digital twin simulation

Existing digital twin (DT) research lacks a cross-level unified architecture and empirical validation across manufacturing tiers. Method: This paper proposes a generic DT architecture encompassing data flow, core components, and interaction mechanisms; classifies multi-tier DT types and modeling techniques; and critically analyzes discrete-event simulation (DES) for dynamic system modeling. Contribution/Results: Through industrial case studies, the work empirically validates DT’s efficacy in enhancing operational efficiency, reducing downtime, and optimizing lifecycle management. It identifies three critical implementation barriers: data integration complexity, cybersecurity risks, and high deployment costs. The study provides both theoretical foundations and practical guidelines for standardized DT development and scalable industrial deployment across product/production-line, production-system, and enterprise levels.

Subsymbolic AI struggles with training under few-shot and low-quality data, while existing virtual simulation approaches lack systematic, standards-aligned frameworks. Method: We conduct a systematic literature review covering 22 state-of-the-art works and propose, for the first time, a unified reference framework for digital twin–driven AI simulation—deeply integrating digital twins with AI agents to establish a closed-loop, cyber-physical data orchestration mechanism. We further achieve systematic alignment of this framework with the ISO 23247 international standard for digital twins. Contribution/Results: We distill key technological evolution trends, identify five core challenges and several open research directions, and deliver a reusable architectural guideline and reference framework. This work provides a standardized, methodology-driven foundation for high-fidelity AI simulation, advancing both theoretical rigor and practical deployability in industrial AI applications.

To address the loss of fidelity in digital twins caused by dynamic physical system evolution—such as maintenance, wear, and human intervention—this paper proposes a model-verification-based continuous validation framework. The framework integrates real-time monitoring with historical data comparison to construct an interpretable validation metric system, incorporates a lightweight anomaly detection mechanism, and introduces a data-driven parameter self-adaptation estimation algorithm for online twin diagnosis and closed-loop model updating. Unlike conventional static calibration methods, our approach enables long-term trustworthiness preservation and autonomous evolution of the digital twin. Evaluated on an industrial quay crane use case, the framework accurately detects system deviations and dynamically refines model parameters, reducing modeling error by 37.2% and improving maintenance response timeliness by 52%. These results demonstrate significant enhancements in the representativeness, robustness, and engineering practicality of digital twins.

Digital twins are increasingly critical for real-time monitoring and decision-making in complex systems; however, their dynamic behavior, integration of multi-source heterogeneous data, and requirements for real-time synchronization pose significant challenges to verifying accuracy, reliability, and trustworthiness. To address these challenges, this paper proposes the first comprehensive, lifecycle-oriented TEVV (Testing, Evaluation, Verification, and Validation) framework for digital twins. The framework systematically integrates model-driven engineering, formal verification, simulation-based comparison, data provenance tracking, and uncertainty quantification, augmented with real-time monitoring and feedback mechanisms to enable multi-level, multi-dimensional trust assessment. Designed for cross-domain scalability, the framework has been empirically validated across multiple industrial applications, demonstrating substantial improvements in digital twin model credibility and decision-support effectiveness.

Complex cyber-physical systems (CPS) in agriculture and manufacturing often suffer from hardware constraints and limited upgradability, hindering intelligent enhancement and fault resilience. Method: This paper proposes a lightweight digital twin framework targeting functional augmentation and fault tolerance, featuring an edge–cloud collaborative twin deployment architecture that integrates real-time data synchronization, bidirectional control, and model-based fault prediction and recovery. Contribution/Results: To our knowledge, this is the first industrial validation of digital twins enabling functional offloading and cooperative fault tolerance for CPS on production lines. Experiments demonstrate a 37% increase in mean time between failures and a 62% reduction in operational response latency. The framework eliminates reliance on hardware retrofitting, significantly improving CPS robustness, scalability, and operational efficiency under resource-constrained conditions—providing a reusable technical pathway for intelligent upgrading of legacy systems.

Manual pre-deployment testing and validation of communication software in autonomous network evolution is time-consuming and labor-intensive. Method: This paper proposes a digital twin (DT) automated generation method aligned with the ITU-T Autonomous Networks architecture, integrating network modeling, automated orchestration, and parameter-driven simulation to generate executable, high-fidelity DT instances directly from real-world network configurations. Contribution/Results: The approach significantly reduces manual configuration overhead and enables seamless integration of the DT environment into existing verification workflows, supporting efficient execution of experimental subsystems. Experimental evaluation demonstrates that the generated DTs meet practical testing requirements in both accuracy and runtime efficiency. To the best of our knowledge, this work achieves the first end-to-end automated construction and closed-loop validation of digital twins compliant with the ITU-T G.1000 series standards.

This study addresses the challenges of non-standardized, time-consuming, and resource-intensive digital twin development by proposing a tool-supported framework that, for the first time, enables the automatic derivation of purpose-specific digital twins from existing engineering models. By reusing pre-existing structural and behavioral models of physical assets and integrating lightweight customization and configuration, the approach leverages model-based engineering methods and automated generation techniques to rapidly align digital twins with operational objectives. The framework’s feasibility, generality, and engineering practicality are demonstrated through the successful automatic generation of digital twin instances across four heterogeneous use cases.

This work addresses the challenge that existing cross-domain digital twin approaches struggle to effectively coordinate the intrinsic operational relationships among heterogeneous domains in terms of states, errors, objectives, constraints, and control. To overcome this limitation, the paper proposes a novel cross-domain digital twin framework featuring an original seven-layer conceptual architecture and a cross-domain orchestration core. The framework enables deep multi-domain coordination through shared state alignment, explicit coupling modeling, heterogeneous temporal coordination, joint decision-making, and a feedback-driven adaptive mechanism. It incorporates a single offline training phase with bounded online adaptation and integrates model lifecycle management, runtime safety, and provenance tracking. Compatible with mainstream digital twin and simulation standards, the approach is accompanied by validation criteria, a maturity model, and a deployment architecture, thereby establishing a foundation for benchmarking and real-world implementation.

This study addresses the absence of a unified model capable of consistently characterizing both core and optional features of digital twins. To bridge this gap, the authors propose, for the first time, a generic feature model encompassing the three canonical forms—digital models, digital shadows, and digital twins—derived through a systematic literature mapping. The model is empirically validated across three distinct application domains: emergency response, intelligent vehicles, and smart manufacturing. By providing a coherent foundation for design decisions, model-driven development, and test case generation in digital twin systems, the proposed framework significantly enhances development efficiency and model reusability.

Existing digital twin definitions and reference models are overly abstract, leading to a significant disconnect between theoretical concepts and industrial practice. Method: This paper proposes a unified, CPS-oriented digital twin reference model, systematically synthesizing mainstream frameworks to establish a fine-grained architecture with explicit hierarchical structure, well-defined component relationships, and operational interfaces. The model integrates strengths from ISO/IEC 23053 and the Digital Twin Capability Maturity (DTCM) framework. Contribution/Results: It advances conceptual clarity, modeling granularity, and engineering mapping: (1) improves terminology consistency and semantic interpretability; (2) explicitly specifies data, control, and service flows governing cyber-physical interaction; and (3) provides actionable implementation pathways across modeling, integration, and verification phases. Experimental validation in three representative industrial scenarios demonstrates that the model supports digital twin system development, reducing modeling time by approximately 35% and enhancing cross-organizational collaboration efficiency.

This study addresses the challenge that existing simulation testing struggles to seamlessly integrate real vehicles and field data, resulting in insufficient high-fidelity replication of dynamic real-world interactions for connected and automated vehicle validation. To overcome this limitation, the authors propose a real-time hybrid digital twin platform that leverages a custom middleware and low-latency V2X communication to map the motion states of physical vehicles into shadow vehicles within a coupled CARLA-SUMO simulation environment. Virtual control commands generated in simulation are transmitted via CAN bus to actuate the physical vehicle’s chassis, establishing a tightly coupled cyber-physical closed loop. Integrating photogrammetry-based modeling and a cloud-edge协同 architecture, the platform demonstrates low latency, high synchronization, and effective closed-loop control across multiple scenarios, significantly enhancing both the efficiency and realism of validation for connected autonomous driving systems.

Current digital twin (DT) and simulation platform integrations in IoT and IIoT suffer from rigidity and insufficient runtime coordination, hindering adaptive system operation and closed-loop interaction between virtual models and physical assets. Method: This paper proposes a bidirectional integration framework centered on a novel “Digital Twin–Simulation Bridge” mechanism, enabling dynamic model updates, parameter self-adaptation, and deep integration of virtual commissioning with real-time behavioral analysis. The framework adopts a modular architecture and standardized bidirectional data interfaces to support flexible, scalable interconnection across the DT lifecycle—encompassing design, verification, and real-time execution—with heterogeneous simulation platforms. Contribution/Results: Experimental evaluation demonstrates that the framework significantly enhances design agility and enables high-fidelity, closed-loop co-simulation and physical-device synchronization across diverse industrial scenarios, thereby improving operational responsiveness and system adaptability.