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Nicholas Tsoulfanidis—ANS member since 1969
We welcome ANS members who have careered in the community to submit their own Nuclear Legacy stories, so that the personal history of nuclear power can be captured. For information on submitting your stories, contact nucnews@ans.org.
As an undergraduate I studied physics at the University of Athens. I entered the university in 1955 after successfully passing a national exam (came up fourth in a field of about 700 candidates). Upon graduation and finishing my mandatory two-year military service, the plan was to teach physics either in a public high school or as a tutor for a private for-profit institution, preparing high school students for the national exam.
Anthony Michael Scopatz
Nuclear Science and Engineering | Volume 186 | Number 1 | April 2017 | Pages 83-97
Technical Paper | doi.org/10.1080/00295639.2016.1272384
Articles are hosted by Taylor and Francis Online.
A method for quickly determining deployment schedules that meet any given fuel cycle demands is presented here. This algorithm is fast enough to perform in situ within low-fidelity fuel cycle simulators. It uses Gaussian process regression models to predict the production curve as a function of time and the number of deployed facilities. Each of these predictions is measured against the demand curve using the dynamic time warping distance. The minimum-distance deployment schedule is evaluated in a full fuel cycle simulation, and the generated production curve then informs the model on the next optimization iteration. The method converges within five to ten iterations to a distance that is less than 1% of the total deployable production. This speed of convergence makes it suitable for use even when fuel cycle realizations are expensive, as in higher-fidelity or agent-based simulators. A representative once-through fuel cycle is used to demonstrate the methodology for reactor deployment. However, the algorithm itself is multivariate and may be used to determine the deployment schedules of many facility types that meet a number of independent criteria simultaneously. The once-through, electricity production example was chosen for the simplicity of illustrating the method.