Mario Felipe Campuzano Ochoa (Cornell Energy Workshop)
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Transcript of Mario Felipe Campuzano Ochoa (Cornell Energy Workshop)
1
CFD in Aeronautics and AerospaceNumerical Computation and Modeling of Internal and External Flows
Progress in Aerospace Planes, Aerodynamics, and High-Speed Combustion
Mario Felipe Campuzano Ochoa Terra Global Energia Investments Ltd.
NASA Fellow ’95 -’[email protected]
Cornell Workshop on Large-Scale Wind Generated PowerJune 12-13, 2009
Cornell UniversityIthaca, New York 14853
2
External Flows - Aerodynamics
Samples of Flows – Subsonic and Separation
Flow Separation
Turbulence
Design is MULTIDISCIPLINARY
(everything is related and dependent)
3
The Fluid Dynamics Equation (Navier-Stokes)
dxidxidt
dfvi=dfi+dw
sijuj + k dT/dxiruiH
si3ru3ui
si2fvi =ru2uifi =
si1ru1ui
0rui
rE
ru3
ru2w =
ru1
r
p = (g -1) r {E - 1/2(ui ui)}
Equation solved numerically using optimal and iterative algorithms
4
CFD Stages of Design and Testing
Inflatable Reentry Vehicle Concept
Flow Modeling w/ “complex” Boundary Conditions
Automatic ModelingState of the Art Aerodynamic Design
Interactive CalculationRapid Prediction of Flows
• Integrate the capabilities into an automatic method that incorporates computer optimization.
• Can be done when flow calculation can be performed fast enough• But does NOT provide any direction on how to change the
conditions if performance is not desirable.
• Predict the flow past an aerodynamic body or its components in different flight regimes and paths such as take-off or cruise and off-design conditions.
Design by Numerical Finite Modeling
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Issues with the Discretization Models
The number of aerodynamic calculations is proportional to the number of design variables
Using 2016 grid points on the wing surface as design variables
Boeing 747
2016+ flow calculations ~ 2-5 minutes each (Euler not Viscous Flow)
Cost Prohibited for Industrial Design
System and Feedback Theory in Aero Design
Minimization of Drag Optimal Control of Aerodynamic Equations subject to body changes
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and
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GOAL : Reduction of Computational Costs
e.g. Minimize CD
System and Feedback Theory in Aero Design II
SS
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Flow Physics Solution + Ad-joint Solution
(Adjoint)
(Gradient)
2016 variables In grid
Ad-joint Method Characteristics:
• Gradient for N variableswith cost equal to 2 flow solutions
• Minimal memory needs in comparison with auto differentiation
• Shapes can be designed as free surface• No need for specific shape function• No constraints on the design space
2016 variables
Design Loop
Final Solution
Ad-joint solution
Gradient/PDE Calc.
Sobolev Solution
Contour/Grid Modification
Itera
te to
Con
verg
ence
an
d O
ptim
um S
hape
Summary Flow and Ad-joint Modeling
31423322221
332
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(gradient) Flow of Change
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problem Inverse for the sBC'
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scoordinate gridWith
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Sobolev Modeling
Continuous descent trajectory
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Key issue for implementation of Continuous adjoint solution.
Computational Costs - N Variables
(K independent of N)
(K )Sobolev Grad.
(N ) Quasi-Newton
(N2)Steep Descent
Cost of Algorithm
(N )Ad-joint Grad.
+ Quasi-Newton Search
(K independent of N)
(K )Adjoint Gradient
+ Sobolev Grad.
(N2) Fin. Diff. Gradients
+ Quasi-Newton Search or Response surface
(N3)Fin. Diff. Gradients
+ Steepest Descent
Total Computational Cost
- N~2000- Huge Savings- Enables Calculationson a small PC or iPAD
Design of Boeing 747 Wing at its Cruise Mach Number
Constraint: : Fixed CL = 0.42: Fixed load distribution: Fixed thickness for wing 14% wing drag saves
(7 minutes cpu time - 1proce.)
~5% aircraft drag saving
baseline
New design
Euler Calc.
Planform and Aero-Structural Optimization
270Total
___
27515Other
25522Nacelles
23522Tail
21555Fuselage
16045Wing friction
(15 shock, 100 induced)
125 counts125 countsWing Pressure
Cumulative CDCDItem
Boeing 747 at CL ~ .47 (fuselage lift ~ 16%)
Drag (the) largest component
Comments
• Aerodynamic design by a small team of engineers focusing on design issues.
• Significant reduction time and cost.
• Superior and unconventional aero designs.
• Aerodynamic wing design is complex due to complexity of flow around the wing.
• The adjoint method, aerodynamic wing design is carried out quick and cost effective
Pay-Off
17
Airbreathing Propulsion CFD
X-43A- Integrated Vehicle- Scramjet Engine Design- Short Duration Test (Heat Sink Tech.)
Dual Mode Scramjet- Cooled Structure for long flight
Turbine Combined Cycle Rig
X-51A
Durable Combustor Set
HIFiRE
Flight Experimentation
Long Duration
Combined Cycle
18
Test Cases for CFD
Objective: Duplicate hydrocarbon scramjet acceleration and performance during flight
NASA data analysis and CFD code validation using full-scale X-51 test data
X-51 in the NASA Langley 8’ High Temperature Tunnel
X-51A flight hardware at Edwards Air Force Base
19
Turbine Combined Cycle Propulsion
Objective: Demonstrate transition between turbine and simulated scramjet
NASA finished the design of a large scale inlet
Turbine Flowpath
Scramjet Flowpath
Dual Inlet
Rotating Doors
Drawing of full TBCC Test Rig
Centerline view of inlet
Assembled Inlet hardware at manufacturer
Turbine
Dual Inlet
Simulated Scramjet
20
Fan Rotor Blisk
Turbine-Based Combined Cycle Propulsion
Objective: Validate tools for Mach 4 stage with/without distortion
NASA finished evaluation of Mach 4 stage
RTA: GE 57 / NASA Mach 4 capable Turbine EngineInlet Distortion Screens
Turbo Code Calculation
Combustion in High-Speed Flows - SCRAMJET
• CFD usage in scramjet engine design/analysis– Why is it a critical tool?– How is it used and developed?
• CFD practices in scramjet analysis and design– Reynolds stress tensor closure– Reynolds flux vector closure– Turbulence-chemistry interactions (i.e. internal and external)– Unsteady formulation (e.g. turbulence models)
• Concluding Remarks
• Sample FAP NRA projects currently underway– Hybrid RAS/LES– FDF and PDF (Filtered/Probability Density Function) development– Reduced chemical kinetics model development (MS thesis @ Syracuse
University – NASA LaRC)
Role of CFD in Scramjet Development
• CFD role in scramjet development
– Not possible to exactly reproduce hypersonic flight conditions at ground test facilities
i. CFD used to extrapolate/approximate results to flight
ii. CFD used to examine effects of “modeled-conditions”
– Not possible to measure all relevant properties at ground test facilities
i. CFD used to complete gaps due to lack of measurements and instrumentation (overcome maybe by nanotechnology in near future)
ii. CFD used to model outcomes from perturbations made from a calibrated condition
– Not possible to copy from designs of existing vehicles and engines
i. CFD used to examine candidate configurations
ii. CFD used to build databases
iii. Sensitivity studies performed on most designs
Scramjet Propulsion System
Scramjet Flow Dynamics
• NASP Model Space Plane
CFD in Scramjet Design & Development
• Current CFD
– 3-D steady-state RAS (parabolized versions for some analyses)
– Turbulence models use eddy viscosity/gradient diffusion concepts
– Chemical reactions handled via reduced finite rate kinetics
– Turbulence-chemistry interactions typically ignored (but some studies have been done by Givi @ SUNY Buffalo for example).
– Acceptable turn-around time for solutions is measured in days
• Limitations of current methodology
– Uncertainty related to turbulence model is often unacceptable
– Crude chemistry Flame-holding limits can not be obtained
– Unsteady effects (very important) are ignored and/or “poorly modeled”
Reynolds Averaged EquationsReynolds Averaged Equations
Reynolds Turbulence Stress Model
• Most common closure is the Boussinesq assumption:
• Typical eddy viscosity models:
– Zero-equation models (e.g. Baldwin-Lomax)
– One-equation models (e.g. Spalart-Allmaras)
– Two-equation models (e.g. k-ε, k-ω)
– Three-equation models (e.g. Durbin k-ε-v2)
• LEVMs are deficient in several areas:
– Unable to capture stress-induced secondary flow structures (Reynolds-stress anisotropies)
– No direct avenue to incorporate pressure-strain correlation effects
– No rigorous accounting for streamline curvature effects
Con’t - Reynolds Stress
• Second order models can address these deficiencies:
• Cost of solving these equations is significant (i.e. computational time)– Algebraic models extracted by enforcing equilibrium assumptions– These models retain much of the information from the full Reynolds stress
equation– When recast as explicit relationships, the cost is comparable to LEVMs
Reynolds Stress Comparison Models
• Mach 3.0 flow through a symmetric square duct
• Linear k-ω model unable to predict secondary flow
• EARS k-ω predicts anisotropy secondary motions
Measured Linear k-ω Measured EARS k-ω
X/h = 40
Scalar Flux Models
• Closure used is the gradient diffusion model:
• Diffusion is tested by the specification of σt
Vector Flux Models
• Scramjet Flow Path
Scalar Flux Models
• Scalar flux transport equation:
• The cost of solving the additional equations is prohibitive (3*ns additional transport equations)
• Algebraic models have been explored, but not to a level that compares with algebraic closures for the Reynolds stress tensor
Turbulence - Chemistry Models
• Common closures are for laminar-chemistry situations, i.e.
• Turbulent fluctuation effects on the chemistry can be modeled using PDF’s (i.e. Givi SUNY @ Buffalo):
• The form of the PDF can be assumed before test, or an evolution equation can be integrated for it
• To date, results from various turbulence-chemistry combinations modes had small changes than results obtained from variations ofturbulent models
Supersonic Axi-symmetric Burner
• New Injector Design
Turbulence - Chemistry Models
CFD
Hybrid RAS and LES
• Real Concept: LES far from walls, RAS near walls
• Hybrid RAS/LES value (relative to flow-state RAS)
– Temporal accuracy requires 4-8 times more work per iteration
– Flow must be integrated to a stationary state (N) followed by more iterations (on the order of N) to gather meaninful data
– Spatial resolution increase– Nearly isotropic grid regions in LES spaces
– Cost of a hybrid RAS/LES is roughly 100 to 500 times that of steady-state RAS
– Time history data dumps hundreds of GB’s to tens (even hundreds) of TB’s in future
Hybrid RAS and LES
Concluding Remarks
• Steady-state RAS will be the primary governing equation - for some time - for high-speed internal flows studies
• RAS models must focus on the scalar transport closures
• Closures of higher-order for the Reynolds stress equations can be used ideally for the shock-dominated scramjet flows
• Turbulence-chemistry interactions may be a secondary (BUT IMPORTANT) issue for high-speed flows
• Hybrid RAS/LES can be the next step for CFD analysis