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.
Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2025)
February 3–6, 2025
Amelia Island, FL|Omni Amelia Island Resort
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!
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Latest News
Biden executive order to facilitate AI data center power
As demand for artificial intelligence and data centers grows, President Biden issued an executive order yesterday aimed to ensure clean-energy power supply for the technology.
Roberto P. Domingos, Roberto Schirru, Cláudio M. N. A. Pereira
Nuclear Science and Engineering | Volume 152 | Number 2 | February 2006 | Pages 197-203
Technical Paper | doi.org/10.13182/NSE06-A2575
Articles are hosted by Taylor and Francis Online.
This work presents particle swarm optimization (PSO) as an alternative method for solving an optimization problem that arises during nuclear reactor core design. The method is introduced and applied to a simplified core optimization problem found in literature. When compared with other evolutionary computation-based methods, PSO performs better. Moreover, PSO presents easier modeling and demands less computational effort in the optimization process. The obtained results are shown and discussed.