Digital twins for design in the presence of uncertainties
Andrade, L https://orcid.org/0000-0003-0679-9354
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.
Online Publication Date
Design sensitivity toolbox, Design key performance indicator, Design entropy, Fisher information, Probability of acceptance, Likelihood ratio method
Mechanical Systems and Signal Processing
Engineering and Physical Sciences Research Council (EP/R006768/1)