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HPS's Eric Goldin: On health physics
Eric Goldin, president of the Health Physics Society, is a radiation safety specialist with 40 years of experience in power reactor health physics, supporting worker and public radiation safety programs. A certified health physicist since 1984, he has served on the American Board of Health Physics, and since 2004, he has been a member of the National Council on Radiation Protection and Measurements’ Program Area Committee 2, which provides guidance for radiation safety in occupational settings for a variety of industries and activities. He was awarded HPS Fellow status in 2012 and was elected to the NCRP in 2014.
Goldin’s radiological engineering experience includes ALARA programs, instrumentation, radioactive waste management, emergency planning, dosimetry, decommissioning, licensing, effluents, and environmental monitoring.
The HPS, headquartered in Herndon, Va., is the largest radiation safety society in the world. Its membership includes scientists, safety professionals, physicists, engineers, attorneys, and other professionals from academia, industry, medical institutions, state and federal government, the national laboratories, the military, and other organizations.
The HPS’s activities include encouraging research in radiation science, developing standards, and disseminating radiation safety information. Its members are involved in understanding, evaluating, and controlling the potential risks from radiation relative to the benefits.
Goldin talked about the HPS and health physics activities with Rick Michal, editor-in-chief of Nuclear News.
Anthony Michael Scopatz
Nuclear Science and Engineering | Volume 186 | Number 1 | April 2017 | Pages 83-97
Technical Paper | dx.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.