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Found 8 papers, 3 papers with code
Integrating LLMs for Explainable Fault Diagnosis in Complex Systems
This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for informed decision-making.
A Safe Reinforcement Learning Algorithm for Supervisory Control of Power Plants
In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the control scheme accordingly.
Physics-informed State-space Neural Networks for Transport Phenomena
This work introduces Physics-informed State-space neural network Models (PSMs), a novel solution to achieving real-time optimization, flexibility, and fault tolerance in autonomous systems, particularly in transport-dominated systems such as chemical, biomedical, and power plants.
Design of a Supervisory Control System for Autonomous Operation of Advanced Reactors
The hierarchical control system consists of a supervisory layer and low-level layer.
Numerical Demonstration of Multiple Actuator Constraint Enforcement Algorithm for a Molten Salt Loop
In this work, we focus on the specific problem of optimal control during autonomous operation.
Empirical Models for Multidimensional Regression of Fission Systems
Findings from this work establish guidelines for developing empirical models for multidimensional regression of neutron transport.
Deep Surrogate Models for Multi-dimensional Regression of Reactor Power
The results indicate that neural networks are an appropriate choice for surrogate models to implement in an autonomous reactor control framework.
Inference of Gas-liquid Flowrate using Neural Networks
The WMS is an experimental tool that records high-resolution high-frequency 3D void fraction distributions in gas-liquid flows.
Fluid Dynamics Computational Engineering, Finance, and Science
