Reactor Optimization

Reactor optimization aims to enhance reactor performance, efficiency, and safety by systematically identifying optimal design parameters and operational strategies. Current research heavily utilizes machine learning, particularly deep reinforcement learning and Bayesian optimization methods, often coupled with high-fidelity simulations, to navigate complex, high-dimensional design spaces and handle multiple objectives and constraints. These advancements are improving the speed and effectiveness of reactor design across various applications, from nuclear power generation to chemical processing, leading to more efficient and cost-effective systems.

Papers

October 15, 2024

Differentiable Programming for Computational Plasma Physics
Nick McGreivy
Automatic Differentiation PDE Solver Differentiable Programming Plasma Dynamic Reactor Optimization

September 12, 2024

Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning
Jinsu Kim, Jaemin Seo
Deep Reinforcement Learning Design Optimization Fusion Energy Reactor Optimization

August 31, 2024

Using Deep Learning to Design High Aspect Ratio Fusion Devices
P. Curvo, D. R. Ferreira, R. Jorge
Deep Learning Plasma Dynamic Magnetic Field Fusion Energy Reactor Optimization

August 5, 2024

Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems
Markus Grimm, Sébastien Paul, Pierre Chainais
Bayesian Optimization Yield Prediction Catalyst Design Reactor Optimization

March 21, 2024

Reactor Optimization Benchmark by Reinforcement Learning
Deborah Schwarcz, Nadav Schneider, Gal Oren, Uri Steinitz
Reinforcement Learning Criticality Analysis Reactor Optimization

December 15, 2023

Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization
Paul Seurin, Koroush Shirvan
Reinforcement Learning Multi Objective Multi Objective Reinforcement Learning Learning Based Approach Pareto Frontier Reactor Optimization

May 9, 2023

Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization
Paul Seurin, Koroush Shirvan
Deep Reinforcement Learning Proximal Policy Optimization Direct Assessment Stochastic Optimization Design Optimization Reactor Optimization

April 20, 2023

Prediction of the evolution of the nuclear reactor core parameters using artificial neural network
Krzysztof Palmi, Wojciech Kubinski, Piotr Darnowski
Neural Network Human Prediction Specie Evolution Nuclear Reactor Reactor Geometry Reactor Optimization

October 31, 2022

Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors
Tom Savage, Nausheen Basha, Omar Matar, Ehecatl Antonio Del-Rio Chanona
Multiphase Flow Multi Fidelity Bayesian Optimization Reactor Geometry Reactor Optimization Gaussian Neural Network Tubular Reactor

April 1, 2022

MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems
Brian Andersen, Gregory Delipei, David Kropaczek, Jason Hou
Optimization Purpose Optimization Algorithm Optimization Method Modular Framework Reactor Optimization Engineering Problem Modular Optimization Framework

Reactor Optimization

Papers

Differentiable Programming for Computational Plasma Physics

Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning

Using Deep Learning to Design High Aspect Ratio Fusion Devices

Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems

Reactor Optimization Benchmark by Reinforcement Learning

Multi-Objective Reinforcement Learning-based Approach for Pressurized Water Reactor Optimization

Assessment of Reinforcement Learning Algorithms for Nuclear Power Plant Fuel Optimization

Prediction of the evolution of the nuclear reactor core parameters using artificial neural network

Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors

MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems