Rotorcraft flight simulation to support aircraft certification: a review of the state of the art with an eye to future applications

© The AuthorsThis paper presents the approach for Rotorcraft Certification by Simulation proposed within the RoCS project. In particular, the aspects of model validation and credibility assessment through the usage of uncertainty quantification techniques are reviewed, and some lesson learned are presented. It is shown that the increase of effort required to thoroughly evaluate the capability of the simulation model is often counterbalanced by the advantages of the insight that can be obtained and possibly exploited also for design purposes. It is shown that the numerical approaches, and in some cases even the tools required to perform the necessary uncertainty analyses are publicly available and can be directly employed. This paper is one of a set presented at the 49th European Rotorcraft Forum discussing results from the EU Clean Sky 2 project, Rotorcraft Certification by Simulation (RoCS)

Daftar Ebook Engineering Science Terbitan Springer Tahun 2018

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Combining Interval, Probabilistic, And Other Types Of Uncertainty In Engineering Applications

In many practical application, we process measurement results and expert estimates. Measurements and expert estimates are never absolutely accurate, their result are slightly different from the actual (unknown) values of the corresponding quantities. It is therefore desirable to analyze how this measurement and estimation inaccuracy affects the results of data processing. There exist numerous methods for estimating the accuracy of the results of data processing under different models of measurement and estimation inaccuracies: probabilistic, interval, and fuzzy. To be useful in engineering applications, these methods should provide accurate estimate for the resulting uncertainty, should not take too much computation time, should be understandable to engineers, and should be sufficiently general to cover all kinds of uncertainty. In this dissertation, on several case studies, we show how we can achieve these four objectives. We show that we can get more accurate estimates, for example, by properly taking model inaccuracy into account. We show that we can speed up computations, e.g., by processing different types of uncertainty separately. We show that we can make uncertainty-estimating algorithms more understandable, e.g., by explaining the need for non-realistic Monte-Carlo simulations. We also analyze how how to make decisions under uncertainty and how general uncertainty-estimating algorithms can be