Application of Artificial Neural Networks to Reliable Nuclear Data for Nonproliferation Modeling and Simulation

Abstract

Detection and identification of special nuclear materials (SNMs) are an essential part of the US nonproliferation effort. Modern cutting-edge SNM detection methodologies rely more and more on modeling and simulation techniques. Experiments with radiological samples in realistic configurations, is the ultimate tool that establishes the minimum detection limits of SNMs in a host of different geometries. Modern modeling and simulation approaches have the potential to significantly reduce the number of experiments with radioactive sources needed to determine these detection limits and reduce the financial barrier to SNM detection. Unreliable nuclear data is one of the principal causes of uncertainty in modeling and simulating nuclear systems. In particular, nuclear cross sections introduce a significant uncertainty in the nuclear data. The goal of this research is to develop a methodology that will autonomously extract the correct nuclear resonance characteristics of experimental data in a reliable way, a task previously left to expert judgement. Accurate nuclear data will in turn allow contemporary modeling and simulation to become far more reliable, de-escalating the extent of experimental testing. Consequently, modeling and simulation techniques reduce the use and distribution of radiological sources, while at the same time increase the reliability of the currently used methods for the detection and identification of SNMs.

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

“…The analysis method based on fuzzy logic is applied to deal with various nuclear signals obtained by lowresolution scintillation detectors, especially sodium iodide (NaI) detectors [92]. Lagari et al [93] designed four differ- ent ANNs to automatically determine the nuclear resonance characteristics according to the experimental cross-section data in a fast and reliable way, which made the modeling and simulation more reliable, thus reducing the degree of experimental testing. The multi-layer packet data processing method network is used to simulate the nuclear reactor with UQ (Uncertainty Quantification) and SA (Sensitivity Analysis), and its performance is reliable on high-dimensional problems [94].…”