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Recently, a new class of fatigue crack growth models based on elastoplastic stress‐strain histories at the crack tip region and strain‐life fatigue damage models have been proposed. The fatigue crack propagation is understood as a process of continuous crack initializations, over elementary material blocks, which may be governed by strain‐life data of the plain material. The residual stresses developed at the crack tip play a central role in these models, since they are used to assess the actual crack driving force, taking into account mean stresses and loading sequential effects. The UniGrow model fits this particular class of fatigue crack propagation models. The purpose of this paper is to propose an extension of the UniGrow model to derive probabilistic fatigue crack propagation data, in particular the derivation of the P–da/dN–ΔK–R fields.

An existing deterministic fatigue crack propagation model, based on local strain‐life data is first assessed. In particular, an alternative methodology for residual stress computation is proposed, based on elastoplastic finite element analysis, in order to overcome inconsistencies found in the analytical approximate approaches often used in literature. Then, using probabilistic strain‐life fields, a probabilistic output for the fatigue crack propagation growth rates is generated. A new probabilistic fatigue field is also proposed to take mean stress effects into account, using the Smith‐Watson‐Topper (SWT) damage parameter. The proposed models are assessed using experimental data available for two materials representative from old Portuguese bridges.

A new method to generate probabilistic fatigue crack propagation rates (P–da/dN–ΔK–R fields) is proposed and verified using puddle iron from old Portuguese bridges, usually characterized by significant scatter in fatigue properties. Also, a new probabilistic fatigue field for plain material is proposed to deal with mean stress effects.

A relation between the P–ε–N and the P–da/dN–ΔK–R fields is firstly proposed in this research. Furthermore, a new P–SWT–N field is proposed to deal with mean stress effects.

Aero-engine components endure combined high and low cycle fatigue (CCF) loading during service, which has attracted more research attention in recent years. This study aims to construct a new framework for the prediction of probabilistic fatigue life and reliability evaluation of an aero-engine turbine shaft under CCF loading if considering the material uncertainty.

To study the CCF failure of the aero-engine turbine shaft, a CCF test is carried out. An improved damage accumulation model is first introduced to predict the CCF life and present high prediction accuracy in the CCF loading situation based on the test. Then, the probabilistic fatigue life of the turbine shaft is predicted based on the finite element analysis and Monte Carlo analysis, where the material uncertainty is taken into account. At last, the reliability evaluation of the turbine shaft is conducted by stress-strength interference models based on an improved damage accumulation model.

The results indicate that predictions agree well with the tested data. The improved damage accumulation model can accurately predict the CCF life because of interaction damage between low cycle fatigue loading and high cycle fatigue loading. As a result, a framework is available for accurate probabilistic fatigue life prediction and reliability evaluation.

The proposed framework and the presented testing in this study show high efficiency on probabilistic CCF fatigue life prediction and can provide technical support for fatigue optimization of the turbine shaft.

The novelty of this work is that CCF loading and material uncertainty are considered in probabilistic fatigue life prediction.

A probabilistic‐based design for turbine disk at high temperature can quantify risk and thus identify areas of possible overdesign (conservatism). Moreover, the need for cost‐effective designs has resulted in the development of probabilistic design to quantify the effects of these uncertainties so as to improve the reliability of the component. This paper aims to address these issues.

The flow for probabilistic design established through investigating traditional design methods of the turbine disk at high temperature is divided into the processes of crack initiation and crack growth to find important design inputs at each course, where the probabilistic design criterion has been built based on the deterministic criteria and successful experiences.

The probabilistic‐based design procedure has been demonstrated by taking the reliability design of crack initiation process for turbine disk as the example. The reliability analysis for the disk life after optimization analysis was completed by considering random parameters reflecting the uncertainties. The results showed there was a margin in design for disk life referred to as the probabilistic criterion. This measure was taken by redesigning the structure to reduce the disk's weight within the range of reliability.

The present study provides a method to design aero‐engine components based on probabilistic design for further research.

Moreover, the present study provides a way to design structures based on probabilistic design.

It is proved that probabilistic‐based design could produce a lower weight turbine disk by integrating well‐proved deterministic design methods and tools with probabilistic design techniques while maintaining low failure probability.

The purpose of this study is to address the complexity involved in computing the fatigue life of casted structure with porosity effects in aero engine applications. The uncertainty of porosity defects is addressed by introducing probabilistic models.

One major issue of casted aluminium alloys in the application of aerospace industries is their internal defects such as porosities, which are directly affecting the fatigue life. Since there is huge cost and time effort involved in understanding the effect of fatigue life in terms of the presence of the internal defects, a probabilistic fatigue model approach is applied in order to define the realistic fatigue limit of the casted structures for the known porosity fractions. This paper describes the probabilistic technique to casted structures with measured porosity fractions and its relation to their fatigue life. The predicted fatigue life for various porosity fractions and dendrite arm spacing values is very well matching with the experimentally predicted fatigue data of the casted AS7G06 aluminium alloys with measured internal defects. The probabilistic analysis approach not only predicts the fatigue life limit of the structure but also provides the limit of fatigue life for the known porosity values of any casted aluminium bearing support structure used in aero engines.

The probabilistic fatigue model for addressing porosity in casting structure is verified with experimental results.

This is grey area in aerospace and automotive industry.

This work is original and not published anywhere else.

Integrated vehicle health management has been developed for several years in different industries, to be able to provide the required inputs to determine the optimal maintenance operations depending on the actual health status of the system. The purpose of this paper is to demonstrate the potential of a physics-based model (PbM) for prognostics with a real case study, based on the detection of incipient faults and estimate the remaining useful life of a planetary transmission of an aircraft system.

Most of the research in the area of health assessment algorithms has been focused on data-driven approaches that are not based on the knowledge of the physics of the system, while PbM approaches rely on the understanding of the system and the degradation mechanisms. A physics-based modelling approach to represent metal-metal contact and fatigue in the gears of the planetary transmission of an aircraft system is applied.

Both the failure mode caused by metal-metal contact as caused by fatigue in the gears is described. Furthermore, the real-time application that retrieves the results from the simulations to assess the health of the system is described. Finally the decision making that can be executed during flight in the aircraft is incorporated.

The paper proposes an innovative prognostics health management system that assesses two important failure modes of the planetary transmission that regulates the speed of the generators of an aircraft. The results from the models have been integrated in an application that emulates a real system in the aircraft and computes the remaining useful life in real time.

The purpose of this paper is to estimate probabilistic and regional importance sensitivities of fatigue life, with respect to the laser peening (LP) parameters applied to a Titanium turbine disk.

The sensitivities were calculated from Monte Carlo (MC) analysis of 21,000 simulations and probabilistic sensitivity methods.

The probabilistic sensitivity results indicate that the peak pressure and the mid‐span are the most important variables. The regional importance sensitivity results indicate that probability of failure is the most sensitive to the left tail of peak pressure and middle region of mid‐span and the fatigue life mean is the most sensitive to the left tails of the peak pressure and the mid‐span.

The sensitivity results of this research indicate that more time and energy should be focused on managing peak pressure and mid‐span, as compared to the remaining variables, to design and improve the laser peening process.

The paper presents four sensitivity analysis approaches which were formulated and employed to estimate fatigue life sensitivities with respect to the LP variables.

The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.

In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.

The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.

The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.

The purpose of this paper is to consider an approximate model of accumulation of microdefects in a material under repeated loading which makes it possible to define theoretical parameters of the fatigue failure (durability, fatigue limit, etc.). The model is involving the relevant law of distribution of ultimate (yield) stresses in the material of these members in combination with the basic characteristics of main mechanical properties of a material (ultimate and yield stresses and associated standard deviations).

The model of fatigue failure of brittle and elastoplastic materials based on the use of the structural-probabilistic approach and up-to-date ideas on the mechanism of material fracture is proposed. The model combines statistical fracture criteria, which are expressed in terms of damage concentrations, with the approximate model of microcrack accumulation under repeating loading of the same level. According to these criteria, the fatigue failure begins with the accumulation of separation- or shear-type microdefects up to the level of critical values of their density.

The failure mechanism is associated with the accumulation of dispersed microdamages under repeated loading. The critical value of the density of the microdamages, which are identified with those formed either by separation or shear under static loading in consequence of simple tension, compression or shear, is accepted as the criterion of the onset of fatigue failure. The fatigue being low-cycle or high-cycle is attributed to accumulation of shear microdamages in the region of plastic deformation in the former case and microdamages produced by separation under elastic deformation in the latter one.

The originality of the paper consists in the following. The authors theoretically define parameters of the fatigue failure (durability, fatigue limit, etc.) using the model in combination with the statistical failure (yield) criteria appearing in the damage measures. The constructed fatigue diagram has discontinuities on the conditional boundary dividing domains with the shear-type and separation-type fractures of structural elements. Such results are supported by the experimental results.

The front bearing mount structure in an aero engine has been severely loaded under the fan blade off (FBO) event since imbalance forces at high amplitude but low frequency is transformed to the engine front mount structure. The bearing mount structural forces are estimated by an integrated implicit-explicit analysis process of whole engine model of an aero engine. Since there are many dependent factors which are governing those predicted loads, experimental evidence on FBO is becoming necessary to validate the model used for the load prediction which is more expensive and also time consuming. This paper aims to discuss the above mentioned issues.

The current paper deals with the high impact but low probability nature of FBO load prediction on the bearing mount structure by stochastic approach which could be replaced for FBO experiments which is highly essential for current economic conditions. Several influential factors on the predicted loads have been chosen in the stochastic model and sensitive analysis has also been performed to bring down the variation involved in the predicted load.

The predicted load by proposed stochastic model is then compared with the experimental results. The conclusion on the predicted load with various dependent influential factors is matching well with certain value of damage factor from planned FBO test event.

Limitation of this paper could be that it does not cover with range of load amplitude and is only applicable for civil small and medium engines.

The high amplitude but low frequency load pattern is assessed with impact condition by stochastic model.

Combining experimental and probabilistic load prediction was never done before and read across from previous engine test program could be effectively performed with stochastic model approach.

The purpose of this study is to improve the computational efficiency and accuracy of fatigue reliability analysis.

By absorbing the advantages of Markov chain and active Kriging model into the hierarchical collaborative strategy, an enhanced active Kriging-based hierarchical collaborative model (DCEAK) is proposed.

The analysis results show that the proposed DCEAK method holds high accuracy and efficiency in dealing with fatigue reliability analysis with high nonlinearity and small failure probability.

The effectiveness of the presented method in more complex reliability analysis problems (i.e. noisy problems, high-dimensional issues etc.) should be further validated.

The current efforts can provide a feasible way to analyze the reliability performance and identify the sensitive variables in aeroengine mechanisms.

To improve the computational efficiency and accuracy of fatigue reliability analysis, an enhanced active DCEAK is proposed and the corresponding fatigue reliability framework is established for the first time.