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Education, Training & Workforce Development
The Education, Training & Workforce Development Division provides communication among the academic, industrial, and governmental communities through the exchange of views and information on matters related to education, training and workforce development in nuclear and radiological science, engineering, and technology. Industry leaders, education and training professionals, and interested students work together through Society-sponsored meetings and publications, to enrich their professional development, to educate the general public, and to advance nuclear and radiological science and engineering.
2021 Student Conference
April 8–10, 2021
North Carolina State University|Raleigh Marriott City Center
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A day in the life of the nuclear community
The November issue of Nuclear News is focused on the individuals who make up our nuclear community.
We invited a small group of those individuals to tell us about their day-to-day work in some of the many occupations and applications of nuclear science and technology, and they responded generously. They were ready to tell us about the part they play, together with colleagues and team members, in supplying clean energy, advancing technology, protecting safety and health, and exploring fundamental science.
In these pages, we see a community that can celebrate both those workdays that record progress moving at a steady pace and the exceptional days when a goal is reached, a briefing is delivered, a contract goes through, a discovery is made, or an unforeseen challenge is overcome.
The Nuclear News staff hopes that you enjoy meeting these members of our community—or maybe get reacquainted with friends—through their words and photos.
James E. Bevins, R. N. Slaybaugh
Nuclear Technology | Volume 205 | Number 4 | April 2019 | Pages 542-562
Technical Paper | dx.doi.org/10.1080/00295450.2018.1496692
Articles are hosted by Taylor and Francis Online.
This paper introduces Gnowee, a modular, Python-based, open-source hybrid metaheuristic optimization algorithm (available from https://github.com/SlaybaughLab/Gnowee). Gnowee is designed for rapid convergence to nearly globally optimum solutions for complex, constrained nuclear engineering problems with mixed-integer (MI) and combinatorial design vectors and high-cost, noisy, discontinuous, black box objective function evaluations. Gnowee’s hybrid metaheuristic framework is a new combination of a set of diverse, robust heuristics that appropriately balance diversification and intensification strategies across a wide range of optimization problems. There are many potential applications for this novel algorithm both within the nuclear community and beyond. Given that a set of well-known and studied nuclear benchmarks does not exist for the purpose of testing optimization algorithms, comparisons between Gnowee and several well-established metaheuristic algorithms are made for a set of 18 established continuous, MI, and combinatorial benchmarks representing a wide range of types of engineering problems and solution space behaviors. These results demonstrate Gnoweee to have superior flexibility and convergence characteristics over this diverse set of design spaces. We anticipate this wide range of applicability will make this algorithm desirable for many complex engineering applications.