Design assessments of complex systems based on design oriented modelling and uncertainty analysis

• Published in: Mechanical systems and signal processing Vol. 188; p. 109988
• Main Authors: Yu, Changshuai, Zhu, Yun-Peng, Luo, Haitao, Luo, Zhong, Li, Lei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2023
• Subjects: Complex systems; Design assessment; System identification; Uncertainty analysis; System identification; Complex systems; Uncertainty analysis; Design assessment
• ISSN: 0888-3270
• DOI: 10.1016/j.ymssp.2022.109988

In the design of nonlinear complex systems, the output responses of a complex system subject to demanding loading conditions need to be assessed before the system can be used in practice. Current assessment approaches (including physical model based analysis, Finite Element (FE) simulation and similitude experiments) are often time-consuming and problematic. The objective of this study is to develop a novel data-driven approach to predict the output responses, as well as their uncertainties, of a complex system for the aim of design assessment. The results can be used to decide whether redesign of the complex system is required to avoid damage for actual use. To achieve this goal, a novel design-oriented data driven modelling approach has been developed based on the FROLS (Forward Regression Orthogonal Least Squares) algorithm and K-fold cross validation. In this approach, the NARX (Nonlinear AutoRegressive with eXogenous input) model of the system is identified and validated. After that, the Monte Carlo Simulation (MCS) method is applied to quantify the uncertainties of the NARX model coefficients. Subject to specified inputs, the model coefficients’ uncertainties are propagated to the model predicted output responses for design assessments. Finally, the proposed approach is verified by a case study on the design assessment of a Chinese Space Station Scientific Experiment Rack (CSS-SER). The results indicate the efficiency of the proposed approach and its potential to be applied to solve many engineering design problems.