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Turbomachinery design systems have traditionally been a highly guarded in-house capability of large turbomachinery corporations, though commercial software has started becoming available in the last decade along with the recent release of open source software such as MULTALL. While attempting to use MEANGEN, the meanline solver in MULTALL, for preliminary design optimization of a 2-stage axial turbine for 100 kW micro gas turbine application, it was found that MEANGEN does not provide an estimate of the component losses in a turbomachine such as blade boundary layer loss, trailing edge loss, tip leakage loss, and endwall loss. The present work describes the development of an in-house meanline solver TURBOMEAN with 1D component loss models quantified in terms of entropy generation rate for transonic flow through a multi-stage axial turbine. Special emphasis is put on understanding the physical origins of the loss mechanisms in turbomachinery flows.TURBOMEAN allows the user to run an optimization loop in the first stage of design itself and in turn significantly reduce the time required to optimize a design using 3D-CFD analyses. The 1D component losses being dependent on the flow physics rather than historical correlations may very well be used for modern blade designs which have considerably threedimensional shape. Major effort has been put in realizing those loss models into codes and integrating them with a meanline solver. Practical assumptions were sort after wherever analytical solution was not possible for developing expressions of terms which depended on flow conditions.These assumptions are then tested by validating the design system.The solver and the models are implemented in MATLAB and validated against data available in literature. It is expected that the outcome of the present work can also be extended for axial compressors as well as radial turbomachines due to considerable similarities in underlying aerodynamic loss mechanisms.

Development of One-dimensional Turbomachinery Meanline Solver with Physics-based Detailed Loss Models