International modeFRONTIER Users’ Meeting 2010 ...
Transcript of International modeFRONTIER Users’ Meeting 2010 ...
International modeFRONTIER
Users’
Meeting 2010Applications of modeFRONTIER
on Aeronautical
Problems: an overview of experiences at the German Aerospace Centre (DLR)
Joël
Brezillon*, Frederic Guntzer Institute of Aerodynamics and Flow Technology
*Head of MDO group -
Braunschweig
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1.
General presentation2.
Optimisation of an Aircraft in Take-Off Configuration
3.
Optimal Noise Abatement Flight Procedures4.
Design of a Small Size Super-Sonic Aircraft
5.
Conclusion
Outline
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Institute of Aerodynamics and Flow Technology (AS)
Braunschweig / Göttingen
C.-C. Rossow / A. Dillmann
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Köln-Porz
Göttingen
~ 270 Employees
Braunschweig: ~ 135 Employees
Braunschweig
Göttingen / Köln: ~ 135 Employees
Site Locations
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Covering the complete regime of flow speeds
Research Areas
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Scientific Core Competencies
Multidisciplinary Numerical Simulation and Optimization
Measurement Technologies and Experimental Validation
Conceptual Design and Configuration Analysis
Aerodynamic Design and Analysis
Aeroacoustic
Prediction and Noise Reduction
Aerothermodynamic
Design and Analysis
Flow Technology
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Identification and development of high-fidelity technologies needed in MDO contextin aerodynamics, structure and acousticsin optimisation strategies and architectures
Set up of MDO environment based on mixed high/low fidelities methodsApplication on realistic design configurationsDevelopment of fit purpose optimisation processes and strategies
Multi-Disciplinary Optimisation Process Group
2004: first license of modeFRONTIER (V3.0.2) and application on academicals cases2005: support ESTECO on the design of interface for the use of external gradient2006: first application of NLPQLP with external gradient capability on 2D CFD problem2007: same application on 3D CFD problem2008-09: Multi-objective optimisation of a supersonic business jet 2009: search to the optimal noise abatement flight procedures2010: successful use of krigging approach to solve multi-objectives problem
Mainly used on problems with limited number of variables (~ 20), on multi-objective optimisation problems or on multi-modal design spaces
modeFRONTIER
at DLR-AS
Flap and slat setting optimisation of an aircraft in take-off configuration
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Aircraft in take-off configuration
>1.5VS>1.23VS
>1.13VS
1.13VS
>1.25VS
1.2VS
Airbus A319 with high-lift devices deployed
TAKE-OFF PERFORMANCE
REGULATIONS
min. climb angles
over speed related to VS
engine failure
max. touch-down speed
fast climb to cruise flight level
high altitude at overflow noise point
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Aircraft in take-off configuration
Airbus A319 with high-lift devices deployed
TAKE-OFF PERFORMANCE
REGULATIONS
min. climb angles
over speed related to VS
engine failure
max. touch-down speed
fast climb to cruise flight level
high altitude at overflow noise point
Question: what is the optimal position of the high-lift devices to meet the required performance ?
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Aerodynamic flow around a wing in take-off configuration
Flow is viscous and compressibleComputational Fluid Dynamics (CFD) based on Reynolds-Average Navier-Stokes solver recommended
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Test case specification
Configuration
DLR-F11 geometry in take-off configuration
Objective and constraints
Maximization of
CL > CLinitial
Penalty to limit the deployment of the flap and slat (constraints from the kinematics of the high-lift system)
To complete the optimisation within 2 weeks turn-around time
Flow conditions
M
=0.2 ; Re=20x106 ; =8 º
2
3
33
D
D
CDCLOBJ
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Design parameter Objective
Aerodynamic optimisation process
based on CFD solver
Optimiser
Parametrisation
Mesh process
Analysis
Drag 102 Lift 1.23Moment -0.542
CFD solver
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F
Gap
-O/L
O/L
O/LGap
S
O/L
DoFDoF
Flap & slat position and deflection, constant along spanParametrisation of the geometry with ICEM-CFD (new position and intersection lines between body and slat/flap)
Parametrisation
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Mesh Procedure
Smooth meshto capture the wakes
Mesh generated with ICEM-HexaAbout 2.5 Millions pts in totalSmooth mesh to capture wakes73 pts above the main wing for the wake49 pts between main wing and flat
CFD solverHybrid solver DLR-TAU2nd order accurate methodsfully parallel solversPreconditioning for low Mach numberViscous computation with Spalart-Allmaras-Edwards turb. modeladjoint capability (for fast computation of sensitivities)
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Computation of the sensitivity using adjoint
approach
Good agreement between central finite differences and adjoint computations
2
3
)grad(CDCL
OBJ
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Process in modeFRONTIER
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modeFRONTIER
4.0NLPQLP for fast convergence12 cycles, 13 Aero. evaluations, 2*12 adjoint computationsComputation of the aerodynamic flow on a Linux cluster with 32 proc.Computation of the adjoint flows (CD and CL) on 2 Linux clusters with 16 proc. each3 hours for CFD, 2 hours for AdjointFull optimisation in less than ~ 3 days of computations
Optimisation Strategies
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Synthesis
Leading edge slat Trailing edge flap
Final slat and flap positions for a given span position
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ConclusionDLR developed a computational chain to predict the aerodynamic performance of aircraft in take-off configuration.The time for each simulation is of about 3 hours wall clock time.To complete the optimisation within a reasonable turn around time, the use of gradient based strategy is advisable. In combination of efficient gradient computations (like the adjoint approach in CFD), the procedure is highly efficient and permit to cut down the simulation time.The NLPQLP optimisation combined with the possibility to read the gradient in modeFRONTIER has permitted to reach this goal.
Reference: Numerical Aerodynamic Optimisation of 3D High-Lift Configurations J. Brezillon, R.P. Dwight, J. Wild 26th International Congress of The Aeronautical Sciences, Sept. 2008 Anchorage, USA
Optimal Noise Abatement Flight Procedures of an EC-135
From Frédéric Guntzer, Pierre Spiegel, Markus Lummer and presented at the 35th European Rotorcraft Forum,
September 22-25, 2009, Hamburg, Germany
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Noise Abatement Flight Procedure
Flight path
noise directivities on hemisphere
Ground
EC135 at landing phase
Objective: Finding a feasible flight path that produces the minimum noise at the ground
Problem: Finding a feasible flight path that produces the minimum noise at the ground
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Requirements of the simulation
Cover all noise sources of an helicopter: main rotor, tail rotor, engine, interaction and installation effects.Able to simulate arbitrary procedure noise footprint.Wind effects taken into account:
On flight conditionsOn noise propagation
Ensure flyable procedureCheck criterion on comfortable flightGuarantee safe flight manoeuvre: check avoidance of Vortex Ring State and ensure compatibility with safety requirement in case of engine failure, ensure marging to autorotation….
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Noise propagated on ground including atmospheric/ground
effects
1. Generation of flight path and velocity evolution using cubic splines
2. Flight dynamics calculation of flight conditions (PmAlpha, RmCT, RmMu) and source attitude
using HOST code.
3. HEMISPHERE 2
At each selected steps do step 2 & 3
1
n
t1.1t1.m
tn.m
Control points with velocity at these points
The SELENE noise computation chain of arbitrary flights (Sound Exposure Level starting from the Emitted Noise Evaluation)
DATA BASE of noise directivities on
hemisphere from flight test conducted in 2004
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SELENE and optimization loop flowchart overview
Conditions for Noise Abatement Flight Procedure (Input)
Ensure Flyable procedure generation
Noise on ground simulation
HEMISPHERE 2
Optimizer: ModeFrontier
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Procedure optimization parameters
Final conditions fixed: landing point position and zero velocity
Flight path within a vertical plane
Control of the flight path with 4 free control points : 4 x 2 coordinates + 4 velocity/ground magnitudes = 12 DoF
For some optimizations initial velocity and/or initial height were set free: up to 14 degrees of freedom (DoF)
Optimiser: Multi-Objective Genetic Algorithm (MOGA II)Generations from 50 to up to 500 individualsSimultaneous evaluation on Linux Cluster with up to 32 processors80.000 iterations within 2 days wall clock time
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Verification of the optimised flight procedure Flights test campaign in 2008
Microphones layout in Cochstedt
Reference flight procedure
Tip path plane
Real path of the helicopter
Horizontal velocity
Optimised flight procedure
Ideal path of the helicopter
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Verification of the optimised flight procedure Flights test campaign in 2008
Tip path plane
Horizontal velocity
Optimised flight procedure
Reference flight procedure
Noise Footprint (dBA) at the ground measured with 33 microphonesResults assembled from
2 flightsOptimised flight procedure has permitted to:
decrease maximum noise of about 5dBA
60% reduction of area with more than 80 dB
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Conclusion
DLR developed a computational chain to model noise of arbitrary flight procedures (SELENE)
Coupled with optimisation strategies, the procedure is able to design optimal noise abatement flight procedure
The design space seems to be multi-modal and required robust and global optimisation strategy: the use of ARMOGA-II with simultaneous evaluation has demonstrated the capability to find good minimum in limited turn around time
The prediction of the noise abatement for the optimal flight procedure is confirmed by flight testing
Reference: Genetic Optimizations of EC-135 Noise Abatement Flight Procedures using an Aeroacoustic Database Frédéric Guntzer, Pierre Spiegel, Markus Lummer 35th European Rotorcraft Forum, September 22-25, 2009, Hamburg, Germany.
Design of a Small Size Super Sonic Aircraft
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Motivation
The general objectives of the EU project HISAC (High Speed Aircraft project) was to assess the technical feasibility of environmental compliant supersonic small size transport aircraftsThe commercial characteristics are
Size of cabin for 8 to 16 passengersSpeed up to Mach 1.8Range up to 5000 nm
The DLR contribution was to evaluate the performance potential of given SuperSonic Bussiness Jet concepts using MDO process based on high-fidelity methods.
High-fidelity methods are the best candidate to correctly consider the complex multi-disciplinary interactions that occur on supersonic configuration.
High-fidelity methods (CFD, CSM, …) have reached a mature stage and are routinely used for analysis and assessment of aircraft configurations
DLR, NLR and Onera joint effort to set up such a MDO process
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Realistic transport mission is modelledFour coupled disciplines:
AerodynamicStructureEngineFlight mechanic
High-Fidelity for aerodynamic (cruise) and structure (+2.5g load case)Low-Fidelity for engine, flight mechanic & other flight conditionsOptimisation strategy: MD Analyse driven by optimisation hierarchyNLR provides a core MDA processOnera provide module to compute sonic boom MDO process constructed and applied by DLR Baseline configuration: Low Sonic Boom concept
Multi-Disciplinary Analyse and Optimisation
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Design of a Small Size SuperSonic
AircraftCommon process chain with NLR and Onera
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MDO process operational at DLR
Global optimiser: ARMOGA
Global optimiser: ARMOGA
FEM solver:NASTRAN
FEM solver:NASTRAN
DLR flowsolver: FLOWer
DLR flowsolver: FLOWer
Mesh Generation:
ICEM-CFD
Mesh Generation:
ICEM-CFD
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Process in modeFRONTIER
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Objective:Wing planform optimisation of a low sonic boom supersonic business jet.
Strategy:Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA) as global optimiser
Minimise the sonic boom impact at the groundMinimise the Maximum Take-Off Weight (MTOW)Maximise the range
Population with 50 individuals, initialised with LHS Each analysis takes 1 hour on cluster with 8 cpu
Parametrisation:13 geometrical parameters + MTOW (!) for the global optimiser201 internal wing structural elements supporting structural sizing
Design of a Small Size SuperSonic Aircraft
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+ twist & thicknesses @ C1, C2, C3
Beam, spar and cover thicknesses
Local
(201)
Global (13)
Design variables
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Results:40 generations (2.000 evaluations)200 hours wall clockDecrease MTOW by 1 TDecrease Noise level by 1.7 dBAIncrease range by 263 Nm.
Design of a Small Size SuperSonic Aircraft
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Conclusion
modeFRONTIER has been successfully used at DLR-AS since 2004 for solving wide range of aeronautical problems: drag reduction of wing in cruise configuration, aerodynamic improvement of aircraft in take-off, multi-disciplinary design of small size supersonic aircraft, optimal noise abatement flight procedures of an EC-135, ….
modeFRONTIER offers a bright range of useful optimisation strategies, ranking from efficient gradient based approach (NLPQLP) to robust multi-objective strategies (ARMOGA, MOGA).
What’s next ?
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Thank you ! for your attention