Coupled Thermal Analysis Method with Application to Metallic Thermal Protection Panels

Kontinos, Dean

doi:10.2514/2.6249

Cited by 66 publications

(24 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aircraft structures exposed to extreme environments are subjected to coupled aerodynamic, thermal, and acoustic loading. 2,3,[6][7][8][9][10][11][12] Neglecting these interactions can lead to gross errors in model predictions. [10][11][12][13][14][15] Aerothermoelastic aircraft structures can be modeled at multiple levels of fidelity for structural and thermal effects.…”

Section: Introductionmentioning

confidence: 98%

Error Quantification and Confidence Assessment of Aerothermal Model Predictions for Hypersonic Aircraft

Smarslok¹,

Culler²,

Mahadevan³

2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

34
1
16
0

View full text Add to dashboard Cite

Assessing prediction confidence and enabling its use as a decision-making metric for autonomous model fidelity selection is essential to the USAF's vision of a 'Digital Twin' as a viable approach for condition-based fleet management by tail number. Significant strides have been made in modeling complex interactions of the multi-physics, fluid-thermalstructural coupling applicable to hypersonic flow conditions. However, validation of these models remains a challenge due to limited experimental data for hypersonic conditions. This research addresses quantifying errors and assessing the confidence in aerodynamic pressure and heating predictions for a spherical dome protruding from a flat ramp. Wellcharacterized aerothermal test data from hypersonic wind tunnel experiments are used to calibrate uncertain model parameters and quantify errors through Bayesian techniques. A Bayesian hypothesis testing-based confidence metric is employed to compare the accuracy in various model predictions. A model selection study is performed for 1 st -, 2 nd -, and 3 rd -order piston theories. The results showed that the greatest confidence in model predictions does not necessarily correspond to the highest-order model. NomenclatureB = Bayes factor C = Bayesian hypothesis testing-based confidence metric D = diameter of the spherical dome e = model error H = height of the spherical dome M = Mach number p = aerodynamic pressure q = dynamic pressure (ρU 2 /2) Q = aerodynamic heat flux R eq = equivalence ratio (fuel-to-air ratio divided by stoichiometric fuel-to-air ratio) S = main effect sensitivity index S T = total effect sensitivity index T = temperature w = transverse panel displacement x = model prediction, location along dome y = observed data β = oblique shock angle relative to freestream ϕ = uncertain input parameters γ = ratio of specific heats µ = mean π = probability density function 2 θ = panel inclination angle to freestream ρ = density σ = standard deviation Subscripts aw = adiabatic wall e = edge of boundary layer i = variable index, position along spherical dome true = true value of x pred = model prediction of x w = wall, aerodynamic surface 1 = freestream flow 3 = flow at the leading edge of panel 4 = flow at location of interest along the panel Superscripts fp = flat plate sd = spherical dome * = flow properties evaluated at Eckert's reference temperature

show abstract

Section: Introductionmentioning
confidence: 98%

Error Quantification and Confidence Assessment of Aerothermal Model Predictions for Hypersonic Aircraft
Smarslok¹,
Culler²,
Mahadevan³
2012
53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference
34
1
16
0
View full text Add to dashboard Cite

show abstract

“…Historically, the structural behavior to these different sources has been considered separately. In the first class of problems, the quasi-static panel response (Kontinos, 1997;Culler et al, 2009), and dynamic instabilities (McNamara et al, 2005;Mei et al, 1999;Culler et al, 2009) has been studied by modeling the boundary layer loads and other external acoustic excitations: (1) as a random in time, and uniform in space acoustic load (Przekop and Rizzi, 2006;Spottswood et al, 2010), (2) using semi-empirical models (Maestrello, 1969;Hwang et al, 2009;Hambric et al, 2004;Coe and Chyu, 1972;Wu and Maestrello, 1995), and (3) using high-fidelity Computational Fluid Dynamics (CFD) simulations (Frendi, 1997(Frendi, , 2004. There are limitations of each of these approaches.…”

mentioning
confidence: 99%

Response of skin panels to combined self- and boundary layer-induced fluctuating pressure
Deshmukh
¹
,
Culler
²
,
Miller
³

et al. 2015
Journal of Fluids and Structures
19
2
15
0
View full text Add to dashboard Cite

“…5 indicates that this value is within 16% of the measured value. Comparison of the measured wall static pressure (11,997 Pa) with the wind-tunnel console gauges (12,273 Pa) determined that the data acquisitionsystem and console gauge had a margin of error less than 2%. By using the Mach number value of 3.03 and by knowing the total pressure and temperature, the wind-tunnel ow characteristics can be summarized as given in Table 1.…”

Section: Methodsmentioning
confidence: 99%

Coupled Dual Reciprocity Boundary Element/Finite Volume Method for Transient Conjugate Heat Transfer
Rahaim
¹
,
Kassab
²
,
Cavalleri
³

2000
Journal of Thermophysics and Heat Transfer
21
0
9
0
View full text Add to dashboard Cite

A coupled nite volume/dual reciprocity boundary element method is developed to solve the transient conjugate heat transfer problem of convective heat transfer over and conduction heat transfer within a solid body. In this approach, the ow eld and forced convection heat transfer external to the body is resolved by numerically solving the time-dependent Navier-Stokes equations using a nite volume method, whereas the temperature eld within the body is resolved by numerically solving the heat conduction equation using a dual reciprocity boundary element method. The boundary discretization utilized to generate the computational grid for the external ow eld provides the boundary discretization required for the boundary element method. Coupling of the two elds is accomplished by enforcing interface continuity of heat ux and temperature. Transient heat transfer data needed to verify the code were obtained in a series of experiments that are reported. Details of the experimental setup and test conditions are provided. Numerical simulations of the experiments show good agreement with obtained experimental data. Nomenclature a = nonlinear diffusivity, function of w C(n ) = free term in boundary element method (BEM) equations c = speci c heat D = diagonal matrix E(x, n ) = negative of normal derivative of T ¤ multiplied by k e = speci c energy F = dual reciprocity BEM (DRBEM) interpolating matrix F c = x component of convective ux vector F d = x component of diffusive ux vector f = expansion function G = BEM in uence matrix multiplying vector of nodal uxes G c = y component of convective ux vector G d = y component of diffusive ux vector H = BEM in uence matrix multiplying vector of nodal temperatures h = speci c enthalpy h ¤ = half-channel height k = thermal conductivity k 0 = reference thermal conductivity L = number of additional DRBEM expansion points N = number of BEM nodes P = DRBEM interpolating matrix Pe = Peclet number Pr = Prandtl number p = pressure = negative of normal derivative of u k multiplied by k q(x) = heat ux vector r (x, n ) = Euclidean distance between source (n ) and eld point (x) T = temperature T B = bulk temperature T W = wall temperature T 0 = reference temperature T ¤ = Green's free space solution t = time u = x component of velocity v = y component of velocity W = vector of conserved variables x = location of eld point a = dual reciprocity method expansion coef cient C = boundary of domain X c = speci c heat ratio D t = time step h t , h q = weighting parameters in DRBEM time-marching scheme l = dynamic viscosity n = location of source point r = normal viscous stress s = viscous shear stress U = DRM interpolating matrix u = DRM expansion function w = Kirchhoff parameter w 0 = reference Kirchhoff parameter X = domain

show abstract

Coupled Thermal Analysis Method with Application to Metallic Thermal Protection Panels

Cited by 66 publications

References 8 publications

Error Quantification and Confidence Assessment of Aerothermal Model Predictions for Hypersonic Aircraft

Error Quantification and Confidence Assessment of Aerothermal Model Predictions for Hypersonic Aircraft

Response of skin panels to combined self- and boundary layer-induced fluctuating pressure

Coupled Dual Reciprocity Boundary Element/Finite Volume Method for Transient Conjugate Heat Transfer

Contact Info

Product

Resources

About