The phenomenon identification and ranking table (PIRT) process was employed to evaluate the current capability to assess the ability of liquid-salt-fueled molten salt reactors to achieve their fundamental safety functions (FSFs). The PIRT process provides a structured mechanism to elicit and document expert opinions on the most important phenomena and the corresponding level of knowledge with regard to achieving the FSFs. The PIRT panel included reactor developers, accident progression evaluation tool developers, US Department of Energy (DOE) national laboratory technical staff, university researchers, and US Nuclear Regulatory Commission (NRC) staff. The information-gathering process concentrated on phenomena related to accidents in which the fuel salt-including any in the cover gas-has been released from the first barrier layer. This information-gathering process included identifying all potential accidents that could result in the release of a substantial amount of radionuclides that could breach or bypass the first barrier. The PIRT did not identify any previously unrecognized systemic vulnerabilities. However, the elicitation process identified several areas with low levels of knowledge and significant potential impact on accident progression. These areas either require additional research to improve the state of knowledge or additional design conservatism to accommodate the remaining unknowns.

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The Transformational Challenge Reactor is being designed at Oak Ridge National Laboratory to demonstrate the feasibility of constructing a reactor core using advanced manufacturing technology. This technology includes additive manufacturing combined with machine learning, materials science, and data science technologies in an effort to facilitate the expansion of additive manufacturing into advanced nuclear energy systems and other applications requiring a high level of quality assurance. The Transformational Challenge Reactor is employing additive manufacturing and artificial intelligence to deliver a new approach. Beginning in FY21, the focus of the program has shifted away from demonstrating a reactor, and instead, towards delivering on four key thrust areas: (1) artificial intelligence-informed design, (2) advanced materials, (3) integrated sensing and control, and (4) the digital platform. Of these four thrust areas, the most pertinent to this report is the digital platform. The digital platform has the potential to be a key enabler for a paradigm shift in how components, those derived from advanced manufacturing technologies, are certified for use in nuclear applications. This is achieved primarily using machine learning to discover correlations from the abundance of data produced through additive manufacturing and those physical properties critical to the performance of the component.A proposed approach towards the certification of advanced manufacturing technology-derived components using the digital platform is presented. This approach has similarities to and builds on those industry initiatives already in progress to qualify advanced manufacturing technology-derived components. However, aspects of where data and the digital platform can integrate with, are especially highlighted. This proposed approach is: a. generic (can be applicable to any advanced manufacturing technology, any supported material, and any application which may have a strong safety significance), b. flexible (can be scaled to large programs with a variety of advanced manufacturing technologyderived component applications, or small programs that have only a few, but clearly defined desired applications), c. complementary to existing industry approaches under development (those being developed by the U.S. Nuclear Regulatory Commission and other industry trade groups), and, d. performance-based (allows for operational feedback and building confidence in the digital platform).To envelop these attributes, a simple demonstration has been designed to employ the digital platform for component scale performance predictions. Results from this demonstration are expected around the time of publishing this report and therefore, will be presented in a follow-up report.The digital platform has the potential to transform how concepts of equivalency and expected component performance are measured. Currently, there is no discussion or pathway for using the data and artificial intelligence embedded within the digital platform to demonstrate equivalency ...

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