Deep learning for safety assessment of nuclear power reactors: Reliability, explainability, and research opportunities

Deep learning algorithms provide plausible benefits for efficient prediction and analysis of nuclear reactor safety phenomena. However, research works that discuss the critical challenges with deep learning models from the reactor safety perspective are limited. This article presents the state-of-the-art in deep learning application in nuclear reactor safety analysis, and the inherent limitations in deep learning models. In addition, critical issues such as deep learning model explainability, sensitivity and uncertainty constraints, model reliability, and trustworthiness are discussed from the nuclear safety perspective, and robust solutions to the identified issues are also presented. As a major contribution, a deep feedforward neural network is developed as a surrogate model to predict turbulent eddy viscosity in Reynolds-averaged Navier–Stokes (RANS) simulation. Further, the deep feedforward neural network performance is compared with the conventional Spalart Allmaras closure model in the RANS turbulence closure simulation. In addition, the Shapely Additive Explanation (SHAP) and the local interpretable model-agnostic explanations (LIME) APIs are introduced to explain the deep feedforward neural network predictions. Finally, exciting research opportunities to optimize deep learning-based reactor safety analysis are presented.The work of AA and HA are funded through the Sêr Cymru II 80761-BU-103 project by Welsh European Funding Office (WEFO) under the European Development Fund (ERDF)

Phase transformation of PbSe/CdSe nanocrystals from core-shell to Janus structure studied by photoemission spectroscopy

Photoelectron spectroscopic measurements have been performed, with synchrotron radiation on PbSe/CdSe heteronanocrystals that initially consist of core-shell structures. The study of the chemical states of the main elements in the nanocrystals shows a reproducible and progressive change in the valence-band and core-level spectra under photon irradiation, whatever the core and shell sizes are. Such chemical modifications are explained in light of transmission electron microscopy observations and reveal a phase transformation of the nanocrystals: The core-shell nanocrystals undergo a morphological change toward a Janus structure with the formation of semidetached PbSe and CdSe clusters. Photoelectron spectroscopy gives new insight into the reorganization of the ligands anchored at the surface of the nanocrystals and the modification of the electronic structure of these heteronanocrystals

Soft Dynamics simulation: 2. Elastic spheres undergoing a T1 process in a viscous fluid

Robust empirical constitutive laws for granular materials in air or in a viscous fluid have been expressed in terms of timescales based on the dynamics of a single particle. However, some behaviours such as viscosity bifurcation or shear localization, observed also in foams, emulsions, and block copolymer cubic phases, seem to involve other micro-timescales which may be related to the dynamics of local particle reorganizations. In the present work, we consider a T1 process as an example of a rearrangement. Using the Soft dynamics simulation method introduced in the first paper of this series, we describe theoretically and numerically the motion of four elastic spheres in a viscous fluid. Hydrodynamic interactions are described at the level of lubrication (Poiseuille squeezing and Couette shear flow) and the elastic deflection of the particle surface is modeled as Hertzian. The duration of the simulated T1 process can vary substantially as a consequence of minute changes in the initial separations, consistently with predictions. For the first time, a collective behaviour is thus found to depend on another parameter than the typical volume fraction in particles.Comment: 11 pages - 5 figure