Author: Tafti, Danesh K; He, Long; Nagendra, K
Title: Large eddy simulation for predicting turbulent heat transfer in gas turbines. Cord-id: 0dwi28rn Document date: 2014_1_1
ID: 0dwi28rn
Snippet: Blade cooling technology will play a critical role in the next generation of propulsion and power generation gas turbines. Accurate prediction of blade metal temperature can avoid the use of excessive compressed bypass air and allow higher turbine inlet temperature, increasing fuel efficiency and decreasing emissions. Large eddy simulation (LES) has been established to predict heat transfer coefficients with good accuracy under various non-canonical flows, but is still limited to relatively simp
Document: Blade cooling technology will play a critical role in the next generation of propulsion and power generation gas turbines. Accurate prediction of blade metal temperature can avoid the use of excessive compressed bypass air and allow higher turbine inlet temperature, increasing fuel efficiency and decreasing emissions. Large eddy simulation (LES) has been established to predict heat transfer coefficients with good accuracy under various non-canonical flows, but is still limited to relatively simple geometries and low Reynolds numbers. It is envisioned that the projected increase in computational power combined with a drop in price-to-performance ratio will make system-level simulations using LES in complex blade geometries at engine conditions accessible to the design process in the coming one to two decades. In making this possible, two key challenges are addressed in this paper: working with complex intricate blade geometries and simulating high-Reynolds-number (Re) flows. It is proposed to use the immersed boundary method (IBM) combined with LES wall functions. A ribbed duct at Re=20 000 is simulated using the IBM, and a two-pass ribbed duct is simulated at Re=100 000 with and without rotation (rotation number Ro=0.2) using LES with wall functions. The results validate that the IBM is a viable alternative to body-conforming grids and that LES with wall functions reproduces experimental results at a much lower computational cost.
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