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Division Spotlight
Young Members Group
The Young Members Group works to encourage and enable all young professional members to be actively involved in the efforts and endeavors of the Society at all levels (Professional Divisions, ANS Governance, Local Sections, etc.) as they transition from the role of a student to the role of a professional. It sponsors non-technical workshops and meetings that provide professional development and networking opportunities for young professionals, collaborates with other Divisions and Groups in developing technical and non-technical content for topical and national meetings, encourages its members to participate in the activities of the Groups and Divisions that are closely related to their professional interests as well as in their local sections, introduces young members to the rules and governance structure of the Society, and nominates young professionals for awards and leadership opportunities available to members.
Meeting Spotlight
2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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!
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September 2024
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August 2024
Latest News
Nuclear new build procurement considerations
It may seem counterintuitive, but the best time to enhance the ability to support operations and maintenance for a new plant is before construction starts. This is one of many lessons learned by the currently operating nuclear fleet. As construction and startup of many nuclear facilities was completed, it quickly became evident that the ability to efficiently support operations and maintenance was limited. Most of the information necessary to establish and manage procurement of spare and replacement items, maintenance, and configuration of the facilities was unavailable and had to be gathered on a case-by-case, “on-demand” basis. Absence of necessary information and the associated challenges resulted in the need for staff augmentation and multiyear-long projects to develop equipment bills of material and maintenance programs and to perform technical evaluations for the huge quantities of spare and replacement items being requested.
Xingang Zhao, Xinyan Wang, Michael W. Golay
Nuclear Technology | Volume 209 | Number 3 | March 2023 | Pages 401-418
Technical Paper—Instrumentation and Controls | doi.org/10.1080/00295450.2022.2142445
Articles are hosted by Taylor and Francis Online.
Future advances in nuclear power technologies call for enhanced operator advice and autonomous control capabilities that can leverage simpler designs and increased safety features to reduce reliance on human labor. One of the first tasks in the development of such capabilities is the formulation of symptom-based conditional failure probabilities for the plant structures, systems, and components (SSCs) of interest. The primary goal is to aid plant personnel in (1) deducing the probabilistic performance status of the monitored SSCs and (2) detecting impending faults/failures. The task of estimating conditional failure probability is a bidirectional inference problem, and a logical approach is to use the Bayesian network (BN) method. As a knowledge-based explainable artificial intelligence tool and a probabilistic graphical model, BN offers reasoning capability under uncertainty, graphical representation emulating physical behavior of the target SSC, and interpretability of the model structure and results. This paper provides a systematic overview of the BN technique and the software tools for implementing BN models, along with the associated knowledge representation and reasoning paradigm. Both operational data and expert judgment can be readily incorporated into the knowledge base of a BN model. The challenges with data availability are highlighted, and the general approach to target SSC identification is presented. The focus is on failure-prone and risk-important balance of plant assets, especially for cases with strong operator involvement. Two example case studies on the failure of (1) a centrifugal pump and (2) an electric motor are conducted to demonstrate the usefulness and technical feasibility of the proposed BN-based fault diagnostic system using an expert system shell.