PNNL

Abstract

Optimal data collection and analysis for the goal of assessing nuclear nonproliferation (NN) conditions of monitored facilities is an important research area with significant safeguards, security, and economic consequences for the effective global deployment of nuclear energy. To help with that goal, facility modeling (FM) can integrate and interpret observations collected from monitored facilities in order to assess their NN conditions. FM capabilities include models characterizing nuclear processes and algorithms for data processing and interpretation. Assessing NN conditions is challenging because observations can come from many sources, including local and remote sensors monitoring facility operations, as well as open sources providing particular business information related to monitored facilities. Although many FM capabilities exist for analyzing large amounts of information, these capabilities have not been integrated in an analyst-friendly manner. This paper addresses some of these FM capabilities and illustrates how they could be integrated and utilized for NN analysis. The inverse problem of inferring facility conditions based on collected observations is described, along with an architecture and a computer framework for utilizing FM capabilities. After giving examples of key FM capabilities, the paper illustrates