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 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Apr 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
June 2026
Nuclear Technology
April 2026
Fusion Science and Technology
May 2026
Latest News
Argonne: Where AI research meets education and training
Last September, in the Chicago suburb of Lemont, Ill., Argonne National Laboratory hosted its first AI STEM Education Summit. More than 180 educators from high schools, community colleges, and universities; STEM administrators; and experts in various disciplines convened at “One Ecosystem, Many Pathways–Building an AI-Ready STEM Workforce” to discuss how artificial intelligence is reshaping STEM-related industries, including the implications for the nuclear engineering classroom and workforce.
S. L. Salem, J. S. Wu, G. Apostolakis
Nuclear Technology | Volume 42 | Number 1 | January 1979 | Pages 51-64
Technical Paper | Reactor Siting | doi.org/10.13182/NT79-A32161
Articles are hosted by Taylor and Francis Online.
A systematic methodology for the construction of fault trees based on the use of decision tables has been developed. These tables are used to describe each possible output state of a component as a set of combinations of states of inputs and internal operational or failed states. Two methods for modeling component behavior via decision tables have been developed, one inductive and one deductive. These methods are useful for creating decision tables that realistically model the operational and failure modes of electrical, mechanical, and hydraulic components, as well as human interactions, inhibit conditions, and common-cause events. A computer code CAT (Computer Automated Tree) has been developed to automatically produce fault trees from decision tables. A simple electrical system was chosen to illustrate the basic features of the decision table approach and to provide an example of an actual fault tree produced by this code. This example demonstrates the potential utility of such an automated approach to fault tree construction once a basic set of general decision tables has been developed.