Boundary-Layer Stability Calculations for the HIFiRE-1 Transition Experiment

Alba, Christopher R.; Johnson, Heath B.; Bartkowicz, Matthew D.; Candler, Graham V.; Berger, Karen T.

doi:10.2514/1.37445

Cited by 61 publications

(22 citation statements)

References 21 publications

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“…The primary experiment on HIFiRE-1 was devoted to laminar-turbulent transition on a circular cone at zero angle of attack. [11][12][13][14][15][16][17][18]. The objective of the HIFiRE-1 experiment was to examine second-mode transition.…”

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confidence: 99%

Transition Analysis for the HIFiRE-5 Vehicle

Choudhari

Chang

Jentink

et al. 2009

39th AIAA Fluid Dynamics Conference

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The Hypersonic International Flight Research and Experimentation (HIFiRE) 5 flight experiment by Air Force Research Laboratories and Australian Defense Science and Technology Organization is designed to provide in-flight boundary-layer transition data for a canonical 3D configuration at hypersonic Mach numbers. This paper outlines the progress, to date, on boundary layer stability analysis for the HIFiRE-5 flight configuration, as well as for selected test conditions from the wind tunnel experiments supporting the flight test. At flow conditions corresponding to the end of the test window, rather large values of linear amplification factor are predicted for both second mode (N>40) and crossflow (N>20) instabilities, strongly supporting the feasibility of first in-flight measurements of natural transition on a fully three-dimensional hypersonic configuration. Additional results highlight the rich mixture of instability mechanisms relevant to a large segment of the flight trajectory, as well as the effects of angle of attack and yaw angle on the predicted transition fronts for ground facility experiments at Mach 6. Backgroundue to its influence on surface heat transfer, skin-friction drag, and flow-separation characteristics, prediction of boundary layer transition constitutes an important aspect of hypersonic vehicle design. Laminar to turbulent transition over realistic vehicle surfaces is often caused by surface roughness of sufficiently large magnitude. However, when the surface is relatively smooth, the transition process is initiated by linear instabilities of the laminar boundary layer. Typically, the second (or Mack) mode instability dominates transition in 2D and axisymmetric boundary layers with hypersonic edge Mach numbers, although centrifugal (i.e., Gortler) instabilities may also come into play when the surface has concave curvature along the streamwise direction. Three-dimensional boundary layers involve the additional mechanisms of stationary and traveling modes of crossflow instability and, depending on the geometric configuration, may include the attachment line instability as well [1].Regardless of the speed regime, linear stability correlations have been quite successful in predicting the onset of transition when a single instability mechanism dominates the transition process [2]. Mixed mode transition has been more challenging to predict because of possible nonlinear interactions between the relevant modes of instability. To account for the effect of such interactions, an ad hoc composite metric based on the linear amplification factors for the different instability mechanisms has sometimes been used for transition correlation (see, for instance, [3]). Recent work on crossflow instability in low-speed boundary layers [4] has exposed additional shortcomings in applying purely linear predictive models to crossflow dominated transition in 3D boundary layers.The simplest canonical configuration that includes the necessary elements for studying both mixed mode transition and crossflow evolu...

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confidence: 99%

Transition Analysis for the HIFiRE-5 Vehicle

Choudhari

Chang

Jentink

et al. 2009

39th AIAA Fluid Dynamics Conference

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“…Prior to flight, a series of experimental and numerical studies were performed to aid the design of the aerothermal experiments such as sensor selection and placement, and vehicle geometry and materials. [4][5][6][7][8][9][10][11][12][13][14][15] The cone, cylinder, and flare surfaces of the HIFiRE-1 payload were instrumented with many thermocouples, heat transfer gauges, and pressure transducers. During flight, the data from these transducers were digitized using a sampling scheme with a non-constant time interval between samples.…”

Section: Introductionmentioning

confidence: 99%

Compensation Method for the Estimation of the Autospectral Density Function of the Unevenly Spaced HIFiRE-1 Flight Data

Stanfield

Kimmel

2013

43rd Fluid Dynamics Conference

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The time series data collected during the HIFiRE-1 flight experiment was sampled unevenly due to telemetry dropouts and the sampling scheme selected. Sampling unevenly results in frequency translation of power to artificial sidelobes. These sidelobes distort the real and imaginary components of the Fourier transform such that the spectrum of the sampled data no longer represents the spectrum of the physical process generating the data. These sidelobes can be eliminated by resampling the data onto an evenly spaced grid at the cost of under predicting the power of the higher frequency components in the data. In this paper, a compensation procedure is developed to recover the power loss caused by resampling. This compensation procedure is suitable for stochastic time series data having red-noise spectra such as the pressure fluctuations recorded underneath laminar and turbulent boundary layers. Nomenclaturea i = regression coefficient a 50 = overlap correlation constant b i = regression coefficient c xy = coherency D = location data point of a partition f = frequency, Hz This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Fluid Dynamics and Co-located Conferences f 0 = fundamental frequency, Hz f c = Nyquist frequency, Hz G xx = autospectrum, Pa 2 / Hz G xy = cross-spectrum, Pa 2 / Hz K = number of partitions K eff = effective number of partitions L = number of data samples in a partition N = number data samples in a time series R = sum of squares t = time, sec t f = time used to account for different time origins of time series used in cross spectral analysis, sec t L = time at the end of a partition, sec T p = period, sec T R = sampling interval, sec w n = Hanning window function coefficients y n = time series data σ 2 = variance τ = time shift, sec ω = angular frequency, rad

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“…In conventional tunnels, transition onset usually occurs at N factors around 5. 14 Pate also showed that transition on sharp cones at zero angle of attack can be correlated to measurements of tunnel noise. 15 While quiet tunnels can more accurately simulate flight noise levels, they are incapable of high Reynolds numbers, high Mach numbers, and high enthalpy.…”

Section: Introductionmentioning

confidence: 99%