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Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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Biden executive order to facilitate AI data center power
As demand for artificial intelligence and data centers grows, President Biden issued an executive order yesterday aimed to ensure clean-energy power supply for the technology.
T. Kawano, K. M. Hanson, S. Frankle, P. Talou, M. B. Chadwick, R. C. Little
Nuclear Science and Engineering | Volume 153 | Number 1 | May 2006 | Pages 1-7
Technical Paper | doi.org/10.13182/NSE06-A2589
Articles are hosted by Taylor and Francis Online.
We present an approach to uncertainty quantification for nuclear applications that combines the covariance evaluation of differential cross-section data and the error propagation from matching a criticality experiment using a neutron-transport calculation. We have studied the reduction in uncertainty of 239Pu fission cross sections by using a one-dimensional neutron-transport calculation with the PARTISN code. The evaluation of 239Pu differential cross-section data is combined with a criticality measurement (Jezebel) using a Bayesian method. To quantify the uncertainty in such calculations, we generate a set of random samples of the cross sections, which represents the covariance matrix, and estimate the distribution of calculated quantities, such as criticality. We show that inclusion of the Jezebel data reduces uncertainties in estimating neutron multiplicity.