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Remembering ANS member Gil Brown
Brown
The nuclear community is mourning the loss of Gilbert Brown, who passed away on July 11 at the age of 77 following a battle with cancer.
Brown, an American Nuclear Society Fellow and an ANS member for nearly 50 years, joined the faculty at Lowell Technological Institute—now the University of Massachusetts–Lowell—in 1973 and remained there for the rest of his career. He eventually became director of the UMass Lowell nuclear engineering program. After his retirement, he remained an emeritus professor at the university.
Sukesh Aghara, chair of the Nuclear Engineering Department Heads Organization, noted in an email to NEDHO members and others that “Gil was a relentless advocate for nuclear energy and a deeply respected member of our professional community. He was also a kind and generous friend—and one of the reasons I ended up at UMass Lowell. He served the university with great dedication. . . . Within NEDHO, Gil was a steady presence and served for many years as our treasurer. His contributions to nuclear engineering education and to this community will be dearly missed.”
Jaeseok Heo, Paul J. Turinsky, J. Michael Doster
Nuclear Science and Engineering | Volume 173 | Number 3 | March 2013 | Pages 293-311
Technical Paper | doi.org/10.13182/NSE11-113
Articles are hosted by Taylor and Francis Online.
This paper discusses the utilization of an uncertainty quantification methodology for nuclear power plant thermal-hydraulic transient predictions, with a focus on small modular reactors characterized by the integral pressurized water reactor design, to determine the value of completing experiments in reducing uncertainty. To accomplish this via the improvement of the prediction of key system attributes, e.g., minimum departure from nucleate boiling ratio, a thermal-hydraulic simulator is used to complete data assimilation for input parameters to the simulator employing experimental data generated by the virtual reactor. The mathematical approach that is used to complete this analysis depends upon whether the system responses, i.e., sensor signals, and the system attributes are or are not linearly dependent upon the parameters. For a transient producing mildly nonlinear response sensitivities, a Bayesian-type approach was used to obtain the a posteriori distributions of the parameters assuming Gaussian distributions for the input parameters and responses. For a transient producing highly nonlinear response sensitivities, the Markov chain Monte Carlo method was utilized based upon Bayes' theorem to estimate the a posteriori distributions of the parameters. To evaluate the value of completing experiments, an optimization problem was formulated and solved. The optimization addressed both the experiments to complete and the modifications to be made to the nuclear power plant made possible by using the increased margins resulting from data assimilation. The decision variables of the experiment optimization problem include the selection of sensor types and locations and experiment type imposing realistic constraints. The decision variables of the nuclear power plant modification optimization problem include various design specifications, e.g., power rating, steam generator size, and reactor coolant pump size, with the objective of minimizing cost as constrained by required margins to accommodate the uncertainty. Since the magnitude of the uncertainty is dependent upon the experiments via data assimilation, the nuclear power plant optimization problem is treated as a suboptimization problem within the experiment optimization problem. The experiment optimization problem objective is to maximize the net savings, defined as the savings in nuclear power plant cost due to the modified design specifications minus the cost of the experiments. Both the experiment and the nuclear power plant optimization problems were solved using the simulated annealing method.