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Mike Kramer: Navigating power deals in the new data economy
Mike Kramer has a background in finance, not engineering, but a combined 20 years at Exelon and Constellation and a key role in the deals that have Meta and Microsoft buying power from Constellation’s Clinton and Crane sites have made him something of a nuclear expert.
Kramer spoke with Nuclear News staff writer Susan Gallier in late August, just after a visit to Clinton in central Illinois to celebrate a power purchase agreement (PPA) with Meta that closed in June. As Constellation’s vice president for data economy strategy, Kramer was part of the deal-making—not just the celebration.
John C. Wagner, Douglas E. Peplow, Scott W. Mosher
Nuclear Science and Engineering | Volume 176 | Number 1 | January 2014 | Pages 37-57
Technical Paper | doi.org/10.13182/NSE12-33
Articles are hosted by Taylor and Francis Online.
This paper presents a hybrid (Monte Carlo/deterministic) method for increasing the efficiency of Monte Carlo calculations of distributions, such as flux or dose rate distributions (e.g., mesh tallies), as well as responses at multiple localized detectors and spectra. This method, referred to as Forward-Weighted CADIS (FW-CADIS), is an extension of the Consistent Adjoint Driven Importance Sampling (CADIS) method, which has been used for more than a decade to very effectively improve the efficiency of Monte Carlo calculations of localized quantities (e.g., flux, dose, or reaction rate at a specific location). The basis of this method is the development of an importance function that represents the importance of particles to the objective of uniform Monte Carlo particle density in the desired tally regions. Implementation of this method utilizes the results from a forward deterministic calculation to develop a forward-weighted source for a deterministic adjoint calculation. The resulting adjoint function is then used to generate consistent space- and energy-dependent source biasing parameters and weight windows that are used in a forward Monte Carlo calculation to obtain more uniform statistical uncertainties in the desired tally regions. The FW-CADIS method has been implemented and demonstrated within the MAVRIC (Monaco with Automated Variance Reduction using Importance Calculations) sequence of SCALE and the ADVANTG (Automated Deterministic Variance Reduction Generator)/MCNP framework. Application of the method to representative real-world problems, including calculation of dose rate and energy-dependent flux throughout the problem space, dose rates in specific areas, and energy spectra at multiple detectors, is presented and discussed. Results of the FW-CADIS method and other recently developed global variance-reduction approaches are also compared, and the FW-CADIS method outperformed the other methods in all cases considered.
