Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor

Abstract

To improve simulation accuracy and efficiency of probabilistic fatigue life evaluation for turbine rotor, a decomposed collaborative modeling approach is presented. In this approach, the intelligent Kriging modeling (IKM) is firstly proposed by combining the Kriging model (KM) and an intelligent algorithm (named as dynamic multi-island genetic algorithm), to tackle the multi-modality issues for obtaining optimal Kriging parameters. Then, the decomposed collaborative IKM (DCIKM) comes up by fusing the IKM into decomposed collaborative (DC) strategy, to address the high-nonlinearity problems for accelerating simulation efficiency. Moreover, the DCIKM-based probabilistic fatigue life evaluation theory is introduced. The probabilistic fatigue life evaluation of turbine rotor is regarded as case study to verify the presented approach; the evaluation results reveal that the probabilistic fatigue life of turbine rotor is 3296 cycles. The plastic strain range ∆εp and fatigue strength coefficient σf′ are the main affecting factors to fatigue life, whose effect probability are 28% and 22%, respectively. By comparing with direct Monte Carlo method, KM method, IKM method and DC response surface method, the presented DCIKM is validated to hold high efficiency and accuracy in probabilistic fatigue life evaluation.

Recent advances in reliability analysis of aeroengine rotor system: a review

“…Compared with the direct MCS method, the surrogate model requires fewer samples and holds lower time consumptions (Zhang et al, 2016). Current surrogate model methods include polynomial function (Meng et al, 2019b(Meng et al, , 2020a, Kriging model (Huang et al, 2020;Zhang et al, 2015Zhang et al, , 2020b, support vector machine (Hurtado, 2007;Gao and Bai, 2015;Dai et al, 2012) and artificial neural network (Song et al, 2018;Barbosa et al, 2020;Liu et al, 2019a).…”

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

“…Figure 16 depicts the thermal-structural interaction-based simulation nephograms, where the material is set as GH4133B. 4 The blade root with the maximum stress and strain is regarded as the reliability hazard location. To effectively quantify the LCF life dispersion, the distribution traits of material variables (elastic modulus E, material density ρ, thermal conductivity h, Poisson ratio μ, thermal expansion coefficient α, and specific heat v), and physical loads (rotational speed ω, blade-root temperature T 1 , and blade-tip temperature T 2 ) are shown in Table 9.…”

“…Numerical application of the AOK‐ES is further demonstrated by performing the high‐nonlinearity low cycle fatigue (LCF) reliability analysis of a turbine rotor, and considering its cyclic symmetry, a 1/40 turbine rotor is selected as the computing object. Figure 16 depicts the thermal‐structural interaction‐based simulation nephograms, where the material is set as GH4133B 4 . The blade root with the maximum stress and strain is regarded as the reliability hazard location.…”

“…Uncertainties widely exist in practical engineering structural systems, such as industrial robots 1,2 and aerospace equipment. [3][4][5] Reliability analysis is an effective technique to quantify uncertainty, which is of great significance to the reliable operation of complex equipment systems. [6][7][8][9] The key of reliability analysis is to evaluate the failure probability P f , i.e., to solve the following integral:…”

Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

“…Only needing a small amount of black-box function calls, the surrogate model can be established and is promising to reduce simulation cost [7][8][9][10][11]. Classic surrogate models include polynomial response surface [12][13][14], Kriging model (KM) [15][16][17], artificial neural network [18][19][20], and support vector regression [21][22][23]. Among them, with integrating global nonlinear approximation ability and local precise description ability, the Kriging model possesses the potentials to approximate complex structural responses and ensure calculation accuracy [24,25], which is suitable for complex multiobjective optimization problems of the multicomponent system.…”