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 9–12, 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
Sep 2025
Jan 2025
Latest Journal Issues
Nuclear Science and Engineering
September 2025
Nuclear Technology
Fusion Science and Technology
October 2025
Latest News
A wave of new U.S.-U.K. deals ahead of Trump’s state visit
President Trump will arrive in the United Kingdom this week for a state visit that promises to include the usual pomp and ceremony alongside the signing of a landmark new agreement on U.S.-U.K. nuclear collaboration.
S. R. Dwivedi, H. C. Gupta
Nuclear Science and Engineering | Volume 92 | Number 4 | April 1986 | Pages 545-549
Technical Paper | doi.org/10.13182/NSE86-A18611
Articles are hosted by Taylor and Francis Online.
The Monte Carlo scheme for deep-penetration problems, where both transport and collision kernels are biased synergistically, leads to minimum variance. Obtaining a proper biasing parameter is still a problem. For certain values of biasing parameter, the variance could be infinite even in a very simple problem. Using moment equations of statistical error prediction, a critical biasing parameter is obtained. A biasing parameter greater than the critical parameter may lead to an unbounded second moment in a simple one-dimensional homogeneous shield problem. A prescription is provided that may help to avoid a poor selection of the biasing parameter.