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​​​​​​​​​​​​​​​​​​​Plant Fuel-Reload Optimization​​


Goal

To develop an integrated platform that uses state-of-the art computational and modeling techniques and integrates all tasks required for plant fuel-reload into one automated process.​ 

  • Development of an artificial intelligence (e.g., genetic algorithm) automatized reactor core designing platform plug-and-play type with various physics simulation tools.​

  • Multiphysics simulation with feedback from system analysis and fuel performance including uncertainty analysis.

Outcome

Researchers will develop an integrated platform that combines tasks for a fuel-reload analysis and fuel-pattern optimization. The integrated platform removes manual data transfer between tasks, eliminating human-error, and the optimization capability allows for reduced fuel-batch size. This integrated platform also allows the transition from a deterministic approach to transient and accident analyses to a probabilistic (risk-informed) approach. The risk-informed approach to safety evaluations will enable additional reductions in fuel-batch size. The integrated platform will be ready for deployment to industry, giving utilities the option to perform fuel analyses in-house, independent from fuel vendors, which will provide additional savings. This work will assist industry with the transition to accident-tolerant fuels because the developed integrated platform and computational platform will be capable of performing evaluations of accident-tolerant fuels during both the licensing phase and normal plant operations.

Planned Major Accomplishments

  • ​​​Complete an economic benefit assessment for the new fuel-management plan and extend the framework's capabilities to other core configurations (i.e., pressurized water reactors with accident-tolerant fuels​and 24-month refueling cycle, generic boiling-water reactors).

  • Complete demonstration of ready for deployment framework to the industry, including equilibrium scenarios (i.e., normal plant operation).

  • Prepare a topical report that demonstrates the framework and its capabilities, including regulatory-required steps for a typical license-amendment request. Support the topical report review and approval process by the Nuclear Regulatory Commission.

Reports​

 

 

Pressurized-Water Reactor Core Design Demonstration with Genetic Algorithm Based Multi-Objective Plant Fuel Reload Optimization Platform, INL/RPT-23-74498288044This report summarizes development and demonstration activities of the Plant Reload Optimization (PRLO) platform which aims to provide optimized core design solution during fuel reloading by using artificial intelligence technology. During FY23, the project focused on the improvement of the platform by implying multiobjective and multiphysics problem solver to handle large size of objectives and constraints and its demonstration. The platform is now fully capable to produce optimized nuclear reactor core design along with the system safety and fuel performance analysis results which are required for the licensing during fuel reloading. This report summarizes development and demonstration activities of the Plant Reload Optimization (PRLO) platform which aims to provide optimized core design solution during fuel reloading by using artificial intelligence technology. During FY23, the project focused on the improvement of the platform by implying multiobjective and multiphysics problem solver to handle large size of objectives and constraints and its demonstration. The platform is now fully capable to produce optimized nuclear reactor core design along with the system safety and fuel performance analysis results which are required for the licensing during fuel reloading. 9/13/2023 10:23:21 PMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government 127https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
Development of Genetic Algorithm Based Multi-Objective Plant Reload Optimization Platform, INL/RPT-23-7166711561The purpose of this report is to develop and demonstrate artificial intelligence (i.e., Genetic Algorithm) based nuclear reactor fuel reloading optimization platform by integrating the non-dominated sorting genetic algorithm II (NSGA-II). This allowed solving the multi-objective optimization framework for realistic plant reload optimization problem with improved termination criteria, constraints handling and active subspaces. Demonstrations were performed with verification test and benchmark case. The purpose of this report is to develop and demonstrate artificial intelligence (i.e., Genetic Algorithm) based nuclear reactor fuel reloading optimization platform by integrating the non-dominated sorting genetic algorithm II (NSGA-II). This allowed solving the multi-objective optimization framework for realistic plant reload optimization problem with improved termination criteria, constraints handling and active subspaces. Demonstrations were performed with verification test and benchmark case. 3/27/2023 3:53:00 PMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government It is challenging to create a 203https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
Development of Plant Reload Optimization Platform Capabilities for Core Design and Fuel Performance Analysis, INL/RPT-22-70382269184The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses, and also uses artificial intelligence to support optimization of core design solutions.The Plant Reload Optimization Platform development project aims to build a reactor core design tool that includes reactor safety and fuel performance analyses, and also uses artificial intelligence to support optimization of core design solutions.12/14/2022 2:46:26 PMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government The LWRS Program promotes a wide 221https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis, INL/RPT-22-6862853489Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis.Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis.8/22/2022 9:13:14 PMINL/RPT-22-68628 Light Water Reactor Sustainability Program Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis August 259https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
Demonstration of the Plant Fuel Reload Process Optimization for an Operating PWR, INL/EXT-21-64549234769This report summarizes the research outcomes in FY-2021, which the project progressed from the planning and methodology development phase to the early demonstration phase.This report summarizes the research outcomes in FY-2021, which the project progressed from the planning and methodology development phase to the early demonstration phase.9/29/2021 4:12:16 PMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government The LWRS Program is promoting a 421https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf
RISA Plant Reload Process Optimization: Development of design basis accident methods for plant reload license optimization, INL/EXT-20-59614199544RISA Plant Reload Process Optimization: Development of design basis accident methods for plant reload license optimization, INL/EXT-20-59614RISA Plant Reload Process Optimization: Development of design basis accident methods for plant reload license optimization, INL/EXT-20-596149/1/2020 2:01:12 AMU.S. Department of Energy Office of Nuclear Energy This information was prepared as an account of work sponsored by an agency of the U.S. Government Safety is a key parameter to all 415https://lwrs.inl.gov/RiskInformed Safety Margin Characterization/Forms/AllItems.aspxpdfFalsepdf

​​For more information, contact

Svetlana (Lana) Lawrence
Risk-Informed Systems Analysis, Pathway Lead
Idaho National Laboratory
PWR Fuel Cycle

Sample optimiz​​ed core design for maximizing 17x17 PWR fuel cycle length