Assessment of the National Transonic Facility for Natural Laminar Flow Testing

Crouch, J. D.; Sutanto, Mary I.; Witkowski, David; Watkins, A. Neal; Rivers, Melissa B.; Campbell, Richard L.

doi:10.2514/6.2010-1302

Cited by 22 publications

(17 citation statements)

References 9 publications

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“…[9][10][11][12][13][14] Other related activities under the SFW project focused on in-flight characterization of crossflow receptivity to surface roughness, 15 design tools 16 and high Reynolds number testing for natural laminar flow (NLF) wings. 17 These activities were subsequently transitioned to the ERA project. The recently initiated Aeronautical Sciences (AS) project of NASA is aimed at developing computer-based tools and models as well as scientific knowledge that will lead to significant advances in our ability to understand and predict flight performance for a wide variety of air vehicles.…”

Section: Introductionmentioning

confidence: 99%

Towards Bridging the Gaps in Holistic Transition Prediction via Numerical Simulations

Choudhari

Chang

et al. 2013

21st AIAA Computational Fluid Dynamics Conference

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The economic and environmental benefits of laminar flow technology via reduced fuel burn of subsonic and supersonic aircraft cannot be realized without minimizing the uncertainty in drag prediction in general and transition prediction in particular. Transition research under NASA's Aeronautical Sciences Project seeks to develop a validated set of variable fidelity prediction tools with known strengths and limitations, so as to enable "sufficiently" accurate transition prediction and practical transition control for future vehicle concepts. This paper provides a summary of research activities targeting selected gaps in high-fidelity transition prediction, specifically those related to the receptivity and laminar breakdown phases of crossflow induced transition in a subsonic swept-wing boundary layer. The results of direct numerical simulations are used to obtain an enhanced understanding of the laminar breakdown region as well as to validate reduced order prediction methods. Nomenclature A= amplitude of crossflow instability mode or secondary instability mode, measured in terms of peak chordwise velocity perturbation and normalized with respect to freestream velocity c = wing chord length normal to the leading edge C = coupling coefficient for receptivity D = diameter of cylindrical roughness element f = frequency of instability oscillations (Hz) F = forcing function representing external disturbance environment F = geometry factor from receptivity theory G = gain factor associated with instability amplification i = (-1) 1/2 k = grid index along wall-normal direction M = freestream Mach number N = N-factor (i.e., logarithmic amplification ratio) of linear crossflow instability or secondary instability q = arbitrary flow variable R = transfer function associated with receptivity Re c = Reynolds number based on wing chord t = time (u,v,w) = Cartesian velocity components aligned with (x,y,z) axes x = chordwise coordinate in direction perpendicular to leading edge y = Cartesian coordinate normal to x-y plane z = spanwise coordinate, i.e., the coordinate parallel to airfoil leading edge α = chordwise wavenumber 1 2 β = spanwise wavenumber Γ = wing sweep angle λ = spanwise wavelength of crossflow instability λ w = chordwise wavelength of wall waviness Λ = efficiency function for localized receptivity ξ = dummy integration variable in chordwise direction π = normalized mode shape of instability wave θ = phase function φ = normalized mode shape of instability wave ω = radian frequency of instability wave Subscripts c = crossflow direction i = initial or reference location (typically chosen to be lower branch neutral station for primary instability and inflow location of x/c=0.502 for secondary instability) init = initial or reference location (used interchangeably with subscript i) ins = instability wave lb = lower branch neutral station for the instability mode of interest L = linear amplification phase N = nonlinear amplification phase r = roughness w = wall τ = edge streamline direction Superscripts + = wall units

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Section: Introductionmentioning

confidence: 99%

Towards Bridging the Gaps in Holistic Transition Prediction via Numerical Simulations

Choudhari

Chang

et al. 2013

21st AIAA Computational Fluid Dynamics Conference

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“…Versions of this TSP have been used for transition detection at cryogenic conditions at the National Transonic Facility (NTF) [21] and for measurement of heating properties at hypersonic conditions [22]. The formulation is based on a polyurethane sealant in which a ruthenium-based luminophore is dissolved.…”

Section: Methodsmentioning

confidence: 99%

“…This allows easy discrimination of the excitation light from the emitted luminescence using optical filters. For this work, the TSP employed bis-(2,2′,2′′-terpyridine) ruthenium(II) chloride (Ru(trpy) 2 ) as the luminophore, which has good sensitivity and luminescence output at cryogenic conditions [13,15,21]. …”

Section: Methodsmentioning

confidence: 99%

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer

Goodman

Lipford

Watkins

2016

Sensors

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Detection of flow transition on aircraft surfaces and models can be vital to the development of future vehicles and computational methods for evaluating vehicle concepts. In testing at ambient conditions, IR thermography is ideal for this measurement. However, for higher Reynolds number testing, cryogenic facilities are often used, in which IR thermography is difficult to employ. In these facilities, temperature sensitive paint is an alternative with a temperature step introduced to enhance the natural temperature change from transition. Traditional methods for inducing the temperature step by changing the liquid nitrogen injection rate often change the tunnel conditions. Recent work has shown that adding a layer consisting of carbon nanotubes to the surface can be used to impart a temperature step on the model surface with little change in the operating conditions. Unfortunately, this system physically degraded at 130 K and lost heating capability. This paper describes a modification of this technique enabling operation down to at least 77 K, well below the temperature reached in cryogenic facilities. This is possible because the CNT layer is in a polyurethane binder. This was tested on a Natural Laminar Flow model in a cryogenic facility and transition detection was successfully visualized at conditions from 200 K to 110 K. Results were also compared with the traditional temperature step method.

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“…In 2009 Boeing and NASA conducted a joint investigation to assess the National Transonic Facility (NTF) at NASA Langley Research Center for potential laminar-flow testing [3]. This test made use of a natural laminar-flow (NLF) wing coated with temperature-sensitive paint to measure the location of transition.…”

Section: A Research Demonstrations and Applicationsmentioning

confidence: 99%

Boundary-Layer Transition Prediction for Laminar Flow Control (Invited)

Crouch

2015

45th AIAA Fluid Dynamics Conference

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Issues related to the successful application of laminar-flow-control technology are discussed, with particular emphasis on boundary-layer transition prediction. A survey of wind-tunnel and flight-demonstration campaigns conducted over the past couple decades is presented, along with a status summary of recent applications of the technology. Different approaches to transition prediction are discussed, with an emphasis on the flow physics and real-world issues afforded by the different methods. A case is made for methods based on linear-amplitude correlations because they offer the ability to capture receptivity and growth-modifier effects on transition. These methods have the ability to account for realistic controllers of transition, without depending on simple avoidance criteria. Strengths and shortcomings of these transition-prediction schemes are considered in the context of laminar-flow-control applications. Nomenclature A = amplitude of instabilityA 0 = initial amplitude of instability A T = linear threshold amplitude of instability for transition onset h rms = root mean square of surface roughness m = linear growth factor for a single physical mode of an instability n = envelope of physical mode growth factors, m N = critical value of n corresponding to transition onset R CF = crossflow Reynolds number R  = Reynolds number based on momentum thickness Tu = turbulence intensity x T = streamwise or chordwise location of transition  = spatial growth rate of instability  = spanwise wavenumber of instability   = boundary layer displacement thickness  = boundary layer momentum thickness  = angular frequency of instability Subscripts SCF = stationary cross-flow instability TCF = traveling cross-flow instability TS = Tollmien-Schlichting instability crit = corresponding to the critical mode

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Assessment of the National Transonic Facility for Natural Laminar Flow Testing

Cited by 22 publications

References 9 publications

Towards Bridging the Gaps in Holistic Transition Prediction via Numerical Simulations

Towards Bridging the Gaps in Holistic Transition Prediction via Numerical Simulations

Boundary-Layer Detection at Cryogenic Conditions Using Temperature Sensitive Paint Coupled with a Carbon Nanotube Heating Layer

Boundary-Layer Transition Prediction for Laminar Flow Control (Invited)

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