Fuel sprays produce high-velocity, jet-like flows that impart turbulence onto the ambient flow field. This spray-induced turbulence augments rapid fuel-air mixing, which has a primary role in controlling pollutant formation and cyclic variability in direct-injection engines. This paper presents tomographic particle image velocimetry (TPIV) measurements to analyse the 3D spray-induced turbulence during the intake stroke of a direct-injection spark-ignition (DISI) engine. The spray produces a strong spray-induced jet (SIJ) in the far field, which travels through the cylinder and imparts turbulence onto the surrounding flow. Planar high-speed PIV measurements at 4.8 kHz are combined with TPIV at 3.3 Hz to evaluate spray particle distributions and validate TPIV measurements in the particle laden flow. A comprehensive uncertainty analysis is performed to assess the uncertainty associated with individual vorticity and strain rate components. TPIV analyses quantify the spatial domain of the turbulence in relation to the SIJ and describe how turbulent flow features such as turbulent kinetic energy (TKE), strain rate (S) and vorticity (Ω) evolve into the surrounding flow field. Access to the full S and Ω tensors facilitate the evaluation of turbulence for individual spray events. TPIV images reveal the presence of strong shear layers (visualized by high S magnitudes) and pockets of elevated vorticity along the immediate boundary of the SIJ. S and Ω values are extracted from spatial domains extending in 1mm increments from the SIJ. Turbulence levels are greatest within the 0-1mm region from the SIJ boarder and dissipate with radial distance. Individual strain rate and vorticity components are analyzed in detail to describe the relationship between local strain rates and 3D vortical structures produced within strong shear layers of the SIJ. Analyses are intended to understand the flow features responsible for rapid fuel-air mixing and provide valuable data for the development of numerical models.
Circulation control inlet guide vanes (IGVs) may provide significant benefits over current IGVs that employ mechanical means for flow turning. This paper presents the results of a two-dimensional computational study on a circulation control IGV that takes advantage of the Coanda effect for flow vectoring. The IGV in this study is an uncambered airfoil that alters circulation around itself by means of a Coanda jet that exhausts along the IGV’s trailing edge surface. The IGV is designed for an axial inlet flow at a Mach number of 0.54 and an exit flow angle of 11 degrees. These conditions were selected to match the operating conditions of the 90% span section of the IGV of the TESCOM compressor rig at the Compressor Aero Research Laboratory (CARL) located at Wright-Patterson AFB, the hardware that is being used as the baseline in this study. The goal of the optimization was to determine the optimal jet height, trailing edge radius, and supply pressure that would meet the design criteria while minimizing the mass flow rate and pressure losses. The optimal geometry that was able to meet the design requirements had a jet height of h/Cn = 0.0057 and a trailing edge Radius R/Cn = 0.16. This geometry needed a jet to inflow total pressure ratio of 1.8 to meet the exit turning angle requirement. At this supply pressure ratio the mass flow rate required by the flow control system was 0.71 percent of the total mass flow rate through the engine. The optimal circulation control IGV had slightly lower pressure losses when compared with a reference cambered IGV.
The IGV in this study is an uncambered airfoil, designed to use the Coanda effect to achieve flow vectoring. For this internal flow problem, two isotropic turbulence models are compared to an anisotropic model. Good trend comparison was seen for turning angle and pressure loss performance characteristics given a nominal fixed trailing edge geometry. However, as the trailing edge geometry is modified in an attempt to increase turning, discrepancies become evident, possibly due to the effects of streamline curvature. As the trailing edge radius decreases, significant variations in the jet separation location are predicted, which translates directly into flow turning predictions. Further, one turbulence model was examined using a second CFD code to ensure software independence of the solutions.
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