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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Empowering the next generation: ANS’s newest book focuses on careers in nuclear energy
A new career guide for the nuclear energy industry is now available: The Nuclear Empowered Workforce by Earnestine Johnson. Drawing on more than 30 years of experience across 16 nuclear facilities, Johnson offers a practical, insightful look into some of the many career paths available in commercial nuclear power. To mark the release, Johnson sat down with Nuclear News for a wide-ranging conversation about her career, her motivation for writing the book, and her advice for the next generation of nuclear professionals.
When Johnson began her career at engineering services company Stone & Webster, she entered a field still reeling from the effects of the Three Mile Island incident in 1979, nearly 15 years earlier. Her hiring cohort was the first group of new engineering graduates the company had brought on since TMI, a reflection of the industry-wide pause in nuclear construction. Her first long-term assignment—at the Millstone site in Waterford, Conn., helping resolve design issues stemming from TMI—marked the beginning of a long and varied career that spanned positions across the country.
Man Gyun Na, Belle R. Upadhyaya, Xiaojia Xu, In Joon Hwang
Nuclear Science and Engineering | Volume 154 | Number 3 | November 2006 | Pages 353-366
Technical Paper | doi.org/10.13182/NSE06-A2638
Articles are hosted by Taylor and Francis Online.
In this paper, a space reactor core dynamics is identified online by a recursive least-squares method. Based on this identified reactor model consisting of the control reactivity and the thermal electric generator power, the future thermoelectric (TE) generator power is predicted. A model predictive control method is applied to design an automatic controller for TE generator power control for a space reactor of the SP-100 system. The basic concept of the model predictive control is to solve an optimization problem for a finite future at current time and to implement as the current control input only the first optimal control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The objectives of the proposed model predictive controller are to minimize both the difference between the predicted TE generator power and the desired power and the variation of the control reactivity. Also, the control constraints are subjected to maximum and minimum reactivity and to maximum reactivity change. Therefore, the genetic algorithm that is appropriate to accomplish multiple objectives is used to optimize the model predictive controller. A lumped parameter simulation model of the SP-100 nuclear space reactor is used to verify the proposed controller. The results of numerical simulation to check the performance of the proposed controller show that the TE generator power level controlled by the proposed controller could track the target power level effectively, satisfying all control constraints.
