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
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
March 2026
Nuclear Technology
February 2026
Fusion Science and Technology
January 2026
Latest News
Fusion energy: Progress, partnerships, and the path to deployment
Over the past decade, fusion energy has moved decisively from scientific aspiration toward a credible pathway to a new energy technology. Thanks to long-term federal support, we have significantly advanced our fundamental understanding of plasma physics—the behavior of the superheated gases at the heart of fusion devices. This knowledge will enable the creation and control of fusion fuel under conditions required for future power plants. Our progress is exemplified by breakthroughs at the National Ignition Facility and the Joint European Torus.
Kai Yao, Shengyuan Yan, Cong Chi Tran
Nuclear Technology | Volume 208 | Number 4 | April 2022 | Pages 761-774
Technical Note | doi.org/10.1080/00295450.2021.1947123
Articles are hosted by Taylor and Francis Online.
Human error is an important factor leading to nuclear power plant (NPP) accidents. The increasing of the amount of information improves the operators’ human error probability (HEP) in the digital main control room of NPPs. Human reliability analysis (HRA) is considered to be an effective method to reduce human error. The Cognitive Reliability and Error Analysis Method (CREAM) is one of the widely accepted HRA methods. However, there are shortcomings that weaken the applicability of this method. Therefore, this research proposes a fuzzy CREAM method based on a combination of fuzzy logic theory and the CREAM method. The study considers the weight of common performance conditions (CPCs) and constructs a logical relationship between CPCs and control modes. Finally, the effectiveness of the proposed method is determined using a widely accepted method to validate the evaluation results. The validated results showed that the evaluation result has a consistency between the fuzzy CREAM method and the traditional CREAM method. They indicated that the fuzzy CREAM method can obtain reliable HEP.