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The nuclear power industry has recently recognized the potential cost savings that can be achieved from migrating the current manual and labor-intensive surveillance and preventive maintenance activities to data-driven online monitoring methods. Consequently, several efforts have been launched with various degrees of momentum to tackle specific surveillance or preventive maintenance activities. The U.S. Department of Energy's Light Water Reactor Sustainability (LWRS) program has anticipated this need and launched an effort aimed to develop a technology roadmap for the nuclear power industry. A holistic technology roadmap will reduce long-term investment costs by prioritizing improvements that support the end-state vision rather than just the next incremental capability. Because a comprehensive roadmap has to be focused towards the plant's needs, and because nuclear power plants (NPPs) have different process efficiencies and deficiencies, the extent of details described by this technology roadmap was optimized to be plant-independent. The roadmap, therefore, is developed to describe processes and equipment, regardless of an NPP's specific need to target a certain process or equipment sequence. For example, the steps required to migrate the inspection of a pump are plant independent. However, defining that the migration of feed water pump inspections to online monitoring should be performed before inspections of feed water valves is dependent on multiple plant factors such as the plant requirements, labor work capacity, and process or equipment conditions.The technology roadmap was broken down into six elements, four of which have a sequential path of advancement, while the other two are supporting elements to the sequential elements. The sequential part of the roadmap consists of data collection, analytics, visualization, and management. For these elements of the technology roadmap, the adoption of the technology to perform an activity is defined by the availability and usage of the technology in one of three states. The base state is defined as the most primitive, manual, and/or labor-dependent process used by some NPPs. The modern state is defined as the state that can be achieved if the activities are augmented or replaced by current technologies that are either commercially available or soon to be available. The state of the art is defined as technologies of the future (i.e., concepts and technologies that are being researched).The data collection element of the technology roadmap is described in terms of a process. However, the data analytics element is described in terms of equipment, because automating an activity performed on one piece of equipment requires considering multiple data sources from various data collection processes. The visualization and data management elements are process and equipment independent. Visualization describes the effective use of human factors science and technological advancements to present the collected data and data analytics results. Data management targets storage, commun...

This case study describes the development of technologies that enable digital-engineering and digital-twinning efforts in proliferation detection. The project presents a state-of-the-art approach to supporting IAEA safeguards by incorporating diversion-pathway analysis, facility misuse, and detection of indicators within the reactor core, applying the safeguards-by-design concept, and demonstrates its applicability as a sensitive monitoring system for advanced reactors and power plants. There are two pathways a proliferating state might take using the reactor core. One is “diversion,” where special fissionable nuclear material—i.e., Pu-239, U-233, U enriched in U-233/235—that has been declared to the International Atomic Energy Agency (IAEA) is removed surreptitiously, either by taking small amounts of nuclear material over a long time (known as protracted diversion) or large amounts in a short time (known as abrupt diversion). The second pathway is “misuse,” where undeclared source material—material that can be transmuted into special fissionable nuclear material: depleted uranium, natural uranium, and thorium—is placed in the core, where it uses the neutron flux for transmutation. Digital twinning and digital engineering have demonstrated significant performance improvement and schedule reduction in the aerospace, automotive, and construction industries. This integrated modeling approach has not been fully applied to nuclear safeguards programs in the past. Digital twinning, combined with machine learning technologies, can lead to new innovations in process-monitoring detection, specifically in event classification, real-time notification, and data tampering. It represents a technological leap in evaluation and detection capability to safeguard any nuclear facility.

This report presents the initial application design of the Cost Risk Analysis Framework Tool (CRAFT). At the beginning, the concept of CRAFT was formulated from a need to perform economic risk analysis that can be quickly deployable through the nuclear industry to evaluate the risk associated to capital projects and to perform plant asset management. The design evolved into a framework that can integrate several forms of risk (e.g., not just economic, but also safety related) into a single analysis. In order to reduce the research, development and deployment time, we have decided to use the Risk Analysis Virtual ENvironment (RAVEN) statistical framework as a basis for the CRAFT architecture. In this report we summarize the most recent developments accomplished during the second part of FY18 for the two use cases of the Cost and Risk Categorization Applications path of the RISA project. These two use cases directly target plant health and plant capital SSC management. Regarding the plant health management use case, we have started to investigate methods to extract information from text-based data: text data mining. The Risk Informed Asset management is the main target of this report and several methods are here shown in order to solve the capital SSC replacement issue. This has been performed by presenting: several models designed to determine the effective cost through the lifetime of a capital SSCs, and the optimization algorithms that will be employed to determine the optimal replacement schedule of a given set of capital SSC. The interface between RAVEN and the PRA code SAPHIRE is the first step toward the integration of several plant risk models (e.g., economic and safety) and plant data (e.g., databases from the plant maintenance and diagnostics center) into a single analysis framework. In this respect, RAVEN is the ideal platform to connect several models and to manage data streaming among them.