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
October 2025
Nuclear Technology
September 2025
Fusion Science and Technology
Latest News
NRC’s hybrid AI workshop coming up
The Nuclear Regulatory Commission will host a hybrid public workshop on September 24 from 9 a.m.-5 p.m. Eastern time to discuss its activities for the safe and secure use of artificial intelligence in NRC-regulated activities.
Thomas E. Booth, James E. Gubernatis
Nuclear Science and Engineering | Volume 165 | Number 3 | July 2010 | Pages 283-291
Technical Paper | doi.org/10.13182/NSE09-62
Articles are hosted by Taylor and Francis Online.
Recently, we proposed a modified power iteration method that simultaneously determines the dominant and subdominant eigenvalues and eigenfunctions of a matrix or a continuous operator. One advantage of this method is the convergence rate to the dominant eigenfunction being [vertical bar]k3[vertical bar]/k1 instead of [vertical bar]k2[vertical bar]/k1, a potentially significant acceleration. One challenge for a Monte Carlo implementation of this method is that the second eigenfunction is represented by particles of both positive and negative weights that somehow must sum (cancel) to estimate the second eigenfunction faithfully. Our previous Monte Carlo work has demonstrated the improved convergence rate by using a point flux estimator method and a binning method to effect this cancellation. This paper presents an exact method that cancels over a region instead of at points or in small bins and has the potential of being significantly more efficient than the other two.