Transient probabilistic analysis for turbine blade-tip radial clearance with multi-component and multi-physics fields based on DCERSM

Fei, Chengwei; Choy, Yat Sze; Hu, Dian Yin; Bai, Guang-Chen; Tang, Wen

doi:10.1016/j.ast.2015.11.025

Cited by 28 publications

(13 citation statements)

References 21 publications

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“…Working in the extreme environment, turbine blisk endures high temperature, high pressure and high speed. To simulate the variation of turbine blisk stress under different operation status, the analytical range of start, idle, take off, climb and cruise is selected from the flight profile of the aeroengine in the time domain [0 s, 215 s] [37,38]. In this time domain [0 s, 215 s], 12 critical points of angular speed shown in Figure 3 are selected during the aeroengine operation of time domain.…”

Section: Deterministic Analysis For Turbine Bliskmentioning

confidence: 99%

Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis

Feng

Fei

2019

Energies

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The parameters considered in structural dynamic reliability analysis have strong uncertainties during machinery operation, and affect analytical precision and efficiency. To improve structural dynamic fuzzy reliability analysis, we propose the weighted regression-based extremum response surface method (WR-ERSM) based on extremum response surface method (ERSM) and weighted regression (WR), by considering the randomness of design parameters and the fuzziness of the safety criterion. Therein, we utilize the ERSM to process the transient to improve computational efficiency, by transforming the random process of structural output response into a random variable. We employ the WR to find the efficient samples with larger weights to improve the calculative accuracy. The fuzziness of the safety criterion is regarded to improve computational precision in the WR-ERSM. The WR-ERSM is applied to perform the dynamic fuzzy reliability analysis of an aeroengine turbine blisk with the fluid-structure coupling technique, and is verified by the comparison of the Monte Carlo (MC) method, equivalent stochastic transformation method (ESTM) and ERSM, with the emphasis on model-fitting property and simulation performance. As revealed from this investigation, (1) the ERSM has the capacity of processing the transient of the structural dynamic reliability evaluation, and (2) the WR approach is able to improve modeling accuracy, and (3) regarding the fuzzy safety criterion is promising to improve the precision of structural dynamic fuzzy reliability evaluation, and (4) the change rule of turbine blisk structural stress from start to cruise for the aircraft is acquired with the maximum value of structural stress at t = 165 s and the reliability degree (Pr = 0.997) of turbine blisk. The proposed WR-ERSM can improve the efficiency and precision of structural dynamic reliability analysis. Therefore, the efforts of this study provide a promising method for structural dynamic reliability evaluation with respect to working processes.

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Section: Deterministic Analysis For Turbine Bliskmentioning

confidence: 99%

Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis

Feng

Fei

2019

Energies

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“…in which N and N f refer to the total number of extracting samples and the number of failures (as defined in (12) with a given Y allow ), respectively. Sensitivity analysis of multicomponent structure can reflect the effect of the variation of input variables on failure probability or reliability probability, which help us determine the important degrees of input parameters on the output response of analytical object and then provide guidance for the design parameters of complex structures [1,23,40]. Sensitivity analysis typically contains sensitivity degree (S d ) analysis and impact probability (I d ) analysis [25].…”

Section: Dynamic Probabilistic Analysismentioning

confidence: 99%

“…Mechanical system generally comprises multiple complex structures, and the complex structure is assembled by multiple components. For instance, turbine blisk in an aeroengine contains blade and disk [1]. Each structure plays a key role and is subjected to rigorous workloads during operation.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reliability Design of Multicomponent Structure with Improved Weighted Regression Distributed Collaborative Surrogate Model Method

Dong

Zhu

et al. 2018

Advances in Materials Science and Engineering

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For dynamic reliability design of complex structures with multiple components, an improved weighted regression distributed collaborative surrogate model method (IWRDCSMM) is developed from the extremum response surface method (ERSM), decomposed-coordinated thought, and improved weighted regression principle. e ERSM is used to address the dynamic reliability and sensitivity analyses of multicomponent structures and enhance the computing efficiency. e decomposedcoordinated thought is applied to handle the relationship among multiple components. e improved weighted regression method is used to find the efficient samples with smaller errors to improve the modeling accuracy. e proposed method is first introduced for dynamic probabilistic analysis (including reliability analysis and sensitivity analysis) of multicomponent structures. e method is then mathematically modeled by adopting the efficient samples selected based on the improved weighted regression method. Finally, the radial deformation dynamic probabilistic analysis of an aeroengine turbine blisk assembled by blade and disk is accomplished, in respect of the IWRDCSMM, fluid-thermal-structure interaction, and the randomness of input parameters within the time domain [0, T]. e results illustrate that the reliability degree of turbine blisk radial deformation is 0.9951 when the allowable value u allow is 2.30 × 10 −3 m, and all the input parameters affecting the turbine blisk radial deformation are gas temperature, angular speed, inlet velocity, outlet pressure, material density, and inlet pressure, successively. As revealed by the comparison of different methods, the IWRDCSMM has high fitting speed and simulation efficiency with the guarantee of accuracy. e efforts of this study provide a promising dynamic probabilistic analysis technique for complex structures with multiple components and enrich mechanical reliability theory.

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“…Mechanical structures are usually assembled by a several components; for example, the rotor system of an aero engine is assembled by a spindle, disk, blade, and other components [1]. If we directly establish the reliability optimization design model of an overall structure involving multi-material, multi-disciplinary, and multi-physics structures, the computational burden will become very large in analysis, so that computational efficiency is unacceptable [2]. Therefore, it is significant to propose an efficient method for an overall reliability optimization design of multi-component and multi-failure modes, to make computational precision and efficiency satisfy engineering requirements.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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To effectively perform the probabilistic fatigue/creep coupling optimization of a turbine bladed disk, this paper develops the fuzzy multi-extremum response surface method (FMERSM) for the comprehensive probabilistic optimization of multi-failure/multi-component structures, which absorbs the ideas of the extremum response surface method, hierarchical strategy, and fuzzy theory. We studied the approaches of FMERSM modeling and fatigue/creep damage evaluation of turbine bladed disks, and gave the procedure for the fuzzy probabilistic fatigue/creep optimization of a multi-component structure with FMERSM. The probabilistic fatigue/creep coupling optimization of turbine bladed disks was implemented by regarding the rotor speed, temperature, and density as optimization parameters; the creep stress, creep strain, fatigue damage, and creep damage as optimization objectives; and the reliability and GH4133B fatigue/creep damages as constraint functions. The results show that gas temperature T and rotor speed ω are the key parameters that should be controlled in bladed disk optimization, and respectively reduce by 85 K and 113 rad/s after optimization, which is promising to extend bladed disk life and decrease failure damages. The simulation results show that this method has a higher modeling accuracy and computational efficiency than the Monte Carlo method (MCM). The efforts of this study provide a new useful method for overall probabilistic multi-failure optimization and enrich mechanical reliability theory.

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Transient probabilistic analysis for turbine blade-tip radial clearance with multi-component and multi-physics fields based on DCERSM

Cited by 28 publications

References 21 publications

Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis

Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis

Dynamic Reliability Design of Multicomponent Structure with Improved Weighted Regression Distributed Collaborative Surrogate Model Method

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

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