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 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
Latest Magazine Issues
Aug 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
October 2026
Nuclear Technology
September 2026
Fusion Science and Technology
August 2026
Latest News
Front-end nuclear fuel supply cooperation: Turning allied interdependence into strategic advantage
The global nuclear revival, which is fueled by unprecedented demand for firm, affordable, dispatchable power for artificial intelligence and data center build-out, energy security imperatives, and climate commitments, has exposed a structural reality of the Western fuel cycle: No single allied nation currently possesses the full suite of front-end capabilities. From mining through conversion, enrichment, fabrication, and the emerging deconversion and metallization steps required for reactor fuels, capability is distributed across Canada, France, Japan, the United Kingdom, and the United States (collectively, the “Sapporo Five”), as well as a small group of close partners.
O. J. Wallace
Nuclear Science and Engineering | Volume 78 | Number 1 | May 1981 | Pages 78-85
Technical Note | doi.org/10.13182/NSE81-A19609
Articles are hosted by Taylor and Francis Online.
Calculations based on the integration of the point kernel over a finite source region are widely used in obtaining gamma-ray fluxes, dose rates, and heating rates. For most cases of practical interest, this integration must be done numerically. The relative merits of the trapezoidal rule, Gauss quadrature, and the semi-Gauss automatic quadrature algorithm of Patterson are discussed as they apply to the integration of the point kernel. The Patterson algorithm is superior to other quadrature algorithms for this application because it allows results to be calculated to a predetermined relative error, wastes no function evaluations, is accurate, and supplies relative error data along with the answer. It is efficient with respect to both engineering and computer time. The implementation of this algorithm for point-kernel integrations is described in detail.