Nuclear plants have high up-front costs, complex processes occurring all the way down to the molecular level throughout their decades-long lifetimes, and strict safety criteria. Modelling all the parameters and predicting the outcomes has traditionally begun with theory and observation followed by simulations, the results of which are fed back into the next round of theories, and repeated until those results look valid. The quality of the results, applied to plant operation and design, affect costs, lifetimes and safety. Dan Yurman looks at two new methods using artificial intelligence that can significantly improve predictive power. The first is VERA (Virtual Environment for Reactor Applications) which has just been licensed for commercial use. Its use could improve performance and extend the lifetimes of the current reactor fleet. Modelling includes nucleate boiling, corrosion deposits on fuel rods, pellet expansion, and performance of reactor parts when exposed to high temperatures and radiation. Meanwhile, Argonne National Laboratory is using AI to create fast-running models of various nuclear thermal-hydraulic processes. Traditional methods need hundreds to thousands of repeated analyses which carry a high computational burden. Yurman explains that AI should allow analysts not only to get to the answer faster but dig deeper into the data and improve results for existing and new plants.
Energy Post 31st March 2020 read more »