Fusion Reactor Plasma Control Using Adaptive Sensor Correlations
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Solution Overview
Problem
Designing and operating fusion reactors is complex due to conflicting requirements among systems and processes, and the hostile environment causes sensor degradation and failure, limiting the effectiveness of current plasma control systems.
Innovation Solution
A method using machine learning to control plasma in a fusion reactor by integrating primary and secondary sensors, where an initial control model is adjusted based on correlations between sensor readings to adapt and maintain plasma state, even after primary sensor failure, leveraging a training mode to ensure accurate operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If primary sensors are placed close to the plasma to monitor plasma state accurately, then measurement precision is improved, but reliability deteriorates due to sensor degradation from neutron flux
Solution Approach 1:
The patent introduces a machine learning model as an intermediary that processes data from both primary sensors (close to plasma) and secondary sensors (remote sensors) to infer plasma state. This mediator allows the system to maintain measurement precision by combining multiple sensor inputs while reducing reliance on any single vulnerable primary sensor, thereby improving overall reliability.
Solution Approach 2:
The patent creates a virtual copy of the plasma state through machine learning models that replicate plasma behavior based on sensor data. These digital twins allow operators to monitor plasma state accurately without requiring all primary physical sensors to function perfectly, compensating for sensor degradation through computational reconstruction of plasma parameters.
2Productivity
If complex systems are integrated to achieve high plasma temperatures and densities, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service through automated machine learning models that autonomously analyze sensor data, detect sensor failures, and adjust plasma control strategies without requiring complex manual intervention. The system self-diagnoses sensor degradation and self-corrects by switching to alternative sensor combinations, reducing the operational complexity burden despite high system integration.
3Measurement precision
If primary sensors are used exclusively for plasma control, then measurement precision is maintained, but adaptability deteriorates when sensors fail
Solution Approach 1:
The patent makes the control system universal by designing it to handle multiple sensor types and configurations through a unified machine learning framework. The system can universally process data from various primary and secondary sensors, adapting its measurement strategy based on which sensors are functional, thereby maintaining precision while gaining adaptability to different failure scenarios.
Solution Approach 2:
The patent introduces dynamics by making the sensor selection and weighting in the control system flexible and adaptive rather than fixed. The machine learning model dynamically adjusts which sensors to trust and how to weight their contributions based on real-time sensor performance assessment, allowing the system to transition smoothly between different operational modes as sensors degrade or fail.
Data Source
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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; and adjusting the control model based on the determined correlations.