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Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
Conference on Nuclear Training and Education: A Biennial International Forum (CONTE 2023)
February 6–9, 2023
Amelia Island, FL|Omni Amelia Island Resort
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Nuclear Science and Engineering
Fusion Science and Technology
Nuclear energy: enabling production of food, fiber, hydrocarbon biofuels, and negative carbon emissions
In the 1960s, Alvin Weinberg at Oak Ridge National Laboratory initiated a series of studies on nuclear agro-industrial complexes1 to address the needs of the world’s growing population. Agriculture was a central component of these studies, as it must be. Much of the emphasis was on desalination of seawater to provide fresh water for irrigation of crops. Remarkable advances have lowered the cost of desalination to make that option viable in countries like Israel. Later studies2 asked the question, are there sufficient minerals (potassium, phosphorous, copper, nickel, etc.) to enable a prosperous global society assuming sufficient nuclear energy? The answer was a qualified “yes,” with the caveat that mineral resources will limit some technological options. These studies were defined by the characteristic of looking across agricultural and industrial sectors to address multiple challenges using nuclear energy.
Pedro Mena, R. A. Borrelli, Leslie Kerby
Nuclear Technology | Volume 208 | Number 2 | February 2022 | Pages 232-245
Technical Paper | doi.org/10.1080/00295450.2021.1905470
Articles are hosted by Taylor and Francis Online.
Artificial intelligence is becoming a larger part of operations for many industries. One industry where this is occurring rapidly is the nuclear industry. Researchers from around the world are looking to implement this technology in various areas of the nuclear industry. This paper explores the use of machine learning to diagnose problems. This project makes use of synthetic data collected from a Generic Pressurized Water Reactor (GPWR) simulator on whether a reactor is operating normally or experiencing one of four different transient events. A dataset was created consisting of over 30 000 reactor operational states. The data were explored and wrangled using Python and the Pandas package, using a variety of methods. Once ready, the data were randomly shuffled, with half the data being used for training and the other half being used for testing. Six different machine learning models were created using scikit-learn and the AutoML package Tree-based Pipeline Optimization Tool (TPOT). These models were created using six data scaling methods along with six feature reduction/selection methods. These models were validated using accuracy, precision, recall, and F1 score. The accuracy of the individual transients was also calculated. All six of the models had validation scores above 95%, with the decision tree and logistic regression models performing the best. These results are promising for the possible future use of machine learning in reactor diagnostics.