Abstract
Body force models of fans and compressors are widely employed for predicting performance due to the reduction in computational cost associated with their use, particularly in nonuniform inflows. Such models are generally divided into a portion responsible for flow turning and another for loss generation. Recently, accurate, uncalibrated turning force models have been developed, but accurate loss generation models have typically required calibration against higher fidelity computations (especially when flow separation occurs). In this paper, a blade profile loss model is introduced which requires the trailing edge boundary layer momentum thicknesses. To estimate the momentum thickness for a given blade section, an artificial neural network is trained using over 400,000 combinations of blade section shape and flow conditions. A blade-to-blade flow field solver is used to generate the training data. The model obtained depends only on blade geometry information and the local flow conditions, making its implementation in a typical computational fluid dynamics framework straightforward. We show good agreement in the prediction of profile loss for 2D cascades both on and off design in the defined ranges for the neural network training.
Highlights
Future turbofan engines are likely to encounter nonuniform inflows due to boundary layer ingestion (BLI) [1] or reduced length nacelles [2]
Due to the body force limitations related to obtaining velocity distributions on either side of the blade and due to high computational costs for the solution of Drela’s boundary layer equations, a data-driven approach has been used in this study to provide a direct analytical model to predict the trailing edge normalized momentum thickness
Cascade 1 experiences deviation angles between 6 to 9 degrees for a range of −6 to 6 incidence angles, and the maximum boundary layer displacement thickness to pitch ratio occurs at an incidence angle of 6 degrees that is 0.04
Summary
Future turbofan engines are likely to encounter nonuniform inflows due to boundary layer ingestion (BLI) [1] or reduced length nacelles [2]. Due to the body force limitations related to obtaining velocity distributions on either side of the blade and due to high computational costs for the solution of Drela’s boundary layer equations, a data-driven approach has been used in this study to provide a direct analytical model to predict the trailing edge normalized momentum thickness. The key findings are: (1) the new viscous body force model captures the viscousinviscid interaction effects on the relative total pressure drop, (2) a novel momentum thickness equation shows that the machine-learning-based approach is a reliable, fast model that predicts the loss both on and off-design. A new shock loss body force model based on Denton’s shock entropy generation equation [17] is introduced. Implementation of the new viscous and shock loss models on a 3D compressor for uniform and nonuniform inflows is discussed in Pazireh’s PhD dissertation [25]
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