Fusion Reactor Plasma Control with Virtual Sensor Recovery
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Solution Overview
Problem
Designing and operating fusion reactors is complex due to conflicting requirements among integrated systems, and sensors used for plasma control are fragile and prone to failure from neutron flux, limiting the ability to achieve optimal plasma states and maintain accurate control.
Innovation Solution
A machine learning approach is applied to fusion reactor design and operation, using a control system that interfaces with both primary and secondary sensors to establish correlations and adjust control models, allowing for optimal plasma control even after primary sensor failure, and to simulate and optimize reactor parameters efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are placed close to the plasma to improve measurement accuracy, then measurement precision is improved, but reliability deteriorates due to neutron flux damage and sensor failure
Solution Approach 1:
The patent creates a virtual copy of the failed primary sensor using machine learning models trained on historical sensor data. When a primary sensor fails, the system generates synthetic sensor readings that replicate the expected behavior of the failed sensor, allowing continuous accurate plasma control without physical redundancy
Solution Approach 2:
The patent replaces the mechanical/physical sensor system with a computational system. Machine learning models substitute for physical sensors by processing data from remaining functional sensors and generating estimates of plasma parameters that would be measured by failed sensors, eliminating the need for physical sensor proximity
2Power
If complex systems are integrated to achieve high plasma temperatures and densities, then fusion performance is improved, but device complexity increases making design and control more difficult
Solution Approach 1:
The patent implements a universal machine learning control framework that handles multiple plasma control functions through a single integrated system. The same neural network architecture manages diverse plasma parameters (temperature, density, confinement) and coordinates multiple actuators, reducing the complexity of managing integrated systems while achieving high fusion performance
Solution Approach 2:
The patent transforms the control problem from managing complex physical system interactions to optimizing mathematical parameters in machine learning models. By changing control parameters through software rather than physical adjustments, the system manages complexity while maintaining the ability to achieve high plasma temperatures and densities
3Ease of operation
If traditional control methods are used with fragile sensors, then initial plasma control is achieved, but control accuracy deteriorates over time due to sensor degradation from neutron flux
Solution Approach 1:
The patent implements a self-healing control system where the machine learning framework automatically detects sensor failures and compensates without external intervention. The system serves itself by generating virtual sensor readings from available data, eliminating the need for manual sensor replacement or recalibration and maintaining control accuracy throughout the reactor lifecycle
Solution Approach 2:
The patent implements continuous feedback loops where the machine learning model constantly monitors the performance and consistency of sensor readings. When degradation is detected, the system adjusts by weighting remaining functional sensors differently and relying more on model-based estimates, maintaining control accuracy despite ongoing sensor degradation from neutron flux
Data Source
AI summary
A method of controlling a plasma in a nuclear fusion reactor. The nuclear fusion reactor comprises sensors and plasma control inputs. An initial control model is provided, relating readings of at least a subset of the sensors to control of the plasma control inputs. A control loop is performed, comprising:operating the plasma control inputs in dependence upon the sensors according to the control model;determining correlations between readings of each of the sensors, and/or between readings of the sensors and states of the plasma control inputs; andadjusting the control model based on the determined correlations.