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Marta Antonelli, Andrea Mazzino, and Umberto Rizza

Abstract

Temperature fluctuations in an atmospheric convective boundary layer are investigated by means of large eddy simulations (LESs). A novel statistical characterization for both weak temperature fluctuations and strong temperature fluctuations has been found. Despite the nontriviality of the dynamics of temperature fluctuations, the data presented here support the idea that the most relevant statistical properties can be captured solely in terms of two scaling exponents, characterizing the entire mixed layer. Such exponents control asymptotic (i.e., core and tails) rescaling properties of the probability density functions of equal-time temperature differences Δr θ between points separated by a distance r. A link between statistical properties of large temperature fluctuations and geometrical properties of the set hosting such fluctuations is also provided. Finally, a possible application of these new findings to the problem of subgrid-scale parameterizations for the temperature field in a convective boundary layer is discussed.

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Marta Antonelli, Alessandra Lanotte, and Andrea Mazzino

Abstract

Turbulent fluctuations of both velocity and temperature fields, issuing from high-resolution large-eddy simulations, have been analyzed in convective boundary layers. The numerically simulated flows are strongly anisotropic at large scales: this is due both to the action of buoyancy and to the imposed geostrophic wind. Their relative weight is varied so that one experiment’s results are much more convective than the other. To properly disentangle anisotropic properties, the authors exploit both standard statistical indicators, like skewness coefficients, and the three-dimensional rotational group decomposition SO(3). Two main conclusions can be drawn. First, despite the strong anisotropies at large scales, isotropy is statistically recovered at scales much smaller than the large ones. Second, relevant statistical indicators of turbulence such as the scaling exponents, of both velocity and temperature fields, are remarkably close for the two experiments. Implications of these findings for the problem of subgrid-scale modeling are discussed.

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Federico Cassola, Massimiliano Burlando, Marta Antonelli, and Corrado F. Ratto

Abstract

In contrast to conventional power generation, wind energy is not a controllable resource because of its stochastic nature, and the cumulative energy input of several wind power plants into the electric grid may cause undesired fluctuations in the power system. To mitigate this effect, the authors propose a procedure to calculate the optimal allocation of wind power plants over an extended territory to obtain a low temporal variability without penalizing too much the overall wind energy input into the power system. The procedure has been tested over Corsica (France), the fourth largest island in the Mediterranean Basin. The regional power supply system of Corsica could be sensitive to large fluctuations in power generation like wind power swings caused by the wind intermittency. The proposed methodology is based on the analysis of wind measurements from 10 anemometric stations located along the shoreline of the island, where most of the population resides, in a reasonably even distribution. First the territory of Corsica has been preliminarily subdivided into three anemological regions through a cluster analysis of the wind data, and the optimal spatial distribution of wind power plants among these regions has been calculated. Subsequently, the 10 areas around each station have been considered independent anemological regions, and the procedure to calculate the optimal distribution of wind power plants has been further refined to evaluate the improvements related to this more resolved spatial scale of analysis.

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