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.
Highlights
As a hot-end core component of aeroengine, a turbine rotor operates in severe loading environment at a high temperature, high pressure and high speed
The unacceptable accuracy and efficiency problems would occur if directly adopting DC strategy with regular surrogate model. In this situation, considering the potentials of intelligent Kriging model, we further developed a decomposed collaborative intelligent Kriging model to improve the simulation accuracy and efficiency for probabilistic fatigue life evaluation of a turbine rotor
MCM, Kriging model (KM) and intelligent Kriging modeling (IKM) calculate the relationship between input variable X0 = [ω, T, ρ, E, λ, α, σf 0, c, b, εf 0 ] and output response Nf directly, while DCRSM and decomposed collaborative IKM (DCIKM) adopt parallel calculation by simulating elastic strain range ∆εe, plastic strain range ∆εp, mean stress σm, and fatigue life Nf in several computer devices
Summary
As a hot-end core component of aeroengine, a turbine rotor operates in severe loading environment at a high temperature, high pressure and high speed. These loads often present complex alterability and cyclicity. In this case, the turbine rotor is prone to generate complex plastic deformation, which inevitably leads to low-cycle fatigue failure. Effective probabilistic fatigue life evaluation of turbine rotor is urgently required to describe these uncertainties and evaluate its reliability. Under these circumstances, probabilistic analysis techniques have emerged to tackle with the multiple points of uncertainty [7,8]
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