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

Abstract

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications.

1. Introduction

The concept of digital twin technology was initially proposed by NASA (National Aeronautics and Space Administration) in 2002 with the goal of enhancing the reliability and safety of missions through the creation of virtual replicas of complex spacecraft systems [1]. Since then, the concept has rapidly evolved and expanded from the aerospace sector to other industrial fields, becoming an integral part of the Industry 4.0 strategy [2,3,4,5]. Digital twins integrate technologies such as the Internet of Things (IoT) [6,7,8], artificial intelligence (AI) [9,10,11], machine learning (ML) [12,13,14], and big data analytics [15,16,17]. Through highly accurate simulations, they can monitor and replicate the status and behavior of their physical counterparts in real time. This not only optimizes the design and production processes but also significantly enhances operational efficiency and system reliability.

In manufacturing, digital twins are utilized to simulate production lines, optimize the manufacturing process, and predict failures to reduce downtime and enhance production efficiency [18,19,20,21]. For instance, General Electric (GE) employs digital twin technology to optimize the performance of its gas turbines, using real-time data analysis to predict equipment failures and optimize maintenance schedules [22]. In the automotive industry, digital twin technology is applied throughout the entire lifecycle of a vehicle, from design and manufacturing to after-market services [23,24,25]. It allows designers and engineers to test and verify vehicle performance in a virtual environment, such as crash tests and climate adaptability, thereby reducing the cost and time of physical testing. In the healthcare sector, digital twin technology is progressively being used for personalized medical treatment and surgical planning [26,27,28]. By creating a digital twin of a patient, doctors can simulate the effects of various treatment options, optimize the treatment process, and predict potential complications, thus providing more accurate medical services. Additionally, digital twins demonstrate tremendous potential in fields like energy and urban management. In the energy sector, digital twins are used to optimize the operation of energy systems, including wind farms and smart grids [29,30,31]. In urban management, by creating digital twins of cities, managers can effectively monitor and manage urban infrastructure and services such as traffic flow, energy consumption, and public safety [32,33,34].

As digital twin technology matures, its potential and value in the aerospace sector have been widely recognized and practically implemented [35,36,37,38]. As shown in Figure 1, after more than two decades of development, digital twin technology has evolved from a conceptual idea to a tool capable of managing the entire lifecycle of spacecraft. In the future, with the deep integration of artificial intelligence, digital twins are expected to play an even more significant role. Specifically, digital twin models can reflect the real-time status, dynamic processes, and behaviors of their corresponding entities, providing unprecedented support for design optimization, status monitoring, fault prediction, and health management [39,40,41,42]. Particularly in space environments, which are inaccessible or unsustainable for long-term human presence, digital twin technology showcases its unique value. Space environments are extreme and unpredictable, and every space mission requires high precision and reliability [43,44,45,46]. In such settings, traditional monitoring and maintenance methods face significant challenges, but digital twin technology offers an effective solution. By establishing digital twins of spacecraft or space stations, scientists and engineers can simulate and analyze various scenarios in space environments on the ground, thereby predicting potential issues and devising countermeasures in advance, greatly enhancing the safety and success rates of missions. Moreover, the application of digital twin technology significantly improves resource efficiency. In space missions, resources such as energy, time, and space are incredibly valuable. Digital twin models allow scientists and engineers to conduct extensive experiments and tests on the ground, reducing the need for trials in actual space environments and thus saving substantial resources. For example, by simulating different maintenance strategies and pathways, maintenance plans and schedules can be optimized, reducing maintenance time for space stations and enhancing operational efficiency.

Another important aspect of digital twin technology in space applications is its role in astronaut education and training. For astronauts, digital twin models can be used not only to simulate and train for upcoming missions but also to provide real-time technical support during the execution of missions. Through interaction with digital twin models, astronauts can gain a more intuitive understanding of the workings and current status of equipment, which is crucial for making rapid decisions and responding to emergencies in extreme environments [47,48,49]. However, despite the significant potential of digital twin technology in the aerospace sector, its implementation and development still face many technical and practical challenges. Building highly accurate digital twin models with limited data and incomplete models, ensuring real-time data transmission and processing, and designing intuitive and effective user interfaces are key issues that need to be addressed in current technological developments [50,51,52,53].

In summary, as technology advances and digital twin technology continues to mature, its applications in space exploration and operations will become more extensive and profound. In the future, digital twins will become an indispensable component of space exploration, not only enhancing mission safety and success rates but also significantly advancing the development and application of space technologies. Through in-depth research and practice, we believe that digital twin technology will play an increasingly important role in future space exploration.

Hence, the primary objective of the present review paper is to comprehensively assess the applications of digital twin technology in the fields of space exploration and operations, highlighting its importance as a cutting-edge technology and its diverse potential applications. More specifically, by analyzing key technologies of digital twins and their actual performance in space missions, this article aims to provide a systematic and informative reference for researchers, technology developers, and policymakers, thereby supporting their use of this technology in future projects and decision-making. Additionally, this review also explores the technical challenges and future development directions faced by digital twin technology, intending to stimulate broader research and discussion to promote technological innovation and application. Since there are many abbreviations in the article,

Table 1. The summary table of abbreviations.

NASA	National Aeronautics and Space Administration (U.S.)	IoT	Internet of Things
AI	Artificial Intelligence	ML	Machine Learning
GE	General Electric	RQ	Research Question
SQx	Sub-questions	AR	Augmented Reality
MR	Mixed Reality	VR	Virtual Reality
PLM	Product Lifecycle Management	AFRL	Air Force Research Laboratory
HUMS	Health and Usage Monitoring System	EKF	Extended Kalman Filter
LMO	Low Mars Orbit	ESA	European Space Agency
GEO	Geostationary Orbit	GAC	German Aerospace Center
SOA	Service-Oriented Architecture	ESB	Enterprise Service Buses
GNSS	Global Navigation Satellite Systems	FEA	Finite Element Analysis
CFD	Computational Fluid Dynamics	GUIs	User Interfaces

illustrates the abbreviations in the article in a uniform format.

1.1. Research Questions

Aiming at the application and challenge of digital twin technology in the 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?

• SQ1: What are the main technical challenges of using digital twins in the space environment?
• SQ2: How to deal with data transmission delays, model accuracy, and user interface design?
• SQ3: How does the application of digital twins in space contribute to efficiency and safety in mission planning, astronaut training, and deep space exploration?
• 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, this paper systematically evaluates the application potential of digital twins in the 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: We 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 provides 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 provides the basic concept of the “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 a 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.

2. Development of Digital Twins in Space Environments

In the current field of aerospace, as exploration activities increase and mission complexities rise, traditional space technology methods are gradually showing their limitations. Facing these challenges, the introduction of digital twin technology not only provides new solutions but also expands the possibilities for space missions. This section delves into the integration of digital twin technology with space technology, covering specific applications and several key practical cases to highlight its value and effectiveness in space environments.

2.1. Requirements and Challenges of Digitalization in Space Environments

As space exploration deepens, future trends point toward ambitious goals such as deep space exploration and long-term manned spaceflight. The complexity and risks of these missions require more innovative and reliable methods in the development and application of space technologies. In this context, the demand for digital twins in space environments becomes particularly urgent as they offer a new way to design, test, and execute space missions. Figure 2 shows that digital twin technology allows for the creation of precise replicas of space assets in virtual environments. By integrating real-time data, these virtual replicas can reflect the status and behavior of their physical counterparts. Specifically, for deep space exploration missions, this means that the performance of probes in distant star systems can be simulated on Earth, allowing for thorough testing and validation before actual launch. Digital twins can also be used to simulate and predict the unknown environments and challenges that probes may encounter, such as extreme temperatures, radiation levels, and micro-meteorite impacts, thereby optimizing the design and operational strategies of the probes. In terms of long-term manned spaceflight, by creating digital twins of space stations or spacecraft, we can simulate the living and working conditions of astronauts in space on the ground, thereby studying and addressing issues that may affect the health and safety of astronauts. Additionally, digital twins can be used to simulate emergency situations, such as spacecraft leakage or life support system failures, thereby enhancing astronauts’ emergency response capabilities and the overall safety of the mission.

Despite the rapid development of digital twin technology, it still faces several challenges. Among these, data security and privacy protection are major issues, as digital twins involve the real-time transmission and processing of a large amount of sensitive data. Additionally, the complexity of technology integration requires further research and solutions, particularly regarding customization and standardization issues across different industries and applications. In the future, the development of digital twin technology will focus more on cross-disciplinary integration and innovation, such as combining augmented reality (AR) and virtual reality (VR) technologies to provide more intuitive data interaction and presentation. The application of digital twin technology not only changes the operational modes of traditional industries but also has a profound impact on the economic structure of society. By optimizing production processes and improving resource efficiency, digital twins contribute to achieving sustainable development goals. At the same time, this technology also promotes the creation of new business models and job opportunities, such as maintenance and analysis services based on digital twins.

2.2. Fundamental Concepts of Digital Twins in Space Environments

Digital twin technology, serving as a bridge between the physical and digital worlds, has garnered widespread attention and application in modern industry [54,55,56,57]. This technology constructs high-precision digital models of physical entities and establishes dynamic communication mechanisms between the model and the entity, achieving bidirectional mapping. This mapping allows for the continuous updating of the twin model with real-time data from the physical entity, facilitating diagnostics, predictions, and evaluations within the model. The results of these simulations can then be fed back into the control systems to optimize the operation and maintenance of the physical entities. The concept of the digital twin can be traced back to NASA’s Apollo program, which built two identical spacecraft, one of which served as a ground simulator—a so-called “digital twin”—to mirror the status of the spacecraft on a mission [1]. In 2003, Professor Michael Grieves from the University of Michigan further formalized this concept, defining it as a crucial component of Product Lifecycle Management (PLM), specifically as a digital replica of a specific device or device group that can be tested in real or simulated environments [58]. Although initially limited by technological and conceptual understanding, digital twin technology did not gain widespread attention until 2012. With the collaboration of the Air Force Research Laboratory (AFRL) and NASA and the publication of the “Modeling, Simulation, Information Technology & Processing” roadmap, digital twin technology entered the public eye [59]. With the rapid advancement of information technology, digital twins have expanded from their initial application in manufacturing to sectors like construction, healthcare, and transportation, playing an increasingly significant role in industrial production, integrated manufacturing, aerospace, smart agriculture, and smart water management [60,61,62,63].

Despite its demonstrated potential in various industries, the application of digital twin technology in space environments is still in its early stages. The uniqueness of space environments lies in their extreme and variable conditions, such as microgravity, high radiation, extreme temperature variations, the ionosphere, and vacuum conditions. These conditions profoundly affect the materials, structures, and system performance of spacecraft. However, most existing digital twin models are developed for terrestrial environments and applications, lacking models specifically tailored for space environments. This leads to inaccuracies in simulating and predicting the behavior of equipment and systems in space, failing to meet the high demands for reliability and safety required by space missions. Therefore, digital twin technology in space must consider the natural, orbital, electromagnetic, and artificial environments specific to space (Figure 3). More specifically, it needs clear integration and application strategies tailored to the unique requirements of space missions for real-time data processing, communication delays, and system autonomy. Integrating digital twin technology with existing space mission operation and management systems is essential to achieving efficient data exchange, real-time monitoring, and remote control.

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

(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.

2.3. Application Cases of Digital Twins of Space Environments

Digital twins in space environments have been applied across multiple domains, involving astronaut training, spacecraft health management, space rovers, space station maintenance, and reusable rockets, among others.

Table 2. Summary and comparison of application cases of digital twins of space environments.

No.	Application Field	Key Indicators	Application Case Description
1	Astronaut training [47,64]	High-fidelity, mission-execution simulation	The digital twin simulates the space environment and astronauts’ missions, providing a ground training environment.
2	Aircraft health management [65]	Condition monitoring, life prediction, health management	By building a virtual mapping of each component, subsystem, and even the overall status of the spacecraft in virtual space, it is possible to conduct real-time maintenance and performance monitoring of the spacecraft throughout its life cycle.
3	Space exploration rover [66]	Design iteration speed, cost-effectiveness, accuracy	It integrates complex geometry, kinematics, and dynamics models, as well as sensors and control systems, to accurately simulate real-world exploration scenarios.
4	Interstellar shuttle digital twin [67]	Design and test the environmental adaptability of the aircraft	From preliminary design to operational performance in extreme space conditions, it enables engineers to anticipate and resolve potential problems without actually building and testing a full prototype.
5	European Space Agency’s spacecraft-servicing robot [68]	Mission planning and simulation to ensure safe space operations	Spacecraft maintenance robots use digital twin technology for mission planning and simulation to ensure that complex space operations can be performed safely and efficiently.
6	Digital twin of reusable rocket [69]	Multiple launch mission process simulations to accelerate design iterations	Digital twin technology is introduced to simulate the multiple launch processes of reusable rockets to optimize the rocket design and improve reuse efficiency.

lists some of these application cases, with specific details to be elaborated on in the following sections.

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 [47]. 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 participant’s interaction with the digital twin scenarios were 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) [64].

To enhance spacecraft health management and optimize its operational efficiency in orbit, aerospace engineers are incorporating digital twin technology into the maintenance and performance monitoring of spacecraft throughout their lifecycle [65]. Figure 5 illustrates the specific workflow of the spacecraft system fault diagnosis model, showing that this approach not only revolutionizes traditional maintenance strategies but also significantly enhances the autonomous management capabilities of space missions. The construction of digital twin models allows for the real-time virtual mapping of every component, subsystem, and the overall state of the spacecraft, acting like a precise health mirror, enabling ground control personnel to instantly understand the “health” of the spacecraft. Before faults occur, digital twins can predict potential failures through the analysis of differences between the model and actual behavior, helping decision-makers quickly pinpoint problems and devise solutions, significantly reducing the time and cost associated with fault diagnosis. For example, by comparing the propulsion system data of the digital twin with that of the actual spacecraft, engineers successfully identified a slight decline in propulsion efficiency and took timely measures to prevent more severe failures. The introduction of digital twins has shown enhanced adaptability and reliability of spacecraft in the complex and harsh conditions of space, laying a solid foundation for the sustainability of future deep space exploration and long-term space missions, marking a significant leap forward in the field of aerospace health management.

Recently, a project named “DIGES,” a collaboration between the Polytechnic University of Italy and the Italian Space Agency, is dedicated to developing a digital twin system specifically designed for space exploration rovers. This system aims to enhance the Health and Usage Monitoring System (HUMS) to ensure the safety and efficiency of space missions [66]. Utilizing MATLAB-Simulink as the modeling platform, the digital twin system integrates complex geometrical, kinematic, and dynamic models, as well as sensor and control systems, allowing for precise simulation of real-world exploration scenarios (Figure 6). Through high-fidelity virtual prototype testing, the system accelerates the design process of the rover and advances the development of damage detection algorithms. The system’s flexibility enables the reproduction of diverse terrains, environmental conditions, and operational situations, significantly enhancing the rover’s research and development efficiency. One highlight is that the digital twin system can effectively handle critical faults, such as internal battery shorts, using an Extended Kalman Filter (EKF) to rapidly detect anomalies and predict battery status, thus ensuring the reliability of the energy system. Additionally, the system is highly sensitive to drive motor faults, which aids in distinguishing between sensor failures and actual motor issues, thereby guiding more precise fault troubleshooting strategies. By simulating the interactions between multiple subsystems, the project team can build a comprehensive damage database, providing a solid foundation for real-time monitoring and decision support. The digital twin of the space exploration rover marks a significant step toward the intelligence and autonomy of aerospace engineering.

SpaceX utilizes digital twin technology to optimize the design and testing of its Starship spacecraft. By accurately simulating the entire lifecycle of the Starship in a virtual environment—from initial design to performance under extreme space conditions—engineers can predict and address potential issues without the need to physically construct and test a complete prototype [67]. To assess the feasibility of the Starship missions, researchers have integrated all available data and performed calculations and extrapolations based on information publicly released by SpaceX, including subsystem design, mass budget, and propellant generation using existing technologies. Figure 7 illustrates the basic process of SpaceX’s Starship conducting a human Mars exploration mission. The research team used the Lambert solver to plan the interplanetary transfer orbit, considering the total Δv requirements from Earth to Mars and back, especially the challenges of launching from the Martian surface into the Low Mars Orbit (LMO). However, even under assumed ideal conditions (such as 100% recycling of crew consumables), the application of digital twin technology suggests that the mission concept of the Starship still faces significant obstacles, including technological shortcomings, such as energy supply technologies on the Martian surface. Therefore, this paper emphasizes the need for international cooperation to accelerate technological development, bridge technological gaps, and enhance the feasibility of the mission. This demonstrates that while digital twin technology provides SpaceX with powerful design and testing tools, a series of technological and strategic challenges must be overcome before implementing human Mars exploration missions.

European Space Agency (ESA) has made significant advancements in the field of on-orbit services by incorporating digital twin technology into its spacecraft maintenance robots [68]. This is particularly crucial for operations such as grappling and refueling of geostationary orbit (GEO) satellites. By creating precise virtual models of spacecraft and their maintenance robots, comprehensive mission planning, simulation, and optimization can be conducted before actual task execution. ESA’s ASSIST initiative focuses on standardizing the internal and external configurations required for on-orbit services, including modifying satellite platforms to enable servicing without the need for extensive design changes. The application of digital twin technology allows for in-depth analysis of these modifications’ compatibility, the precision of docking mechanisms, and the feasibility of service operations in a virtual environment. This approach enables the identification and resolution of potential issues before physical implementation, reducing risks and optimizing service procedures to ensure efficient and safe operations such as docking and refueling. Within this framework, digital twin models not only simulate the mechanical arm movements and docking processes of the maintenance robots but also integrate simulations of complex interactions such as fluid transfer, electrical connections, and data communications, providing a highly realistic rehearsal platform for actual missions. Additionally, the technology supports the analysis of dynamic responses during service tasks, such as performance evaluation under different orbital conditions or in unexpected scenarios, ensuring that maintenance robots can flexibly handle various challenges.

To save on space launch costs, reusable rocket propulsion systems have become a hot topic in aerospace research. The German Aerospace Center (GAC), with its SpaceLiner 7 and SpaceX’s ongoing development of Starship, has incorporated digital twin technology to simulate the multiple launch processes of reusable rockets, optimizing rocket design and enhancing reuse efficiency [69]. By creating virtual replicas of rockets and their launch processes, engineers can safely test different flight trajectories and design options in a digital environment. Specifically, during the design iterations of SpaceLiner 7, a smaller wing design was considered to mimic Starship’s upper atmospheric gliding strategy. Digital twin technology simulated the impacts of these design changes on re-entry and landing phases, shortening the validation cycle and enhancing accuracy, thus accelerating the design validation process for the next generation of reusable spacecraft. Digital twin technology, serving as a bridge between reality and the virtual, plays a crucial role in advancing reusable rocket technologies. It not only provides a more cost-effective solution for space launches but also opens up new avenues for exploring ultra-high-speed transportation modes on Earth while ensuring design efficiency and controllable environmental impacts.

Through the applications and cases mentioned, we can see the potential and practical benefits of digital twin technology in the aerospace sector. As this technology continues to develop, it is expected to play an increasingly central and revolutionary role in future space exploration and operational activities.

3. Key Technologies and Challenges

Although the application of digital twin technology in the space sector offers vast potential, its implementation and optimization process still face numerous technical challenges. This chapter first outlines three key technologies involved in system construction and management: the mapping of physical entities to virtual models, information systems and data storage, and operation and management within space environments. It then discusses challenges related to data collection and processing, the accuracy and reliability of models, and user interaction and interface design, providing potential solutions for these issues.

3.1. Key Technologies in System Construction and Management

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 refer to the natural and artificial objects that exist in the physical environment of space, mainly including the space environment (solar radiation, cosmic rays, solar particles, micrometeoroids and space debris, etc.), natural celestial bodies (planets such as the sun, Earth, Moon, and Mars and their satellites, bodies and orbits such as asteroids and comets), and 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 refer 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’s 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.

The construction and management of digital twin systems in space involve heightened technical requirements and challenges due to complex conditions such as electromagnetic environments, extreme weather, and space debris. This section provides a comprehensive review of three critical technologies in the digital twin environment of space: mapping physical entities to virtual models, information systems and data storage, and operation and management in space. It highlights the key and core elements of these technologies, offering insights into the technical nuances and challenges to assist readers in understanding the crucial points and difficulties involved.

3.1.1. Physical Entity to Virtual Model Mapping

In the construction of digital twin systems for space environments, the technology of building and managing virtual models forms the foundation of the entire system. This technical field is dedicated to creating a virtual replica that can accurately map a physical entity, enabling simulation, analysis, and prediction of complex dynamics in digital space [70,71,72]. Here are four essential requirements for achieving high-precision and real-time mapping from physical entities to virtual models:

(1)

High-Precision 3D Modeling Technology: This is fundamental for constructing virtual models. It requires precise digital representation of the geometric shape, structural layout, and material properties of space assets. This typically involves advanced Computer-Aided Design (CAD) tools and 3D scanning technologies to ensure geometric accuracy and richness of details;

(2)

Simulation of Complex Physical Fields: The complexity of space environments demands that virtual models can simulate interactions among various physical fields such as electromagnetic, thermal, and mechanical fields. Multiphysics simulation engines integrate these different physical processes, providing a unified simulation platform for modeling complex phenomena in space, such as the effects of solar wind on satellite surfaces and temperature changes on material performance;

(3)

Parametric Modeling Technology: This allows models to automatically adjust their structure and performance based on changes in input parameters. This technology is particularly important in design optimization, sensitivity analysis, and uncertainty quantification. Through parametric modeling, researchers can evaluate the performance of space assets under different design variables and operating conditions, providing a scientific basis for design decisions;

(4)

Rigorous Verification and Calibration: To ensure the accuracy and reliability of virtual models, they must undergo stringent verification and calibration. This includes comparisons with actual measurement data, analysis of model prediction uncertainties, and adjustment of model parameters. The verification and calibration process is iterative, ensuring that models accurately reflect the behavior of physical entities and provide a solid foundation for subsequent data analysis and decision-making.

Furthermore, in constructing digital twin systems for space environments, ensuring high consistency between the virtual model and the physical entity is crucial. To achieve this, a series of precise physical entity monitoring and control technologies must be employed to accurately perceive the physical characteristics of space environments and precisely manipulate space assets [73,74,75]. Initially, space environment sensors were key components for acquiring space environment data, capable of measuring critical parameters such as temperature, pressure, radiation levels, and magnetic field strength. These sensors must possess high reliability and stability to withstand extreme conditions in space, such as extreme temperature variations, high vacuum, and intense radiation. Additionally, the miniaturization and low-power design of sensors are vital for integration into satellites and other space assets. Further, the signals collected need to be promptly transmitted back to the ground control center. Satellite communication systems provide the necessary link for data transmission between space assets and the ground control center. With the development of high-throughput satellites and laser communication technologies, data transmission rates and bandwidth have significantly improved, allowing for rapid and accurate transmission of large volumes of real-time data back to Earth, supporting real-time updates of digital twin systems. Moreover, relevant telemetry and telecommand technologies allow ground control personnel to remotely monitor and control space assets, including adjusting satellite orbits, operating scientific instruments, and performing on-orbit maintenance tasks.

However, for the success of space missions, the efficiency and reliability of telemetry and telecommand systems must be ensured. In practice, considering contingencies and timeliness, automated control systems need to provide certain autonomous operational capabilities for space assets. These systems, utilizing advanced algorithms and artificial intelligence technologies, can autonomously execute tasks, perform fault diagnosis, and conduct emergency responses with limited intervention from the ground control center. The introduction of automated control systems significantly enhances the operational efficiency and mission adaptability of space assets. Through the integrated application of these technologies, digital twin systems for space environments can achieve comprehensive monitoring of space assets, providing strong technical support for the planning, execution, and maintenance of space missions. These technological advancements not only enhance the safety and success rates of space exploration but also open up new possibilities for future space activities.

3.1.2. Information Systems and Data Storage

In the construction and management of digital twin systems for space environments, the role of information systems is pivotal. They not only act as the hub for data flow but also facilitate multidisciplinary integration, resource optimization, and automation of tasks. A complete information system consists of three components: software architecture, communication middleware platforms, and microservice architecture. Below is a brief introduction to the typical forms of these components in space environments:

• Software Architecture: The design of software architecture modularizes the various functional modules of the information system into independent services. These services communicate through well-defined interfaces and contracts, allowing for flexible combination and reuse. In space environments, Service-Oriented Architecture (SOA) supports cross-system service integration, enabling various task planning, monitoring, and analysis services to collaborate effectively, thereby enhancing the system’s responsiveness and adaptability [76,77,78].
• Communication Middleware Platforms: These platforms typically feature capabilities such as messaging, data transformation, and service orchestration. In digital twin systems for space, Enterprise Service Buses (ESB) are commonly used as the core for service integration, ensuring correct routing and processing of data from various sources and formats [79,80]. Furthermore, ESB supports the dynamic discovery and binding of services, ensuring the system’s scalability and flexibility.
• Microservice Architecture: This architecture involves breaking down an application into a set of small, independent services, each designed around a specific business function. This setup allows services to be independently deployed, scaled, and updated, thus enhancing the system’s reliability and maintainability. In space environments, microservice architecture aids in the rapid iteration and deployment of new functionalities to meet the evolving needs of missions [81,82].

Service Governance: Service governance ensures the quality and performance of services within the information system. It includes lifecycle management, performance monitoring, fault recovery, and security strategies. Service governance is particularly crucial in space environments as it involves supporting critical missions and controlling potential risks. Effective service governance mechanisms ensure system stability and reliability under extreme conditions and uncertainties. As space technology rapidly advances, information systems must be compatible with new hardware, software, and communication protocols. Compatibility management involves assessing and ensuring interoperability between system components and compatibility with both current and future technologies. This requires consideration of long-term technological evolution during the system design phase and continuous compatibility testing and upgrades during implementation.

Furthermore, in the architecture of digital twin systems for space environments, the storage and management of twin data are key supports for efficient system operation. With the increasing complexity of space missions, the volume of data generated is growing exponentially, necessitating advanced data management strategies. Space environment systems may involve various types of data sources, including remote sensing data, telemetry data, and scientific experiment data. Data standardization and conversion technologies aim to unify these heterogeneous data into a common format for easier storage, processing, and analysis. This requires the development of unified data models and interfaces, as well as tools that support the conversion of multiple data formats.

With the surge in data volumes, traditional database management systems face performance bottlenecks. High-performance database management systems (DBMS) enhance data storage and query efficiency through distributed architectures, in-memory computing, and parallel processing technologies [83,84,85,86]. These systems handle large-scale datasets, support complex query operations, and ensure data consistency and integrity. Cloud computing platforms provide scalable computing resources and storage capabilities, allowing flexible storage, processing, and analysis of space environment data in cloud settings. Big data processing frameworks like Apache Hadoop and Apache Spark offer robust support for handling large datasets, including batch processing and real-time stream processing.

Finally, data security and privacy protection are crucial when handling sensitive space environment data. This requires implementing strict access controls, data encryption, and audit logs. Compliance with relevant laws and regulations is also necessary to ensure lawful data use and protect personal privacy. Through the integrated application of these key technologies, digital twin systems for space environments can effectively manage massive data, providing solid data support for the planning, execution, and maintenance of space missions. As technology continues to evolve, these technologies are expected to be further optimized to meet the higher demands of future space exploration for data management and processing.

3.1.3. Operations and Management in a Space Environment

In the implementation of digital twin systems for space environments, the development of space environment applications and service technologies is key to achieving intelligent system operations. These technologies aim to enhance the efficiency, safety, and scientific output of space missions through highly integrated software solutions. Initially, space environment monitoring technology is essential to ensure the safe operation of space assets. It involves real-time monitoring of the Earth’s magnetic field, solar activity, cosmic rays, and other space weather phenomena. These data are crucial for predicting and mitigating potential space weather events, thereby protecting satellites and other space assets from damage. Furthermore, satellite navigation systems provide precise timing and location information for space missions. With the continuous improvement of Global Navigation Satellite Systems (GNSS), digital twin systems for space environments can utilize this information for accurate orbit prediction, attitude control, and ground positioning, enhancing the precision and reliability of missions [87,88,89]. Based on these conditions, timely and accurate mission planning technology is required to complete detailed planning for space missions, including mission objectives, resource allocation, risk assessment, and time management. Finally, precise execution technology is necessary to ensure that missions proceed smoothly according to the planned schedule.

These technologies often combine artificial intelligence and machine learning algorithms to optimize task processes and increase the level of automation. Notably, the vast amount of data generated by space environment systems needs to be processed through advanced data analysis techniques. This includes data mining, pattern recognition, and statistical analysis to extract valuable information and insights. Artificial intelligence and machine learning algorithms play a key role in this process, as they can discover trends, predict future events, and support decision-making from complex data. Through the integrated application of these key technologies, digital twin systems for space environments can provide comprehensive intelligent support for space missions. The advancements in these technologies not only enhance the efficiency and safety of space exploration but also open new possibilities for space science research and commercial applications. As technology continues to evolve, we anticipate that these application and service technologies will be further optimized to meet the higher requirements for intelligent operations in future space activities.

3.2. Technical Challenges

Due to the unique nature of space environments, constructing a robust and reliable digital twin system faces numerous technical challenges. Here, we emphasize three key challenges: the accuracy and reliability of model construction, digital data collection and processing, and system interaction and interface design. More specifically, as shown in Figure 9, these challenges are interconnected and impact each other; they must be completely overcome through continuous technological iteration and optimization to achieve a qualified digital twin system for space environments.

3.2.1. Model Accuracy and Reliability

The accuracy and reliability of the digital twin model are crucial for the success of its technical applications. The model must be able to map and predict the state of the physical entity in real time accurately, relying on advanced modeling techniques and extensive experimental data. High-fidelity simulation of complex systems requires a combination of physical modeling and data-driven modeling methods [90,91,92]. This involves using physical modeling techniques such as Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) to provide a theoretical foundation for the digital twin. Simultaneously, high-precision sensors collect real-time data to provide necessary calibration for the model, thereby enhancing its accuracy. Moreover, manually improving and evaluating model accuracy and reliability can be costly. Currently, there is an attempt to leverage advancements in artificial intelligence to optimize the construction and adjustment processes of the model, hoping that deep learning technologies can extract complex patterns and relationships from large datasets to achieve adaptive model updates, thus maintaining precise predictions of system status. However, the interpretability issues of deep learning techniques can raise questions about their reliability. Therefore, to ensure model reliability in predicting critical system failures and making decisions, developing complex verification and testing procedures is essential. These tests not only include assessments under normal operating conditions but also simulations under various potential fault conditions to evaluate the model’s robustness and reliability.

Additionally, introducing fault simulation and error correction mechanisms by simulating various possible fault scenarios, evaluating system responses when faults occur, and developing corresponding error correction algorithms can help identify weak links in the system and ensure its continuous, stable operation. In summary, the accuracy and reliability of the digital twin model result from the combined effects of multiple factors. By integrating advanced modeling techniques, extensive experimental data, artificial intelligence and machine learning algorithms, and complex verification and testing procedures, the model’s performance and reliability can be enhanced. Introducing fault simulation and error correction mechanisms further enhances the model’s adaptability and robustness in complex environments. However, integrating these technologies remains the primary technical challenge in ensuring the model’s accuracy and reliability.

3.2.2. Data Collection and Transmission

The extreme characteristics of space environments present significant challenges for data collection and processing. In space missions, the sensors used must withstand extreme temperature variations, high radiation levels, and mechanical vibrations. These environmental demands require sensors not only to have high reliability but also self-calibration capabilities to adapt to long-term changes in space conditions. During the design and manufacturing processes, the durability of materials and the radiation resistance of electronic components must be considered. Researchers have employed technologies such as radiation hardening and advanced materials science to enhance the stability and durability of sensors in extreme environments. Additionally, challenges in data transmission include signal delays and interruptions, especially when space missions exceed the direct communication range of Earth. To overcome these issues, the development of new communication technologies like laser communication has become particularly important. This technology, by transmitting data at shorter wavelengths, can significantly improve the speed and reliability of data transmission [93,94,95]. Laser communication not only supports higher data transfer rates but also reduces communication delays, which is especially valuable in deep space missions. On the other hand, applying edge computing technology allows for substantial initial data processing at the data generation site (such as satellites or other spacecraft), effectively reducing the transmission burden and increasing system response speed. Edge computing enables spacecraft to perform local data preprocessing, filtering, and compression, thereby reducing the amount of data transmitted back to Earth. This method not only enhances data transmission efficiency but also increases the real-time and reliability of data processing. To further improve data management efficiency in space missions, advanced data compression algorithms and intelligent data filtering techniques are also widely applied. These technologies can significantly reduce the volume of data without substantial loss of data quality, improving transmission and storage efficiency. By integrating these advanced technologies, the efficiency of data collection and processing in space missions has been significantly improved, ensuring the successful implementation of missions.

In summary, data collection and processing in space environments face multiple challenges, but by adopting radiation-resistant sensor technologies, new communication means such as laser communication, and edge computing technology, these challenges can be addressed, enhancing data management capabilities in space missions.

3.2.3. System Interaction and Interface Design

Optimizing the user interface is a key direction in enhancing the application effectiveness of digital twin technology. The interface should provide an intuitive and clear information display, allowing operators to quickly understand complex data and model predictions [96,97,98]. This intuitive presentation requires that the interface design be not only aesthetically pleasing but also well structured in terms of information hierarchy to ensure that users can quickly find the information they need. By utilizing graphical user interfaces (GUIs), data visualization tools, and dashboards, complex datasets and analysis results can be presented in a more user-friendly manner, enabling users to make more efficient decisions. Given the complexity of operations, the interface should support multimodal interactions, including touch screens, voice commands, and virtual reality (VR) technology. Multimodal interaction not only enhances the flexibility of the system but also meets the operational habits and environmental needs of different users. For example, in certain special environments, voice commands can free up users’ hands, improving operational efficiency, while virtual reality technology can provide a more immersive and interactive experience, which is especially valuable in training and simulation operations. Methods to further enhance the practicality of the interface include conducting user research to identify pain points and needs during operation and developing customized interaction schemes that meet specific task requirements.

With the development of adaptive interface design, systems can dynamically adjust the interface layout and functionalities based on user habits and feedback, providing a more personalized and efficient user experience. Adaptive interfaces can automatically adjust various settings of the interface by learning users’ behavior patterns and preferences, making it more in line with user habits. This dynamic adjustment not only improves user operational efficiency but also reduces the learning curve, enabling new users to get started with the system more quickly. Additionally, to ensure the compatibility and usability of the interface, the design process should follow the best practices and standards of human–computer interaction. For example, the interface layout should maintain consistency, the choice of colors and fonts should consider visual readability, and the design of interactive elements should meet user expectations. By optimizing these details, the usability and user satisfaction of the interface can be further enhanced. In summary, optimizing the user interface plays a crucial role in enhancing the effectiveness of digital twin technology applications. Through multimodal interaction, user research, and adaptive interface design, the system’s flexibility, practicality, and user experience can be significantly improved. These optimization measures not only meet the actual needs of users but also promote the widespread application and development of digital twin technology across various fields.

4. Potential Applications and Development Prospects

The application of digital twin technology in the space sector is redefining the design, execution, and management of space missions with broad and profound implications. From improving mission planning efficiency to enhancing the safety of spacecraft, the potential of digital twin technology is gradually being realized. Overall, the prospects for the application of digital twin technology in space are vast, not only enhancing mission safety and success rates but also promoting sustainable space exploration activities through efficient resource management and environmental monitoring. As the technology matures and its applications deepen, digital twins may become an indispensable part of space missions, bringing revolutionary changes to human space exploration activities. This section will explore in detail the potential applications and development prospects of digital twin technology in space.

4.1. Environmental Monitoring and Sustainable Development

Digital twin technology shows great potential in space environment monitoring and promoting sustainable development. Through continuous monitoring and simulation of the space environment, digital twins can help scientists better understand the conditions of outer space and predict the impacts of space weather and other environmental factors on space missions. This not only enhances understanding of the space environment but also provides valuable data and insights for Earth’s environmental protection and global climate change research. Space debris threatening space assets can also be effectively tracked and managed through digital twin technology, maximizing the safety of spacecraft and personnel in space.

4.1.1. Space Environment Monitoring and Prediction

Within the framework of digital twin systems for space environments, space environment monitoring and prediction constitute a crucial application, utilizing digital twin technology for high-precision simulation and analysis of complex physical phenomena in space. With digital twin technology, physical phenomena in the space environment can be simulated at different scales, including macro-level cyclical changes in solar activity to micro-level dynamics of cosmic rays and particle streams. Through such multiscale simulations, we can comprehensively understand the impact of the space environment on space assets (Figure 10). Additionally, digital twin technology can collect real-time data from space environment monitoring systems, including telemetry from satellites, space stations, and other space assets, as well as measurements from ground-based observatories. Efficient data processing algorithms can then ensure these data are quickly integrated into digital twin models, providing a real-time status of the space environment. With sufficient data accumulated, in-depth analysis and learning from historical space weather events could identify potential risk patterns and trends. This analysis not only helps understand past space weather events but also provides crucial reference information for future predictions. Based on physical laws and statistical principles, models developed using digital twin technology can accurately predict space weather events such as solar storms and geomagnetic storms, detailing the likelihood and impact range of such events. Reliable predictions of space environment conditions can be used for risk assessment, aiding decision-makers in formulating response strategies. For example, in anticipation of high-risk space weather events, satellite operational modes could be adjusted or sensitive space missions delayed to protect space assets and astronaut safety.

Through the integrated application of these technologies, digital twin systems for space environments provide robust support for the protection of space assets, mission planning, and scientific research. These advancements not only increase the safety and success rate of space activities but also offer new tools for deep understanding and long-term monitoring of space environments.

4.1.2. Space Debris Tracking and Management

In space environments, space debris poses a significant potential threat to space assets, with debris impacts potentially causing severe damage. Against this backdrop, tracking and managing space debris has become a critical application of digital twin systems to safeguard space assets. The core of this application area is the use of digital twin technology to accurately simulate and analyze the dynamic behavior of space debris. By creating precise digital replicas of space debris, digital twin technology allows us to simulate and predict orbital changes and potential collision risks. This highly accurate simulation not only provides real-time data about the current location of debris but also predicts future orbital paths. Additionally, the digital twin platform can integrate data from multiple sensors and observatories, enhancing the precision and response speed of debris behavior monitoring. Through this integration and simulation, space asset managers can devise more effective collision avoidance strategies, timely adjusting the orbits of space assets to avoid potential debris threats. Furthermore, digital twin systems can also help analyze the cost-effectiveness of different avoidance strategies, optimizing safety planning and resource allocation for space missions. In essence, space debris tracking and management is a key application of digital twin systems in space environments, significantly enhancing the safety and operational efficiency of space assets through highly accurate debris behavior simulation and risk prediction.

As shown in Figure 10, the specific process involves the following six steps:

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High-Precision Debris Modeling: The digital twin system constructs high-precision 3D models of space debris by integrating data from ground-based radars, optical telescopes, and other sensors. These models include the size, shape, and mass distribution of the debris, as well as its motion state in orbit, such as velocity, acceleration, and direction;

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Debris Trajectory Prediction: Using physical laws and advanced numerical simulation methods, the digital twin system can predict the future motion trajectories of space debris. This prediction is crucial for assessing the potential collision risks with in-orbit assets and helps in planning avoidance strategies in advance;

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Collision Risk Assessment: Through the simulation and analysis of debris trajectories, the digital twin system can assess the probability of collision and the potential impacts. This assessment takes into account the physical characteristics of the debris and the protective capabilities of space assets, as well as the distribution of debris clouds post-collision;

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Avoidance Strategy Formulation: Once high-risk debris is identified, the digital twin system can assist in formulating effective avoidance strategies. This may include adjusting the orbital parameters of satellites, changing the timing of astronaut extravehicular activities, or replanning the sequence of space mission executions;

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Debris Mitigation Measures: In addition to avoidance strategies, the digital twin system can support the development and evaluation of measures to mitigate the impact of space debris. This includes designing impact-resistant satellite structures, deploying protective barriers, and developing debris removal technologies;

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Long-Term Monitoring and Database Construction: The digital twin system supports long-term monitoring of space debris, continuously updating the debris database to provide real-time data support for space debris management. These databases are valuable for the establishment of a global space debris monitoring network and international cooperation.

Through the integrated application of these key technologies, space debris tracking and management play a crucial role in digital twin systems for space environments. These technologies not only enhance the safety of space assets but also provide a scientific basis for international governance and mitigation of space debris. As technology continues to advance, we anticipate that these technologies will play an even more significant role in future space activities, contributing to the sustainable development of space.

4.2. Mission Planning and Decision Support

Digital twin technology significantly enhances the efficiency of space mission planning and the accuracy of decision-making by providing high-precision simulations and predictions. With digital twins, engineers and mission planners can perform comprehensive mission simulations on the ground, assessing the feasibility and effectiveness of different strategies and optimizing mission workflows. For example, when planning a new space station construction or repair mission, the relevant teams can use digital twin models to simulate various construction scenarios and emergency response strategies, thereby devising the most optimal operational plan (Figure 11). Additionally, the application of digital twin technology can also assist aerospace teams in resource allocation and time management, identifying optimal paths for resource use and potential bottlenecks through predictive models, thereby reducing waste and improving mission efficiency. This precise planning and resource optimization not only saves costs but also significantly reduces the risks associated with mission execution.

4.2.1. Scientific Data Analysis and Decision Support

In the digital twin systems for space environments, scientific data analysis and decision support play a crucial role. The core of this area involves using digital twin technology for efficient integration and in-depth analysis of the massive scientific data generated by space missions, providing robust data-driven support for scientific exploration and mission planning. Digital twin systems can integrate data streams from various space assets and scientific instruments, such as remote sensing data, experimental data, telemetry, and astronaut operation logs. By building a unified data management platform, the system ensures the consistency, integrity, and accessibility of data, laying a solid foundation for subsequent advanced data analysis. These analysis activities employ advanced technologies like machine learning, data mining, and statistical modeling to reveal complex patterns and correlations in the data, helping to extract valuable scientific information from large datasets and advancing new discoveries and theoretical developments. In terms of scientific discovery and validation, digital twin systems, through in-depth data analysis, assist scientists in validating scientific hypotheses and discovering new natural phenomena or laws. For instance, the system can help identify and classify characteristics of distant galaxies in astrophysics research or analyze the impacts of climate change in Earth science missions. Digital twin technology also serves as a powerful decision support tool, simulating different decision scenarios and predicting their possible outcomes, helping decision-makers evaluate the pros and cons of various options, thus making more scientific and rational decisions. Additionally, the system assesses various risk factors during space mission planning and execution, such as technical failures, environmental changes, and operational errors, and proposes risk mitigation strategies based on these assessments, reducing the risk of mission failure.

Ultimately, the digital twin system is not only a platform for data analysis but also a medium for knowledge extraction and sharing. It transforms analysis results into easily understandable visual representations, promoting the dissemination and application of scientific knowledge. Through the integrated application of these key technologies, scientific data analysis and decision support play a crucial role in digital twin systems for space environments, not only enhancing the efficiency and quality of scientific research but also providing robust data support for the planning and execution of space missions. As technology continues to advance, these technologies are expected to play an even more significant role in future space exploration, opening new paths for humanity’s understanding and utilization of the universe.

4.2.2. Satellite Navigation and Orbit Optimization

Within the scope of digital twin systems for space environments, satellite navigation and orbital optimization are key application areas that are essential for the efficient execution of space missions. The core goal in this domain is to use digital twin technology to precisely simulate and optimize satellite orbits, thereby enhancing the overall performance of satellite navigation systems. Digital twin models can accurately simulate the orbital dynamics of satellites, including gravitational influences, atmospheric drag, gravitational perturbations from the sun and moon, and other non-conservative forces. This high-precision modeling provides a solid theoretical foundation for orbital design, ensuring that satellites operate accurately along their predetermined trajectories.

By precisely simulating the propagation paths of satellite signals, digital twin technology further helps to improve the positioning accuracy of satellite navigation systems. This involves considering factors such as signal propagation delays, multipath effects, and signal attenuation to ensure that ground users receive more accurate positioning information. Additionally, by employing advanced optimization algorithms, such as genetic algorithms and particle swarm optimization, digital twin systems can minimize fuel consumption while optimizing satellite launch and operational costs, all while meeting mission requirements. Moreover, precise orbital simulation and optimization help reduce fuel consumption during satellite orbital adjustments and maintenance, thereby extending the satellite’s operational lifespan and reducing operational costs. Digital twin models also support complex mission planning and scheduling, including the selection of satellite launch windows, adjustment of orbital phases, and the coordinated operation of multiple satellite systems, facilitating the efficient execution of space missions and ensuring the timely completion of critical tasks. During the orbital optimization process, digital twin technology can also assess potential risks, such as possible collisions and orbital changes caused by solar activity, providing decision support for risk management to satellite operators and ensuring the safety of space assets. Through the integrated application of these key technologies, satellite navigation and orbital optimization play a crucial role in digital twin systems for space environments. Not only do they enhance the accuracy and reliability of satellite navigation, but they also provide robust support for the planning and execution of space missions. These advancements ensure that space missions can be conducted more efficiently and safely, leveraging cutting-edge technology to enhance our capabilities in space exploration and operations.

4.3. Safety and Emergency Operations

In the field of aerospace safety and emergency response operations, digital twin technology provides a powerful tool to enhance the safety of spacecraft and space stations. As shown in Figure 12, digital twin models can continuously monitor the status of spacecraft, promptly warning of potential system failures and performance degradation. This allows maintenance teams to intervene and repair issues before they develop into more serious failures. Additionally, by continuously optimizing and simulating emergency evacuation processes through model mission simulation and resource management, digital twins ensure that all evacuation operations can be safely and efficiently executed in real emergencies.

4.3.1. Space Asset Maintenance and Fault Diagnosis

Within the architecture of digital twin systems for space environments, the maintenance and fault diagnosis of space assets are crucial applications to ensure mission continuity and long-term stable operation of assets. The core of this technology domain lies in using digital twin models to continuously monitor and analyze the health status of space assets to detect and resolve potential issues in a timely manner. Digital twin systems integrate sensor data to enable real-time monitoring of critical performance indicators of space assets, such as temperature, pressure, vibration, current, and voltage. These data can reflect the operational state and potential anomalies of the assets. Based on these data, the system can predict potential failures and performance degradation, employing complex data analysis techniques such as pattern recognition, statistical analysis, and machine learning to identify early signs of faults. Furthermore, based on predictive outcomes, digital twin systems help formulate and optimize maintenance plans, determine the optimal timing for maintenance, select appropriate maintenance strategies, and allocate necessary resources to minimize the impact of maintenance activities on missions and reduce costs. For space assets that cannot be directly intervened by ground control centers, the system also provides remote fault handling capabilities, including remote diagnostics, fault isolation, and the implementation of automated repair strategies, enhancing the autonomy and reliability of space assets. Digital twin technology is also used to assess the remaining life and performance degradation trends of space assets, aiding in planning asset decommissioning and replacement to ensure smooth mission transitions and effective asset utilization. The system also acts as a knowledge repository, recording and accumulating maintenance history, fault cases, and solutions, providing valuable experience for future mission planning and execution. Through the integrated application of these key technologies, maintenance and fault diagnosis of space assets play a crucial role in digital twin systems for space environments, not only enhancing operational efficiency and reliability but also providing a solid guarantee for the continuous success of space missions.

4.3.2. Space Environment Simulation and Emergency Operation

In the framework of digital twin systems for space environments, space mission planning and simulation are key applications that ensure the efficient execution of missions and the achievement of scientific objectives. With digital twin technology, mission planners can comprehensively rehearse and evaluate space missions in a virtual environment, which not only enhances the feasibility of mission plans but also optimizes resource allocation and strengthens risk management capabilities. Digital twin systems provide a highly interactive simulation environment that allows planners to simulate various mission scenarios, from launch window selection and orbital insertion to scientific experiment operations and return to Earth. These simulation activities help identify potential risk points and optimize mission plans, thereby improving the feasibility and safety of the missions. Additionally, the optimization of resource allocation for space missions is crucial. Digital twin technology ensures the achievement of critical mission objectives through precise resource management simulations while minimizing resource consumption. Risk management and emergency response are indispensable parts of mission planning. Digital twin systems can simulate various anomalies and emergency situations, such as equipment failures, environmental changes, and unexpected collisions, and develop effective response strategies to enhance the mission’s adaptability and robustness.

For astronaut training, digital twin technology, combined with virtual reality (VR) and augmented reality (AR), provides a nearly real training platform that enhances astronauts’ operational skills and emergency response capabilities, ensuring their performance in actual missions. For example:

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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;

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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;

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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;

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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.

Moreover, the complexity of space missions often requires multidisciplinary collaboration. Digital twin systems support experts in aerospace engineering, earth sciences, biology, and other disciplines to work collaboratively on the same platform, optimizing mission design and improving the quality of scientific output. The system also supports continuous iteration and improvement of mission planning. As new data are acquired and the mission environment changes, the models are continuously updated, making mission planning more flexible and precise. In summary, space mission planning and simulation play a crucial role in digital twin systems for space environments. The application of these technologies not only enhances the success rate and scientific value of missions but also provides solid support for astronaut safety and the protection of space assets.

Facing these challenges, the future development direction in the aerospace field will focus on integrated technological innovations, including the use of new materials and technologies to improve sensor design, the application of advanced algorithms to enhance data processing capabilities, and the optimization of model updates and interaction design through intelligent systems. Through the integrated application of these technologies, digital twins will better support space exploration and operation, enhance the safety and efficiency of space missions, and drive the advancement of space technology to higher levels.

5. Conclusions

The introduction of digital twin technology in the space sector marks a new development phase in aerospace technology. With its highly accurate simulations, real-time status monitoring, and advanced decision support systems, digital twin technology not only provides unprecedented support for space missions but also brings profound transformations to the aerospace field. This review article has comprehensively explored and discussed the integration of digital twin technology with physical space technology, the technical challenges faced, and the future prospects and potential impacts of this technology. It has been understood that the digital twin technology makes the design, execution, and maintenance of space missions more efficient and safer. By accurately simulating spacecraft and space stations, scientists and engineers can conduct comprehensive tests and analyses on the ground, greatly reducing the need for physical trials, saving costs, and minimizing risks. Additionally, real-time data monitoring and analysis provide strong technical support for diagnosing and resolving issues during mission execution, significantly increasing the success rate and safety of space missions.

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 the following:

• 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;
• 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;
• 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;
• 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 these data is a major challenge;
• 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;
• 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 consume many 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, such as 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 mechanisms. Using encryption and anonymization technology can protect data security and privacy.

Looking ahead, as related technologies continue to advance and mature, digital twins are expected to play an increasingly critical role in space exploration and operation. The deep application of this technology is anticipated to extend not only to spacecraft and space stations but also to the monitoring and management of the entire space environment, thereby providing more comprehensive and in-depth support for human space activities. The development of digital twin technology will continue to drive innovation in aerospace technology, opening new possibilities for humanity’s space exploration endeavors and offering a broader future perspective.