Insider Brief
- Digital twins could improve the design, testing, and real-time monitoring of Hall thrusters, reducing costs and increasing reliability for space missions.
- Researchers from Imperial College London propose a multi-model computing framework integrating real-world data and machine learning to enhance predictive capabilities.
- Challenges include the high computational cost of plasma simulations and the need for standardized validation, but cloud-based solutions and industry collaboration may help scale digital twin adoption.
Digital twins could accelerate the development and deployment of Hall thrusters, a key propulsion technology for space missions, by improving design accuracy, reducing costs, and enabling real-time monitoring. In a study posted on the pre-print server Research Gate, Researchers from Imperial College London’s Plasma Propulsion Laboratory outlined the requirements and computing infrastructure needed to make these digital models viable for the space industry.
Digital Twins for Space Propulsion
The space industry is moving toward more efficient, cost-effective propulsion systems as missions become more ambitious and sustainability concerns grow, according to the researchers. Electric propulsion (EP), particularly Hall thrusters, offers fuel efficiency advantages over chemical propulsion, but qualification and testing remain expensive and time-consuming. Digital twins — virtual models that evolve with real-world data — could revolutionize the development and operation of these thrusters.
The study describes digital twins as a means to streamline EP system development, qualification, and operation by providing a comprehensive, cost-effective, and reliable approach to designing, testing, and operating these technologies. Unlike traditional simulations, digital twins dynamically update based on real-world data, offering predictive insights into system behavior.
Overcoming Development Bottlenecks
Hall thrusters, widely used for satellite station-keeping and interplanetary missions, require thousands of hours of reliable operation, according to the study. Current testing methods rely on high-vacuum facilities, which cannot fully replicate space conditions. This limitation increases the risk of discrepancies between ground testing and in-orbit performance. The study points out that conventional qualification methods are costly and inadequate for assessing long-term performance and risk.
The researchers argue that digital twins could provide a more effective alternative by continuously refining their models with real-time data. This would allow engineers to predict failures, optimize performance, and extend thruster lifetimes more efficiently than existing empirical methods.
Computing Infrastructure and Requirements
For digital twins to be effective, they must integrate real-world data with high-fidelity simulations while maintaining computational efficiency. The study proposes a computing framework composed of several sub-models representing different aspects of a Hall thruster, including plasma dynamics, gas flow, electromagnetic field, and interactions with the spacecraft environment.
The authors highlight the importance of modularity in these models, allowing updates without requiring full system revalidation. They also emphasize uncertainty quantification, a process that ensures confidence in predictions by measuring the reliability of the twin’s output.
The team writes: “A digital twin is a technology, designed for practical applications in industrial and operational contexts. Thus, much like physical assets, a DT must be developed, verified, and validated against structured requirements to ensure its functional reliability, industrial acceptance, and successful deployment. Establishing well-defined requirements is a critical step toward the widespread adoption of DT technology.”
Machine learning plays a critical role in this framework. Researchers have developed a Hierarchical Multiscale Neural Network (HMNN) to predict thruster behavior over time while minimizing errors. Unlike traditional simulations, which may struggle with long-term forecasting, HMNN balances accuracy and computational efficiency by integrating multiple time scales into a single model.
Another component, the Shallow Recurrent Decoder (SHRED), reconstructs a full system state using limited sensor data. This allows real-time monitoring of thrusters in space without requiring extensive onboard diagnostics. According to the researchers, SHRED can infer crucial plasma properties from a few sensor readings, making it particularly useful for autonomous spacecraft operations.
Challenges and Future Directions
While digital twins offer significant potential, key challenges remain. High-fidelity simulations, particularly those using the particle-in-cell (PIC) method for plasma modeling, are computationally demanding. The study introduces a reduced-order PIC (RO-PIC) method developed at Imperial College London, which retains predictive accuracy while reducing computational costs.
“By the merit of the continuous spectrum of resolution and accuracy to which the RO-PIC provides access, it enables ‘fit-for-purpose’ kinetic simulations – simulations that tailor to various modelling purposes and needs,” the study states. “The RO-PIC approach has been extensively verified in various 2D plasma configurations, several of which represent the operating physics of Hall thrusters.”
Another challenge is the integration of digital twins with real-time operational data from spacecraft. The researchers propose using cloud-based and distributed computing solutions to support scalability. They also highlight the need for industry-wide collaboration to establish standardized validation and verification frameworks for digital twins in propulsion applications.
Broader Implications
The development of digital twins for Hall thrusters could extend to other electric propulsion technologies, such as gridded ion thrusters and even futuristic nuclear fusion plasma propulsion systems. The study underscores the importance of designing digital twin frameworks with generalizability in mind, ensuring that advancements in one area of propulsion can be applied across different systems.
The potential market for digital twins is substantial. The study cites industry reports estimating that the digital twin market across aerospace, manufacturing, and transportation could grow from $6.5 billion in 2021 to $125.7 billion by 2030. With the European Space Agency and other organizations investing in digital twin technology, researchers expect significant advancements in the coming years.
According to the researchers, if successfully implemented, digital twins represent a fundamental shift in how space propulsion systems are designed, tested, and operated. In the long-term, by combining high-fidelity simulations with real-time data integration, they could dramatically reduce costs and improve the reliability of Hall thrusters and other electric propulsion systems.
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