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Acrylic bone cement is widely used as a structural material in orthopaedics, dentistry, and orofacial surgery. Although bone cement celebrates four decades of success, it remains susceptible to fatigue fracture. This type of failure can directly lead to implant loosening, revision surgery, and increased healthcare expenditures. The mechanism of fatigue failure is divided into three stages: 1) fatigue crack initiation, 2) fatigue crack propagation, and 3) fast, brittle fracture. Adding reinforcing fibers and particles to bone cement is a proposed solution for improving fatigue performance. The mechanical performance of these reinforced bone cements is limited by fiber ductility, fibermatrix de-bonding, elevated viscosity, and mismatch of fiber size and scale of fatigue induced damage. In this dissertation, I report that adding small amounts (0% - 10% by weight) of multiwall carbon nanotubes (MWNTs) enhances the strength and fatigue performance of single phase bone cement. MWNTs (diameters of 10-9 10-8 m; lengths of 10-6 10-3 m) are a recently discovered nanomaterial with high surface area to volume ratios (conferring MWNT bone cement composites with large interfaces for stress transfer) that are capable of directly addressing sub-microscale, fatigue induced damage. MWNTs (2wt%) significantly increased the flexural strength of single phase bone cement by a modest 12%; whereas, similar additions of MWNTs dramatically enhanced fatigue performance by 340% and 592% in ambient and physiologically relevant conditions, respectively. Comparing the fatigue crack propagation behaviors of reinforced and unreinforced single phase bone cements revealed that the reinforcing mechanisms of MWNTs are strongly dependent on stress intensity factor, K, a numerical parameter that accounts for the combinatorial effect of the applied load and the crack size. As the crack grows the apparent stress at the crack tip intensified and the MWNTs lost their reinforcing capabilities. For that reason, it is likely that the predominant role of the MWNTs is to reinforce the bone cement matrix prior to crack initiation and during the early stages of crack propagation. Therefore, MWNTs are an excellent candidate for improving the clinical performance of bone cement, thereby improving implant longevity and reducing patient risk and healthcare costs.

In this thesis, fatigue life of a stationary component of F110-GE-100 jet engine is assessed. Three-dimensional finite element model of the component itself and the neighboring components are modeled by using a finite element package program, ANSYS, in order to perform thermal, stress and fracture mechanics analyses. Coupled-field (thermal-stress) analysis is performed to identify fracture-critical locations and to describe the stress histories of the components. After determining the critical location, fracture mechanics calculations are performed by modeling a crack of various lengths at the critical locations with FRANC3D in order to calculate mode I and II stress intensity factors and geometry factors beta. Combining the outputs of coupled-field and fracture mechanics analyses, fatigue lives and creep rupture times are calculated with a crack growth life prediction program, AFGROW. A linear damage summation method is used to assess the fatigue life of the component of interest.

The goal this work is solve the case of customer complaint. The solved part is loaded by high pressure and extend the service life will be considered a number of possible solu-tions. Among them will be selected to best in terms of technical, economic, and rate of delivery.

Aerospace design requirements mandate acceptable levels of structural failure risk. Probabilistic fatigue models enable estimation of the likelihood of fatigue failure. A key step in the development of these models is the accurate inference of the probability distributions for dominant parameters. Since data sets for these inferences are of limited size, the fatigue model parameter distributions are themselves uncertain. A hierarchical Bayesian approach is adopted to account for the uncertainties in both the parameters and their distribution. Variables specifying the distribution of the fatigue model parameters are cast as hyperparameters whose uncertainty is modeled with a hyperprior distribution. Bayes' rule is used to determine the posterior hyperparameter distribution, given available data, thus specifying the probabilistic model. The Bayesian formulation provides an additional advantage by allowing the posterior distribution to be updated as new data becomes available through inspections. By updating the probabilistic model, uncertainty in the hyperparameters can be reduced, and the appropriate level of conservatism can be achieved. In this work, techniques for Bayesian inference and updating of probabilistic fatigue models for metallic components are developed. Both safe-life and damage-tolerant methods are considered. Uncertainty in damage rates, crack growth behavior, damage, and initial flaws are quantified. Efficient computational techniques are developed to perform the inference and updating analyses. The developed capabilities are demonstrated through a series of case studies.