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
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Jan 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
February 2026
Nuclear Technology
December 2025
Fusion Science and Technology
November 2025
Latest News
The top 10 states of nuclear
The past few years have seen a concerted effort from many U.S. states to encourage nuclear development. The momentum behind nuclear-friendly policies has grown considerably, with many states repealing moratoriums, courting nuclear developers and suppliers, and in some cases creating advisory groups and road maps to push deployment of new nuclear reactors.
Taro Ueki, Edward W. Larsen
Nuclear Science and Engineering | Volume 130 | Number 3 | November 1998 | Pages 269-291
Technical Paper | doi.org/10.13182/NSE98-A2006
Articles are hosted by Taylor and Francis Online.
A new Boltzmann Monte Carlo (BMC) equation is proposed to describe the transport of Monte Carlo particles governed by a set of nonanalog rules for the transition of space, velocity, and weight. The BMC equation is a kinetic equation that includes weight as an extra independent variable. The solution of the BMC equation is the pointwise distribution of velocity and weight throughout the physical system. The BMC equation is derived for the simulation of a transmitted current, utilizing the exponential transform with angular biasing. The weight moments of the solution of the BMC equation are used to predict the score moments of the transmission current. (Also, it is shown that an adjoint BMC equation can be used for this purpose.) Integrating the solution of the forward BMC equation over space, velocity, and weight, the mean number of flights per history is obtained. This is used to determine theoretically the figure of merit for any choice of biasing parameters. Also, a maximum safe value of the exponential transform parameter is proposed, which ensures the finite variance of variance estimate (sample variance) for any penetration distance. Finally, numerical results that validate the new theory are provided.