Uncertainty Propagation in Integrated Airframe Propulsion System Analysis for Hypersonic Vehicles

Lamorte, Nicolas; Friedmann, Peretz P.; Dalle, Derek J.; Torrez, Sean M.; Driscoll, James F.

doi:10.2514/6.2011-2394

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…This is primarily a result of the errors in p 4 and Q 4 predictions being more easily scaled as defined in Eqs. (12) and (13). Thus, calibrating the errors did result in a shift in the mean values, as seen in Fig.…”

Section: Figure 5 Bayes Network For Calibrating Model Inputs and Errmentioning

confidence: 68%

“…On-going research is investigating models for each of the four aerothermoelastic system components, as well as their integration to study the importance of different types of coupling. 3,4,13 In order to validate these models and quantify the confidence in their predictions, experimental data from this extreme, hypersonic environment is required. A candidate for validation data under these conditions are the experiments performed by Glass and Hunt, in which a series of tests were conducted in a hypersonic wind tunnel to investigate the aerodynamic loads on deformed surface panels.…”

Section: Aerothermal Model Definition and Experimentsmentioning

confidence: 99%

“…17 Related work expanded on uncertainty propagation in aerothermoelastic analysis for hypersonic vehicles with emphasis on assessing the impact of aerothermoelastic deformation on aerodynamic heating. 13 Culler et al also identified two-way coupling between structural deformation and aerodynamic heating as an important consideration in modeling an aerothermoelastic panel. 11 These efforts underscore the importance of understanding the uncertainty in a coupled aerothermoelastic model; however, many questions remain about the significant deterministic and stochastic sources of uncertainty and how to assess the confidence in model predictions.…”

Section: Introductionmentioning

confidence: 97%

“…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. However, there are limitations in computational resources, which make the degree of model fidelity and the level of coupling necessary for a particular problem play a key role in the computational tractability of the model used.…”

Section: Introductionmentioning

confidence: 99%

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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

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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

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Section: Figure 5 Bayes Network For Calibrating Model Inputs and Errmentioning
confidence: 68%

Section: Aerothermal Model Definition and Experimentsmentioning
confidence: 99%

Section: Introductionmentioning
confidence: 97%

Section: Introductionmentioning
confidence: 99%

See 2 more Smart Citations
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
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“…This type of vehicle has a unique design, incorporating a supersonic combustion scramjet engine located beneath the fuselage. This configuration can exhibit good performance in the range of Mach 4-7, but it causes severe aero-propulsion interactions and uncertainties, which result in highly nonlinear, strongly coupling, and fast time varying, with a great amount of parameter uncertainties as well as unknown external disturbances [6] [7]. Therefore, the dynamics modeling and flight control design of AHV become one of the most challenging problems.…”

Section: Introductionmentioning
confidence: 99%

Modeling and nonlinear control for air-breathing hypersonic vehicle with variable geometry inlet
Dou
¹
,
Su
²
,
Ding
³

2017
Aerospace Science and Technology
35
0
17
0
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This paper develops a control method for an air-breathing hypersonic vehicle with variable geometry inlet (AHV-VGI). For the AHV-VGI, a movable translating cowl is used to track the shock on lip conditions to capture enough air mass flow, which can ensure a more powerful thrust. Compared with traditional air-breathing hypersonic vehicle with fixed geometry inlet (AHV-FGI), this AHV-VGI extends the velocity range, which is favorable to the acceleration and maneuvering flight. However, the VGI causes the unknown changes of the aerodynamic forces, moment and the thrust in the meanwhile. Therefore, we firstly establish a longitudinal dynamic for AHV-VGI, which includes the uncertain changes induced by VGI. A conception of the optimal elongation distance of translating cowl is introduced, and its estimated value is obtained by curve fitted approximation. And then, the control process for AHV-VGI is divided into two subsystems. For each subsystem, a sliding mode controller is designed, and interval type-2 fuzzy logic systems (FLSs) are adopted to approximate nonlinear parts including the uncertain changes induced by VGI. Furthermore, uniformly stability of the whole system is proved by Lyapunov approach. Finally, simulation results demonstrate that AHV have a better control performance under the condition of VGI compared to the FGI.

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The determination of physical dimensions of a hypersonic three-stage compression system considering panel vibration effects
Gou
¹
,
Jia
²
,
Li
³

et al. 2023
Aerospace Science and Technology
0
0
0
0
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Uncertainty Propagation in Integrated Airframe Propulsion System Analysis for Hypersonic Vehicles

Cited by 7 publications

References 36 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

Modeling and nonlinear control for air-breathing hypersonic vehicle with variable geometry inlet

The determination of physical dimensions of a hypersonic three-stage compression system considering panel vibration effects

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