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ANALYSIS AND OPTIMIZATION OF DENSE GAS FLOWS: APPLICATION TO ORGANIC RANKINE CYCLES TURBINES

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ANALYSIS AND OPTIMIZATION OF DENSE GAS FLOWS: APPLICATION TO ORGANIC RANKINE CYCLES TURBINES ( analysis-and-optimization-dense-gas-flows-application-to-org )

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critical value (the critical Mach number, Mc), the total drag coefficient increases abruptly. Nevertheless, if M∞ is just slightly above Mc, shock waves appearing in the flow field remain relatively weak, since the maximum Mach number in the flow is relatively close to one. For a population of airfoils of given relative thickness, shock waves will be more or less strong according to the particular shape of an individual; individuals closest to the global optimum, i.e. with the lowest drag coefficient, are expected to produce particularly weak shocks or, eventually, a completely shock-free flow. In the latter case, CD exactly equals zero for all airfoil shapes such that Mmax≤1, where Mmax is the maximum Mach number in the flow-field, and a continuous subset of global optima exists. Of course, this is due to limitations of the adopted flow model, which excludes viscous effects from considerations. For this reason, it was chosen to optimize the airfoil shape for flow conditions somewhat above the critical ones, in order to get supercritical flow for all individuals in the search space and, consequently, to be able to select a unique global optimum. In practice, because of numerical errors introduced by the numerical scheme and boundary conditions, CD is not exactly zero even for completely subsonic flow-fields, but takes instead small positive values. In order to distinguish between subcritical and supercritical airfoils, a numerical check on the flow maximum Mach number is introduced, and CD is forced to be zero if Mmax≤1. For supercritical flows characterized by strong shock waves, contribution of numerical errors to the total drag is generally negligible. Problems can arise for flow-fields characterized by very weak shock waves, i.e. for individuals close to the global optimum. For these individuals, numerical drag can represent a considerable amount of the total drag if the numerical evaluation of the fitness function is not sufficiently accurate; conversely, the more accurate is the numerical scheme and the finer the computational grid, the smaller the numerical error on CD. Firstly, two optimization runs are performed using different randomly-generated starting populations. For each run, the GA-driven population appears to have reached a stabilized optimal (minimal) value of the drag coefficient after the prescribed number of generations: the maximum and mean value of the drag coefficient in the final population of airfoil shapes are almost the same in each case, which hopefully indicates that the algorithm has converged to a population of almost identical geometries. Nonetheless, optimal airfoils obtained in the two runs display considerable geometric differences, precisely, about 8%, based on the distance measure ((Eq. 19). Since both optimization runs provide stabilized 107

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