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      A digital twin modeling and application for gear rack drilling rigs lifting system

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          Abstract

          A comprehensive digital transformation has been undergone by the oil and gas industry, wherein digital twins are leveraged to enable real-time data analysis, providing predictive and diagnostic engineering insights. The potential for developing intelligent oil and gas fields is substantial with the implementation of digital twins. A digital twin framework for gear rack drilling rigs is proposed, built upon an understanding of the digital twin composition and characteristics of the gear rack drilling rig lifting system. The framework encompasses descriptions of digital twin characteristics specific to drilling rigs, the application environment, and behavioral rules. The modeling approach integrates mechanism modeling, real-time performance response, instantaneous data transmission, and data visualization. To illustrate this framework, exemplary case studies involving the transmission unit and support unit of the lifting system are presented. Mechanism models are constructed to analyze dynamic gear performance and support unit response. Real-time data transmission is facilitated through sensor-based monitoring, enhancing the prediction speed and accuracy of dynamic performance through a synergy of mechanism modeling, machine learning, and real-time data analysis. The digital twin of the lifting system is visualized utilizing the Unity3D platform. Furthermore, functionalities on data acquisition, processing, and visualization across diverse application scenarios are encapsulated into modular components, streamlining the creation of high-fidelity digital twins. The frameworks and modeling methodologies presented herein can serve as a foundational and methodological guide for the exploration and implementation of digital twin technology within the oil and gas industry, ultimately fostering its advancement in this sector.

          Supplementary Information

          The online version contains supplementary material available at 10.1038/s41598-024-73954-z.

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          Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems

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            Enabling technologies and tools for digital twin

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              Degradation curves integration in physics-based models: Towards the predictive maintenance of industrial robots

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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                10 October 2024
                10 October 2024
                2024
                : 14
                : 23711
                Affiliations
                [1 ]School of Mechanical Engineering, Yangtze University, ( https://ror.org/05bhmhz54) Jingzhou, 434023 Hubei China
                [2 ]Hubei Engineering Research Center for Oil and Gas Drilling and Completion Tools, Jingzhou, 434023 Hubei China
                [3 ]College of Mechanical Engineering, Chongqing University of Technology, ( https://ror.org/04vgbd477) Chongqing, 401135 China
                Article
                73954
                10.1038/s41598-024-73954-z
                11467295
                39390008
                fb7e6871-e6f3-4246-8d26-6e4f76df9b3a
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 22 March 2024
                : 23 September 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100014717, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China;
                Award ID: 52204002
                Funded by: FundRef http://dx.doi.org/10.13039/501100018537, National Science and Technology Major Project;
                Award ID: 2016ZX05038-002-LH001
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                drilling and completion engineering,gear rack drilling rig,digital twin,real-time prediction.,energy science and technology,engineering

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