Deformations occur gradually in the gas turbine components since they are working under high temperature and stress. In the turbine blade alloys, creep is the most significant failure mechanism. In this research, creep life has been estimated for the blade alloys by considering humidity. A method is proposed to estimate the creep life by direct consideration of humidity on the creep life of the gas turbine blade. In the proposed model, the humidity factor is added to the classic Larson Miller creep life estimation method. This model is capable of predicting creep life with known dry temperature (Water Air Ratio=0), mechanical stress, and humidity. In this approach, there is no need to measure blade temperature variation during operation. As a case study, the creep life of first-stage turbine blade alloy is predicted using the proposed method and benchmarked with published (Finite element analysis) FEA results. The reliability of the blades was estimated by considering different success criteria using Monte Carlo simulation. The reliability of the creep rupture life of Nimonic-90 steel was carried out using SCRI mode based on the Z-parameter. The scattered data has been considered for creep rupture of materials in this part. The results show that creep life increases with humidity increase. It is also shown that with an increase in mechanical stress and temperature fluctuations, the reliability of the turbine blade creep life decreases sharply.
Unmanned Aerial Vehicles (UAV) are increasingly being popular in many applications. Their operation requires a high level of safety and reliability to accomplish successful missions. In this study, their reliability is comparatively analyzed by different available approaches to select the efficient method. Firstly, the failure model of the system is developed. Then, three different scenarios are considered to study the effect of redundancies on the reliability of the system. In the first scenario, there is no redundancy, whereas in the second scenario there is one redundant component and in the third scenario, there are three redundant components. Static reliability analysis implemented on the proposed scenarios using methods of Fault Tree Analysis (FTA), Reliability Block Diagram (RBD), Markov Chain (MC), and Bayesian Networks (BN) and the results are obtained. Also, regarding the time dependencies between redundant components, a dynamic-based methodology is developed by applying Dynamic Fault Tree (DFT) analysis. Then, the proposed static and dynamic approaches are applied to a UAV as a case study and the results are discussed. Finally, the characteristics of each methodology and the related conditions are clarified for selecting the efficient reliability analysis approach.
MEMS reliability analysis is a challenging area of research which comprises various physics of failure and diverse failure mechanisms. Reliability issues are critical in both design and fabrication phases of MEMS devices as their commercialization is still delayed by these problems. In this research, a hybrid methodology is developed for the reliability evaluation of MEMS devices. Its first step is the identification of dominant failure modes by FMEA, evaluation of failure mechanisms and an updated lifetime estimation by Bayesian method. The reliability of MEMS devices is studied using probabilistic physics of failure (PPoF) by determining the dominant failure mechanism. Accordingly, a deterministic model is selected for the analysis of the life and reliability of the dominant failure mechanisms. To convert the deterministic model to a probabilistic model, the uncertainty sources affecting the dielectric lifetime are determined. This model is simulated by utilization of Monte Carlo method. In the final stage, the results of life estimation are updated using the Bayesian method. Considering the wide applications and advantages of RF MEMS capacitive switches, they have been selected for a case study. A framework is developed for the reliability evaluation of these switches failures due to stiction mechanism. The results contain FMEA table, lifetime estimation in different voltages, number of duty cycles and at the end, updated results of life estimation using Bayesian method.
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