Digital twin dynamic-polymorphic uncertainty surrogate model generation using a sparse polynomial chaos expansion with application in aviation hydraulic pump

Full lifecycle high fidelity digital twin is a complex model set contains multiple functions with high dimensions and multiple variables. Quantifying uncertainty for such complex models often encounters time-consuming challenges, as the number of calculated terms increases exponentially with the dimensionality of the input. This paper based on the multi-stage model and high time consumption problem of digital twins, proposed a sparse polynomial chaos expansions method to generate the digital twin dynamic-polymorphic uncertainty surrogate model, striving to strike a balance between the accuracy and time consumption of models used for digital twin uncertainty quantification. Firstly, an analysis and clarification were conducted on the dynamic-polymorphic uncertainty of the full lifetime running digital twins. Secondly, a sparse polynomial chaos expansions model response was developed based on partial least squares technology with the effectively quantified and selected basis polynomials which sorted by significant influence. In the end, the accuracy of the proxy model is evaluated by leave-one-out cross-validation. The effectiveness of this method was verified through examples, and the results showed that it achieved a balance between maintaining model accuracy and complexity.