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

作　　者：Dong LIU Shaoping WANG Jian SHI Di LIU 

机构地区：[1]School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China [2]Tianmushan Laboratory,Hangzhou 310023,China

出　　处：《Chinese Journal of Aeronautics》2024年第12期231-244,共14页中国航空学报（英文版）

基　　金：co-supported by the National Natural Science Foundation of China(Nos.51875014,U2233212 and 51875015);the Natural Science Foundation of Beijing Municipality,China(No.L221008);the Science,Technology Innovation 2025 Major Project of Ningbo of China(No.2022Z005);the Tianmushan Laboratory Project,China(No.TK-2023-B-001).

摘　　要：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.

关 键 词：Digital Twin Uncertainty surrogate model Dynamic-polymorphic uncertainty Sparse polynomial chaos expansions Aviation hydraulic pump 

分 类 号：TP399[自动化与计算机技术—计算机应用技术] V245.1[自动化与计算机技术—计算机科学与技术]