Demonstration of the Plant Fuel Reload Process Optimization for an Operating Pressurized Water Reactor (PWR)

The United States (U.S.) Department of Energy's (DOE's) Light Water Reactor Sustainability (LWRS) Program-under the Risk-Informed Systems Analysis (RISA) Pathway Plant Reload Optimization Project-aims to develop and demonstrate an automatized generic platform that can generate optimized fuel load configurations in the reactor core of a nuclear power plant. The project targets to optimize reactor core thermal limits through the implementation of state-of-the-art computational and modeling techniques. The optimization of core thermal limits allows a smaller fuel batch size to produce the same amount of electricity, which reduces new fuel costs and saves a significant amount of money on the back-end of the fuel cycle by reducing the volume of spent fuel that needs to be processed. The cost of a typical fuel reload for a light water reactor (LWR) is about $50M. This project is leading towards a cost reduction of at least 5%, which is attainable by consolidating methods and core design procedures and practices. The project includes the development of an artificial intelligence-based 'genetic algorithm' for the platform and demonstration of plant reload optimization with selective design basis accident scenarios for licensing support during fuel reloading. This platform integrates workflow that incorporates seamlessly all the steps required for the fuel reload analysis, which traditionally is a labor-intensive and time-consuming process. This paper summarizes the recent research outcomes, which progressed from the planning and methodology development phase to the early demonstration phase-including the development of a multi-objective optimization process using genetic algorithms; development and testing of an approach for acceleration of optimization using artificial intelligence that significantly reduces the computational burden; demonstration of the fuel reload optimization framework for a generic pressurized water reactor; and demonstration of selective scenarios for evaluation of the transition from deterministic to risk-informed approach for fuel analyses. On-going activities and plans are also summarized.