In most of the nuclear power reactors, neutrons generated in the fission reaction are of fast energies and are slowed down to thermal energies through the process of scattering with moderator materials. This slowing down process, known as neutron thermalization, is contingent upon precise knowledge of thermal scattering cross sections, which are governed by the Thermal Scattering Law (TSL). Discrepancies in thermal scattering cross-section data have been known to significantly affect criticality safety studies and reactor physics simulations, with observed deviations reaching several hundreds of pcms in some critical benchmarks sensitive to TSL.
Over recent years, extensive efforts have been directed towards quantifying the impact of nuclear data uncertainties on nuclear systems using covariance data. Despite several efforts , the complexity of the mechanisms involved in the generation of the covariance matrix for TSLs has hindered its development. The absence of covariance matrices for TSL limits the estimation of the impact of TSL uncertainty on thermal systems. This postdoctoral work is to expand our capabilities to estimate the impact of TSL uncertainties by developing Random TSL files for moderator materials, thereby working towards a better understanding of the uncertainty in the thermal scattering cross-section data.

The complexity of thermal scattering data is due to their highly non-linear distributions. The Total Monte Carlo (TMC) approach, a method that utilizes random sampling techniques to evaluate uncertainties in nuclear data, has been effectively used to propagate these uncertainties. This post doc subject focuses on generating random variations in the input parameters (such as phonon spectrum, diffusion coefficients, free gas cross sections, etc,.) of the TSL thermalization model for different moderator materials, leading to the creation of random TSLs. Numerous double-differential experimental data and phonon spectrum data are available in the literature from methods such as time-of-flight experiments, Raman spectroscopy, molecular dynamics simulations, and ab initio simulations. Additionally, recent advancements made in the TSL domain by the European Spallation Source (ESS), specifically with the development of NCrystal [6], will be of significant value in exploring various datasets. The objective is to leverage the existing data pool to perturb the TSL parameters based on experimental/simulation data available in the open literature and simulate the inherent uncertainties of the TSL model to propagate them in neutronics simulations. This postdoctoral proposal outlines a crucial project, TARA (Thermal scattering law uncertainty Analysis using RAndom files), aimed at advancing our understanding of uncertainties in thermal scattering cross-section data, which is essential for the safety and efficiency of nuclear reactors.

Expected Results and Relevance
The outcome of this work is expected to be an open-source python tool, “TARA”, enabling the generation of Random TSL files for moderator materials at any required temperatures. The expected result of this project would provide us with better uncertainty information on TSL data that is presently unavailable. This endeavor will further refine our understanding of the sensitivity of the TSL input parameters, which would aid in the development of TSL covariance matrix in the future. Additionally, the study of the impact of uncertainties on TSL can guide adjustments to the nuclear data library to enhance its congruence with experimental and simulated results. The open-source tool "TARA" and the generated Random TSL files will be contributed to the forthcoming JEFF-4 library, marking a significant advancement in the field.
Work towards the quantification of the uncertainty of the TSL is an ongoing activity at ASNR. Some preliminary work has already been carried out and demonstrated the methodology to generate Random TSL files for light water. No new experimental campaign is needed, and this work will rely solely on the available differential data/phonon spectrum data in the literature. The postdoctoral work will be carried out at ASNR in the frame of the APRENDE European project, with occasional travel to ESS Sweden to integrate functionalities from the NCrystal code into the "TARA" tool.

The ideal candidate for this position will have a strong interest in nuclear data, with a particular focus on uncertainty quantification. The candidate should have a solid background in physics, nuclear physics, or nuclear engineering. Proficiency in programming languages such as Python and C++, along with experience in high-performance computing and familiarity with Unix/Linux environments, is essential. The candidate should possess strong interpersonal skills, demonstrating the ability to effectively communicate and collaborate with international nuclear-data communities. The successful candidate will have the opportunity to contribute significantly to the field of nuclear data, working with leading institutions and experts.

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