Digital twins for design in the presence of uncertainties
Published version
Peer-reviewed
Repository URI
Repository DOI
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Authors
Abstract
Successful application of digital twins in the design process requires a tailored approach to identify high value information from the uncertain data. We propose a non-intrusive sensitivity metric toolbox that integrates black-box digital twins in the design and decision process under uncertainties. The toolbox captures the evolving nature of the key design performance indicators (KPI) and provide both KPI-free and KPI-based metrics. The KPI-free metrics, which are based on entropy and Fisher information but independent of design KPIs, is shown to give good indication of the most influential data for KPI-based metrics. This suggests a consistent identification of high value data throughout the design process.
Description
Keywords
Design sensitivity toolbox, Design key performance indicator, Design entropy, Fisher information, Probability of acceptance, Likelihood ratio method
Journal Title
Mechanical Systems and Signal Processing
Conference Name
Journal ISSN
0888-3270
1096-1216
1096-1216
Volume Title
179
Publisher
Elsevier BV
Publisher DOI
Sponsorship
Engineering and Physical Sciences Research Council (EP/R006768/1)