Abstract
Real-world products and physics-based simulations are becoming interconnected. In particular, real-time capable dynamic simulation has made it possible for simulation models to run in parallel and simultaneously with operating machinery. This capability combined with state observer techniques such as Kalman filtering have enabled the synchronization between simulation and the real world. State estimator techniques can be applied to estimate unmeasured quantities, also referred as virtual sensing, or to enhance the quality of measured signals. Although synchronized models could be used in a number of ways, value creation and business model development are currently defining the most practical and beneficial use cases from a business perspective. The research reported here reveals the communication and collaboration methods that lead to economically relevant technology solutions. Two case examples are given that demonstrate the proposed methodology. The work benefited from the broad perspective of researchers from different backgrounds and the joint effort to drive the technology development towards business relevant cases.
| Original language | English |
|---|---|
| Pages (from-to) | 45962-45978 |
| Number of pages | 17 |
| Journal | IEEE Access |
| Volume | 10 |
| DOIs | |
| Publication status | Published - 25 Apr 2022 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Computational modeling
- Digital twin
- Finite element analysis
- Finite Element Method
- Kalman Filter
- Kalman filters
- Mathematical models
- Multibody simulation
- Parameter estimation
- Physics-based simulation
- Predictive models
- Real-time systems
- State estimation
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Open Access
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