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

Li, Xueqin; Song, Lu-Kai; Bai, Guang-Chen

doi:10.1108/ijsi-10-2021-0111

Cited by 101 publications

(54 citation statements)

References 113 publications

(95 reference statements)

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“…However, the recently developed technique like surrogate modeling, hybrid MCS coupled with artificial intelligence, deep learning regression-based stratified probabilistic combined cycle fatigue damage evaluation is likely to obtain significantly accurate solutions to non-linear problems (Li et al. , 2022a, b, c). A detailed review of the recent advances in reliability analysis is provided in the literature (Li et al.…”

Section: Methodology For Reliability-based Design Of Tmsmentioning

confidence: 99%

On computation of reliability index for tensile membrane structures based on limit state of deflection

Rana

Dutta²,

Maiti³

et al. 2022

IJSI

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PurposeThe present study is based on finding the structural response of a tensile membrane structure (TMS) through deformation. The intention of the present research is to develop a basic understanding of reliability analysis and deflection behavior of a pre-tensioned TMS. The mean value first-order second-moment method (MVFOSM) method is used here to evaluate stochastic moments of a performance function with random input variables. Results suggest the influence of modulus of elasticity, the thickness of the membrane, and edge span length are significant for reliability based TMS design.Design/methodology/approachA simple TMS is designed and simulated by applying external forces (along with prestress), as a manifestation of wind and snow load. A nonlinear analysis is executed to evaluate TMS deflection, followed by calculating the reliability index. Parametric study is done to consider the effect of membrane material, thickness and load location. First-order second moment (FOSM) is used to evaluative the reliability. A comparison of reliability index is done and deflection variations from μ − 3s to μ + 3s are accounted for in this approach.FindingsThe effectiveness of deflection is highlighted for the reliability assessment of TMS. Reliability and parametric study collectively examine the proposed geometry and material to facilitate infield design requirements. The estimated β value indicates that most suitable fabric material for a simple TMS should possess an elasticity modulus in the range of 1,000–1,500 MPa, the thickness may be considered to be around 1.00 mm, and additional adjustment of around 5–10 mm is suggested for edge length. The loading position in case of TMS structures can be a sensitive aspect where the rigidity of the surface is dependent on the pre-tensioning of the membrane.Research limitations/implicationsThe significance of the parametric study on material and loading for deflection of TMS is emphasized. Due to the lack of consolidated literature in the field combining reliability with deflection limits of a TMS, this work can be very useful for researchers.Practical implicationsThe present work outcome may facilitate practitioners in determining effective design methodology and material selection for TMS construction.Originality/valueThe significance of parametric study for serviceability criteria is emphasized. Parameters like pre-stress can be included in future parametric studies to witness in depth behavior of TMS. Due to lack of consolidated literature in the field combining reliability with deflection limits of a TMS, this work can be very useful for the researchers. The present work outcome may facilitate practitioners in determining effective design methodology and material selection for TMS construction.

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Section: Methodology For Reliability-based Design Of Tmsmentioning

confidence: 99%

On computation of reliability index for tensile membrane structures based on limit state of deflection

Rana

Dutta²,

Maiti³

et al. 2022

IJSI

View full text Add to dashboard Cite

show abstract

“…, 2022; Hariri-Ardebili and Pourkamali-Anaraki, 2018; Keshtegar et al. , 2021), artificial neural network (ANN) (Li et al. , 2021; Cherid et al.…”

Section: Dynamic Reliability Analysis Of Single-objective Structurementioning

confidence: 99%

“…stress, deformation, strain and so forth) is determined by multiple dynamic deterministic analyses based on the material property, structure loads and dimensions, and the samples of input variables and output responses are obtained. Then the limit state function is approximated by the response surface method (RSM) (Lu et al, 2020;Zhang et al, 2017;Kaymaz and McMahon, 2005), support vector regression (SVM) (Feng et al, 2019;Chen et al, 2022;Hariri-Ardebili and Pourkamali-Anaraki, 2018;Keshtegar et al, 2021), artificial neural network (ANN) (Li et al, 2021;Cherid et al, 2021;Peng et al, 2019) and Kriging (Teng et al, 2022;Zhang et al, 2021a, b;Jiang et al, 2019a, b) surrogate model, and the reliability is analyzed by combining the allowable value. Zhang and Bai (2012) presented the extremum RSM used for the reliability analysis of a two-link flexible robot manipulator.…”

mentioning

confidence: 99%

Structural dynamic reliability analysis: review and prospects

Teng

Feng

Chen

et al. 2022

IJSI

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PurposeThe purpose of this paper is to briefly summarize and review the theories and methods of complex structures’ dynamic reliability. Complex structures are usually assembled from multiple components and subjected to time-varying loads of aerodynamic, structural, thermal and other physical fields; its reliability analysis is of great significance to ensure the safe operation of large-scale equipment such as aviation and machinery.Design/methodology/approachIn this paper for the single-objective dynamic reliability analysis of complex structures, the calculation can be categorized into Monte Carlo (MC), outcrossing rate, envelope functions and extreme value methods. The series-parallel and expansion methods, multi-extremum surrogate models and decomposed-coordinated surrogate models are summarized for the multiobjective dynamic reliability analysis of complex structures.FindingsThe numerical complex compound function and turbine blisk are used as examples to illustrate the performance of single-objective and multiobjective dynamic reliability analysis methods. Then the future development direction of dynamic reliability analysis of complex structures is prospected.Originality/valueThe paper provides a useful reference for further theoretical research and engineering application.

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“…There are many ways to classify uncertainties. For engineering problems, the most common classifications are aleatory uncertainty and epistemic uncertainty (Dantan et al, 2013;Shah et al, 2015;Li et al, 2021;Yang et al, 2022), which is shown in Figure 2. Aleatory uncertainties exist objectively and cannot be reduced.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization of engineering systems under uncertainty: a review

Meng

Yang

et al. 2022

IJSI

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PurposeAs an advanced calculation methodology, reliability-based multidisciplinary design optimization (RBMDO) has been widely acknowledged for the design problems of modern complex engineering systems, not only because of the accurate evaluation of the impact of uncertain factors but also the relatively good balance between economy and safety of performance. However, with the increasing complexity of engineering technology, the proposed RBMDO method gradually cannot effectively solve the higher nonlinear coupled multidisciplinary uncertainty design optimization problems, which limits the engineering application of RBMDO. Many valuable works have been done in the RBMDO field in recent decades to tackle the above challenges. This study is to review these studies systematically, highlight the research opportunities and challenges, and attempt to guide future research efforts.Design/methodology/approachThis study presents a comprehensive review of the RBMDO theory, mainly including the reliability analysis methods of different uncertainties and the decoupling strategies of RBMDO.FindingsFirst, the multidisciplinary design optimization (MDO) preliminaries are given. The basic MDO concepts and the corresponding mathematical formulas are illustrated. Then, the procedures of three RBMDO methods with different reliability analysis strategies are introduced in detail. These RBMDO methods were proposed for the design optimization problems under different uncertainty types. Furtherly, an optimization problem for a certain operating condition of a turbine runner blade is introduced to illustrate the engineering application of the above method. Finally, three aspects of future challenges for RBMDO, namely, time-varying uncertainty analysis; high-precision surrogate models, and verification, validation and accreditation (VVA) for the model, are discussed followed by the conclusion.Originality/valueThe scope of this study is to introduce the RBMDO theory systematically. Three commonly used RBMDO-SORA methods are reviewed comprehensively, including the methods' general procedures and mathematical models.

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

Cited by 101 publications

References 113 publications

On computation of reliability index for tensile membrane structures based on limit state of deflection

On computation of reliability index for tensile membrane structures based on limit state of deflection

Structural dynamic reliability analysis: review and prospects

Multidisciplinary design optimization of engineering systems under uncertainty: a review

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