Fusion Plasma Disruption Forecasting With Physics-Based Event Models
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Solution Overview
Problem
Accurately determining and forecasting plasma disruptions in fusion devices is challenging due to the complexity of these systems, which involves thousands of state variables and model parameters, exceeding the capabilities of current supercomputers, and existing methods fail to provide the physical understanding needed to prevent disruptions.
Innovation Solution
A system and method using physics-based models to analyze fusion device data, determine the occurrence and criticality of events, and instruct control systems to take preventive actions, such as controlled shutdowns or parameter changes, to avoid plasma disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physics-based models with thousands of state variables and model parameters are used to analyze fusion device operation, then measurement precision and reliability of disruption detection improve, but device complexity and computational requirements exceed current supercomputer capabilities
Solution Approach 1:
The patent segments the complex plasma disruption prediction problem into multiple manageable components: (1) identifying critical parameters from thousands of state variables, (2) developing separate physics-based models for different disruption mechanisms, (3) creating a modular architecture where models can be independently validated and combined. This segmentation reduces the computational burden while maintaining high detection accuracy through focused analysis of key physical processes.
Solution Approach 2:
The patent extracts and focuses on the most critical physical parameters and mechanisms that drive plasma disruptions, separating them from the overwhelming number of次要 state variables. By identifying and isolating the key predictive parameters through physics-based analysis, the system achieves high disruption detection accuracy without requiring computational resources proportional to the total number of system parameters.
2Reliability
If existing methods are used to analyze fusion device data, then device complexity remains manageable, but the physical understanding needed to prevent disruptions is insufficient
Solution Approach 1:
The patent implements feedback mechanisms where physics-based models continuously compare predicted disruption conditions with actual device operation data. The system provides feedback loops that refine parameter identification and model accuracy over time, enabling progressive improvement in disruption prevention capability. This feedback-driven approach builds physical understanding incrementally while managing system complexity through iterative validation.
Solution Approach 2:
The patent dynamically adjusts and refines the identification of critical parameters based on changing plasma conditions and operational scenarios. By adapting which parameters are monitored and how they are weighted in prediction models, the system enhances disruption prevention reliability without requiring a fixed complex structure, allowing flexibility in managing analysis system complexity.
Data Source
AI summary
Techniques for detecting, forecasting, and reducing or avoiding disruptions in fusion devices are described. Data corresponding to operation of a fusion device may be received. Based on the data and one or more physics-based models, an occurrence of an event and a criticality level of the event may be determined. Based on criticality level of the event, whether a disruption of plasm will occur may be determined. An indication identifying the event and whether the disruption of plasma in the fusion device will occur may be output to a computing device associated with a user. In some embodiments, based, at least in part, on the criticality level of the first event, a control system associated with the fusion device may be instructed to perform one or more actions.