Digital Twin Platform
Title: |
Digital Twin Platform |
DNr: |
NAISS 2023/22-458 |
Project Type: |
NAISS Small Compute |
Principal Investigator: |
Mohammad Rad <radmo@chalmers.se> |
Affiliation: |
Chalmers tekniska högskola |
Duration: |
2023-04-26 – 2023-11-01 |
Classification: |
20307 |
Keywords: |
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Abstract
System-level design space exploration for complex products is limited in design qualifications. Relying on physical tests makes the qualification costly and relying on digital simulations makes it inaccurate. When designing a complex product, being able to predict the system's final performance with chosen configurations, even roughly, can enable wide design space explorations with lower costs on prototyping, qualifying, and optimizing. There is a lack of understanding of how design knowledge at the component level can be leveraged to assess performance at the system level.