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One of the great challenges of engineering teams nowadays is to overcome long and costly project experimentation phases. One effective way of decreasing such project demands is to come up with a firsthand prototype with high success probability. In order to do so, the project team should rely on robust numerical models, which can represent most of the real-life product behaviors, for instance system dynamics. For rolling element bearings, such dynamic models have to consider the dynamic interactions between its components, i.e., rolling elements and raceways. The only vibration transmitting points on rolling element bearings are the lubricated contacts. Therefore, in order to represent the full bearing dynamic behavior on a numerical model, an efficient transient contact model, which depicts the actual contact behavior, is fundamental.

This paper presents a software developed to apply the Hertz contact formulation to bearings with rolling elements, estimating the occurrence of surface fatigue failure in this kind of parts. Considering the wide range of designs in the automotive industry using this kind of bearings, a dedicated software, using a simple language, becomes a useful tool to reduce the design time, predicting in a satisfactory manner the failure of the bearing. The software developed shows a high level of accuracy, when compared to data provided by rolling bearing manufacturers.

This work presents an application of the simulated annealing algorithm to redundant system reliability optimization, and its objective is to analyze this method and compare to other optimization methods, which are Genetic Algorithm, Evolution Strategy and Lagrange Multipliers. A hidrid optimization algorithm, composed by Simulated Annealing and Genetic Algorithm, is also developed in this work, to conciliate the advantages of both methods. The results presented by the Simulated Annealing and the hybrid algorithm are significant and validate these methods as robust tools for parameters optimization in engineering systems developments

The reliability optimization of redundant systems aims to improve some design parameters as reliability or cost, using optimization methods, in order to determine the optimum number of redundancies in each component of the system. This work compares some optimization methods (Lagrange Multipliers, Evolution Strategy and Genetic Algorithm) and its objective is to determine which method is more adjusted to the problem.