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Articles 1 - 3 of 3
Full-Text Articles in Mechanical Engineering
Inverse Design Of And Experimental Measurements In A Double-Passage Transonic Turbine Cascade Model, G. M. Laskowski, A. Vicharelli, G. Medic, C. J. Elkins, J. K. Eaton, Paul A. Durbin
Inverse Design Of And Experimental Measurements In A Double-Passage Transonic Turbine Cascade Model, G. M. Laskowski, A. Vicharelli, G. Medic, C. J. Elkins, J. K. Eaton, Paul A. Durbin
Paul A. Durbin
A new transonic turbine cascade model that accurately produces infinite cascade flow conditions with minimal compressor requirements is presented. An inverse design procedure using the Favre-averaged Navier-Stokes equations and k-ε turbulence model based on the method of steepest descent was applied to a geometry consisting of a single turbine blade in a passage. For a fixed blade geometry, the passage walls were designed such that the surface isentropic Mach number (SIMN) distribution on the blade in the passage matched the SIMN distribution on the blade in an infinite cascade, while maintaining attached flow along both passage walls. An experimental rig …
Unsteady Effects On Trailing Edge Cooling, G. Medic, Paul A. Durbin
Unsteady Effects On Trailing Edge Cooling, G. Medic, Paul A. Durbin
Paul A. Durbin
It is shown how natural and forced unsteadiness play a major role in turbine blade trailing edge cooling flows. Reynolds averaged simulations are presented for a surface jet in coflow, resembling the geometry of the pressure side breakout on a turbine blade. Steady computations show very effective cooling; however when natural-or even moreso, forced-unsteadiness is allowed, the adiabatic effectiveness decreases substantially. Streamwise vortices in the mean flow are found to be the cause of the increased heat transfer.
A Lagrangian Stochastic Model For Dispersion In Stratified Turbulence, S. K. Das, Paul A. Durbin
A Lagrangian Stochastic Model For Dispersion In Stratified Turbulence, S. K. Das, Paul A. Durbin
Paul A. Durbin
In this paper we discuss the development of a Lagrangian stochastic model (LSM) for turbulent dispersion of a scalar (species). Given any tensorally linear second-moment closure (SMC) turbulence model we show how to derive a mathematically equivalent set of stochastic differential equations (SDEs), i.e., the second-moment equations constructed from these SDEs are exactly the same (within a realizability constraint) as the given SMC. This set of equations forms the LSM. Both turbulence anisotropy and buoyancy effects are incorporated by this method. In order to achieve the correct critical Richardson number and to obtain the simplest Lagrangian formulation, a revised set …