ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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!
Latest Magazine Issues
Oct 2025
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
December 2025
Nuclear Technology
November 2025
Fusion Science and Technology
Latest News
Illinois legislature lifts ban on nuclear energy, funds clean energy
The Illinois General Assembly passed a clean energy bill on October 30 that would, in part, lift a 30-year moratorium on new nuclear energy in the state and create incentives for more energy storage.
R. R. Coveyou, V. R. Cain, and K. J. Yost
Nuclear Science and Engineering | Volume 27 | Number 2 | February 1967 | Pages 219-234
Technical Paper | doi.org/10.13182/NSE67-A18262
Articles are hosted by Taylor and Francis Online.
The use of the Monte Carlo method for the study of deep penetration of radiation into and through shields entails the use of sophisticated methods of variance reduction to make such calculations economical or even feasible. This paper presents an exposition of the most useful methods of variance reduction. The exposition is unified by consistent exploitation of adjoint formulations to estimate expected values, as in previous work, and further to evaluate the variance of the resulting estimates., The connection between adjoint formulations and the choice of biasing schemes is also investigated. In particular, it is shown that the value function (the solution of the integral equation of the adjoint formulation) is always a good choice for importance function biasing; a sharp upper bound, independent of the particular problem, is found for the resulting variance. Predicted (analytic) and experimental (Monte Carlo) results are also given for a simple one-dimensional problem.