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The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows its use in simulations where the computation of a single realization is very costly. This work presents a methodology for the parallelization of the MC method, in the context of cloud computing. This strategy is based on the MapReduce paradigm, and allows an efficient distribution of tasks in the cloud. This methodology is illustrated on a problem of structural dynamics that is subject to uncertainties. The results show that the technique is capable of producing good results concerning statistical moments of low order. It is shown that even a simple problem may require many realizations for convergence of histograms, which makes the cloud computing strategy very attractive (due to its high scalability capacity and low-cost). Additionally, the results regarding the time of processing and storage space usage allow one to qualify this new methodology as a solution for simulations that require a number of MC realizations beyond the standard.

The influence of material time dependency and anisotropy in the context of two specific flywheel designs-preload and multi-directional composite (MDC)--is investigated. In particular, we focus on the following aspects: 1) geometric constraints, 2) material constraints, 3) loading type, and 4) the fundamental character of the time-dependent response, i.e., reversible or irreversible. The bulk of the results presented were obtained using a composite (PMC IM7/8552 at 135 C) material system. The material was characterized using a general multimechanism hereditary (viscoelastoplastic) model. As a general conclusion, the results have clearly shown that both the preload and the MDC rotor designs are significantly affected by time-dependent material behavior, which may impact the state of rotor balance and potentially reduce its operating life. In view of the results of the parametric studies and predictions made in the present study, the need for actual experimentation focusing on the time-dependent behavior of full-scale flywheel rotors is self-evident.