Crossplane Velocimetry of a Transverse Supersonic Jet in a Transonic Crossflow

Beresh, Steven J.; Henfling, John F.; Erven, Rocky J.; Spillers, Russell Wayne

doi:10.2514/1.22311

Cited by 69 publications

(57 citation statements)

References 28 publications

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“…The test case is described in detail in [3,18,19] and we produce a summary here. A supersonic jet exits from a nozzle into a 12in × 12in test section of a blow-down wind-tunnel.…”

Section: Jet-in-crossflow Predictionsmentioning

confidence: 99%

“…3 and 5) and, for each simulation, quantify the vorticity in R by calculating the circulation, the centroid (z, y) of the vorticity distribution and its radius of gyration (as an approximate measure of the vorticity distribution's spatial extent). These metrics are normalized by their counterparts calculated from experimental measurements [18,19]. In Fig.…”

Section: Jet-in-crossflow Predictionsmentioning

confidence: 99%

“…The problem is strongly vortical and has been extensively studied experimentally [18,19,21] (henceforth, collectively known as the "Beresh experiments"). The flowfield is dominated by a counter-rotating vortex pair, formed by a Kelvin-Helmholtz roll-up of the shear layer at the boundary between the jet and the crossflow.…”

Section: Introductionmentioning

confidence: 99%

“…Thereafter, we will sample the joint (C µ ,C ε2 ,C ε1 ) distribution to seed an ensemble of 100 3D JIC simulations. The ensemble results will provide a probabilistic prediction of the flowfield and we will check its accuracy against available JIC experimental data [18,19].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Tuning a RANS k-e model for jet-in-crossflow simulations.

Lefantzi¹,

Ray²,

Arunajatesan³

et al. 2013

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We develop a novel calibration approach to address the problem of predictive k − ε RANS simulations of jet-incrossflow. Our approach is based on the hypothesis that predictive k − ε parameters can be obtained by estimating them from a strongly vortical flow, specifically, flow over a square cylinder. In this study, we estimate three k − ε parameters, C µ , C ε2 and C ε1 by fitting 2D RANS simulations to experimental data. We use polynomial surrogates of 2D RANS for this purpose. We conduct an ensemble of 2D RANS runs using samples of (C µ ,C ε2 ,C ε1 ) and regress Reynolds stresses to the samples using a simple polynomial. We then use this surrogate of the 2D RANS model to infer a joint distribution for the k − ε parameters by solving a Bayesian inverse problem, conditioned on the experimental data. The calibrated (C µ ,C ε2 ,C ε1 ) distribution is used to seed an ensemble of 3D jet-in-crossflow simulations. We compare the ensemble's predictions of the flowfield, at two planes, to PIV measurements and estimate the predictive skill of the calibrated 3D RANS model. We also compare it against 3D RANS predictions using the nominal (uncalibrated) values of (C µ ,C ε2 ,C ε1 ), and find that calibration delivers a significant improvement to the predictive skill of the 3D RANS model. We repeat the calibration using surrogate models based on kriging and find that the calibration, based on these more accurate models, is not much better that those obtained with simple polynomial surrogates. We discuss the reasons for this rather surprising outcome.

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“…The test case is described in detail in [3,18,19] and we produce a summary here. A supersonic jet exits from a nozzle into a 12in × 12in test section of a blow-down wind-tunnel.…”

Section: Jet-in-crossflow Predictionsmentioning

confidence: 99%

Section: Jet-in-crossflow Predictionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tuning a RANS k-e model for jet-in-crossflow simulations.

Lefantzi¹,

Ray²,

Arunajatesan³

et al. 2013

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show abstract

“…Research on plume/freestream interaction for axisymmetric nozzles [1][2][3][4][5][6][7][8] indicates that the nondimensional parameter J has been successful in correlating plume-freestream interaction phenomena such as plume penetration into the freestream. J is defined by…”

Section: Experimental Characterization Of Spin Motor Nozzle Flow Intrmentioning

confidence: 99%

Experimental characterization of spin motor nozzle flow.

Erven¹,

Peterson²,

Henfling³

2006

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The Mach number in the inviscid core of the flow exiting scarfed supersonic nozzles was measured using pitot probes. Nozzle characterization experiments were conducted in a modified section of an obsolete M = 7.3 test section/nozzle assembly on Sandia's Hypersonic Wind Tunnel. By capitalizing on existing hardware, the cost and time required for tunnel modifications were significantly reduced. Repeatability of pitot pressure measurements was excellent, and instrumentation errors were reduced by optimizing the pressure range of the transducers used for each test run. Bias errors in probe position prevented us from performing a successful in situ calibration of probe angle effects using pitot probes placed at an angle to the nozzle centerline.The abrupt throat geometry used in the Baseline and Configuration A and B nozzles modeled the throat geometry of the flight vehicle's spin motor nozzles. Survey data indicates that small ("unmeasurable") differences in the nozzle throat geometries produced measurable flow asymmetries and differences in the flow fields generated by supposedly identical nozzles. Therefore, data from the Baseline and Configuration A and B nozzles cannot be used for computational fluid dynamics (CFD) code validation.Configuration C and D nozzles replaced the abrupt throat geometry of Baseline and Configuration A and B nozzles with a 0.500-inch streamwise radius of curvature in the throat region. This throat geometry eliminated the flow asymmetries, flow separation in the nozzle throat, and measurable differences between the flow fields from identical nozzles that were observed in Baseline/A/B nozzles. Data from Configuration C and D nozzles can be used for CFD code validation.-4 -

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Comparison of PIV data using multiple configurations and processing techniques

Beresh

2009

Exp Fluids

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Crossplane Velocimetry of a Transverse Supersonic Jet in a Transonic Crossflow

Cited by 69 publications

References 28 publications

Tuning a RANS k-e model for jet-in-crossflow simulations.

Tuning a RANS k-e model for jet-in-crossflow simulations.

Experimental characterization of spin motor nozzle flow.

Comparison of PIV data using multiple configurations and processing techniques

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