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NOVEL APPROACH FOR LOSS AND FLOW-TURNING PREDICTION USING OPTIMIZED SURROGATE MODELS IN TWO-DIMENSIONAL COMPRESSOR DESIGN

Schmitz, Andreas and Aulich, Marcel and Nicke, Eberhard (2011) NOVEL APPROACH FOR LOSS AND FLOW-TURNING PREDICTION USING OPTIMIZED SURROGATE MODELS IN TWO-DIMENSIONAL COMPRESSOR DESIGN. ASME Turbo Expo 2011: Power for Land, Sea and Air, Vancouver, Canada.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1115/GT2011-45086

Abstract

wo-dimensional (2D) streamline curvature methods are still an important tool in modern compressor design. In the past most of the streamline curvature methods made use of empirical correlations to approximate the blade row losses and deviation functions on which the accuracy of streamline curvature methods mainly depend. These empirical correlations are just accurate for a small set of geometric airfoil design parameters for which they where obtained and the prediction of airfoil performance at high Mach numbers or at off-design condition is inaccurate. Nowadays, a new approach is needed to consider highly customized, modern airfoil geometries with an increased number of design parameters. A new method with the possibility to predict the performance of these highly customized airfoils also at off-design condition and high Mach numbers is presented in this paper. This method uses a large airfoil database together with optimized surrogate models to accurately predict airfoil performance. The database consists of approximately 106 randomly created airfoils with randomly created inflow conditions and the airfoil performance which results from the 2D Euler-boundary layer code MISES. The airfoil geometry in this database is described by ten geometrical parameters, e.g. stagger angle, chord length etc.. The flow condition is described by four flow parameters such as the relative inflow Mach number, MVDR, relative inflow angle and Reynolds number. Airfoil performance is represented by total pressure loss and flow-turning. This database was used to train neural networks that provides the relationship between the geometrical/flow parameters and the airfoil performance. The topology of the neural networks was optimized to achieve a model which represents this highly nonlinear functionality at best. This model was integrated in the DLR's in-house streamline curvature tool ACDC which is based on the equations of MÖNIG et al. , GALLIMORE. The code allows viscous throughflow calculations taking into account radial mixing by turbulent diffusion, endwall boundary layers and a model for tip clearance based on the work of DENTON and KRÖGER

Document Type:Conference or Workshop Item (Paper)
Title:NOVEL APPROACH FOR LOSS AND FLOW-TURNING PREDICTION USING OPTIMIZED SURROGATE MODELS IN TWO-DIMENSIONAL COMPRESSOR DESIGN
Authors:
AuthorsInstitution or Email of Authors
Schmitz, Andreasandreas.schmitz@dlr.de
Aulich, Marcelmarcel.aulich@dlr.de
Nicke, Eberhardeberhard.nicke@dlr.de
Date:2011
Refereed publication:Yes
In ISI Web of Science:No
Status:Published
Keywords:Throughflow, 2D solver, neural networks, compressor design, loss, flow turning, total pressure loss
Event Title:ASME Turbo Expo 2011: Power for Land, Sea and Air
Event Location:Vancouver, Canada
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Aircraft Research
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Simulation & Validation
Location: Köln-Porz
Institutes and Institutions:Institute of Propulsion Technology > Fan and Compressor
Deposited By: Andreas Schmitz
Deposited On:16 Nov 2012 11:41
Last Modified:16 Nov 2012 11:41

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