Digital twin:Multi-dimensional model reduction method for performance optimization of the virtual entity
Autor: | Kari Koskinen, Arttu Heininen, Ananda Chakraborti, Ville Lämsä |
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Přispěvatelé: | Gao, Robert X., Ehmann, Kornel, Tampere University, Automation Technology and Mechanical Engineering |
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Model reduction Distributed computing Multi-physics simulation 02 engineering and technology 010501 environmental sciences 01 natural sciences Digital twins Spectral clustering Data-driven Reduction (complexity) 214 Mechanical engineering 020901 industrial engineering & automation Model fusion Asynchronous communication Metric (mathematics) General Earth and Planetary Sciences Graph (abstract data type) State (computer science) Representation (mathematics) 0105 earth and related environmental sciences General Environmental Science |
Zdroj: | Chakraborti, A, Heininen, A, Koskinen, K T & Lämsä, V 2020, ' Digital twin : Multi-dimensional model reduction method for performance optimization of the virtual entity ', Procedia CIRP, vol. 93, pp. 240-245 . https://doi.org/10.1016/j.procir.2020.04.050 |
Popis: | Digital Twin (DT) is an emerging technology that allows manufacturers to simulate and predict states of complex machine systems during operation. This requires that the physical machine state is integrated in a virtual entity, instantaneously. However, if the virtual entity uses computationally demanding models like physics-based finite element models or data driven prediction models, the virtual entity may become asynchronous with its physical entity. This creates an increasing lag between the twins, reducing the effectiveness of the virtual entity. Therefore, in this article, a model reduction method is described for a graph-based representation of multi-dimensional DT model based on spectral clustering and graph centrality metric. This method identifies and optimizes high-importance variables from computationally demanding models to minimize the total number of variables required for improving the performance of the DT. publishedVersion |
Databáze: | OpenAIRE |
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