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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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NN Asks: What did you learn from ANS’s Nuclear 101?
Mike Harkin
When ANS first announced its new Nuclear 101 certificate course, I was excited. This felt like a course tailor-made for me, a transplant into the commercial nuclear world. I enrolled for the inaugural session held in November 2024, knowing it was going to be hard (this is nuclear power, of course)—but I had been working on ramping up my knowledge base for the past year, through both my employer and at a local college.
The course was a fast-and-furious roller-coaster ride through all the key components of the nuclear power industry, in one highly challenging week. In fact, the challenges the students experienced caught even the instructors by surprise. Thankfully, the shared intellectual stretch we students all felt helped us band together to push through to the end.
We were all impressed with the quality of the instructors, who are some of the top experts in the field. We appreciated not only their knowledge base but their support whenever someone struggled to understand a concept.
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.
