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
Apr 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
June 2026
Nuclear Technology
March 2026
Fusion Science and Technology
May 2026
Latest News
DOE selects first companies for nuclear launch pad
The Department of Energy’s Office of Nuclear Energy and the National Reactor Innovation Center have announced their first selections for the Nuclear Energy Launch Pad: three companies developing microreactors and one developing fuel supply.
The four companies—Deployable Energy, General Matter, NuCube Energy, and Radiant Industries—were selected from the initial pool of Reactor Pilot Program and Fuel Line Pilot Program applicants, the two precursor programs to the launch pad.
Scott A. Turner, Edward W. Larsen
Nuclear Science and Engineering | Volume 127 | Number 1 | September 1997 | Pages 22-35
Technical Paper | doi.org/10.13182/NSE127-22
Articles are hosted by Taylor and Francis Online.
A new automated variance reduction method for the Monte Carlo simulation of multigroup neutron transport source-detector problems is described. The method is based on a modified transport problem that can be solved by analog Monte Carlo with zero variance. The implementation of this modified problem is impractical, in part because it requires the exact solution of an adjoint transport problem. The new local importance function transform (LIFT) method is developed to overcome this difficulty by approximating the exact adjoint solution with a piecewise-continuous function containing parameters that are obtained from a deterministic adjoint calculation. The transport and collision processes of the transformed Monte Carlo problem bias source distribution, distance to collision, and selection of postcollision energy groups and directions. A companion paper provides numerical results that demonstrate the efficiency of the LIFT method.