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    Design, Manufacturing and Setting up the Injection and Mixing Teststand of Shear and Swirl injectors in Transcritical Condition

    , M.Sc. Thesis Sharif University of Technology Shokrzadeh Damirchi, Alireza (Author) ; Farshchi, Mohammad (Supervisor)
    Abstract
    The arena of supercritical combustion involves a set of unique physical phenomena that are different from what is observed in the spraying and combustion of a subcritical regime. Understand the complex physics of the flow field in transcritical and supercritical operating conditions and discovery of factors affecting them and the extent to which each of these factors affects the structure of the jet and consequently the quality of the mixing and combustion process, is one of the basic steps to optimize of cryogenic liquid fuel engine combustion chamber design. In this thesis, by studying the experimental researches of injection at sub-critical to super-critical conditions, it has been tried... 

    Numerical Simulations of the Coaxial Shear and Swirl Injectors’ Cryogenic Flowfields under Trans- and Supercritical Conditions

    , Ph.D. Dissertation Sharif University of Technology Poormahmood, Ata’allah (Author) ; Farshchi, Mohammad (Supervisor)
    Abstract
    Characterizing the dynamics dictating injection and mixing of the coaxial shear and swirl injectors is of central importance in the context of space rockets, diesel and direct injection reciprocating engines, and modern turbine gases. Sophisticated with precise thermo-physical models along with high-fidelity turbulence approaches, a computational platform –based on OpenFOAM open source code– is developed to simulate the practical injectors flow-fields under trans- and supercritical conditions. Aiming at providing a deep insight on the underlying physics, the investigations are conducted in a hierarchical manner; the flow complexities are added step-by-step to eventually construct the... 

    Liquid-liquid coaxial swirl injector performance prediction using general regression neural network

    , Article Particle and Particle Systems Characterization ; Volume 25, Issue 5-6 , 2009 , Pages 454-464 ; 09340866 (ISSN) Ghorbanian, K ; Soltani, M. R ; Ashjaee, M ; Morad, M. R ; Sharif University of Technology
    2009
    Abstract
    A general regression neural network technique was applied to design optimization of a liquid-liquid coaxial swirl injector. Phase Doppler Anemometry measurements were used to train the neural network. A general regression neural network was employed to predict droplet velocity and Sauter mean diameter at any axial or radial position for the operating range of a liquid-liquid coaxial swirl injector. The results predicted by neural network agreed satisfactorily with the experimental data. A general performance map of the liquid-liquid coaxial swirl (LLCS) injector was generated by converting the predicted result to actual fuel/oxidizer ratios. © 2008 WILEY-VCH Verlag GmbH & Co. KGaA