The need for new EXFOR formats to apply machine learning techniques to its data [Slides]
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DILUTE AND DISPOSE • Less than 40 MT of Pu to dispose • 34 MT under Plutonium Management and Disposition Agreement (PMDA) • Mostly “clean” – recent and concurrent oxidation • Remainder from multiple sources • Cleanout, decommissioning, various wastes • Higher rate of items other than oxide
There exist many methods for sensitivity analysis readily available to the practitioner. While each seeks to help the modeler answer the same general question – How do sources of uncertainty or changes in the model inputs relate to uncertainty in the output? – different methods are associated with different assumptions, constraints, and required resources, leading to conclusions that may vary in interpretability and level of detail. Thus, it is crucial that the practitioner selects the desired sensitivity analysis method judiciously, making sure to match the selected approach to the specifics of their problem and to their desired objectives. In this chapter, we provide a practical overview of a collection of widely used, widely available sensitivity analysis methods. We focus on global sensitivity approaches, which seek to characterize how uncertainty in the model output may be allocated to sources of uncertainty in model inputs across the entire input space. Generally, this will require the practitioner to specify a probability distribution over the input space. On the other hand, methods for local sensitivity analysis do not require this specification but they have more limited utility, providing insight into sources of uncertainty associated only with a particular, specified location in the input space. Our hope is that this chapter may serve as a decision-making tool for practitioners, helping to guide the selection of a sensitivity analysis approach that will best fit their needs. To support this goal, we have selected a suite of approaches to cover, which, while not exhaustive, we believe provides a flexible and robust sensitivity analysis toolkit. All methods included are widely used and available in standard software packages.
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The purpose of this Cooperative Research and Development Agreement (CRADA) was to test a heat exchanger defrosting approach using radiant energy, including ultraviolet-C, -B, and -A. A preliminary test conducted at Oak Ridge National Laboratory demonstrated the concept of ultraviolet-based defrosting, which melts frost on the fin surface of a heat exchanger without increasing its overall temperature, thereby offering a new defrosting method that does not interrupt heat exchanger operation. Building on this key finding, this CRADA outlines a plan for upgrading the test equipment and for further testing to bridge the gap between the concept and its feasibility for industrial applications. This report also provides a brief review of existing defrosting technologies, the current state of the experimental system, and future parametric experiment plans.
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International Neutron Coincidence Counting (INCC6) is the IAEA’s neutron NDA software. With FY25 SGTech support we implemented self-diagnostic features into INCC6. For FY26 we propose to implement advanced analysis methods that have been splintered into custom software for one-off instruments. Incorporating these capabilities in INCC6 will make them available for any new or existing application on the ubiquitous INCC6 platform, increasing safeguards efficiency and effectiveness.
The purpose of this Cooperative Research and Development Agreement (CRADA) was to test a heat exchanger defrosting approach using radiant energy, including ultraviolet-C, -B, and -A. A preliminary test conducted at Oak Ridge National Laboratory demonstrated the concept of ultraviolet-based defrosting, which melts frost on the fin surface of a heat exchanger without increasing its overall temperature, thereby offering a new defrosting method that does not interrupt heat exchanger operation. Building on this key finding, this CRADA outlines a plan for upgrading the test equipment and for further testing to bridge the gap between the concept and its feasibility for industrial applications. This report also provides a brief review of existing defrosting technologies, the current state of the experimental system, and future parametric experiment plans.
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This small experimental report evaluates a variation of Retrieval Augmented Generation (RAG), called Self-RAG. This method uses a generative language model that incorporates retrieved facts into its generation and is explicitly trained to be able to determine whether retrieved information is enough to answer the input query, with a user-defined threshold for confidence. We performed an experiment using data from the publicly available CISA Common Security Advisory Framework (CSAF) repository (https://github.com/cisagov/CSAF) as the database of facts to be used in retrieval. Qualitative results from the experiment demonstrate that the Self-RAG method has some ability to provide reasonable answers to queries that are in the dataset and will often ignore irrelevant information when asked outside of domain questions (e.g., general facts). In settings with deliberately confusing questions (the question is within domain, but asks about a fabricated advisory), it was able to refuse 40% of the time without further adjustments to the original framework. While this performance is not sufficient for current practical use, further improvements to data formatting, disambiguating results, and leveraging threshold values could improve performance significantly. However, evaluating this will require more extensive evaluations on larger datasets and potentially better models.
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