Using of renewable energy sources, instead of traditional oil, gas and coal, is the one of the ways by which it’s possible to hold global warming mechanisms. One of these sources is the wind energy and the device-transformer for it is the wind generator. This paper deals with the problem of the optimal wind generator blade design which allows getting the device efficiency maximum. The alternative wind generators realisations and their advantages are showed in the work. The numerical methods for fluid dynamic equations solving were applied for the optimal profile definition. The advantages of this way were showed, also the model, which was the best fitted for the concerned conditions, was chosen. Modelling was realized in ANSYS Fluent. The results were compared with the real wind generations data.

We obtain new sufficient conditions of strong convergence of distributions of Brownian sojourn times in procedures of approzimation by functionals of integral type. Bibliography: 5 titles.We consider a sequence of stochastic processes {~,(t)}7=~, ~(t), t E T c R, whose trajectories a.s. lie in a function space X.Let {Pn}~=l be distributions corresponding to {~,}~~ The goal of the present paper is to obtain conditions which guarantee the strong convergence (convergence with respect to variation)for certain functionals and stochastic processes approximating the limiting stochastic process. A systematic study of problems concerning the strong convergence for distributions of functionals of processes was initiated by Yu. A. Davydov (see the bibliography in [3]). He proved, in particular, that in the case of Ganssian processes, the weak convergence Pn =~ P and the fact that f belongs to a certain class ,Alp determined only by the limiting process imply the strong convergence (1). This result is not universal, e.g., it cannot be applied to the following important class of functionals of sojourn-time type:where G is a subset of R 2 (the configuration space) and X = C[0,1] is the space of continuous functions on [0,1]. We consider these functionals in the present paper. We consider the Wiener process w(t), t E [0, 1], as a (limiting) stochastic process. We denote by P = W the distribution of this process. The following three theorems extend and complete Davydov's results mentioned above.

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