Abstract
For multicomponent structures enduring dynamic workloads coming from multi-physical fields, safety assessment is significant to guarantee the normal operation of entire structure system. In this paper, an enhanced extremum Kriging-based decomposed coordinated framework (E2K-DCF) is proposed to improve the dynamic probabilistic failure analyses of multicomponent structures. In this method, extremum Kriging model (EKM) is developed by introducing Kriging model into extremum response surface method (ERSM) to process the transient response problem and shorten computational burden in dynamic probabilistic failure analyses. Multiple population genetic algorithm (MPGA) is employed to solve maximum likelihood equation (MLE) and find the optimal hyperparameter $\boldsymbol \theta $ in the EKM, which is promising to enhance approximate accuracy; decomposed-coordinated (DC) strategy is used to handle the coordinated relationship of multiple analytical objectives. To validate the proposed E2K-DCF, the probabilistic failure analysis of turbine blisk radial deformation is conducted by comparing with different methods within time domain [0 s, 215 s], considering fluid-thermal-structural interaction. It is revealed that the failure probability of blisk radial deformation is only 0.0022 when the allowable value is $2.5702\times 10^{-3}$ m acquired from real world practice. Compared to the other approaches, this E2K-DCF has obvious advantages in fitting time and accuracy as well as simulation efficiency and accuracy. The results illustrate that the E2K-DCF is effective and applicable in dynamic probabilistic failure analysis. The efforts of this paper provide a novel viewpoint for the transient reliability evaluation of multicomponent structures, which is likely to enrich mechanical reliability theory.
Highlights
Complex structure in gas turbine always involves multiple components and endures complicated workloads induced by multi-physical fields during operation [1]
To overcome the two defects, we develop extremum Kriging model (EKM) for structural dynamic probabilistic failure analyses by absorbing the superiorities of extremum response surface method (ERSM) and Kriging model
The results show that the mean value of root mean square error (RMSE) of E2KDCF for turbine blisk deformation (1.63 × 10−5 m) is less than those of the DCERSM and DCIEKS (2.43×10−5 m and 1.79 × 10−5 m), which reveals that the developed E2K-based DC framework (E2K-DCF) has good learning ability and high modeling accuracy to some extent
Summary
Complex structure in gas turbine always involves multiple components and endures complicated workloads induced by multi-physical fields during operation [1]. To guarantee the safety and performance of the structure, it is urgent to investigate the probabilistic failure analysis of multicomponent structures considering the effect of dynamic loads and the randomness of influencing parameters. Despite the aforementioned methods are effective in structural probabilistic failure analysis, they cannot be applied to the dynamic reliability analyses of multicomponent structures, due to the required thousands of iterations for real models. The objective of this paper is to provide an enhanced extremum Kriging-based decomposed-coordinated framework (E2K-DCF) for multicomponent structural probabilistic failure analysis with dynamic loads and random parameters, by integrating the strengths of Kriging model, ERSM, multiple population genetic algorithm (MPGA) and decomposedcoordinated (DC) strategy. To validate the developed method, the dynamic probabilistic failure analysis of turbine blisk is conducted to verify the proposed approach in engineering, by considering fluid-thermalstructural interaction.
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