Twin Platform Research Articles

Research on the method of digital twin operation and maintenance platform for intelligent early warning of wind turbine tower

Wind power generators have a complex structure and operate in harsh environments, where working conditions are highly variable. As a result, the operation and maintenance of wind turbines face numerous challenges. In response to the need for the development of wind power operation and maintenance informatization, it is necessary to satisfy the requirements for multi-party collaborative monitoring to ensure the long-term safe and reliable operation of wind turbines. In this paper,we proposed a method for building an intelligent early-warning digital twin platform focused on the simulation of wind turbines and tower components. The platform construction method proposed in this article is based on the Web and from the perspective of intelligent operation and maintenance of wind turbines. It establishes a warning model for tower agent simulation and vibration signal time series prediction. The tower mechanism model is established based on the operating data set of a 4MW wind turbine at Shanghai Electric. Different physical responses of the tower under different wind speeds are simulated, and an agent model using LSTM and decision tree models is established for predictive analysis. To account for uncertainty, a Bayesian-LSTM model is established to warn against predictive errors. Finally, a data-driven digital twin wind turbine platform is achieved on the Web.

Supply chain management for modular construction using building information modeling

This paper presents an example of an integrated con¬struction system that uses building information model¬ing (BIM), the internet of things (IoT), and geographic information system (GIS) technology to improve supply chain logistics management in construction projects, thereby increasing project efficiencies and controlling construction costs. The authors developed and tested a digital twin platform for the proposed system, which allows real-time simulation of logistics in modular con-struction. The proposed digital twin platform uses BIM and GIS as its foundation because BIM includes infor¬mation on the plans, detailed geometry, properties, and quantities of modules, whereas GIS provides geospatial data for the modules along with vehicular information during transport. This system with integrated BIM, IoT, and GIS modules was applied in a new manufacturing company within its first five years. The results show that potential logistical risks and accurate module arrival time can be detected via the suggested digital twin platform. The integrated system proved to be effective in product delivery management tasks.

Construction and simulation analysis of lithium‐ion batteries thermoelectric coupling model based on digital twin

AbstractWith the rapid development of energy storage technology, it is significant to evaluate the operating status of lithium‐ion batteries efficiently and accurately, so as to ensure their safe operation and reduce the probability of accidents. For the problems of long simulation time and low accuracy in existing models, this paper proposes a construction method of lithium‐ion batteries thermoelectric coupling model based on digital twin. First, the digital twin structure system of lithium‐ion batteries is proposed. Second, considering the coupling effects of the thermodynamic model and the equivalent circuit model, the thermoelectric coupling model is constructed based on the digital twin platform ANSYS TwinBuilder. The thermodynamic model is reduced in order and the simulation time is reduced to second level, which improves the simulation efficiency and meets the real time simulation requirements of the digital twin. Furthermore, considering that the operating parameters of lithium‐ion batteries are variable, the parameters of the equivalent circuit model are identified online based on the variable forgetting factor recursive least squares algorithm. It updates parameters of the model and improves the simulation accuracy. Finally, the efficiency and accuracy of the model are verified through simulation analysis.

Vision for the 12 LABOURS Digital Twin Platform.

Clinical translation of personalised computational physiology workflows and digital twins can revolutionise healthcare by providing a better understanding of an individual's physiological processes and any changes that could lead to serious health consequences. However, the lack of common infrastructure for developing these workflows and digital twins has hampered the realisation of this vision. The Auckland Bioengineering Institute's 12 LABOURS project aims to address these challenges by developing a Digital Twin Platform to enable researchers to develop and personalise computational physiology models to an individual's health data in clinical workflows. This will allow clinical trials to be more efficiently conducted to demonstrate the efficacy of these personalised clinical workflows. We present a demonstration of the platform's capabilities using publicly available data and an existing automated computational physiology workflow developed to assist clinicians with diagnosing and treating breast cancer. We also demonstrate how the platform facilitates the discovery and exploration of data and the presentation of workflow results as part of clinical reports through a web portal. Future developments will involve integrating the platform with health systems and remote-monitoring devices such as wearables and implantables to support home-based healthcare. Integrating outputs from multiple workflows that are applied to the same individual's health data will also enable the generation of their personalised digital twin.Clinical Relevance- The proposed 12 LABOURS Digital Twin Platform will enable researchers to 1) more efficiently conduct clinical trials to assess the efficacy of their computational physiology workflows and support the clinical translation of their research; 2) reuse primary and derived data from these workflows to generate novel workflows; and 3) generate personalised digital twins by integrating the outputs of different computational physiology workflows.

The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.

The Impact of the Platform Economy on Urban–Rural Integration Development: Evidence from China

As a new economic system composed of data-driven, platform-supported, and network-coordinated economic activity units, the platform economy provides a new impetus for the integrated development of urban and rural areas. This paper uses China’s provincial panel data from 2011–2021 to measure the development level of the platform economy and the urban–rural integration development index using the entropy weight method and empirically tests the direct and indirect effects of the platform economy on urban–rural integration development. The research finds that (1) the platform economy has a significant role in promoting the development of urban–rural integration. After introducing a series of robustness tests such as instrumental variables estimation, the results are still significant. (2) Regional innovation capability is an important transmission mechanism for the platform economy to enable urban–rural integrated development. The platform economy can indirectly promote urban–rural integrated development by improving regional innovation capability. (3) There is a significant mesomeric effect between the traditional factors of production and the platform economy and the mesomeric effect of capital, technology, land, and labor factors increased in turn. (4) The digital twin platform is a kind of digital land generated in the platform economy and has become an important place and value source for products in the digital era.

Construction Method for a Three-Dimensional Tunnel General Monomer Model Based on Parallel Pathfinding

Existing approaches for the 3D modeling of tunnels suffer from several problems, such as highly difficult data acquisition, redundancy of model data, large computational burden, and the inability of the resulting models to be monolithic. Therefore, solutions to the tunnel network modeling problem for complex structures need to be proposed and elaborated in detail. In this paper, a construction method for a three-dimensional tunnel general monomer model based on parallel pathfinding is proposed. Widely used tunnel CAD drawings are analyzed and read, a disordered arc ensemble intersection trend decision method is developed, and an automatic path extraction solution algorithm for unidirectional modeling of tunnel centerlines is constructed. By constructing and splicing the surface elements of the 3D model, a monomeric 3D tunnel model representing the complex network structure is finally obtained. Moreover, the modeling of shafts is realized based on the monomer model, allowing for the three-dimensional topological relationships between different sub-levels of the tunnel and the ground to be established. The automatic modeling method proposed in this paper is applied to the digital twin platform of a filling project in a mining area in Gansu province, China. The experimental results demonstrate that the 3D tunnel models constructed in this way have a smaller data volume, higher modeling accuracy, and more stable growth of modeling speed.

A Theoretical Open Architecture Framework and Technology Stack for Digital Twins in Energy Sector Applications

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

Requirements and challenges for infusion of SHM systems within Digital Twin platforms

The need for measurable data from physical assets to actively feed a living Digital Twin (DT) is paramount. The requirements and needs that the gathered data should fulfill in order to be practically implemented in the stream data pipeline are heterogeneous, some of them general and other case-specific. This article summarizes a set of identified challenges and requirements for a seamless infusion of well-established Structural Health Monitoring (SHM) systems within DT platforms, without the objective of solve all of them. This identification is performed based on a review of traditional SHM systems with a vast array of information sources as well as on the review of techniques for the systematic digitalization of existing assets. On the other hand, ten real demo cases belonging to Ashvin, an H2020 Research and Innovation project, are providing real world testing beds for an active development of SHM infused in DT systems. Multiple information sources are studied in those sites, which also enriches with more realism, the identification of requirements and challenges presented herein. These assets provide a perspective to researchers about practical implications of these needs.

Applicability of BIM-IoT-GIS integrated digital twins for post occupancy evaluations

Post Occupancy Evaluations (POE) provided a systematic methodology for determining the performance gap between expected and actual performance. Monitoring quality of the indoor environment is essential for understanding building performance in relation to occupant health, wellbeing, and comfort. Because of the global COVID-19 pandemic, researchers faced numerous issues accessing the building for collecting data and making spot measurements of the indoor environment. Technologies such as Building Information Modeling (BIM), Internet of Things (IoT), and Geographical Information Systems (GIS) have the potential to address existing challenges for data collection, analysis, and visualization in post occupancy evaluations. This study aims to explore the applications of a BIM-IoT-GIS-integrated digital twin for post occupancy evaluations. First, high-level use case scenarios are developed to derive system requirements for a digital twin platform. Second, four tests are conducted that provide a step-by-step procedure for BIM-IoT-GIS integration. Third, the integration is validated by geo-reference checks, data transfer checks, and visual checks. Based on the tests, a streamlined workflow is recommended for similar/future projects. The results demonstrate that Revit-ArcGIS Pro integration meets the system requirements for post occupancy evaluations. Moreover, as shown in the graphical abstract (Figure), the spatial-temporal capabilities of ArcGIS Pro enable continuous monitoring and visualization of building performance in 4D. In conclusion, BIM-IoT-GIS integration can provide a solid foundation for developing a centralized digital twin for post occupancy evaluations and enables researcher to collect and analyze the data without being physically present in the building.

A Lean Scheduling Framework for Underground Mines Based on Short Interval Control

Production scheduling management is crucial for optimizing mine productivity. With the trend towards intelligent mines, a lean scheduling management mode is required to align with intelligent conditions. This paper proposes a lean scheduling framework, based on short interval control as an effective tool to adapt intelligent scheduling in underground mines. The framework shortens the production monitoring and adjustment cycle to near-real-time, enabling timely corrective measures to minimize schedule deviations and improve overall production efficiency. An intelligent scheduling platform is implemented by adopting the digital twin platform framework, the intelligent scheduling mobile terminal module, and the integrated scheduling control cockpit module. The results indicate that the platform is effective in promoting mine intelligence by providing benefits in information transparency, flexible scheduling, lean production, and scientific decision-making. The proposed framework provides a practical solution for implementing intelligent scheduling in underground mines, contributing to the overall improvement of mine productivity. Overall, this paper provides insights for implementing intelligent scheduling in underground mines.

A pattern catalog for augmenting Digital Twin models with behavior

Abstract Digital Twins are emerging as a solution to build and extend existing software systems to make better use of data produced by physical systems. For supporting the development of Digital Twins, several software vendors are offering dedicated tool support, often referred to as Digital Twin platforms. The modeling capabilities of these platforms are mostly concerned with structural viewpoints, i.e., providing an overview of available components including their current and historical sensor values. However, behavioral viewpoints did not yet receive much attention on these platforms. As behavioral models are often used during the design processes, e.g., for simulation and synthesis, it would be beneficial for having them included in Digital Twin platforms, e.g., for reasoning on the set of possible next actions or for checking the execution history to perform runtime validation. In this paper, we present a catalog of modeling patterns for augmenting Digital Twin models with behavioral models and their corresponding runtime information without requiring any extension of the code bases of Digital Twin platforms. We demonstrate the presented modeling patterns by applying them to the Digital Twin platform offered by Microsoft, in an additive manufacturing use case of a 3D printer in a production line.

Time-space intelligent management and dispatch of electric Beidou based on Beidou and digital twin technology

Aiming at the increasingly complex management and dispatching problems of power system, this paper combines Beidou precise space-time technology with digital twin system theory, and puts forward a method of constructing intelligent management and dispatching platform of power grid. The method combines Beidou and IoT (Internet of Things) sensors to form a digital twin management and scheduling platform which can perform state prediction, fault diagnosis, self-healing and other functions. Firstly, this paper analyzes the overall architecture of Beidou digital power twin system, then discusses the construction method of digital twin in power system from the aspects of digital twin modeling theory, data acquisition intelligent terminal based on Beidou, operation mode of digital twin platform, etc. Finally, the application test analysis is carried out. The research results show that the method proposed in this paper has reference value for improving the management and dispatching capability of smart grid.

Increasing the Flexibility of Hydropower with Reinforcement Learning on a Digital Twin Platform

The increasing demand for flexibility in hydropower systems requires pumped storage power plants to change operating modes and compensate reactive power more frequently. In this work, we demonstrate the potential of applying reinforcement learning (RL) to control the blow-out process of a hydraulic machine during pump start-up and when operating in synchronous condenser mode. Even though RL is a promising method that is currently getting much attention, safety concerns are stalling research on RL for the control of energy systems. Therefore, we present a concept that enables process control with RL through the use of a digital twin platform. This enables the safe and effective transfer of the algorithm’s learning strategy from a virtual test environment to the physical asset. The successful implementation of RL in a test environment is presented and an outlook on future research on the transfer to a model test rig is given.

Overview of Digital Twin Platforms for EV Applications.

Digital twin (DT) technology has been used in a wide range of applications, including electric vehicles. The DT platform provides a virtual representation or advanced simulation of a physical object in real-time. The implementation of DT on various aspects of EVs has recently transpired in different research studies. Generally, DT can emulate the actual vehicle on the road to predict/optimize its performance and improve vehicle safety. Additionally, DT can be used for the optimization of manufacturing processes, real-time condition monitoring (at all levels and in all powertrain components), energy management optimization, repurposing of the components, and even recycling processes. This paper presents an overview of different DT platforms that can be used in EV applications. A deductive comparison between model-based and data-driven DT was performed. EV main systems have been discussed regarding the usable DT platform. DT platforms used in the EV industry were addressed. Finally, the review showed the superiority of data-driven DTs over model-based DTs due to their ability to handle systems with high complexity.

Predictive digital twin for offshore wind farms

As wind turbines continue to grow in size, they are increasingly being deployed offshore. This causes operation and maintenance of wind turbines becoming more challenging. Digitalization is a key enabling technology to manage wind farms in hostile environments and potentially increasing safety and reducing operational and maintenance costs. Digital infrastructure based on Industry 4.0 concept, such as digital twin, enables data collection, visualization, and analysis of wind power analytic at either individual turbine or wind farm level. In this paper, the concept of predictive digital twin for wind farm applications is introduced and demonstrated. To this end, a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication is developed. The platform is completed with the Prophet prediction algorithm to detect potential failure of wind turbine components in the near future and presented in augmented reality to enhance user experience. The presentation is intuitive and easy to use. The limitations of the platform include a lack of support for specific features like electronic signature, enhanced failover, and historical data sources. Simulation results based on the Hywind Tampen floating wind farm configuration show our proposed platform has promising potentials for offshore wind farm applications.

A Machine-Learning-based Surrogate Modeling Methodology for Submodel Integration in the Holistic Railway Digital Twin Platform

A holistic railway infrastructure digital twin (DT) platform is sophisticated and consists of a series of submodels (e.g., turnouts, tracks, vehicles, etc.) that are built through various methodologies and software. However, integrating these submodels into the DT platform is tremendously challenging due to considerable computational complexity, software and interface restrictions. To this end, we designed a machine learning (ML) based surrogate modeling methodology for the submodel integration in the holistic railway infrastructure DT platform and illustrated the methodology through a case study. In this case study, an ML-based surrogate model for multibody simulation of railway vehicle-track dynamics is created, which can replace the railway vehicle-track simulation executed with the Multibody Dynamics (MBD) Simulation commercial software SimPACK. The well-built ML model can accurately and quickly predict the vehicle-track system's dynamic responses to different track irregularities. Besides, the integration process of the ML-based surrogate model into the DT platform through a standardized open-source Functional Mock-up Interface (FMI) is also proposed. The developed surrogate modeling methodology shows great promise owing to its high fidelity, which is verified by the measurement data collected from the Austrian national railway track system. The main contribution of our work lies in the well-built ML-based surrogate modeling methodology for reducing the computation complexity and time of different submodels, which facilitates the unification and integration of different submodels. Furthermore, this approach can also be applied to other submodels and help to build the holistic railway DT platform collaboratively.

Customized Digital Twin Platform for SMEs in South Korea

With the development of information and communication technology, the era of the 4th industrial revolution has arrived, which has enabled hyper-connection, -intelligence, and -convergence. Digital twin (DT), known as the core technology of cyber-physical system (CPS) implementation, has become increasingly important for industrial digitalization. However, a majority of small and medium-sized enterprises (SMEs) lack innovation resources and face hurdles while building their own customized smart manufacturing processes. Thus, this paper proposes a customized DT platform that reflects international standards to strengthen the competitiveness of various SMEs. This platform can be used to support rapid decision-making at minimal cost without disrupting the production site. The proposed platform is validated through a case study involving a die-casting manufacturer in South Korea.

The capability of Distributed Generation in Digital Twin platform

The digital Twin of distributed electricity generation is the concept of a smart grid. A smart grid uses advanced digital technologies, including sensors, communication networks, and analytics software, to optimize the distribution and consumption of energy in real time. The future of distributed generation is promising. As more people adopt renewable energy systems like rooftop solar panels, the demand for traditional centralized power grids will decrease. This creates opportunities for smaller, community-based microgrids that can generate and distribute energy locally. By leveraging digital technologies like Digital Twin, the Internet of Things (IoT), blockchain, and artificial intelligence, these microgrids would improve energy efficiency, reduce costs, and enhance reliability. Overall, the combination of distributed energy generation and digital technologies has the potential to revolutionize the energy industry, making it more sustainable, resilient, and equitable. In this paper, the effective factors in the development of distributed production in the digital hybrid platform were identified and it is investigated with the cause and effect loops of the system dynamics.

Potentials of game engines for wind power digital twin development: an investigation of the Unreal Engine

Digital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines.