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Numerical Investigation of the NASA Glenn Propulsion Systems Laboratory
Technical Paper
2019-01-1924
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
NASA Glenn Research Center’s Propulsion Systems Laboratory (PSL) allows ice-crystal ice accretion tests on jet engines. This pressurized wind tunnel facility allows engines to be operated at flight altitudes and temperatures. Steady state and unsteady computational fluid dynamics simulations were performed for the PSL geometry, including the spray bars with their supports, and the converging duct section. These simulation results help to characterize the performance of the tunnel and are important for understanding the flow and particle behavior leading up to the engine test section. The results indicate complex flow structures, with vortex shedding and non-uniform flow features. Flow separation is observed in several regions. Several flow features and vortices are seen to persist to the duct exit plane where the fan section of a jet engine would be mounted for testing. Unsteady simulations show that periodic vortex shedding is created by the spray bars, and that the generated crosswise vorticity is converted to streamwise and antistreamwise vorticity through the acceleration and stretching of the flow structures in the converging duct section. Flow nonuniformity is observed on the duct exit plane for both steady state and unsteady simulations with velocity deficits on the sides and bottom of the exit plane. Particles of various diameters were released from either the upstream inlet plane or the spray bars, and corresponding trajectory simulations predict that the larger diameter particles will exit the duct closer to the centerline of the tunnel. Particle distribution at the duct exit plane is not uniform and is influenced by the upstream tunnel geometry features. These three dimensional simulation results will inform future testing efforts.
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Feier, I., "Numerical Investigation of the NASA Glenn Propulsion Systems Laboratory," SAE Technical Paper 2019-01-1924, 2019, https://doi.org/10.4271/2019-01-1924.Data Sets - Support Documents
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