Fatigue life prediction of a turbine blade using finite element analysis – Complete Phd and Masters Thesis

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Introduction

In the field of aerospace engineering, turbine blades play a crucial role in the performance and efficiency of gas turbine engines. These components are subjected to high temperatures, centrifugal forces, and aerodynamic loads, which can lead to fatigue failure over time. Therefore, predicting the fatigue life of turbine blades is essential to ensure the safety and reliability of the engine. Finite Element Analysis (FEA) is a powerful tool that can be used to simulate the behavior of turbine blades under different loading conditions and predict their fatigue life.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Turbine Blades
2.2 Fatigue Failure Mechanisms
2.3 Finite Element Analysis in Aerospace Engineering
2.4 Previous Studies on Fatigue Life Prediction of Turbine Blades
2.5 Material Properties and Constitutive Models
2.6 Crack Propagation Analysis
2.7 Damage Tolerance and Fracture Mechanics
2.8 Sensitivity Analysis
2.9 Validation of FEA Models
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Introduction
3.2 Selection of FEA Software
3.3 CAD Modeling of Turbine Blade
3.4 Mesh Generation
3.5 Material Assignment and Boundary Conditions
3.6 Loading Conditions
3.7 Fatigue Life Prediction Methods
3.8 Post-Processing and Results Analysis
3.9 Sensitivity Analysis
3.10 Validation of FEA Models

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Comparison of Different Fatigue Life Prediction Methods
4.3 Influence of Material Properties on Fatigue Life
4.4 Effect of Loading Conditions on Fatigue Life
4.5 Sensitivity Analysis Results
4.6 Validation of FEA Models
4.7 Limitations of the Study
4.8 Recommendations for Future Research
4.9 Implications for Aerospace Industry
4.10 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to the Field
5.4 Practical Implications
5.5 Recommendations for Practitioners
5.6 Recommendations for Future Research
5.7 Limitations of the Study
5.8 Conclusion

Thesis Overview on Fatigue life prediction of a turbine blade using finite element analysis:

The aim of this thesis is to investigate the fatigue life prediction of a turbine blade using finite element analysis. In the aerospace industry, turbine blades are subjected to harsh operating conditions that can lead to fatigue failure. The prediction of fatigue life is essential to ensure the safe and reliable operation of gas turbine engines.

Chapter 1 provides an introduction to the research topic, including the background, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. Chapter 2 presents a comprehensive literature review on turbine blades, fatigue failure mechanisms, FEA in aerospace engineering, previous studies, material properties, crack propagation analysis, damage tolerance, fracture mechanics, sensitivity analysis, and validation of FEA models.

Chapter 3 outlines the research methodology, including the selection of FEA software, CAD modeling, mesh generation, material assignment, boundary conditions, loading conditions, fatigue life prediction methods, post-processing, results analysis, sensitivity analysis, and validation. Chapter 4 discusses the findings of the study, including the comparison of different prediction methods, the influence of material properties and loading conditions, sensitivity analysis, validation of models, limitations, recommendations, and implications for the aerospace industry.

Chapter 5 presents the conclusion and summary of the thesis, summarizing the findings, conclusions, contributions to the field, practical implications, recommendations for practitioners and future research, limitations, and final conclusions. This thesis aims to contribute to the understanding of fatigue life prediction in turbine blades and provide valuable insights for the aerospace industry.

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