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This work aims to prediction of laminar/turbulent transition which plays an important role on aerodynamics of wing section. In this respect the flow around the NACA2415 airfoil simulated in a Computational Fluid Dynamics (CFD) solver in different regimes with and without propeller flowfield. For predicting the transition onset, two approaches were used: The first is based on time history of the skin-friction coefficient for determining the transition onset and the transition length on the airfoil. The second is to apply transition γ-〖Re〗_θ model for laminar/turbulent transition simulation. For investigation of transition effect, the simulation repeated by use of a classical turbulent model and both results was compared with experimental data. The comparison shows that taking into account the transition effects gives a good agreement with experiment. Relative error of calculated drag coefficients for the transition based simulation is lower than 10%, while fully turbulent simulation are 70% overestimated in some incidences. Slipstream of upstream propeller changes flow pattern and boundary layer characteristics over the wing. Indeed in presence of propeller, spanwise load distribution and laminar/turbulent transition onset were affected. In propeller flowfield, increasing of velocity normal component over wing surface causes transition delay. Movement of transition onset to trailing edge on the upper surface in propeller downwash is representative of such phenomenon. On the other hand, in upwash region, the transition onset moves upstream. With the increasing propeller rotational speed, this tendency augments and so the transition onset on the wing upper surface moves far downstream in propeller downwash.

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Grain design is the most important part of a solid rocket motor. The aim of this study is finocyl grain design based on predetermined objective function with respect to ballistic curves in order to satisfy various thrust performance requirements through an innovative design approach using a genetic algorithm optimization method. The classical sampling method has been used for design space-filling. The level set method has been used for simulating the evolution of the burning surface in the propellant grain. An algorithm has been developed beside the level set code that prepares the initial grain configuration using Pro/Engineer software to export generated models to level set code. The lumped method has been used to perform internal ballistic analysis. Two meta-models are used to surrogate the level set method in the optimization design loop. The first method is based on adaptive basis function construction and the second method is based on the artificial neural network. In order to validate the proposed algorithm, a grain finosyl sample has been investigated. The results show that both grain design method reduced the design time significantly and this algorithm can be used in designing of any grain configuration. In addition, data have more accuracy in grain design based on the artificial neural network, so this method is the more effective and practical method to grain burn-back training.