Moreover, the analytical framework commonly used to describe neutron correlations relies on strong physical assumptions and is thus inherently biased. This paper addresses dual goals. Firstly, surrogate models are used to improve neutron correlations predictions and quantify the errors on those predictions. Then, the inverse uncertainty quantification is performed to include the impact of measurement error alongside the residual model bias.

stat.CO↗