Probabilistic Fatigue/Creep Optimization of Turbine Bladed Disk with Fuzzy Multi-Extremum Response Surface Method

Zhang, Chunyi; Yuan, Zhe-Shan; Wang, Ze; Fei, Chengwei; Lu, Cheng

doi:10.3390/ma12203367

Cited by 18 publications

(13 citation statements)

References 37 publications

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“…Along with the theory and concept of the DCGRENN method and BTRRC reliability analysis, in the GRENN model, the Gauss function is selected as the transfer function in the hidden layer, in which the weights matrix LW 1,1 was computed using the Euclidean distance function [33,34,35]. Then the outputs of the hidden layer were regarded as the connected weights matrix LW 2,1 of the hidden layer and the output layer.…”

Section: Reliability Analysis Of Blade-tip Radial Running Clearancementioning

confidence: 99%

Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

et al. 2019

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To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

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Section: Reliability Analysis Of Blade-tip Radial Running Clearancementioning

confidence: 99%

Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

et al. 2019

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“…Kriging model holds good approximate performance in prediction, and thus is widely applied in performance analysis [20], [24], probabilistic failure analysis [25], [26], sensitivity assessment [27], [28], and design optimization [29], [30], for structure system. However, Kriging model is inefficient for dynamic structural probabilistic analysis, because multiple models are required modeling for dynamic operational process and greatly enlarge computational complexity and consumptions.…”

Section: A Extremum Kriging Modelmentioning

confidence: 99%

Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Feng

Fei

et al. 2019

IEEE Access

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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 θ 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 × 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. INDEX TERMS E2K-DCF, multicomponent structure, multiple population genetic algorithm, probabilistic failure, turbine blisk.

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“…Zhang's group from the Harbin University of Science and Technology, Fudan University and Northwestern Polytechnical University (China) developed a fuzzy multi-extremum response surface method (FMERSM) for the comprehensive probabilistic optimization of multi-failure/multi-component structures, where the probabilistic fatigue/creep coupling optimization of turbine bladed disks was implemented-the rotor speed, temperature, and density as optimization parameters, and the creep stress, creep strain, fatigue damage, and creep damage as optimization objectives [9].…”

Section: Overview On the Themed Issuementioning

confidence: 99%

Advanced Simulation Tools Applied to Materials Development and Design Predictions

et al. 2019

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This thematic issue on advanced simulation tools applied to materials development and design predictions gathers selected extended papers related to power generation systems, presented at the XIX International Colloquium on Mechanical Fatigue of Metals (ICMFM XIX) organized at University of Porto, Portugal, in 2018. Guest editors express special thanks to all contributors for the success of this special issue—authors, reviewers, and journal staff.

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Probabilistic Fatigue/Creep Optimization of Turbine Bladed Disk with Fuzzy Multi-Extremum Response Surface Method

Cited by 18 publications

References 37 publications

Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Decomposed-Coordinated Framework With Enhanced Extremum Kriging for Multicomponent Dynamic Probabilistic Failure Analyses

Advanced Simulation Tools Applied to Materials Development and Design Predictions

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