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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.
Nathan W. Porter, Maria N. Avramova, Vincent A. Mousseau
Nuclear Science and Engineering | Volume 190 | Number 3 | June 2018 | Pages 271-286
Technical Paper | dx.doi.org/10.1080/00295639.2018.1435135
Articles are hosted by Taylor and Francis Online.
This work describes the results of a quantitative uncertainty analysis of the thermal-hydraulic subchannel code for nuclear engineering applications, Coolant Boiling in Rod Arrays-Three Field (COBRA-TF). CTF is used, which is a version of COBRA-TF developed in cooperation between the Consortium for Advanced Simulation of Light Water Reactors and North Carolina State University. Four steady-state cases from Phase II Exercise 3 of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency Light Water Reactor Uncertainty Analysis in Modeling (UAM) Benchmark are analyzed using the statistical analysis tool, Design Analysis Kit for Optimization and Terascale Applications (Dakota). The input parameters include boundary condition, geometry, and modeling uncertainties, which are selected using a sensitivity study and then defined based on expert judgment. A forward uncertainty quantification method with Latin hypercube sampling (LHS) is used, where the sample size is based on available computational resources.
The means and standard deviations of thermal-hydraulic quantities of interest are reported, as well as the Spearman rank correlation coefficients between the inputs and outputs. The means and standard deviations are accompanied by their respective standard errors, and the correlation coefficients are tested for statistical significance. The quantities of interest include void fractions, temperatures, and pressure drops. The predicted uncertainty in all parameters remains relatively low for all quantities of interest. The dominant sources of uncertainty are identified. For cases based on experiments, two different validation metrics are used to quantify the difference between measured and predicted void fractions.
The results compare well with past studies, but with a number of improvements: the use of an updated CTF input deck using the current UAM specification and the most recent version of CTF, the use of an LHS method, an analysis of standard errors for the statistical results, and a quantitative comparison to experimental data.
Though the statistical uncertainty analysis framework presented herein is applied to thermal-hydraulic analyses, it is generally applicable to any simulation tool. Given a specified amount of computational resources, it can be used to quantify statistical significance through the use of fundamental statistical analyses. This is in contrast with the prevailing methods in nuclear engineering, which provide a sample size necessary to achieve a specified level of statistical certainty.