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摘要
Reliability analysis of a multidisciplinary system is computationally intensive due to the involvement of multiple disciplinary models and coupling between the individual models. When the system inputs and outputs are varying over time and space, the reliability analysis is even more challenging. This paper proposes a surrogate model-based method for the reliability analysis of a multidisciplinary system with spatio-temporal output. The transient characteristics of the multidisciplinary system output under time-dependent variability are analyzed first. Based on the transient analysis, surrogate models are built for individual disciplinary analyses instead of a single surrogate model for the fully coupled analysis. To address the challenge introduced by the high-dimensionality of spatially varying inter-disciplinary coupling variables, a data compression method is first employed to convert the high-dimensional coupling variables into low-dimensional latent space. Kriging surrogate modeling is then used to build surrogates for the individual disciplinary models in the latent space. Based on the individual disciplinary surrogate models, reliability analysis of the coupled multidisciplinary system under time-dependent uncertainty is investigated. Further, epistemic uncertainty sources, such as data uncertainty and model uncertainty, lead to uncertainty in the reliability estimate. Therefore, an auxiliary variable approach is used to efficiently include the epistemic uncertainty sources within the reliability analysis. An aircraft panel subjected to hypersonic flow conditions is used to demonstrate the proposed method. The analysis involves four interacting disciplinary models, namely, aerodynamics, aerothermal analysis, heat transfer, and structural analysis. The results show that the proposed method is able to effectively perform reliability analysis of a multidisciplinary system with spatio-temporal output.
- 出版日期2017-9
