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Robotics & Remote Systems
The Mission of the Robotics and Remote Systems Division is to promote the development and application of immersive simulation, robotics, and remote systems for hazardous environments for the purpose of reducing hazardous exposure to individuals, reducing environmental hazards and reducing the cost of performing work.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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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Nuclear News 40 Under 40 discuss the future of nuclear
Seven members of the inaugural Nuclear News 40 Under 40 came together on March 4 to discuss the current state of nuclear energy and what the future might hold for science, industry, and the public in terms of nuclear development.
To hear more insights from this talented group of young professionals, watch the “40 Under 40 Roundtable: Perspectives from Nuclear’s Rising Stars” on the ANS website.
Tejashree S. Phatak, Jayalekshmi Nair, Sangeetha Prasanna Ram, B. J. Roy
Nuclear Science and Engineering | Volume 198 | Number 8 | August 2024 | Pages 1583-1606
Research Article | doi.org/10.1080/00295639.2023.2259748
Articles are hosted by Taylor and Francis Online.
For the accurate estimation of neutron cross-section data, evaluation of nuclear data is mandatory to fulfill the need of nuclear science and technology. In this work, the evaluation of 232Th(n,2n)231Th, 241Am(n,2n)240Am, 100Mo(n,2n)99Mo, and 96Mo(n,p)96Nb reaction cross-section data is carried out using a novel method. This novel method of evaluation employs a cluster-based piecewise evaluation followed by a digital filter for merging the evaluated curves. The clusters in the experimental data and model data are identified using the probabilistic method of the Gaussian Mixture Model. The clustered experimental data are then regressed using the polynomial regression technique. The model data are generated using the Talys 1.9 code, and the model deficiency due to the complex random nature of nuclear reactions is also accounted here using chi-squared analysis. Evaluation in each cluster is then carried out independently using the popular Kalman filter technique. For obtaining a single smooth evaluated curve for the whole energy range, the popular smoothing digital filter, the Savitzky-Golay Filter, is employed for the first time in nuclear data evaluation. The proposed evaluated curves and existing evaluated curves of 232Th(n,2n)231Th, 241Am(n,2n)240Am, 100Mo(n,2n)99Mo, and 96Mo(n,p)96Nb reactions from nuclear data libraries such as ENDF/BVIII.0, JEFF-3.3, JENDL-4.0, CENDL-3.1, and TENDL 2021 are compared and found to be in good agreement. It is also found that generally, evaluation methods are data dependent, and so, a single evaluation method may not be applicable for all reactions of all nuclides. Since piecewise evaluation is cluster dependent, selecting the appropriate cluster makes this method robust for almost all reactions of all nuclides. Also, it is proven that this novel method of evaluation is a promising method demonstrating the potential of this approach for evaluation based on the chi-squared goodness-of-fit test with respect to standard evaluated library ENDF/BVIII.0 and experimental data.