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DOE OSTI · 1886293

EUCLID: Experiments Underpinned by Computational Learning for Improvements in Nuclear Data [Slides]

Hutchinson, Jesson D.·Alwin, Jennifer Louise·Clark, Alexander Rich·Cutler, Theresa Elizabeth·Grosskopf, Michael John·Haeck, Wim·Herman, Michal W.·Kleedtke, Noah Andrew·Lamproe, Juliann Rose·Little, Robert Currier·Michaud, Isaac James·Neudecker, Denise·Rising, Michael Evan·Smith, Travis Austin·Thompson, Nicholas William·Vander Wiel, Scott Alan
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Abstract

EUCLID will design validation experiments optimized to resolve compensating errors and adjust nuclear data to experiments. A big part of the success of the EUCLID proposal was due to previous work supported by NCSP (MCNP, nuclear data, and NCERC capabilities). The work performed under EUCLID will similarly benefit the NCSP mission. It will lead to new MCNP capabilities, improved nuclear data and nuclear data capabilities, and new methodology and tools that will have large impact on future NCERC experiments.

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73 NUCLEAR PHYSICS AND RADIATION PHYSICS97 MATHEMATICS AND COMPUTINGintegral experimentsLDRDMCNP6NCERCNuclear Criticality Safety Program (NCSP)

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Hutchinson, Jesson D., Alwin, Jennifer Louise, Clark, Alexander Rich, Cutler, Theresa Elizabeth, Grosskopf, Michael John, Haeck, Wim, Herman, Michal W., Kleedtke, Noah Andrew, Lamproe, Juliann Rose, Little, Robert Currier, Michaud, Isaac James, Neudecker, Denise, Rising, Michael Evan, Smith, Travis Austin, Thompson, Nicholas William, Vander Wiel, Scott Alan. 2022-02-16. EUCLID: Experiments Underpinned by Computational Learning for Improvements in Nuclear Data [Slides]. https://doi.org/10.2172/1886293

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