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
Nov 2025
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
December 2025
Nuclear Technology
Fusion Science and Technology
November 2025
Latest News
X-energy raises $700M in latest funding round
Advanced reactor developer X-energy has announced that it has closed an oversubscribed Series D financing round of approximately $700 million. The funding proceeds are expected to be used to help continue the expansion of its supply chain and the commercial pipeline for its Xe-100 advanced small modular reactor and TRISO-X fuel, according the company.
Dean V. Power
Nuclear Technology | Volume 27 | Number 4 | December 1975 | Pages 680-691
Technical Paper | Nuclear Explosive | doi.org/10.13182/NT75-A24341
Articles are hosted by Taylor and Francis Online.
The coherency transfer function (CTF) is a method for summing seismograms from multiple nearly coherent sources by using a frequency domain transformation. Ground motion predictions for the nuclear explosive Rio Blanco experiment are calculated for peak vector amplitudes of acceleration, velocity, and displacement and are compared to the Rio Blanco data and the results of other prediction techniques. Predictions of amplitudes are higher than experimental results by a few percent for acceleration and displacement and by 20% for velocity. Data regression slopes are ∼12% greater than predicted values for acceleration but <5% greater for displacement and velocity. CTF predictions are found to agree with experimental results as good as or better than values predicted by other methods.