Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging regression model and a distributed collaborative response surface method (DCRSM) for th...
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Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging regression model and a distributed collaborative response surface method (DCRSM) for the reliability assessment of turbo-fan blades, considering the relevant uncertainty. Following a series of deterministic analyses, such as steady-state aerodynamic analysis, harmonic response analysis and Campbell diagram, and based on the assumption that vibration stress is mainly from aerodynamic load, the fatigue strength is calculated for turbo-fan blades under coupling aerodynamic forces, according to a modtfied Goodman curve of titanium-allay. Giving consideration to the uncertainty of the resonance frequencies and material properties, the fatigue strength of the turbo-fan blade is evaluated, including probabilistic analysis and sensitivity analysis. In the case study analyzed, the conclusions are that the fatigue strength reliability reaches 96.808% with confidence level of 0.95 for the turbo-fan blade folder the coupling aerodynamic forces, and the first three-order resonant frequencies are found to have important influence on the fatigue performance of turbo-fan blades.
The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material *** order to ameliorate r...
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The fatigue life of aeroengine turbine disc presents great dispersion due to the randomness of the basic variables,such as applied load,working temperature,geometrical dimensions and material *** order to ameliorate reliability analysis efficiency without loss of reliability,the distributed collaborative response surface method(DCRSM) was proposed,and its basic theories were established in this *** the failure dependency among the failure modes,the distributedresponsesurface was constructed to establish the relationship between the failure mode and the relevant random ***,the failure modes were considered as the random variables of system response to obtain the distributedcollaborativeresponsesurface model based on structure failure ***,the given turbine disc structure was employed to illustrate the feasibility and validity of the presented *** the comparison of DCRSM,Monte Carlo method(MCM) and the traditional responsesurfacemethod(RSM),the results show that the computational precision for DCRSM is more consistent with MCM than RSM,while DCRSM needs far less computing time than MCM and RSM under the same simulation ***,DCRSM is demonstrated to be a feasible and valid approach for improving the computational efficiency of reliability analysis for aeroengine turbine disc fatigue life with multiple random variables,and has great potential value for the complicated mechanical structure with multi-component and multi-failure mode.
To develop the high-performance high-reliability of aeroengine, the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) with three objects (disk, blade, and casing) and two d...
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To develop the high-performance high-reliability of aeroengine, the probabilistic design of high-pressure turbine (HPT) blade-tip radial running clearance (BTRRC) with three objects (disk, blade, and casing) and two disciplines (heat and mechanical loads) was completed based on distributed collaborative response surface method (DCRSM) from a probabilistic perspective considering dynamic loads and nonlinear material properties. The mathematical model of DCRSM was established based on the quadratic responsesurface function. The basic idea of BTRRC probabilistic design based on DCRSM was introduced. The BTRRC probabilistic analysis results, consisting of the probabilistic distribution characteristics of input-output variables, the failure probability, and reliability of BTRRC under different static blade-tip clearances delta and the major factors affecting the BTRRC, reveal that the static blade-tip clearance delta = 1.865 x 10(-3) m is an optimally acceptable option for BTRRC design. The comparison of methods shows that the DCRSM has high accuracy and high efficiency in the BTRRC probabilistic analysis;meanwhile, the strengths become more obvious with the increasing times of simulations. The present research offers an effective way for HPT BTRRC probabilistic design, as well as provides a promising method for the further probabilistic optimal design of complex mechanical system. (C) 2014 American Society of Civil Engineers.
To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributedcollaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as D...
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To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributedcollaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design. (C) 2017 Published by Elsevier Ltd.
To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributedcollaborative generalized regression extremum neural network is proposed by ...
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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 distributedcollaborative 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 10(4) 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 th
To improve the computational accuracy and efficiency of complex mechanical component like engine turbine structure, distributed collaborative response surface method is applied to the reliability analysis of aeroengin...
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To improve the computational accuracy and efficiency of complex mechanical component like engine turbine structure, distributed collaborative response surface method is applied to the reliability analysis of aeroengine turbine blade low-cycle fatigue damage. The improved Manson-Coffin formulas with different confidence levels are established based on the linear variance regression analysis in the application of the fatigue data of nickel-based superalloy GH4133. The distributedresponsesurfaces of strain range A epsilon(t) and mean stress sigma(m) are established by considering the randomness of the design sizes, working loads and material parameters. And then A epsilon(t) and sigma(m) are regarded as the basic input variables of fatigue life N-f to complete turbine blade low-cycle fatigue damage reliability analysis by the Miner cumulative damage theory. The probabilistic sensitivity analyses demonstrate that the fatigue performance parameters hold important influence on the low-cycle fatigue life of turbine blade. Through the comparison of methods, it is revealed that distributed collaborative response surface method is superior to responsesurfacemethod in computational precision and efficiency, especially for low confidence level. The efforts give the conclusion that distributed collaborative response surface method is a promising approach in ameliorating the computational precision and efficiency of reliability analysis, which enriches the reliability theory and method of complex mechanical structure with multi-component and multi-failure mode. (C) 2015 Elsevier Masson SAS. All rights reserved.
To improve the performance and reliability of gas turbine like an aeroengine, the multi-object multi-discipline (MOMD) reliability optimization design of high press turbine (HPT) blade-tip radial running clearance (BT...
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To improve the performance and reliability of gas turbine like an aeroengine, the multi-object multi-discipline (MOMD) reliability optimization design of high press turbine (HPT) blade-tip radial running clearance (BTRRC) was first accomplished based on the mechanical dynamic assembly reliability (MDAR) theory and distributed collaborative response surface method (DCRSM). Four optimization models of MDAR were developed based on the features of assembly machinery and the thought of DCRSM, which are, respectively, called as the direct reliability optimization model (denoted by M1), the multilayer reliability optimization models (denoted by M2), the direct reliability optimization model-based probabilistic analysis (denoted by M3), and the multilayer reliability optimization model-based probabilistic analysis (denoted by M4). Through the MDAR optimization design of BTRRC by the four standard optimization models, some conclusions are drawn as follows: (1) the DCRSM is proved to be effective and feasible for MOMD MDAR optimization design with high computational efficiency and precision;(2) all the reliability optimization results of BTRRC and assembly objects satisfy the requirements of optimization design, and the optimized BTTRC variations are reduced by about 10% and obey the normal distribution, which are quite promising in improving the design and control of HPT BTRRC;(3) in computational efficiency, the computing time of M1 and M3 is far less than those of M2 and M4, meanwhile M3 and M4 are superior to M1 and M2;(4) in computational accuracy, M1 and M2 are better than M3 and M4, as well as M2 and M4 are higher than M1 and M3 theoretically. The presented study does not only fulfill the HPT BTRRC dynamic assembly design from a probabilistic optimization perspective and improve the performance and reliability of gas turbine engine, but also provides a promising approach and four valuable optimization models for MDAR optimization design. Besides, the present efforts ar
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