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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This presentation covers why we should care about Cu nuclear data. It also covers the current state of Cu63 and Cu65 covariances and the Impact on specific critical experiments.
FSEN provides an opportunity to quickly calculate sensitivities to orthogonal measurements during integral experiments. Toy problem has been deployed to expand on verification of FSEN for reaction rate ratios. While there is decent agreement, further investigation must be done on multiplication’s impact on sensitivity vector.
Current capabilities in MCNP allow for full modeling of TRISO particles to handle uncertainties.
This presentation covers motivations and DNCSH, individual particle measurements, infinite lattice study, application study, and conclusions.
This presentation touches on reducing uncertainties in intermediate-energy actinide nuclear data and this continues to be a high priority for many applications. The goal of PARADIGM (PARallel Approach of Differential and InteGral Measurements): accelerate efforts to reduce biases and uncertainties in nuclear data through improvements to the nuclear data pipeline. This presentation includes integral experiments and a summarization of existing copper nuclear data.
Nuclear data (ND) underpins predictive neutron transport simulations like MCNP. The current nuclear data pipeline is time intensive and iterative. PARADIGM aims to reduce uncertainties by restructuring the ND Pipeline.
he project PARADIGM (PARallel Approach of Differential and InteGral Measurements) answers this question by selecting via machine learning (ML) an optimal combination of differential and integral experiments to reduce 239 Pu nuclear data uncertainties from 1-600 keV by 50%..
This presentation covers flattop description, original benchmark model, final revised benchmark models, computational responses, modern validation suite, and future work.
This presentation finds that as it was designed, Cerberus is extremely sensitive to copper nuclear data. Just changing copper nuclear data to another library can swing simulated keff by >1000 pcm. ENDF/B-VIII.1 and JENDL-5.0 both show large downward trends in keff as copper interstitial thickness is increased
Abstract not provided.
This presentation touches on an Introduction to Thales Method for Low Fidelity Optimization, Progression to High Fidelity, Preliminary Uncertainty and finishes with some Concluding Remarks.
How do we accelerate scientific progress in the Nuclear Data (ND) field? By selecting via Machine Learning (ML) an optimum combination of differential and integral experiments to reduce ND uncertainties.
Integral Measurements to support Pu intermediate energy nuclear data were successfully performed. They were designed using an ML-based approach, with a novel direct tie to improved nuclear theory and Differential Measurements.
Abstract not provided.
Nuclear data uncertainties and similarity metrics were investigated for the Thales design. Nuclear data uncertainties decrease dramatically from BLO to ENDF/B-VIII.1 covariances. Similarity metrics were utilize to down-select among proposed experiment design files.
Abstract not provided.
This report documents the release of evaluated neutron-induced 238,240−242 Pu prompt fission neutron multiplicities, $ \overline{ν}$ p , for ENDF/B-IX.0. For each isotope, one evaluation out of four is chosen. Each chosen evaluation is based on experimental $ \overline{ν}$ p and model values from the CGMF code.
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