Computational Discovery of Hypersonic ... - APUS Lab
Transcript of Computational Discovery of Hypersonic ... - APUS Lab
Computational Discovery ofHypersonic Aerothermoelastic Scaling Laws
Dr. Daning Huang
APUS Lab, apus.psu.edu
Aerospace multi-Physical and Unconventional Systems
Prepared for AERSP Seminar, 10/30/2019
Hypersonic: โฅ Mach 5
A conceptual hypersonic commercial jet
Image source: Boeing 2018
Forget about 14 hours one-way trip.
Letโs do round trip in 4 hours!Image source:Google Maps2
โขMaturing propulsionโขAdvanced materialsโขSupercomputers
Hypersonic flight: A historical view
3USAF SAB report โWhy and Whither Hypersonic Research in the USAFโ
Hypersonic Commercial Jet Image source: Boeing
SR-72Image source: Lockheed Martin
Res
earc
h e
ffo
rt
X-51 WaveriderImage source: Boeing
2020+
Modeling and Testing challenges from 1988 NASP report:Because of the uncertainties ... in aerodynamic
loads and heating, ... precision of computation and lack of ground test facilities to replicate thermal and structural flight loads, the current ability to meet the structural designers requirements are marginal to non existent.
A technical barrier: Aerothermoelasticity
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Aerothermoelasticity
SR-72Image source: Lockheed Martin
Aerothermoelastic response of a 2D skin panel
To Understand Aerothermoelasticity
Hypersonic
Aerothermoelasticity
Analysis &
Design
Validate
Understand &
Validate
Modeling Testing?5
??
Modeling: Multi-Physics
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Hypersonic
Aerothermodynamics
Heat
Conduction
Structural
Dynamics
Heat flux
Temperature Deformation
Pressure
Temperature
Deformation
โข Real gas effect
โข Viscous interaction
โข Compressible turbulence
โข Thermal management
โข Material degradation
โข Charring and ablation
โข Flutter and buckling
โข Fatigue and creep
โข Reliability assessment
Modeling: Timescale disparity
HighModel FidelityLow
Brute force simulation:๐๐๐ steps ร sec/step = Weeks
Flight-long simulationโข Culler, McNamara, et al. 2010
Lead to erroneous results:โ Huang, Rokita, Friedmann, 2018
Transient simulation using RANS, LES, DNSโข Ostoich, Bodony, 2013โข McNamara, Crowell, Shinde, et al.,
since 2013
Characteristic times
Flight 1000 s
Thermal 1 s
Structure 0.05 s
Fluid 0.001 s
Computationallyintractable!
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Academic problems using simple analytical models
โข Lamorte, Friedmann, 2013, 2014โข Blades, 2013
Modeling: Accelerating simulationsBrute force simulation:๐๐๐ steps ร sec/step = Weeks
Efficient coupling schemes to reduce number of time steps
Reduced order models (ROMs) to reduce cost per time step
Example:Multi-cycling scheme, Miller, McNamara, AIAAJ 2018
โ Unable to reduce the cost of fluid solver โ the real bottleneck
Example:Kriging-based ROM, Falkiewics, Cesnik, McNamara, AIAAJ 2011
โ Can we do better?Arbitrary geometric scales, structural and thermal responses.
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Testing: Flight test v. s. Wind tunnel test
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Flight test: Full-size prototype
Pros:โข Cheaperโข More detailed measurementโข Controlled environmentโข Testing without compromising safetyCons:โข Is it possible?
Pros:โข Full duplication of flight conditionsCons:โข Expensiveโข Time-consumingโข Limited measurement optionsโข Failure may result in program cancellation
Image source: NASA
Wind tunnel test: Scaled-down replica
Image source: NASA
Scaling law
Model construction
Map back to full scale
Testing: Hypersonic Aerothermoelastic Scaling?
Most studies concentrated in 1960โs (Dugundji 1966) โ analytical dimensional analysis
โข Possible for high supersonic flow (M<3.5)โข For hypersonic flow: Possible only for a
unity scale ratio.
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Full-size prototype
Flight test: High cost/risk
??Scaled-down replica
Wind tunnel test
Image source: NASA Image source: NASA
Objectives
Modeling:
โข Develop a computational framework for fast long-time-duration aerothermoelastic simulation of hypersonic structures.
โข Examine the aerothermoelastic behavior of hypersonic skin panels.
Testing:
โข Develop a two-pronged approach to generating refined hypersonic aerothermoelastic scaling laws.
โข Develop scaled models for composite skin panels in hypersonic flow suitable for testing under realistic wind tunnel conditions.
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I. IntroductionII. Modeling: The HYPATE FrameworkIII. Testing: Numerical Scaling LawsIV. ApplicationsV. Summary and Outlook
Python wrapper
Structural Solver
(C++)
Python wrapper
Thermal Solver
(C++)
HYPersonic AeroThermoElasticsimulation environment
pyJenny library for nonlinear finite element analysis
Huang, Rokita, Friedmann, AIAAJ 2018
Python wrapper
Low-Fidelity (Python)
ROM (C++)
CFD (Fortran)
Fluid Solver
ADflow from UM-MDOLab โ Now open-sourced at github.com/mdolab/adflowc.f. publication in JCP 2019.
Data transfer
Coupling Schemes
โข Loosely-coupled for transient response
โข Tightly-coupled for quasi-steady response
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Python wrapper
Linearized Stability
Analysis (C++)
ROM-based
Aerodynamic
Solver
Accelerating the aerodynamic solver by ๐๐๐
Structural
Solver
Thermal
Solver
Body temperature
Surface deformation
Surface temperature
Pressure distribution
Heat flux distribution
Output:โข Pressure distributionโข Heat flux distribution
Input:โข Surface deformationโข Surface temperature
Precomputed CFD-based sample solutions
Interpolation:Gaussian process
regressionDimension reduction:
Proper orthogonal decomposition
14Falkiewics, Cesnik, McNamara, AIAAJ 2011; Crowell, McNamara, AIAAJ 2012Huang, Rokita, Friedmann, SciTech 2017
CFD-based
Aerodynamic
Solver
ROM: Reduced-Order Model
Extrapolation of an interpolative ROM
๐น๐ถ๐ด๐๐๐
Reference states:โข Fixed flight conditionsโข Fixed geometric scale
๐น๐ถ๐ด๐๐๐
New states:โข Arbitrary flight conditionsโข Arbitrary geometric scale
=๐๐๐๐
Correction factor
*
Conventional ROM is not suitable for analysis and design:
โขROM for a fixed state: geometry + flight conditions
โขROM for all the possible states โ Heavy offline computational burden
Geometric scale
Alt
itu
de
15Huang, Friedmann, Rokita, AIAAJ 2019
๐๐๐๐ =๐ ๐๐๐๐๐
๐ ๐๐๐๐๐
A: Analytical low-fidelity model
โ๐ด๐๐๐(๐๐๐ค ๐๐ก๐๐ก๐๐ )
๐ด๐๐๐(๐ ๐๐ ๐๐ก๐๐ก๐๐ )
Cutting down number of steps by ๐๐๐
Loosely-coupled (Conventional)
Time step size: Fluid time ~ 0.001s
Tightly-coupled
Time step size: Thermal time ~ 1s
16Miller, McNamara, AIAAJ 2015Huang, Friedmann, SciTech 2016 Huang, Friedmann, Rokita, AIAAJ 2019
Tightly-coupled scheme would not work for unstable responses
Full response (based on ROM)
AT ๐ แถ๐ + ๐๐(๐) = ๐๐ (๐ฎ, ๐)
AE ๐ แท๐ฎ + ๐ แถ๐ฎ + ๐ ๐(๐ฎ, ๐) = ๐ ๐(๐ฎ, แถ๐ฎ)
Quasi-steady response, ๐ก๐ด๐ โผ 1๐
AT ๐ แถ๐qs + ๐๐(๐qs) = ๐๐ (๐ฎ
qs, ๐qs)
AE ๐ ๐(๐ฎqs, ๐qs) = ๐ ๐(๐ฎ
qs, ๐)
Transient response (AE only), ๐ก๐ด๐ธ โผ 0.01๐
๐ แท๐ฎuns + ๐ แถ๐ฎuns + ๐ ๐(๐ฎuns, ๐qs) = ๐ ๐(๐ฎ
uns, แถ๐ฎuns)
Tight coupling works for stable response
๐ =๐๐ ๐
๐๐ฎ, ๐๐ด =
๐๐ ๐
๐๐ฎ; Neglect damping
Linearized stability analysis:Generalized eigenvalue problem
๐ โ ๐๐ด เดฅ๐ฎ = ๐๐๐เดฅ๐ฎ
๐ = ๐qs + ๐uns, ๐uns โ ๐๐ฎ = ๐ฎqs + ๐ฎuns
Tikhonovโs Theorem (singular perturbation analysis)โข When stable, full response โ quasi-steady responseโ Tightly-coupled scheme.โข Stability of full response = Stability of transient response โ Linearized stability analysis.
AT: AerothermalAE: Aeroelastic
Errorโผ ๐๐ก๐ด๐ธ
๐ก๐ด๐= ๐(10โ2)
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Real-Time aerothermoelastic simulation
1
2
4
8
16
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CFD Conventional ROM Extrapolative ROM
Days
Hours
Minutes
Online simulation
Offline computation
CFD
Brute force
Conventional ROM
+ loose-coupling
10 days
2 hours
50 hours
30 min
1 hour
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Computational cost of a 30-min flight-long simulation
Extrapolative ROM
+ tight-coupling
* On a computer cluster
* On a workstation using 5 Intel Xeon X5650 processors
I. IntroductionII. Modeling: The HYPATE Framework
III. Testing: Numerical Scaling LawsIV. ApplicationsV. Summary and Outlook
โข Extrapolative ROM: Cost per step, Minutes โ milliseconds
โข Efficient coupling: Number of steps, 106 โ 103
โข Enabled fast high-fidelity flight-long simulation
I. IntroductionII. Modeling: The HYPATE FrameworkIII. Testing: Numerical Scaling LawsIV. ApplicationsV. Summary and Outlook
Example of Analytical scaling law
Subsonic
aerodynamics
Model construction
Map back to full scale
PrototypeFlight conditions[๐โ, ๐โ, ๐โ]
Scaled modelWind tunnel conditions
[๐๐ค๐ก , ๐๐ค๐ก , ๐๐ค๐ก]
Satisfy all similarity parameters
๐ ๐๐๐๐๐ก๐๐ก๐ฆ๐๐ = ๐ ๐๐๐๐๐๐
๐๐๐๐๐๐ก๐๐ก๐ฆ๐๐ = ๐๐๐๐๐๐๐
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Image source: NARAImage source: Airbus
Analytical scaling law for aerothermoelasticity
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Geometryโ
๐ฟ, เดคโ
Thermal characteristic time
๐น๐ =๐๐ ๐ก
เท๐๐ ฦธ๐๐๐ ๐ฟ2
Structural properties
๐๐๐ฅ ๐ฟ2
๐ท๐ฅ๐ฅ
Reference temperatures
๐๐๐๐,๐0๐๐,๐๐น๐๐
ND dynamic pressure
๐๐น =๐พ๐โ๐โ
๐ฟ3
๐ท๐ฅ๐ฅ
Reynolds number ๐ ๐0 =๐0 ๐๐ฟ
๐0
ND heat flux parameter
๐ต๐๐น =๐๐
๐๐ ๐ ๐0๐๐0
๐2
ฦธ๐๐๐ ๐๐
ND: Nondimensional
Flight conditions [๐โ, ๐โ, ๐โ] for aerothermal similarity
Flight conditions [๐โ, ๐โ, ๐โ] for aeroelastic similarity
Principal barrier to complete
aerothermoelastic similarity:
Differing requirements for aeroelastic
and aerothermal similarity.
Assume all satisfied
Two-pronged approach for scaling
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Given
prototype
Design
scaled model
Friedmann, JFS 2004; Huang, Friedmann, SciTech 2019
Maximizes the similarity
in aerothermoelastic
response
Two-pronged approach
Classical approachAnalytical derivation of aerothermoelasticsimilarity parameters
Obtain refinedaerothermoelastic
scaling laws
โModernโ approachNumerical aerothermoelastic
simulations (prototype/scaled)
โข Contains ad hoc assumptions that ignores:o Turbulence and real gas effect in fluid problemo Geometric nonlinearity in structural problemo Temperature-dependent material properties
โข Provides scaling info., but inaccurate.
Refining scaling laws by Optimization
โข Objectives: ๐ฑ(๐ ) = [๐ฝ๐ข(๐ ), ๐ฝ๐(๐ )]
โข Error in structural response: ๐ฝ๐ข ๐ = ฯ๐ ฮค๐๐๐(๐ ) เท๐ข๐ โ ฮค๐๐
๐เท๐ข๐
2 1/2
โข Error in thermal response: ๐ฝ๐ ๐ = ฯ๐ ฮค๐ป๐๐(๐ ) ๐๐ โ ฮค๐ป๐
๐ ๐๐2 1/2
โข Ideal aerothermoelastic scaling: ๐ฝ๐ข = 0, ๐ฝ๐ = 0
โข Design variables: ๐ โข Flow conditions, geometry, materialsโฆ
โข External loading and heating
โข Constraintsโข ๐๐ผ ๐ โค 0: Wind tunnel and manufacturing limitations
โข ๐๐ธ ๐ = 0: Matching a partial set of similarity parameters
โข Incomplete testingโข Parameter relaxation
ND model response
ND prototype response
24Huang, Friedmann, SciTech 2019
Special strategies:
Bayesian optimization for Black-box objectives
โข Bayesian optimization:o Expensive black-box objective functionso A limited computational budgeto โGlobalโ optimum for nonconvex problemo AKA efficient global optimization (EGO)
โข Surrogate:o Gaussian process regressiono Prediction + Uncertainty
โข Acquisition function:o Lower confidence boundo Exploitation & Exploration
Uncertainty of prediction
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๐ฑ ๐ = [๐ฝ๐ข ๐ , ๐ฝ๐(๐ )]๐๐ผ ๐ โค 0๐๐ธ ๐ = 0
Objectives:Subject to:Example: Scalar optimization
Pareto front for Multiple objectives
๐ฝ๐ข
๐ฝ๐
Pareto Front
Pareto Optimal
solutions
Design
Point
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๐ฑ ๐ = [๐ฝ๐ข ๐ , ๐ฝ๐(๐ )]๐๐ผ ๐ โค 0๐๐ธ ๐ = 0
Objectives:Subject to:
Indirect approach:โข Generate Pareto front and select
the design point.โข Suitable for exploring the solution
distribution.
Error in structural response
Erro
r in
th
erm
al r
esp
on
se
I. IntroductionII. Modeling: The HYPATE FrameworkIII. Testing: Numerical Scaling Laws
IV. ApplicationsV. Summary and Outlook
โข Two-pronged approach: Dimensional analysis + Numerical simulation
โข Scaling strategies: Incomplete testing + Parameter relaxation
โข Multi-Objective Bayesian Optimization
I. IntroductionII. Modeling: The HYPATE FrameworkIII. Testing: Numerical Scaling LawsIV. Applications
V. Summary and Outlook
โข Aerothermoelastic analysis of hypersonic skin panels
โข Boundary layer thickness and aspect ratio
โข Flow orientation angle and material orthotropicity
โข Refined scaling laws using two-pronged approach
Case I: Aeroelastic scaling โ Sanity check
Material ๐โ ๐โ ฮ๐ Side Thick
PrototypeInconel
7186.0 104๐๐ 1๐พ 1๐ 2๐๐
Scaled model
Ti 6242 ?? ?? ?? ?? ??
Aeroelastic response with uniform thermal stress in inviscid flowReproduce aeroelastic response on scaled models
Problem:
Objective:
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Analytical scaling v.s. Numerical scaling
Objective: Minimize the error in aeroelastic responses
๐ฝ๐ข ๐ = ๐
ฮค๐๐๐(๐ ) เท๐ข๐ โ ฮค๐๐
๐เท๐ข๐
21/2
Design variables:
Constraints: ND time step size
ฮ๐ก๐ =1
๐2
๐ท๐ฅ๐ฅ๐ แ๐ผm
๐ท๐ฅ๐ฅ๐ แ๐ผp
ฮ๐ก๐
โ (mm) [0.2, 1.2]
๐โ (kPa) [3.0, 11.0]
ฮ๐ (K) [0.5, 4.5]
Thickness-length ratio
โ
๐ฟโ๐ =
1
๐โ๐
ND pressure าง๐๐น =๐พ๐โ
๐ฟ2
๐ท๐ฅ๐ฅ๐โ๐ = ๐3
๐ท๐ฅ๐ฅ๐
๐ท๐ฅ๐ฅ๐ ๐โ
๐
ND thermal stress
ฮ๐ ๐โฒTxL2
Dxxฮ๐๐ = ๐2
๐ท๐ฅ๐ฅ๐ ๐โฒTx
p
๐ท๐ฅ๐ฅ๐ ๐โฒTx
mฮ๐๐
Characteristic time
๐ท๐ฅ๐ฅแ๐ผ ๐ฟ4
ฦธ๐ก ฦธ๐ก๐ =1
๐2
๐ท๐ฅ๐ฅ๐ แ๐ผm
๐ท๐ฅ๐ฅ๐ แ๐ผp
ฦธ๐ก๐
๐ =๐ฟ๐
๐ฟ๐๐: Model๐: Prototype
Assuming same gas (๐พ) and ๐โ:
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Dowell, 1975
Aeroelastic scaling law is recovered
Parametersโ
๐ฟ, 10โ3 าง๐๐น
ฮ๐ ๐โฒTxL2
Dxx
Prototype 2.000 566.1 47.88
๐ = 2 2.008 (0.38%) 566.9 (0.14%) 47.58 (0.63%)
๐ = 3 2.003 (0.17%) 561.4 (0.83%) 47.76 (0.26%)
๐ = 4 2.003 (0.17%) 566.7 (0.11%) 47.67 (0.44%)
Aeroelastic similarity parameters are satisfied with errors < 1%!
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Case II: Aerothermoelastic scaling
๐โ Altitude Side
5.0-7.0 20-30 km 1.0 m
Component Material Thickness
Upper Sheet
Inconel 718
1 mm
Honeycomb Core 16 mm
Lower Sheet 1 mm
Component Material Thickness
Sheet Ti 6242 ??
๐โ ๐โ ๐โ Side
?? ?? ?? ??
Prototype: Composite skin panel
Scaled model: Isotropic panel
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Hypersonic Cruise Vehicle (HCV)
Image source: Zuchowski, 2012
Aerothermoelastic response of hypersonic skin panelsMinimize errors in average temperature and center deflection of aerothermoelastic response
Problem:
Objective:
Design variables and constraints
Design Variables Constraints
Test conditions ๐โ, ๐0, ๐0 Wind tunnel
Side length ๐ฟ (m) [0.1, 0.5]
Front panel length ๐ฟ๐๐ (m) [0.1, 2.0]
Thickness โ (mm) [1.0, 10.0]
Surface emissivity ๐ [0.5, 1.0]
Radiation temperature ๐๐๐๐ (K) [300, 2500]
Thermal characteristic time
๐น๐ =๐๐ ๐ก
เท๐๐ ฦธ๐๐๐ ๐ฟ2
Reference temperatures
๐๐ค๐๐,๐๐๐๐,๐0๐๐,๐๐น๐๐
A partial set of similarity parameters for the parameter relaxation strategy
External radiant heating for the Incomplete testing strategy
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Equality constraints:
Ideal wind tunnel conditions โ Iโข Arbitrary flight conditions in wind tunnel
5.0 โค ๐โ โค 7.0, 20 โค ๐ป โค 30๐๐
โข Prototype flight conditions:
๐โ = 6.0, ๐ป = 25km
โข Four cases:
๐ฟ๐๐๐๐๐ =1
2,1
3,1
4,1
5(๐)
Design Variables Constraints
Ideal wind tunnel
conditions
๐โ [5.0,7.0]
๐0 (MPa) [0.276,86.18]
๐0 (K) [416.5, 2500.0]
Front panel ๐ฟ๐๐ (m) [0.1, 2.0]
Thickness โ (mm) [1.0, 10.0]
๐ =๐ฟ๐๐๐๐ก๐๐ก๐ฆ๐๐
๐ฟ๐๐๐๐๐
Rapid increase in ๐ฝ๐
Rapid increase in ๐ฝ๐ข
Ideal scaling
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Ideal wind tunnel conditions โ IIVariables ๐ = 2 ๐ = 3 ๐ = 4 ๐ = 5
๐โ 6.841 5.653 5.407 6.250
๐0 (MPa) 64.76 39.16 38.58 73.30
๐0 (K) 2280. 1868. 2130. 2187.
๐ฟ๐๐ (m) 1.812 2.000 0.3516 0.1950
โ (mm) 10.00 5.971 4.317 4.150
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๐ =๐ฟ๐๐๐๐ก๐๐ก๐ฆ๐๐
๐ฟ๐๐๐๐๐
Realistic wind tunnel conditions โ I
โข Prototype flight conditions:๐โ = 6.0, ๐ป = 25๐๐
โข Realistic wind tunnel constraints:๐โ = 5.0, 6.0, 7.0
โข Two cases:
Design Variables Constraints
Test conditions ๐0, ๐0 WT5, WT6, WT7
Side length ๐ฟ (m) [0.1, 0.5]
Front panel length ๐ฟ๐๐ (m) [0.1, 2.0]
Thickness โ (mm) [1.0, 10.0]
Surface emissivity ๐ [0.5, 1.0]
Radiation temperature ๐๐๐๐ (K) [300, 2500]
Case 1: Parameter relaxation only
Case 2: Parameter relaxation and incomplete testing
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Hypersonic Tunnel Facility (HTF),NASA Glenn Research Center
๐โ = 6
๐โ = 5
๐โ = 7
๐โ = 6
๐โ = 5
๐โ = 7
Realistic wind tunnel conditions โ II
Prototype: ๐โ = 6.0Model: ๐โ = 7.0
External heating
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Case 1: Parameter
relaxation only
Case 2: Parameter relaxation
and incomplete testing
Aerothermoelastic scaling enabled!
I. IntroductionII. Modeling: The HYPATE FrameworkIII. Testing: Numerical Scaling LawsIV. ApplicationsV. Summary and Outlook
Key Novel Contributions
Testing: First ever hypersonic aerothermoelastic scaling implemented
โขA new, two-pronged approach to aerothermoelastic scaling
o The classical approach augmented with numerical simulations.
o Formulated as a multi-objective optimization problem and solved using a Bayesian approach.
o Applied to the scaling of a finite-dimension panel configuration.
โขPotential applications
o Map aerothermoelastic results from tests of scaled models to an actual vehicle.
o Potential for significant cost saving in hypersonic vehicle development.
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โ Accelerate the advent of Era of hypersonic flight
Future works
โข Joint efforts of the computational and experimental communitiesoComputation: Detailed design of scaled model for wind tunnel testing.
o Experiments: Measurement techniques for aerothermoelastic testing.
โข Robust multidisciplinary design of hypersonic structureso Inclusion of epistemic uncertainties due to modeling, esp. fluid ROM.
o Exploitation of benign aerothermoelastic instabilities.
โข Aerothermoelastic analysis with more complex physics and subsystemso Shock wave/boundary layer interaction, turbulent acoustic radiation, etc.
oCoupling with propulsion and control systems.
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Coupling with complex physics
42
โขCoupling with shock-dominated flow
oChallenges: boundary layer transition,
turbulent acoustic radiation, localized
heating
oRequires: Large Eddy Simulation,
computational aeroacoustics, reduced-
order modeling
โข Current collaborator: Dr. X.I.A. Yang
Shock Wave Boundary Layer Interaction on a 24 deg two-dimensional ramp at Mach 2.3 visualized trough Schlieren image.
Source: https://www.youtube.com/watch?v=aqudZCRiTbQ
Aero-thermo-servo-propulso-elasticity
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โขCoupling with control system
o Time-dependent vehicle dynamics
o Spectrum overlapping of controller and
structural response
โขCoupling with propulsion system
o Integrated airframe-propulsion system
o Aerothermoelastic deformation โ Offset from
engine design point
Kitson and Cesnik, 2016
Lamorte, Friedmann et al., 2014
Multi-physics Uncertainty Quantification
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Uncertainty quantification
and propagation
Hypersonic
Aerothermoelasticity
Modeling Testing
Understand &
Validate
โขIdentify knowledge gaps in modeling tools and
assess impact on analysis and design
oChallenges: Certification of hypersonic
vehicles, Design and optimization under
uncertainty
oRequires: Propagation of uncertainty in
high-dimensional dynamical system
โข Current collaborator: Dr. Puneet Singla
Fluid
Solver
Structural
Solver
Thermal
Solver
Body temperature
Surface deformation
Surface temperature
Pressure distribution
Heat flux distribution
Rotary-wing/eVTOL Aircraft Applications
45
โขWith the VLRCOE folks
โข Example I: Aeromechanics/Aeroacoustics
o Reduced-order modeling for real-time applications
o Numerical scaling laws for eVTOL-class aircraft
โข Example II: Rotorcraft Icing
o Modeling: Develop PSUโs own high-fidelity tools
o Testing: PSU-AERTS, NASA-IRT
Kreeger and Broeren, 2018
Gupta, Halloran, Sankar, Palacios, et al., 2018
Chia, 2017
Thank you!
Questions?
Contact: [email protected] website: apus.psu.edu/join-usPersonal blog: smanist.github.io
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