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 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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
Dec 2025
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
December 2025
Nuclear Technology
Fusion Science and Technology
November 2025
Latest News
INL makes first fuel for Molten Chloride Reactor Experiment
Idaho National Laboratory has announced the creation of the first batch of enriched uranium chloride fuel salt for the Molten Chloride Reactor Experiment (MCRE). INL said that its fuel production team delivered the first fuel salt batch at the end of September, and it intends to produce four additional batches by March 2026. MCRE will require a total of 72–75 batches of fuel salt for the reactor to go critical.
Ely M. Gelbard
Nuclear Science and Engineering | Volume 94 | Number 3 | November 1986 | Pages 274-276
Technical Note | doi.org/10.13182/NSE86-A17271
Articles are hosted by Taylor and Francis Online.
Two different descriptions have been used for Monte Carlo source biasing. One relies on a direct optimization of biasing parameters, the other on an intuitive application of the adjoint flux. But use of the adjoint flux is based on the assumption that importance sampling will be used throughout the calculation, and that source sampling will not be stratified. It is shown that if these conditions are not satisfied, use of the importance functions has no theoretical justification and, in principle, biasing parameters must be optimized directly.