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A Machine Learning Based Body Force Model for Analysis of Fan-Airframe Aerodynamic Interactions

López de Vega, Luis and Dufour, Guillaume and Blanc, Florian and Thollet, William A Machine Learning Based Body Force Model for Analysis of Fan-Airframe Aerodynamic Interactions. (2018) In: Global Power and Propulsion Society Conference 2018, 7 May 2018 - 9 May 2018 (Montréal, Canada).

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Official URL: https://doi.org/10.5281/zenodo.1344526

Abstract

Body force modeling is a numerical strategy that allows an accurate representation of the aerodynamics of turbomachinery blade rows at a reduced computational cost, making it suitable for predicting fan-airframe aerodynamic interactions in boundary layer ingestion (BLI) propulsive architectures. This paper focuses on a new approach for building the body force representation using a machine learning technique, rather than analytically modeling the effects of the blades in the flow. This methodology is developed and assessed in a distorted inflow case representative of a BLI configuration and compared to a full annulus unsteady computation.

Item Type:Conference or Workshop Item (Paper)
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Other partners > Airbus (FRANCE)
Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Laboratory name:
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Deposited By: Guillaume Dufour
Deposited On:12 Oct 2018 13:21

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