Model Order Reduction a Key Technology for Digital Twins

Dirk Hartmann (), Matthias Herz and Utz Wever
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Dirk Hartmann: Siemens AG, Corporate Technology
Matthias Herz: Siemens AG, Corporate Technology
Utz Wever: Siemens AG, Corporate Technology

A chapter in Reduced-Order Modeling (ROM) for Simulation and Optimization, 2018, pp 167-179 from Springer

Abstract: Abstract An increasing number of disruptive innovations with high economic and social impact shape our digitalizing world. Speed and extending scope of these developments are limited by available tools and paradigms to master exploding complexities. Simulation technologies are key enablers of digitalization. They enable digital twins mirroring products and systems into the digital world. Digital twins require a paradigm shift. Instead of expert centric tools, engineering and operation require autonomous assist systems continuously interacting with its physical and digital environment through background simulations. Model order reduction (MOR) is a key technology to transfer highly detailed and complex simulation models to other domains and life cycle phases. Reducing the degree of freedom, i.e., increasing the speed of model execution while maintaining required accuracies and predictability, opens up new applications. Within this contribution, we address the advantages of model order reduction for model-based system engineering and real-time thermal control of electric motors.

Keywords: Model order reduction; Virtual sensor; Systems engineering; Krylov methods; Response surfaces (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-75319-5_8

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DOI: 10.1007/978-3-319-75319-5_8

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