Pub Date : 2024-07-17DOI: 10.12688/digitaltwin.17963.1
Jitao Cai, Jiansong Wu, Yanzhu Hu, Ziqi Han, Yuefei Li, Ming Fu, Xiaofu Zou, Xin Wang
Background Unexpected leakage accidents of the natural gas pipeline inside urban utility tunnels can pose great threats to public safety, property, and the environment. It highlights the modeling of natural gas leakage and dispersion dynamics, especially from a digital twin implementation perspective facilitating effective emergency response in a data-driven way. Methods In this study, a digital twin-based emergency response framework for gas leakage accidents in urban utility tunnels is proposed. Within this framework, the data-calibrated gas concentration prediction (DC-GCP) model is developed by integrating the Lattice Boltzmann Method (LBM) with data assimilation (DA) techniques. This combination enables accurate spatiotemporal predictions of gas concentrations, even with a prior or inaccurate gas leakage source term. Specifically, we develop a high-performance LBM-based gas concentration prediction model using the parallel programming language Taichi Lang. Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. Therefore, the predicted results can be calibrated by the continuous data in the absence of complete source term information. Furthermore, a widely used twin experiment and statistical performance measures (SPMs) are used to evaluate and validate the effectiveness of the proposed approach. Results The results show that all SPMs progressively converge towards their ideal values as calibration progresses. And both the gas concentration predictions and the source term estimations can be calibrated effectively by the proposed approach, achieving a relative error of less than 5%. Conclusions This study helps for dynamic risk assessment and emergency response of natural gas leakage accidents, as well as facilitating the implementation of predictive digital twin in utility tunnels.
背景 城市公共设施隧道内天然气管道的意外泄漏事故会对公共安全、财产和环境造成巨大威胁。该研究强调了天然气泄漏和扩散动态建模，特别是从数字孪生实施的角度，以数据驱动的方式促进有效的应急响应。方法 本研究提出了一个基于数字孪生的城市公用事业隧道天然气泄漏事故应急响应框架。在此框架内，通过将格子波尔兹曼法（LBM）与数据同化（DA）技术相结合，开发了数据校准气体浓度预测（DC-GCP）模型。这种组合能够准确预测气体浓度的时空分布，即使存在先验或不准确的气体泄漏源项。具体来说，我们使用并行编程语言 Taichi Lang 开发了基于 LBM 的高性能气体浓度预测模型。在该模型的基础上，通过 DA 算法实现了对来自公用事业隧道的气体传感器数据的实时整合。因此，在缺乏完整源项信息的情况下，预测结果可以通过连续数据进行校准。此外，还采用了广泛使用的孪生实验和统计性能指标（SPM）来评估和验证所提方法的有效性。结果 结果表明，随着校准的进行，所有 SPM 都逐渐向理想值靠拢。气体浓度预测和源项估算都能通过建议的方法进行有效校准，相对误差小于 5%。结论 本研究有助于天然气泄漏事故的动态风险评估和应急响应，也有助于在公用事业隧道中实施预测性数字孪生技术。
{"title":"Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels","authors":"Jitao Cai, Jiansong Wu, Yanzhu Hu, Ziqi Han, Yuefei Li, Ming Fu, Xiaofu Zou, Xin Wang","doi":"10.12688/digitaltwin.17963.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17963.1","url":null,"abstract":"Background Unexpected leakage accidents of the natural gas pipeline inside urban utility tunnels can pose great threats to public safety, property, and the environment. It highlights the modeling of natural gas leakage and dispersion dynamics, especially from a digital twin implementation perspective facilitating effective emergency response in a data-driven way. Methods In this study, a digital twin-based emergency response framework for gas leakage accidents in urban utility tunnels is proposed. Within this framework, the data-calibrated gas concentration prediction (DC-GCP) model is developed by integrating the Lattice Boltzmann Method (LBM) with data assimilation (DA) techniques. This combination enables accurate spatiotemporal predictions of gas concentrations, even with a prior or inaccurate gas leakage source term. Specifically, we develop a high-performance LBM-based gas concentration prediction model using the parallel programming language Taichi Lang. Based on this model, real-time integration of gas sensor data from utility tunnels is achieved through the DA algorithm. Therefore, the predicted results can be calibrated by the continuous data in the absence of complete source term information. Furthermore, a widely used twin experiment and statistical performance measures (SPMs) are used to evaluate and validate the effectiveness of the proposed approach. Results The results show that all SPMs progressively converge towards their ideal values as calibration progresses. And both the gas concentration predictions and the source term estimations can be calibrated effectively by the proposed approach, achieving a relative error of less than 5%. Conclusions This study helps for dynamic risk assessment and emergency response of natural gas leakage accidents, as well as facilitating the implementation of predictive digital twin in utility tunnels.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141828507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-11DOI: 10.12688/digitaltwin.17734.1
Xin Shi, Fang Fang, Robert Qiu
Background: Power system anomaly detection is of great significance for realizing system situation awareness and early detection of system operating risks. In view of the complex operating conditions of the system, there are a large number of opaque links in the mechanism, and the anomaly detection approach based on physical mechanism modeling is prone to system errors due to assumptions, simplification, and transfer in the modeling process. This paper focuses on digital twin based data-driven approaches for power system anomaly detection to compensate for the limitation of physical methods in dynamical modeling. Methods: First of all, a digital twin framework for power system real-time analysis is constructed based on the concept of digital twin. Then, this paper conducts researches on the core of the designed framework, i.e., digital twin modeling. Considering the complexity of power system operating conditions, data-driven modeling is preferred and a random matrix and free probability theory based model for anomaly detection of system operating situation is constructed. Results: Simulation data with different spatiotemporal structure generated through a Monte Carlo experiment verified the sensitivity of the constructed model for data correlations. Meanwhile, the case on the system operating data generated through the IEEE 118-bus system validate the effectiveness of the proposed model for the system anomaly detection. Conclusions: The constructed data-driven model can accurately characterize the correlations among data elements, has good sensitivity to the variation of data spatial and temporal correlations, and can depict the data residuals better than the M-P law curve, which indicates the practicability and necessity of the constructed data-driven model for the digital twin modeling of power system anomaly detection.
{"title":"Data-driven modeling in digital twin for power system anomaly detection","authors":"Xin Shi, Fang Fang, Robert Qiu","doi":"10.12688/digitaltwin.17734.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17734.1","url":null,"abstract":"Background: Power system anomaly detection is of great significance for realizing system situation awareness and early detection of system operating risks. In view of the complex operating conditions of the system, there are a large number of opaque links in the mechanism, and the anomaly detection approach based on physical mechanism modeling is prone to system errors due to assumptions, simplification, and transfer in the modeling process. This paper focuses on digital twin based data-driven approaches for power system anomaly detection to compensate for the limitation of physical methods in dynamical modeling. Methods: First of all, a digital twin framework for power system real-time analysis is constructed based on the concept of digital twin. Then, this paper conducts researches on the core of the designed framework, i.e., digital twin modeling. Considering the complexity of power system operating conditions, data-driven modeling is preferred and a random matrix and free probability theory based model for anomaly detection of system operating situation is constructed. Results: Simulation data with different spatiotemporal structure generated through a Monte Carlo experiment verified the sensitivity of the constructed model for data correlations. Meanwhile, the case on the system operating data generated through the IEEE 118-bus system validate the effectiveness of the proposed model for the system anomaly detection. Conclusions: The constructed data-driven model can accurately characterize the correlations among data elements, has good sensitivity to the variation of data spatial and temporal correlations, and can depict the data residuals better than the M-P law curve, which indicates the practicability and necessity of the constructed data-driven model for the digital twin modeling of power system anomaly detection.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140715440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.12688/digitaltwin.17931.1
Li Yi, Patrick Ruediger-Flore, Ali Karnoub, Jan Mertes, Moritz Glatt, J. Aurich
Noise monitoring is important in the context of manufacturing because it can help maintain a safe and healthy workspace for employees. Current approaches for noise monitoring in manufacturing are based on acoustic sensors, whose measured sound pressure levels (SPL) are shown as bar/curve charts and acoustic heat maps. In such a way, the noise emission and propagation process is not fully addressed. This paper proposes a digital twin (DT) for noise monitoring in manufacturing using augmented reality (AR) and the phonon tracing method (PTM). In the proposed PTM/AR-based DT, the noise is represented by 3D particles (called phonons) emitting and traversing in a spatial domain. Using a mobile AR device (HoloLens 2), users are able to visualize and interact with the noise emitted by machine tools. To validate the feasibility of the proposed PTM/AR-based DT, two use cases are carried out. The first use case is an offline test, where the noise data from a machine tool are first acquired and used for the implementation of PTM/AR-based DT with different parameter sets. The result of the first use case is the understanding between the AR performance of HoloLens 2 (frame rate) and the setting of the initial number of phonons and sampling frequency. The second use case is an online test to demonstrate the in-situ noise monitoring capability of the proposed PTM/AR-based DT. The result shows that our PTM/AR-based DT is a powerful tool for visualizing and assessing the real-time noise in manufacturing systems.
{"title":"Is it possible to develop a digital twin for noise monitoring in manufacturing?","authors":"Li Yi, Patrick Ruediger-Flore, Ali Karnoub, Jan Mertes, Moritz Glatt, J. Aurich","doi":"10.12688/digitaltwin.17931.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17931.1","url":null,"abstract":"Noise monitoring is important in the context of manufacturing because it can help maintain a safe and healthy workspace for employees. Current approaches for noise monitoring in manufacturing are based on acoustic sensors, whose measured sound pressure levels (SPL) are shown as bar/curve charts and acoustic heat maps. In such a way, the noise emission and propagation process is not fully addressed. This paper proposes a digital twin (DT) for noise monitoring in manufacturing using augmented reality (AR) and the phonon tracing method (PTM). In the proposed PTM/AR-based DT, the noise is represented by 3D particles (called phonons) emitting and traversing in a spatial domain. Using a mobile AR device (HoloLens 2), users are able to visualize and interact with the noise emitted by machine tools. To validate the feasibility of the proposed PTM/AR-based DT, two use cases are carried out. The first use case is an offline test, where the noise data from a machine tool are first acquired and used for the implementation of PTM/AR-based DT with different parameter sets. The result of the first use case is the understanding between the AR performance of HoloLens 2 (frame rate) and the setting of the initial number of phonons and sampling frequency. The second use case is an online test to demonstrate the in-situ noise monitoring capability of the proposed PTM/AR-based DT. The result shows that our PTM/AR-based DT is a powerful tool for visualizing and assessing the real-time noise in manufacturing systems.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140369480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital Twin Workshop(DTW), as an important approach to digitalization and intelligentization of workshop, has gained significant attention in manufacturing industry. Currently, digital twin models for manufacturing resources have progressed from theoretical research to practical implementation. However, as a crucial component of workshop, modeling of human activity in workshop still faces challenges due to the autonomy and uncertainty of human beings. Therefore, we propose a comprehensive approach to the modeling cross-scale human activity in digital twin workshop, which comprises macro activity and micro activity. Macro activity contains human’s occupation and spatial positions in workshop, while micro activity refers to real-time posture and production actions at work. In this paper, we build and integrate macro activity digital twin model and micro activity digital twin model. With the combination of closed-loop interaction between virtual models and physical entities, we achieve semantic mapping and control of production activities, thereby facilitating practical management of human activity in workshop. Finally, we take certain factory’s manufacturing workshop as an example to introduce the application of the proposed approach.
{"title":"Modeling of cross-scale human activity for digital twin workshop","authors":"Tingyu Liu, Mengming Xia, Qing Hong, Yifeng Sun, Pei Zhang, Liang Fu, Ke Chen","doi":"10.12688/digitaltwin.17404.2","DOIUrl":"https://doi.org/10.12688/digitaltwin.17404.2","url":null,"abstract":"Digital Twin Workshop(DTW), as an important approach to digitalization and intelligentization of workshop, has gained significant attention in manufacturing industry. Currently, digital twin models for manufacturing resources have progressed from theoretical research to practical implementation. However, as a crucial component of workshop, modeling of human activity in workshop still faces challenges due to the autonomy and uncertainty of human beings. Therefore, we propose a comprehensive approach to the modeling cross-scale human activity in digital twin workshop, which comprises macro activity and micro activity. Macro activity contains human’s occupation and spatial positions in workshop, while micro activity refers to real-time posture and production actions at work. In this paper, we build and integrate macro activity digital twin model and micro activity digital twin model. With the combination of closed-loop interaction between virtual models and physical entities, we achieve semantic mapping and control of production activities, thereby facilitating practical management of human activity in workshop. Finally, we take certain factory’s manufacturing workshop as an example to introduce the application of the proposed approach.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140217893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The temperature field of oil and gas wells in the field of petroleum engineering presents a core problem and challenge in the digital twin framework due to its ultra-long-distance and highly variable structural characteristics. The varying wellbore cross-sectional structures with depth make it difficult to establish an effective and generalized analytical model for heat transfer. In this study, we propose, for the first time, a method to automate the construction of multi-layered and multi-component heat transfer models by using a general computational model based on non-steady-state single-phase structural modules. This method enables the automated generation of complex multi-layered and multi-component heat transfer models, thereby achieving the construction of a generalized model for temperature field characterization with varying wellbore cross-sectional structures over ultra-long distances. Utilizing this modeling approach, we validate the proposed method through case studies using actual wellbore temperature field data. The results demonstrate the lightweight and efficient computational analysis of temperature field information under non-steady-state conditions.
{"title":"Digital twinning of temperature fields for modular multilayer multiphase pipeline structures","authors":"Wenlan Wei, Maliang Wang, Jiarui Cheng, Yue Hu, Yuqiang Li, Jie Zheng","doi":"10.12688/digitaltwin.17930.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17930.1","url":null,"abstract":"The temperature field of oil and gas wells in the field of petroleum engineering presents a core problem and challenge in the digital twin framework due to its ultra-long-distance and highly variable structural characteristics. The varying wellbore cross-sectional structures with depth make it difficult to establish an effective and generalized analytical model for heat transfer. In this study, we propose, for the first time, a method to automate the construction of multi-layered and multi-component heat transfer models by using a general computational model based on non-steady-state single-phase structural modules. This method enables the automated generation of complex multi-layered and multi-component heat transfer models, thereby achieving the construction of a generalized model for temperature field characterization with varying wellbore cross-sectional structures over ultra-long distances. Utilizing this modeling approach, we validate the proposed method through case studies using actual wellbore temperature field data. The results demonstrate the lightweight and efficient computational analysis of temperature field information under non-steady-state conditions.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-08DOI: 10.12688/digitaltwin.17913.1
Huang Lei, Zhou Fanli, Wang Wei, Shang Shuai, Haocheng Zhou
Spacecrafts are large-scale systems characterized by various on-orbit configurations, multi-disciplinary coupling, and complex mission modes. Research and exploration on the data-driven spacecraft digital twins development methods must be carried out to satisfy various requirements such as spacecraft on-orbit condition monitoring and risk warning, fast flight conditions predictions, intelligent failure location, and virtual verification of failure. In this paper, significant progress is made in multiple key technologies, such as cyber-physical system modeling and simulation, hybrid modeling and model evolution through mechanism-data fusion, and interactive virtual-reality perception and mapping. The spacecraft digital twins’ model is constructed, and the spacecraft digital twin’s platform is designed and developed. Multiple digital twins’ application scenarios, such as on-orbit mission simulation and emulation, real-time interactive monitoring, and fast operating condition prediction, are supported. The research results are applied to the key on-orbit operation tasks, such as entering orbit, rendezvous and docking, position conversion, and astronaut exiting, enabling system-level digital operation for the sub-systems of spacecraft such as energy, power, control, and communication sub-systems.
{"title":"Digital twin method and application practice of spacecraft system driven by mechanism data","authors":"Huang Lei, Zhou Fanli, Wang Wei, Shang Shuai, Haocheng Zhou","doi":"10.12688/digitaltwin.17913.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17913.1","url":null,"abstract":"Spacecrafts are large-scale systems characterized by various on-orbit configurations, multi-disciplinary coupling, and complex mission modes. Research and exploration on the data-driven spacecraft digital twins development methods must be carried out to satisfy various requirements such as spacecraft on-orbit condition monitoring and risk warning, fast flight conditions predictions, intelligent failure location, and virtual verification of failure. In this paper, significant progress is made in multiple key technologies, such as cyber-physical system modeling and simulation, hybrid modeling and model evolution through mechanism-data fusion, and interactive virtual-reality perception and mapping. The spacecraft digital twins’ model is constructed, and the spacecraft digital twin’s platform is designed and developed. Multiple digital twins’ application scenarios, such as on-orbit mission simulation and emulation, real-time interactive monitoring, and fast operating condition prediction, are supported. The research results are applied to the key on-orbit operation tasks, such as entering orbit, rendezvous and docking, position conversion, and astronaut exiting, enabling system-level digital operation for the sub-systems of spacecraft such as energy, power, control, and communication sub-systems.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-01DOI: 10.12688/digitaltwin.17918.1
Xin Wang, Xiaofen Shan, Kang Cui, Zhinan Zhang
The shipbuilding industry plays a pivotal role in national strategic security and economic development, and one critical challenge is the pipeline layout design problem. It predominantly relies on designers’ subjective experience, and it is marked by a lack of efficient knowledge sharing and the absence of smart pipe routing algorithms. This paper proposes an agile design system, which integrates ship pipeline design knowledge management, semi-automatic design that involves frequent interaction with human designers, and automatic rule checking. The framework is refined by digital twin concepts, facilitating close collaboration between physical and digital systems. The paradigm shift holds the potential to substantially enhance the efficiency of ship pipeline layout design, while concurrently reducing the reliance on manual labor.
{"title":"Digital twin enhanced agile design of ship pipeline systems","authors":"Xin Wang, Xiaofen Shan, Kang Cui, Zhinan Zhang","doi":"10.12688/digitaltwin.17918.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17918.1","url":null,"abstract":"The shipbuilding industry plays a pivotal role in national strategic security and economic development, and one critical challenge is the pipeline layout design problem. It predominantly relies on designers’ subjective experience, and it is marked by a lack of efficient knowledge sharing and the absence of smart pipe routing algorithms. This paper proposes an agile design system, which integrates ship pipeline design knowledge management, semi-automatic design that involves frequent interaction with human designers, and automatic rule checking. The framework is refined by digital twin concepts, facilitating close collaboration between physical and digital systems. The paradigm shift holds the potential to substantially enhance the efficiency of ship pipeline layout design, while concurrently reducing the reliance on manual labor.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140084810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-30DOI: 10.12688/digitaltwin.17664.2
Shuaiming Su, Ray Y. Zhong, Yishuo Jiang
The construction industry has a great impact on social and economic development because of its wide coverage and a large number of stakeholders involved. It is precisely owing to its large volume that technological innovation of the construction industry is relatively slow. The birth and rapid development of digital twins brings more hope to the construction industry. This paper summarizes the current development of digital twin and its applications in construction industry. First, the concepts and applications of digital twin are analyzed. Then, the research on digital twins in the construction industry in the past five years is reviewed. The main research directions and key technologies are pointed out in the end. This paper could guide related practitioners to clearly grasp the research application status of digital twin in the construction industry. It could also help to find suitable research directions.
{"title":"Digital twin and its applications in the construction industry: A state-of-art systematic review","authors":"Shuaiming Su, Ray Y. Zhong, Yishuo Jiang","doi":"10.12688/digitaltwin.17664.2","DOIUrl":"https://doi.org/10.12688/digitaltwin.17664.2","url":null,"abstract":"<ns3:p>The construction industry has a great impact on social and economic development because of its wide coverage and a large number of stakeholders involved. It is precisely owing to its large volume that technological innovation of the construction industry is relatively slow. The birth and rapid development of digital twins brings more hope to the construction industry. This paper summarizes the current development of digital twin and its applications in construction industry. First, the concepts and applications of digital twin are analyzed. Then, the research on digital twins in the construction industry in the past five years is reviewed. The main research directions and key technologies are pointed out in the end. This paper could guide related practitioners to clearly grasp the research application status of digital twin in the construction industry. It could also help to find suitable research directions.</ns3:p>","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-30DOI: 10.12688/digitaltwin.17830.1
Zhansheng Liu, Z. Zhu, Zhe Sun, Anxiu Li, Shuxin Ni
Background: Site pollution in construction can be reduced by using high levels of prefabrication and industrialization. However, the lack of green concepts and methods during the prefabrication assembly process hinders its environmental benefits. Digital twin technology can monitor sites in real-time and provide data visualization for decision support, which has been used in construction management and risk control. Methods: We propose a six-dimensional digital twin framework that includes physical and virtual spaces, project management and service layers, twin data, and component connections. The framework integrates green factors of prefabricated construction into a model evolution framework and mechanism that enables real-time green services throughout the process. Results: The proposed framework, modeling method, and evolution method were tested in prefabrication projects in Tianjin. By applying these methods, inadequate management measures were promptly identified and strengthened. Energy consumption and pollution were reduced by comparing with the plan before construction. In addition, the model evolution method optimized green management measures and improved the level of green construction management on site. Conclusions: The application results demonstrate the effectiveness of our proposed framework, the model building method, and the evolution method in improving the green level of prefabricated construction.
{"title":"A digital twin-based green construction management method for prefabricated buildings","authors":"Zhansheng Liu, Z. Zhu, Zhe Sun, Anxiu Li, Shuxin Ni","doi":"10.12688/digitaltwin.17830.1","DOIUrl":"https://doi.org/10.12688/digitaltwin.17830.1","url":null,"abstract":"Background: Site pollution in construction can be reduced by using high levels of prefabrication and industrialization. However, the lack of green concepts and methods during the prefabrication assembly process hinders its environmental benefits. Digital twin technology can monitor sites in real-time and provide data visualization for decision support, which has been used in construction management and risk control. Methods: We propose a six-dimensional digital twin framework that includes physical and virtual spaces, project management and service layers, twin data, and component connections. The framework integrates green factors of prefabricated construction into a model evolution framework and mechanism that enables real-time green services throughout the process. Results: The proposed framework, modeling method, and evolution method were tested in prefabrication projects in Tianjin. By applying these methods, inadequate management measures were promptly identified and strengthened. Energy consumption and pollution were reduced by comparing with the plan before construction. In addition, the model evolution method optimized green management measures and improved the level of green construction management on site. Conclusions: The application results demonstrate the effectiveness of our proposed framework, the model building method, and the evolution method in improving the green level of prefabricated construction.","PeriodicalId":29831,"journal":{"name":"Digital Twin","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49606758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.12688/digitaltwin.17863.1
Junnan Zhi, Yanlong Cao, Tukun Li, Anwer Nabil, Fan Liu, X. Jiang, Jiangxin Yang
Background: In mass production, engineers are more concerned with the statistical distribution accuracy of parts in mass production rather than just the qualification of individual parts. However, currently, the statistical analysis methods designed for product accuracy are relatively scattered, and most of them focus on nominal part models. Therefore, there is a need to design a statistical analysis method for parts in mass production based on the Digital Twin model. Methods: This paper presents a novel method to analyse the geometric variations of parts in batch production in the production line, which is based on digital twins to model and evaluate deviations contributed by the geometrical condition, assembly condition and material condition. Firstly, the statistical descriptions of the parts, particularly the features of a digital twin for parts in batch production related to the geometry and position, are classified into various hierarchies. Secondly, a covariance method is employed to analyse the law of their shape from the descriptions. Thirdly, the parts' shape feature similarity for different terms is derived, including the linear features of pose constraint, rotation deviation, and geometric deviation and the curve features like a geometric deviation. Finally, the probability distribution of discrete points on the manufacturing error caused by different reasons is calculated. Results: Two case studies of reducer and rail highlight the applicability of the proposed approach. The standard deviation of the points has similar trend with sample cases according to normal distribution. Conclusions: This paper categorizes the deviations of batch parts into the linear features of pose constraint, rotation deviation, and geometric deviation. When batch parts exhibit any of these deviation types, the eigenvalues and eigenvectors of their covariance matrix show certain patterns, enabling the identification of the deviation type and calculation of the statistical deviation probability distribution for the corresponding features.
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