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
Jun 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
July 2026
Nuclear Technology
Fusion Science and Technology
May 2026
Latest News
Breaking ground on a new approach to construction
The drive to Kairos Power’s reactor demonstration site in Oak Ridge, Tenn., is not only scenic—it’s historic. Nearly 85 years ago, roughly 30,000 construction workers transformed orchards and farmland into a key Manhattan Project site. Depending on your route, you may pass by one of the three gatehouses that were once military checkpoints controlling access to Atomic Energy Commission production facilities.
Nathan Siu, George Apostolakis
Nuclear Science and Engineering | Volume 94 | Number 3 | November 1986 | Pages 213-226
Technical Paper | doi.org/10.13182/NSE86-A17264
Articles are hosted by Taylor and Francis Online.
The assessment of the fire risk in nuclear power plants requires the analysis of fire scenarios within specified rooms. A methodology that integrates the fire protection features of a given room into an existing fire risk analysis framework is developed. An important component of this methodology is a model for the time required to detect and suppress a fire in a given room, called the “hazard time.” This model accounts for the reliability of fire detection and suppression equipment, as well as for the characteristic rates of the detection and suppression processes. Because the available evidence for fire detection and suppression in nuclear power plants is sparse and often qualitative, a second component of this methodology is a set of methods needed to employ imprecise information in a statistical analysis. These methods can be applied to a wide variety of problems.