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Purpose Hybrid Reynolds-averaged Navier–Stokes large eddy simulation (RANS-LES) methods have become popular for simulation of massively separated flows at high Reynolds numbers due to their reduced computational cost and good accuracy. The current study aims to examine the performance of LES and hybrid RANS-LES model for a given grid resolution. Design/methodology/approach For better assessment and contrast of model performance, both mean and instantaneous flow fields have been investigated. For studying instantaneous flow, proper orthogonal decomposition has been used. Findings Current analysis shows that hybrid RANS-LES is capable of achieving similar accuracy in prediction of both mean and instantaneous flow fields at a very coarse grid as compared to LES. Originality/value Focusing mostly on the practical applications of computation, most of the attention has been given to the prediction of one-point flow statistics and little consideration has been put to two-point statistics. Here, two-point statistics has been considered using POD to investigate unsteady turbulent flow.

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The power generation through renewable energy resources is increasing vastly, Solar energy and Wind Energy are the most abundantly available renewable energy resources. The growth of small scale distributed grid networks increasing rapidly in the modern power systems and Distributed Generation (DG) plays a predominant role. Microgrid is one among the emerging techniques in power systems. Power Management is mainly required to have control over the real and reactive power of individual DG and for smooth operation, maintaining stability and reliability. This paper presents a survey of the research works already reported focusing on power management of hybrid energy systems such as mainly solar and wind systems in microgrid. Six different approaches have been studied in detail for AC,DC and hybrid AC/DC microgrid.

Abstract-B-splines have today become the industry standard for CAD data representation. Freeforrn shape synthesis from point cloud data is an emerging technique. This predominantly involves B-spline curve / snrface fitting to the point cloud data to obtain the CAD definitions. Accurate curve a n d surface fitting from point clouds needs a good parameterization model, i.e. the determination of parameter values of the digitized points in order to perform least squares (LSQ) fitting. Numerous work have been done on selection of such parameters. Nevertheless, it is difficult with the present approaches to estimate better parameters particularly when the points are irregularly spaced and lie on a complex hase curve o r surface. There is a need to evolve from all the available parameterization solutions a n optimum set of parameters which in turn will generate curves / surfaces interpolating the given data closely. An approach based on genetic algorithms for parameter optimization is presented here. A novel population initialization scheme is proposed that ensures that the optimization procedure is both global in nature with less expensive convergence. The present study of parameterization is for Non Uniform B-spline curve fitting.

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