Low-Cycle Fatigue Life of Continuously Cyclic-Hardening Material

A general fatigue life equation is derived by modifying the Tanaka-Mura-Wu dislocation pile-up model for variable strain-amplitude fatigue processes, where the fatigue crack nucleation life is expressed in terms of the root mean square of plastic strain range. Low-cycle fatigue tests were conducted on an austenitic stainless steel. at 400°C and 600°C, the material exhibits continuously cyclic-hardening behaviour. The root mean square of plastic strain ranges is evaluated from the experimental data for each test condition at strain rates ranging from 0.0002/s to 0.02/s. The variable-amplitude Tanaka-Mura-Wu model is found to be in good agreement with the LCF data, which effectively proves Miner’s rule on the stored plastic strain energy basis.

Water Droplet Erosion Life Prediction Method for Steam Turbine Blade Materials Based on Image Recognition and Machine Learning

In this paper, a method for predicting remaining useful life (RUL) of turbine blade under water droplet erosion (WDE) based on image recognition and machine learning is presented. Using the experimental rig for testing the WDE characteristics of materials, the morphology pictures of specimen surface at different times in the process of WDE are collected. According to the data processing method of ASTM-G73 and the cumulative erosion-time curves, the WDE stages of materials is quantitatively divided and the WDE life coefficient (ζ) is defined. The life coefficient (ζ) could be used to calculate the RUL of turbine blades. One convolutional neural network model and three machine learning models are adopted to train and predict the image dataset. Then the training process and feature maps of the Resnet model are studied in detail. It is found that the highest prediction accuracy of the method proposed in this paper can be 0.949, which is considered acceptable to provide reference for turbine overhaul period and blade replacement time.

Computational and Experimental Studies of Cellular Samples Manufactured Using Additive Methods for Use in Advanced Lightweight Designs of Engine Parts

The use of cellular structures is one way to reduce the weight of engine parts. Cellular structures are used to provide rigidity and strength for parts subject to compression, bending, and shock loads. Failure of the individual elements of a lattice/cell structure does not result in the destruction of the entire part; this stands in contrast to the structure of a conventional homogeneous metal object, in which cracks will continue to increase with increasing load, causing the destruction of the entire part. Lattice/cell structures have relatively high characteristics of rigidity and strength, excellent thermal insulation properties, energy absorption characteristics, and high fatigue resistance. The use of this type of structure in engine part construction opens up new opportunities for advanced aviation applications. However, the deformation behavior of porous and metallic structures differs significantly from that of conventional homogeneous materials. Samples with cellular and porous structures are themselves designs. Therefore, procedures for strength testing and interpretation of experimental results for cellular and porous structures differ from those for samples derived from homogeneous materials. The criteria for determining the properties of cellular structures include density, stiffness, ability to accumulate energy, etc. These parameters depend on the configuration of the cells, the size of each cell, and the thickness of the connecting elements. Mechanical properties of cellular structures can be established experimentally and confirmed numerically. Special cellular specimens have been designed for uniaxial tensile, bending, compression, shear, and low-cycle fatigue testing. Several variants of cell structures with relative densities ranging from 13 to 45% were considered. Specifically, the present study examined the stress-strain states of cell structures from brands “CobaltChrome MP1” powder compositions obtained by laser synthesis on an industrial 3D printer Concept Laser M2 Cusing Single Laser 400W. Numerical simulations of the tests were carried out by the finite element method. Then, the most rational cellular structures in terms of mass and strength were established on the basis of both real and numerical experiments.

New Numerical Analysis Method for Creep Lifetime of Gas Turbine Materials by Using the Discrete Cosine Transform

Recently the Discrete Cosine Transform[1], [2], [3] which is a modified Fourier Transform has begun to be used to express coefficients of creep equations using the power law or the exponential law such as Bailey-Norton law[4], [5] and θ Projection[6], [7], [8], [9], [10]. In addition, the Discrete Cosine Transform has begun to be used to express a creep equation itself. We have already found that the Discrete Cosine Transform can express the temperature and stress dependence property of the coefficients of the creep equations at the same time by the two-dimensional Discrete Cosine Transform using 8 × 8 discrete signals[11]. Furthermore, we have already found that the Discrete Cosine Transform can fit measured creep strain values very well from the primary creep region to the tertiary creep region using 8 discrete signals and it can estimate creep strain values between the measured points by interpolation very well[12]. However it has not been known if the Discrete Cosine Transform can predict the long term creep curve by using the short term creep data yet. Therefore, as a next stage, we tried to estimate the long term creep curve from the short term creep data of gas turbine materials by extrapolation using the Discrete Cosine Transform. As a result, we were able to obtain a useful numerical analysis method by utilizing the Discrete Cosine Transform Coefficients and others as a new extrapolation method. It is found that this new numerical method would be able to predict the configuration of 150,000-hour creep curve by using 500-hour to 13,000-hour short term creep data.

Quantification of Low-Cycle Fatigue Life for a Gas Turbine Compressor Vane Carrier Under Varying Operating Conditions

The design of a gas turbine compressor vane carrier (CVC) should meet mechanical integrity requirements on, among others, low-cycle fatigue (LCF). The number of cycles to the LCF failure is the result of cyclic mechanical and thermal strain effects caused by operating conditions on the components. The conventional LCF assessment is usually based on the assumption on standard operating cycles — supplemented by the consideration of predefined extreme operations and safety factors to compensate a potential underestimate on the LCF damage caused by multiple reasons such as non-standard operating cycles. However, real operating cycles can vary significantly from those standard ones considered in the conventional methods. The conventional prediction of LCF life can be very different from real cases, due to the included safety margins. This work presents a probabilistic method to estimate the distributions of the LCF life under varying operating conditions using operational fleet data. Finite element analysis (FEA) results indicate that the first ramp-up loading in each cycle and the turning time before hot-restart cycles are two predominant contributors to the LCF damage. A surrogate model of LCF damage has been built with regard to these two features to reduce the computational cost of FEA. Miner’s rule is applied to calculate the accumulated LCF damage on the component and then obtain the LCF life. The proposed LCF assessment approach has two special points. First, a new data processing technique inspired by the cumulative sum (CUSUM) control chart is proposed to identify the first ramp-up period of each cycle from noised operational data. Second, the probability mass function of the LCF life for a CVC is estimated using the sequential convolution of the single-cycle damage distribution obtained from operational data. The result from the proposed method shows that the mean value of the LCF life at a critical location of the CVC is significantly larger than the calculated result from the deterministic assessment, and the LCF lives for different gas turbines of the same class are also very different. Finally, to avoid high computational cost of sequential convolution, a quick approximation approach for the probability mass function of the LCF life is given. With the capability of dealing with varying operating conditions and noises in the operational data, the enhanced LCF assessment approach proposed in this work provides a probabilistic reference both for reliability analysis in CVC design, and for predictive maintenance in after-sales service.

Application of Damage Mechanics and Polynomial Chaos Expansion for Lifetime Prediction of High-Temperature Components Under Creep-Fatigue Loading

Several (accumulative) lifetime models were developed to assess the lifetime consumption of high-temperature components of steam and gas turbine power plants during flexible operation modes. These accumulative methods have several drawbacks, e.g. that measured loading profiles cannot be used within accumulative lifetime methods without manual corrections, and cannot be combined directly to sophisticated probabilistic methods. Although these methods are widely accepted and used for years, the accumulative lifetime prediction procedures need improvement regarding the lifetime consumption of thermal power plants during flexible operation modes. Furthermore, previous investigations show that the main influencing factor from the materials perspective, the critical damage threshold, cannot be statistically estimated from typical creep-fatigue experiments due to massive experimental effort and a low amount of available data. This paper seeks to investigate simple damage mechanics concepts applied to high-temperature components under creep-fatigue loading to demonstrate that these methods can overcome some drawbacks and use improvement potentials of traditional accumulative lifetime methods. Furthermore, damage mechanics models do not provide any reliability information, and the assessment of the resultant lifetime prediction is nearly impossible. At this point, probabilistic methods are used to quantify the missing information concerning failure probabilities and sensitivities and thus, the combination of both provides rigorous information for engineering judgment. Nearly 50 low cycle fatigue experiments of a high chromium cast steel, including dwell times and service-type cycles, are used to investigate the model properties of a simple damage evolution equation using the strain equivalence hypothesis. Furthermore, different temperatures from 300 °C to 625 °C and different strain ranges from 0.35% to 2% were applied during the experiments. The determination of the specimen stiffness allows a quantification of the damage evolution during the experiment. The model parameters are determined by Nelder-Mead optimization procedure, and the dependencies of the model parameters concerning to different temperatures and strain ranges are investigated. In this paper, polynomial chaos expansion (PCE) is used for uncertainty propagation of the model uncertainties while using non-intrusive methods (regression techniques). In a further post-processing step, the computed PCE coefficients of the damage variable are used to determine the probability of failure as a function of cycles and evolution of the probability density function (pdf). Except for the selected damage mechanics model which is considered simple, the advantages of using damage mechanics concepts combined with sophisticated probabilistic methods are presented in this paper.

Estimation of Applied Stress at Fatigue Cracks of a Single Crystal Superalloy

Needless to say, it is important to estimate the stress applied to a material when conducting failure analysis. In recent years, a material assessment method using electron back-scatter diffraction (EBSD) has been developed. It has been reported that a characteristic misorientation distribution corresponding to the fracture mode is seen in cross-sectional EBSD observation near the fracture surface of a Ni-based superalloy. Furthermore, the authors discovered EBSD striations on the crack cross-section, which is formed with each fatigue crack growth during a turbine shut-down process. This was discovered in misorientation analysis on a single-crystal superalloy blade used in a commercial land-based gas turbine. Since Ni-based superalloys have high deformation resistance, they do not undergo enough ductile deformation to form striations at the crack tip on the fracture surface during fatigue crack growth, and, as a result, striations corresponding to cyclic loadings are rarely observed in fractography. Even in such a Ni-based superalloy with brittle crack growth, the fatigue crack growth rate and the applied stress can be estimated by measuring EBSD striation spacing in misorientation analysis. However, a practical problem in assessment is that the resolution limit fixed with field emission scanning electron microscopes (FE-SEM) determine the range in which crack growth rate can be assessed. Hence, it is difficult to clearly discriminate the EBSD striations when the fatigue crack growth rate is too low, such as in the low stress intensity factor range (ΔK) region. The applied stress can be calculated from ΔK. Therefore, in this paper, in order to estimate the applied stress during fatigue crack growth, we focused on estimating ΔK by measuring the plastic zone size along the crack growth.

On the Probabilistic Endurance Prediction Approach for Turbomachinery Blades and Vanes

The design and aeromechanical assessment of turbomachinery blades and vanes comprises a wide range of complex processes that tend to be based on conventional deterministic methods. These processes often provide a ‘snapshot’ evaluation of the new component designs at the nominal operating conditions. While the deterministic methods can predict the high cycle fatigue (HCF) endurance with reasonable accuracy; they assume that the conservative safety factors applied to cover for the parametric variations, uncertainties and unknowns will not change during the product life cycle. This approach is intended to be conservative and in some cases may overlook the lack of robustness. The present paper proposes a robust design analysis approach based on probabilistic methodology for the aeromechanical assessment of rotor blades and stator vanes of turbomachinery. The robust design approach can account explicitly for the effects of design and manufacturing variability. This methodology can reduce the levels of conservatism in the deterministic approach and can provide a more thorough risk assessment. This paper offers a generalised aeromechanical analysis formulation based on probabilistic methods to evaluate the HCF capability of turbomachinery components. Herein, this methodology is demonstrated by using a typical stator vane of an aero engine compressor and it is based on Monte-Carlo and DOE simulations. The methodology consists of parametric sensitivity studies and identification of the most influential parameters that control the HCF endurance. Future ideas and roadmap of the aeromechanical probabilistic analysis capability development are also discussed.

Stress Rupture Behavior of Disk Superalloys Exposed to Low-Temperature Hot Corrosion Environment

Siemens Energy has a large fleet of aero-derivative gas turbines. The performance and durability of these power turbines largely depend on the capability of hot section components to resist high-temperature surface attacks and to maintain their mechanical properties. Hot corrosion attack occurs due to exposure of turbine components to sulfur-bearing fuels/air together with other corrosive compounds during turbine operation. This paper investigates the impact of low-temperature hot corrosion on the stress rupture of commonly used gas turbine disk alloys, including Inconel 718, Incoloy 901, and A-286. The results indicate that Inconel 718 and Incoloy 901 maintain their creep strength advantage over A-286 in a low-temperature hot corrosion inducing environment at 1100°F. All three materials exhibited an equivalent life reduction in the corrosive environments at 1100°F. Moreover, the results demonstrate that the stress-rupture life of materials in hot-corrosion environments depends on the combined and cumulative effects of corrosion-resistant and hardening elements.

Damage Detection of Composite Materials Using Data Fusion With Deep Neural Networks

Composite materials have enormous applications in various fields. Thus, it is important to have an efficient damage detection method to avoid catastrophic failures. Due to the existence of multiple damage modes and the availability of data in different formats, it is important to employ efficient techniques to consider all the types of damage. Deep neural networks were seen to exhibit the ability to address similar complex problems. The research question in this work is ‘Can data fusion improve damage classification using the convolutional neural network?’ The specific aims developed were to 1) assess the performance of image encoding algorithms, 2) classify the damage using data from separate experimental coupons, and 3) classify the damage using mixed data from multiple experimental coupons. Two different experimental measurements were taken from NASA Ames Prognostic Repository for Carbon Fiber Reinforced polymer. To use data fusion, the piezoelectric signals were converted into images using Gramian Angular Field (GAF) and Markov Transition Field. Using data fusion techniques, the input dataset was created for a convolutional neural network with three hidden layers to determine the damage states. The accuracies of all the image encoding algorithms were compared. The analysis showed that data fusion provided better results as it contained more information on the damages modes that occur in composite materials. Additionally, GAF was shown to perform the best. Thus, the combination of data fusion and deep neural network techniques provides an efficient method for damage detection of composite materials.