Estimation of Pressure Loss Derivatives for Turbine Center Frames via L1-regularized Regression Models

Abstract

The turbine center frame (TCF) is a key component of modern aero engines, guiding the airflow from the high-pressure to the low-pressure turbine. Due to an increasing flow area, it has a diffusing effect, making it prone to flow separations. Open literature offers various layout guidelines based on performance correlations, but in order to generate parsimonious models, many authors restrict their investigations to a small parameter space while neglecting other important factors. On the other hand, a correlation based on a large number of variables might be more accurate yet very impractical. An L1-regularized least squares fit (LASSO) yields a possibility to balance the contradictory demands for a simple and accurate model. In the current work, the LASSO method is used to correlate the total pressure loss of a straight strutted TCF with various geometrical and inlet-flow parameters. The strength of the regularization allows setting the tradeoff between the model's complexity and accuracy. In this way crucial parameters for the performance of the TCF could be identified and a simple polynomial pressure loss model for the design optimization could be found. KEYWORDSTurbine center frame, pressure loss correlations, least squares regression, LASSO algorithm NOMENCLATURE TCF turbine center frame 𝑝 , mass flow avg. inlet total pressure HPT high-pressure turbine 𝑝 , mass flow avg. outlet total pressure LPT low-pressure turbine 𝑝 total pressure loss = , , , OLS ordinary least squares 𝑝 mean total pressure loss CV cross validation 𝑐 correlation coefficient 𝑝 correlation parameter 𝜆 penalty weight LASSO least abs. shrinkage and selection operator