Digital Twin of Space Environment: Development, Challenges, Applications, and Future Outlook

1.2. Contributions

The main contributions of our work to the scientific community are summarized below:

​• Comprehensive perspective of digital twins in space environment: For the first time, the paper systematically evaluates the application potential of digital twins in space environment, which is not only limited to spacecraft design and maintenance, but also covers astronaut training, space station operation, deep space exploration and other dimensions, presenting a comprehensive and in-depth understanding framework for readers.

​• In-depth analysis of technical challenges and solutions: Through detailed case studies and theoretical discussions, we identify and analyze the unique challenges faced by digital twins in the application of space technology, including data transmission delays, model accuracy improvement, and user interface optimization, and propose corresponding solutions.

​• Future application direction and technology integration outlook: not only reviewed the past and current technological progress, but also boldly predicted the future trend of digital twin technology and artificial intelligence, machine learning, augmented reality and other emerging technologies integration, pointing out the direction for researchers and decision makers.

Our work is structured as follows.

Section 1 gives an overview of the background of digital twin technology, emphasizing its application prospects in the field of aerospace, and providing an overall cognitive framework for readers.

Section 2 gives the basic concept of "digital twin of space environment", introduces the bidirectional mapping mechanism between physical and virtual entities, and lists and compares application cases such as astronaut training and spacecraft management.

Section 3 focuses on the difficulties of constructing space digital twin system, such as entity mapping, data management and space operation and maintenance, and analyzes the core technical elements.

Section 4 explores the expansion of digital twin technology in mission planning, decision support, and safe operations, emphasizing its positive impact on space exploration.

Section 5 summarizes the innovative value of digital twin technology, points out its development potential in the space field, and puts forward the focus of future research.

1.1. Research Questions

Aiming at the application and challenge of digital twin technology in space environment, this study proposes a core research question (RQ) and refines it into several sub-questions (SQx) for further exploration:

RQ: What is the status and future potential of digital twins in space exploration?

l   ​SQ1: What are the main technical challenges of using digital twins in the space environment?

l   ​SQ2: How to deal with data transmission delays, model accuracy and user interface design?

l   ​SQ3: How does the application of digital twins in space contribute to efficiency and safety in mission planning, astronaut training, and deep space exploration?

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l   SQ4: How will the convergence of advanced technologies such as artificial intelligence and machine learning with digital twins change the face of space exploration in the future?

​These questions aim to fully assess the applicability of digital twins in the space environment, identify their limitations, and predict their long-term impact in the space sector.

1.2. Contributions

The main contributions of our work to the scientific community are summarized below:

​• Comprehensive perspective of digital twins in space environment: For the first time, the paper systematically evaluates the application potential of digital twins in space environment, which is not only limited to spacecraft design and maintenance, but also covers astronaut training, space station operation, deep space exploration and other dimensions, presenting a comprehensive and in-depth understanding framework for readers.

​• In-depth analysis of technical challenges and solutions: Through detailed case studies and theoretical discussions, we identify and analyze the unique challenges faced by digital twins in the application of space technology, including data transmission delays, model accuracy improvement, and user interface optimization, and propose corresponding solutions.

​• Future application direction and technology integration outlook: not only reviewed the past and current technological progress, but also boldly predicted the future trend of digital twin technology and artificial intelligence, machine learning, augmented reality and other emerging technologies integration, pointing out the direction for researchers and decision makers.

Our work is structured as follows.

Section 1 gives an overview of the background of digital twin technology, emphasizing its application prospects in the field of aerospace, and providing an overall cognitive framework for readers.

Section 2 gives the basic concept of "digital twin of space environment", introduces the bidirectional mapping mechanism between physical and virtual entities, and lists and compares application cases such as astronaut training and spacecraft management.

Section 3 focuses on the difficulties of constructing space digital twin system, such as entity mapping, data management and space operation and maintenance, and analyzes the core technical elements.

Section 4 explores the expansion of digital twin technology in mission planning, decision support, and safe operations, emphasizing its positive impact on space exploration.

Section 5 summarizes the innovative value of digital twin technology, points out its development potential in the space field, and puts forward the focus of future research.

The application of digital twin technology in the space environment involves a variety of algorithms and models, including but not limited to:

(1) State estimation and prediction algorithm: Kalman filter, particle filter and other technologies are used to estimate the current state of physical entities and predict future behavior.

(2) Fault detection and diagnosis algorithm: through pattern recognition, machine learning and other methods to identify abnormal behavior, timely detection of potential faults.

(3) Optimization and control algorithms: based on physical models and real-time data, optimize the performance of physical entities, such as fuel consumption, orbit adjustment, etc.

As shown in Figure 8, the "space environment DT" architecture is mainly divided into four layers: twin mapping layer, twin data layer, system function layer, and application service layer.

​The "Physical entities" in the twin mapping layer refers to the natural and artificial objects that exist in the physical environment of space, mainly including: Space environment (solar radiation, cosmic rays, solar particles, micrometeoroids and space debris, etc.), natural celestial bodies (planets such as the sun, Earth, Moon, Mars and their satellites, bodies and orbits such as asteroids and comets), artificial celestial bodies (spacecraft, launch facilities, ground monitoring and control stations, communication networks, energy facilities, scientific instruments and payloads, etc.). The "Virtual entities" in the twin mapping layer refers to the use of refined modeling technology and advanced communication means to highly restore and dynamically simulate the natural and artificial various elements of the space physical environment to ensure that various situations and challenges that may be encountered in space missions can be simulated and analyzed in real time and comprehensively.

The twin data layer is responsible for integrating, managing and distributing all kinds of data generated within the system, realizing centralized data processing, storage and sharing, ensuring data consistency, reliability and security, and providing solid data support for the digital twin in the space environment.

The system function layer integrates a variety of core functions, such as space data management, space environment simulation, space-based and ground-based observation simulation, scenario inference, case management and evaluation, which enables the Space Environment Digital Twin system to provide comprehensive, efficient and intelligent services.

The application service layer transforms the core capabilities of the "space environment DT system" into actual space mission support, which mainly includes six application fields: space environment monitoring and prediction, space debris tracking and management, satellite navigation and orbit optimization, space asset maintenance and fault diagnosis, space mission planning and simulation, and scientific data analysis and decision support.

In these experiments, data from trainees' interactions with digital twins and virtual environments were recorded and compared with ISS data sets from the Jet Propulsion Laboratory (JPL) and launch telemetry data from SpaceX Falcon Heavy. The mean square error (MSE) index was used to measure the consistency of the digital twins with the real system, and the results confirmed the alignment of the developed digital twins with the real system, verifying their practicality in astronaut training.

To address the high costs and complex facility requirements of astronaut training, Octavio Piñal, Amadeo Arguelles, and others have developed a comprehensive astronaut training platform using mixed reality and digital twin technology [74]. Drawing on the training systems of NASA and ESA, the platform includes three modules and four scenario designs. It not only simulates the propulsion, navigation, and emergency systems of the International Space Station (ISS) but also replicates SpaceX's Falcon Heavy rocket, enabling trainees to experience spacecraft launches, orbital maneuvers, and even spacewalks in a virtual environment, as well as perform maintenance tasks and emergency maneuvers. The data generated by the participants' interaction with the digital twin scenarios was stored in real time and analyzed against the ISS dataset of the Jet Propulsion Laboratory (JPL) to assess the effectiveness of astronaut training. Additionally, the integration of digital twins with Virtual Reality (VR) technology can create a holographic Earth environment, making astronauts feel as if they are in a familiar natural landscape. This integration provides a combination of visual, auditory, and even olfactory stimuli (such as the VR headsets designed by Carulli and Bordegoni with scent output systems) to effectively alleviate the psychological stress caused by isolation and confinement. Meanwhile, Augmented Reality (AR) and Mixed Reality (MR) can merge virtual elements into the actual space environment or allow ground personnel and astronauts to interact in real-time within a shared virtual space. For instance, the ANSIBLE project provides communication and remote medical functions, significantly enhancing astronauts' sense of social connection during simulated Mars missions (Figure 4) [93].

For example:

(1) Theoretical training module: This module uses MR Technology to allow trainees to view and interact with 3D objects through virtual reality equipment to learn about the different systems and operating procedures of the International Space Station (ISS). This module is based on European Space Agency (ESA) training courses and covers theoretical knowledge ranging from Newton's laws to Kepler's laws, as well as detailed information about the ISS such as the Zvezda service module, Zarya module, node module and laboratory module.

(2) Emergency simulation: In the second module, trainees are faced with a space debris collision crisis occurring inside the ISS. In this virtual scenario, trainees must apply Hohmann's knowledge of transfer and circular motion to take action to avoid collisions by interacting with the ISS's digital twin of propulsion, navigation and emergency response systems. The scenario also includes the use of a return capsule, where trainees need to be transferred if an unavoidable collision is detected.

(3) Spacewalk mission: Another practical scenario involves a spacewalk in which trainees are required to perform three tasks: repair an air leak, replace a battery, and recover a sample from a robotic arm. To accomplish these tasks, trainees must become familiar with the module structure and external view of the ISS, all through interaction with the digital twin.

(4) Launch and Ascent Simulation: The third module focuses on preparing astronauts for space launch by simulating the launch and ascent of SpaceX's Falcon Heavy rocket to familiarize trainees with the procedures associated with it. The scene uses Falcon Heavy 3D models and includes digital twins of propulsion, navigation, and staging systems.

In these experiments, data from trainees' interactions with digital twins and virtual environments were recorded and compared with ISS data sets from the Jet Propulsion Laboratory (JPL) and launch telemetry data from SpaceX Falcon Heavy. The mean square error (MSE) index was used to measure the consistency of the digital twins with the real system, and the results confirmed the alignment of the developed digital twins with the real system, verifying their practicality in astronaut training.

However, while digital twins have great potential to improve mission planning accuracy, optimize resource utilization, enhance remote operation and maintenance capabilities, and enhance astronaut training effectiveness, there are still many challenges in their development and application. These challenges include:

l   Data transmission latency and bandwidth limitations: Between Earth and space assets, data transmission faces significant latency and limited bandwidth. This is essential for real-time control and feedback, but it becomes a big problem in space missions that are far from Earth.

l   ​Model accuracy and reliability: Building high-precision digital twins requires accurate physical modeling and sufficient real-world data. In the space environment, models must take into account extreme conditions such as microgravity, high radiation, temperature fluctuations, and vacuum effects, which adds to the complexity of modeling.

l   ​Hardware and Sensor reliability: High-precision sensors and hardware components need to maintain stability and reliability in the extreme conditions of space, which is critical for data accuracy and system functionality. Sensor failure or performance degradation will directly affect the utility of the digital twin.

l   ​Data security and Privacy: Implementing a digital twin means the generation and transmission of large amounts of data, including sensitive astronaut health data and critical system operation parameters. Ensuring the security and privacy of this data is a major challenge.

l   ​System integration and interoperability: Digital twins require seamless integration with existing space assets, communications networks, and ground control centers. Ensuring interoperability and compatibility between different systems is a complex engineering task.

l   ​Cost and resources: Implementing digital twin technology requires significant initial investment in research and development, hardware deployment, and maintenance. In the long term, continuous data processing and analysis also consumes a lot of resources.

​ Despite these challenges, digital twins still show great potential for space missions to significantly improve the efficiency, safety and cost-effectiveness of space exploration. We have also found some breakthroughs to overcome these limitations, for example, using machine learning and artificial intelligence technology to optimize data transmission strategies and reduce latency; The reliability of hardware and sensors can be enhanced through redundant design and self-correction mechanism. Using encryption and anonymization technology can protect data security and privacy.