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Former Exelon CEO Chris Crane remembered for “transformational milestones”
Crane
Exelon announced that Chris Crane, the company’s former chief executive, passed away on Saturday in Chicago at the age of 65.
Crane served as the company’s president and CEO from 2012 until his retirement in December 2022. During his tenure, he steered the energy company through several transformational milestones, including the successful mergers with Constellation Energy in 2012 and Pepco Holdings in 2016, creating the largest utility business by customer count in the United States.
In 2022, with the spin-off of Constellation as the generation and retail side of energy business (with the largest U.S. nuclear fleet), Crane led the creation of a stand-alone transmission and delivery energy company.
Yunzhao Li, E. E. Lewis, Micheal A. Smith, Hongchun Wu, Liangzhi Cao
Nuclear Science and Engineering | Volume 179 | Number 1 | January 2015 | Pages 42-58
Technical Paper | doi.org/10.13182/NSE13-103
Articles are hosted by Taylor and Francis Online.
Combinations of three approaches are examined as options to replace the algorithms presently employed in the variational nodal code VARIANT. They are preconditioned Generalized Minimal Residual (GMRES) algorithms, parallelism in energy, and Wielandt acceleration. Together with partitioned matrix and Gauss-Seidel (GS) preconditioners, two GMRES algorithms are formulated to replace the upscattering iteration and facilitate energy parallelism and Wielandt acceleration. The GMRES algorithms are tested on two-dimensional thermal and fast reactor diffusion problems. The two GMRES algorithms yield higher efficiencies in energy group parallelization and Wielandt acceleration than simple parallelization of the existing GS algorithm. With preconditioning the GMRES algorithms reduce the total computing time by a factor of 2 to 4 and in some cases by a factor of >10. A multilevel iteration optimization scheme is investigated that automatically adjusts the relative error tolerance of the inner iterations according to the estimated convergence rate of the corresponding outer iterations and updates the Wielandt shift magnitude as the calculations progress. Numerical results based on large two-dimensional thermal and fast reactor diffusion problems demonstrate that automated optimization of the multilevel iterative processes reduces iteration numbers by as much as an order of magnitude.