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Division Spotlight
Radiation Protection & Shielding
The Radiation Protection and Shielding Division is developing and promoting radiation protection and shielding aspects of nuclear science and technology — including interaction of nuclear radiation with materials and biological systems, instruments and techniques for the measurement of nuclear radiation fields, and radiation shield design and evaluation.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Latest News
Trump administration begins overhaul of the NRC
Hanson
Since the president's inauguration in January, the Trump Administration has been on course to make big changes at the Nuclear Regulatory Commission to realize its goals of deregulation, energy dominance, and deployment of advanced reactors. Given the many executive orders (EOs) and the work the Department of Government Efficiency has done in cutting the federal workforce, it was a surprise that NRC commissioner Christopher Hanson was dismissed on Friday, according to a statement Hanson posted on his LinkedIn profile early Monday.
Hanson said in the post that President Trump terminated his position “without cause, contrary to existing law and long-standing precedent regarding removal of independent agency appointments.”
Mihály Makai, Zoltán Szatmáry
Nuclear Science and Engineering | Volume 177 | Number 1 | May 2014 | Pages 52-67
Technical Paper | doi.org/10.13182/NSE12-97
Articles are hosted by Taylor and Francis Online.
In the Monte Carlo (MC) method, statistical noise is usually present, and it may become dominant in the calculation of a distribution, usually by iteration, but it is less important in calculating integrals. The subject of the present work is the role of statistical noise in iterations involving stochastic simulation (the MC method). Convergence is checked by comparing two consecutive solutions in the iteration. The statistical noise may randomize or pervert the convergence. We study the probability of convergence and the correct estimation of the variance in a simplified model problem. We also study the statistical properties of the solution to a deterministic problem with a stochastic source obtained from a stochastic calculation. There are iteration strategies resulting in nonconvergence or a randomly stopped iteration.