Data Integration Framework for Virtual Reality Enabled Digital Twins
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916080909287424 |
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| author | Stadtmann, Florian Mahalingam, Hary Pirajan Rasheed, Adil |
| author_facet | Stadtmann, Florian Mahalingam, Hary Pirajan Rasheed, Adil |
| contents | Digital twins are becoming increasingly popular across many industries for real-time data streaming, processing, and visualization. They allow stakeholders to monitor, diagnose, and optimize assets. Emerging technologies used for immersive visualization, such as virtual reality, open many new possibilities for intuitive access and monitoring of remote assets through digital twins. This is specifically relevant for floating wind farms, where access is often limited. However, the integration of data from multiple sources and access through different devices including virtual reality headsets can be challenging. In this work, a data integration framework for static and real-time data from various sources on the assets and their environment is presented that allows collecting and processing of data in Python and deploying the data in real-time through Unity on different devices, including virtual reality headsets. The integration of data from terrain, weather, and asset geometry is explained in detail. A real-time data stream from the asset to the clients is implemented and reviewed, and instructions are given on the code required to connect Python scripts to any Unity application across devices. The data integration framework is implemented for a digital twin of a floating wind turbine and an onshore wind farm, and the potential for future research is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02193 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data Integration Framework for Virtual Reality Enabled Digital Twins Stadtmann, Florian Mahalingam, Hary Pirajan Rasheed, Adil Signal Processing Digital twins are becoming increasingly popular across many industries for real-time data streaming, processing, and visualization. They allow stakeholders to monitor, diagnose, and optimize assets. Emerging technologies used for immersive visualization, such as virtual reality, open many new possibilities for intuitive access and monitoring of remote assets through digital twins. This is specifically relevant for floating wind farms, where access is often limited. However, the integration of data from multiple sources and access through different devices including virtual reality headsets can be challenging. In this work, a data integration framework for static and real-time data from various sources on the assets and their environment is presented that allows collecting and processing of data in Python and deploying the data in real-time through Unity on different devices, including virtual reality headsets. The integration of data from terrain, weather, and asset geometry is explained in detail. A real-time data stream from the asset to the clients is implemented and reviewed, and instructions are given on the code required to connect Python scripts to any Unity application across devices. The data integration framework is implemented for a digital twin of a floating wind turbine and an onshore wind farm, and the potential for future research is discussed. |
| title | Data Integration Framework for Virtual Reality Enabled Digital Twins |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2401.02193 |