Document Details
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Document Type |
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Thesis |
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Document Title |
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RELIABILITY AND PERFORMANCE ANALYSIS OF AIRCRAFT ENGINES OPERATION IN EROSIVE ENVIRONMENT تطوير نموذج متقدم لتحليل موثوقية محركات الطائرات العاملة في البيئات القاسية |
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Subject |
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Faculty of Engineering |
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Document Language |
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Arabic |
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Abstract |
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Sand erosion is a widespread phenomenon in the Gulf Cooperation Council (GCC) region, where a solid particle impacts a wall surface that may cause engine damage. The performance and lifespan of engine compressors and turbines are significantly expected to deteriorate when operating in an erosive environment. Aircraft engine’s reliability has a significant impact on flight safety of modern aircraft. Therefore, proactive maintenance and continuous engine tracking are key methods for enhancing both aircraft reliability and efficiency. Various conventional regression models can be used to predict the failure of equipment and systems; however, there is a growing interest in the application of Artificial Neural Networks (ANN), which outperform regression models. The capacity of neural networks to model multidimensional situations without assuming complex dependencies among the input variables is an advantage over statistical methods. In addition, neural networks extract the underlying nonlinear relationships between the complex input data collected from numerous maintenance records through a process of learning from training data. (FMEA), or Failure Modes and Effects Analysis, is a systematic approach to identifying potential failure modes in a system, as well as the potential effects of those failures and the likelihood of those failures occurring. The objective of FMEA is to identify and prioritize potential failure modes in order to develop and implement preventive and corrective actions that will reduce the risk of failure and mitigate its potential consequences. Flow rate, particle size, and particle concentration are key predictors of erosion rates on and around compressor blades. Computational Fluid Dynamics (CFD) analysis can be used to validate the performance and predict the erosion of compressor blades in gas turbine engines. CFD simulations can provide detailed information about the flow field, including velocity, pressure, and temperature distributions, as well as the trajectory of particles in the flow. This information can be used to predict the location and the rate of blade erosion and to identify the areas of the blade that may be particularly susceptible to erosion. The CFD simulation must first be validated against experimental data, which is done by comparing the simulation results with measurements taken from an actual turbine. This validation process is important to ensure that the CFD model accurately represents the physical system and that the results can be trusted. Once validated, the CFD model can be used to perform sensitivity analysis and explore unique design options to minimize the erosion and increase the life of the compressor blades. The present study investigates the reliability and performance of a Lockheed C-130 T-56 engine operating in the corrosive environment of the Gulf Cooperation Council region. The research work is divided into five major parts.
Part 1 predicts the failure rate of the Lockheed C-130 T-56 engine turbine using both Weibull and lognormal regression models. Initially, the data were fitted into the model using two parameters Weibull analysis, validation of the Weibull model were supported by a straight-line fit to the transformed data. In addition, a validation of Weibull analysis was compared with Weibull and lognormal regression results using the Weibull++7 software package. The comparison indicated excellent agreement with experimental data and validated the accuracy of the method in determining the mean time between failures and a fairly accurate reliability characterization. In addition, using likelihood contour plots for the parameter’s shape parameter and scale parameter, we have explicit boundaries for the variances of all four related parameters. The resultant characteristics indicate that the engine turbine's failure rate increases with time, making a replacement strategy worthwhile. Corrosion, erosion, fatigue, and cracking are the most prevalent reasons for failure within this range. Due to the component's wear-out failure pattern, a hard-time maintenance action consisting of a planned replacement and overhaul program is necessary. Part 2 covers the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. A MATLAB code was developed for this purpose. The code takes in field data and outputs the general failure rate of the T-56 turbine. To validate our results, we have further analyzed the data by using a radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is in better agreement with the radial basis neural network model compared with the actual field data than the failure rate predicted by the Weibull model. Lastly, the general failure rate of the T-56 engine turbine and its six main categorical failures were forecasted using a multilayer perceptron neural network (MLP) model on DTREG commercial software. Part 3 involves the risk assessment and the primacy of corrective action. Failure Modes and Effects Analysis (FMEA) data were ranked using the Risk Priority Number (RPN) ranking. From the FMEA matrix, the major failure mode of the T-56 engine turbine was found to be mechanical damage due to the structural failure caused by factors like erosion and sand ingestion. The results also provide insight into the reliability of the engine turbine under actual operating conditions, which aircraft operators can utilize to assess system and component failures and customize the manufacturer-recommended maintenance plans. In Part 4, a numerical simulation was performed to predict possible erosion patterns, particle distribution, and erosion rate due to the solid particles impacting the blades of NASA rotor 37. A linear erosion cascade experiment performed on NASA rotor 37 provides validation for the failure rate. It was demonstrated that particle concentration has a more substantial effect on blade erosion rate than particle size, whereas particle size has a less significant effect among all other measured parameters. Part 5 describes the development and application of models to calculate surface erosion in T-56 turbomachinery. These models predict particle trajectories in turbomachinery passages to determine impact rates, velocities, and impact angles. For this purpose, a 3D scan of new and damaged blades (due to erosion) was made to design profile data from a T-56 first-stage compressor. The result shows that particles concentration has the most significant effect on blade erosion rate where particles size has less effect among all other measured parameters. It has explained that surface roughness increases with an increase in particle size. The changes in the wall's stress and erosion rate density can also be seen along the streamwise direction, mimicking the trends shown for particle concentration and pressure ratio. It was observed that the average blade erosion rate and density exhibit a rapid rise with the increase in particle size. However, the increase in erosion rate and density slows down for much larger particle |
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Supervisor |
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Prof. Dr. Belkacem Kada |
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Thesis Type |
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Master Thesis |
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Publishing Year |
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1444 AH
2023 AD |
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Added Date |
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Tuesday, September 12, 2023 |
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Researchers
| نزار عوض الله قطان | Qattan, Nizar Awadallah | Researcher | Doctorate | |
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