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Exascale Needs & Challenges for Aeronautics Industry PRACEdays15 - Dublin, May 26th 2015
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Flight-Physics Capability StrategyDr Eric CHAPUT
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Content
HPC in Aeronautics a Historical Perspective
Current HPC Resources and Applications at Airbus
Exascale Vision
Exascale Challenges
Conclusions
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
A historical perspective
In the last 40 years, commercial aviation industry has achieved:
1960 1970 1980 1990 2000 2010 202020%
40%
60%
80%
100%
1950
Relative Fuel Burn per seat
- 70% fuel burn
- 70% CO2 emissions
Noi
se L
evel
1960 1970 1980 1990 2000
2nd Generation Turbofans
Turbojets1st Generation Turbofans
20 dB
More than 20dB improvement
Within Airbus, we have played a key role in reducing the environmental impact from aviation
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Simulation and HPC in the A380 Design
The first Airbus aircraft to have exploited aerodynamics simulation & HPC
Quieter.
Cleaner. Less than 3 litres of fuel per pax per 100 kmLess than 75g of CO2 per pax per km
80828486889092949698
A340-300 A340-600 A380-800
EPNdB 275t
+90t365t
+285t
560t
A380 Bigger & Quieter
Twice as many passengers flown for the same noise level
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Pushing the Boundaries through the A350
An evolution of the aerodynamics design: Heavy use of simulation and HPC – around 5000 data setsCryogenic Wind Tunnel Testing replicating flight conditions
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.10.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.10.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.10.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1
Before
After
Wing Span
Wav
e D
rag Not A350 Distribution
Advanced CFD + Optimisation
Flight Re Testing
Optimised aircraft design, finest aerodynamics and lowest fuel burn
40 % less test than in A380
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Airbus HPC patterns
•Simulation frameworks• Higher process level (e.g. D.O.E./sensitivity analysis, optimization, trade-off studies…)• Massive data production in single process (e.g. aero-data direct production geometry
using HPC)
• Complex processes (e.g. multi-level, multi-disciplinary optimization) Advanced execution & data management
•Grid services / LSF profiles• Virtualization of HPC resources (e.g. separate & global optimization of HPC activities )• Meta-scheduling global virtualization of HPC resources through grid, local
queues management through LSF•FlowSimulator / Open-PALM
• FlowSimulator modular approach (e.g. share mesh parallel services , coupling strategies parallel scripted at solver time loop level)
• Capability to use FlowSimulator brick as a server being one of the component of a distributed simulation
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
-
-
-
Spain
United
Kingdom
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
AIRBUS Cloud & Grid Architecture
AIRBUS GRID SERVICE
AIBUS IndiaToulouse
Filton
HamburgBremen
Bangalore
Getafe
HPC Service
HPC Service HPC Service
AIRBUS IW GRID SERVICE
Suresne
AIRBUS HelicopterGRID SERVICE
Marignane
Donauwörth
Ottobrunn
Private HPC CloudGreen Computing : PUE < 1.2World Grid-Computing : Europe, America and IndiaLSF Platform-Computing(IBM) and SynfinyWay (Fujitsu)HP POD - Cluster PlatformInterconnect - Infiniband
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
AIRBUS HPC4 - Key Characteristics
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Location : Toulouse, HamburgContainers : 3 POD‘sServers : 2 320 Cores : 55 680RMAX : 1 to 2 Pflops (Ivy
Bridge, Broadwell)Memory : 270 TBLocal Storage : 1 500 TBShared Midterm Storage : 750 TBShared ComputingStorage : 1 800 TBOutsourced serviceEnergy Efficiency (PUE < 1.2)Access / SchedulerResource Management : LSF / SynfiniwayInterconnect : Infiniband
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Flight Physics current major HPC customer
Computational Fluid Dynamics• Design & Optimize External Aircraft Shapes• Design Sensitivities & Trades• High Speed an Low Speed Performance• Unsteady Aerodynamic/Aeroelastic Effects• Structural Flexibility Effects • Aircraft Data Model for the whole Envelope• Loads and Aeroelastic Data• Multiphysics : Aeroacoustics, Aerothermics…
2013
80%of HPC Capacity
Aerodynamics Loads & Aeroelastics
Flight-Physics
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Systems, Structure, PowerPlant future big customers
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
20%of HPC Capacity
2013
SystemsSafety & Certification contribution• Electromagnetic compatibility in nominal
and hazardous conditions (EMH)
Fuel Tanks: thermal fluid modelling• Thermal fluid modelling for Fuel tanks is
used throughout the whole A/C development lifecycle
Major Component Optimisation• Box sizing (covers)• Composite fuselage preliminary sizing
(covers thickness)• Hundreds of variables and thousands of
constraintsVirtual testing - Non Linear FE• Generalization of NLFE models with
contact and complex materialStrength analysis
StructureNoise prediction• Integration of noise constraints in the A/C
design process• Robustness of noise assessment along
development cyclePropellers / contra propellers noise• Assessment of noise radiated by SRP and
CRP.• Optimisation of engines installations (MDO)• Extensive use of coupled CFD/CAA
numerical tools
Powerplant
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Progress in Flight Physics Simulation Capability
Physical Complexity
Cap
abilit
y –
Flig
ht E
nvel
ope
Cov
erag
e
- Complete physical modelling - Laminar-Turbulent Transition- Multidisciplinary Optimisation
3D simulation of installed engine -CFD-based Aero Data -
Optimisation on Engine integration -Low speed High-Lift -
- Support to Flight-Tests- Unsteady Aerodynamics- Thermal Aircraft
CFD – Computational Fluid Dynamics
- High speed CFD-based scaling- Simplified geometry
- High fidelity Aerodynamics Simulation - Reducing standard wind tunnel testing- Flexible Aircraft Representation
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Simulation Envelope
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
3g
0g
- 1g
2g
1g
LOA
D F
AC
TOR
3g
0g
- 1g
2g
1g
VC
VD
EAS(at a given altitude)
LOA
D F
AC
TOR
VF
Aerodynamic Flow Predictions vs. Flight Envelope
Buffet boundary
• The colour gradient is intended to show level of confidence in CFD flow solutions
• Beyond buffet boundary flow tends to become more and more separated from the aircraft surface
• Unsteady effects start to become dominant• High lift configuration
flow is physically very complex due to high loading
• Low g flow tends to separate on the lower side of the aircraft
• Local low Mach number / low compressibility flow only weakly coupled to main flow
• Transonic flow effects (shocks) get stronger with increasing Mach number and load
• Interaction of physical effects (shock flow, boundary layer flow) start to dominate
• Maximum lift borders are determined by onset of massive separation causing total lift loss
• Region of highest accuracy and robustness
• Region of most experience
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
3g
0g
- 1g
2g
1g
LOA
D F
AC
TOR
3g
0g
- 1g
2g
1g
VC
VD
EAS(at a given altitude)
LOA
D F
AC
TOR
VF
Aerodynamic Flow Predictions vs. Flight Envelope
Buffet boundary
• The colour gradient is intended to show level of confidence in CFD flow solutions
• Beyond buffet boundary flow tends to become more and more separated from the aircraft surface
• Unsteady effects start to become dominant• High lift configuration
flow is physically very complex due to high loading
• Low g flow tends to separate on the lower side of the aircraft
• Local low Mach number / low compressibility flow only weakly coupled to main flow
• Transonic flow effects (shocks) get stronger with increasing Mach number and load
• Interaction of physical effects (shock flow, boundary layer flow) start to dominate
• Maximum lift borders are determined by onset of massive separation causing total lift loss
• Region of highest accuracy and robustness
• Region of most experience
Uncertainties
Unsteadiness of flow
MD interaction
Prediction accuracy
Comprehensiveness of A/C data
Optimisation
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Exascale Vision
Exascale computing key enabling technology for future aircraft design
Fully multi-disciplinary development and optimization process
Wide use of integrated MDA/MDO capabilities
Real-time/interactive way of working fostering innovation
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Multi-disciplinary analysis and design, supported by affordable CFD-based aerodynamic and aero elastic data prediction will be a significant change of paradigm in aeronautics industry.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Multidisciplinary Design Capability
Twist / Camber /Thickness
-Initial Geom.
-Step 1-Step 2
Parametric Geometry / Shape Generation
Mesh Generation / Deformation
Finite Element Model Generation
Aerodynamic Coefficients
Structural Forces
CFDFlow Solver CSMLoads
Deformation
LOAD
FAC
TOR
3g
2g
1g
0g
-1g
A C D
E
FH
VF VD
EAS
Vs1g
Design SpaceMapping
Full Flight Envelope
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Exascale Challenges: Fly the Virtual Aircraft
Real-time Simulation of Manoeuvre Flight of a Complete Aircraft Full Unsteady aerodynamics simulation and Multi-disciplinary interactions
Full Finite Element Modelling of the Airplane
Full Simulation of Manoeuvre FlightLoads and Stress for the
Airplane in the Whole Envelope
Digital Prediction of “Flight Performance” and “Handling” Prior to First Flight
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
RANS to LES
Move from RANS to unsteady Navier-Stokes simulation, (ranging from current RANS-LES to full LES) and/or Lattice Boltzmann methodSignificantly improve predictions of complex flow phenomena around full aircraft configurations with advanced physical modelling.
Exascale
Petascale
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Complex high lift case: RANS-LES vs LBM
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Nose Landing Gear wake interacting with Main Landing Gear & cavities
Mesh data• 46.8 M nodes• 131 M cells• 72.5 M prisms• 58.5 M tetras• 37 prism layers
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Petaflops to Exaflops Challenges
1980 1990 2000 2010 2020 2030
Capacity: # of Overnight
Loads cases run
Capacity: # of Overnight
Loads cases run Capacity [Flop/s]
CFD-basedLOADS
& HQ
CFD-basedLOADS
& HQ
Aero Optimisation& CFD-CSM
Aero Optimisation& CFD-CSM Full MDOFull MDO
Real timeCFD based
in flightsimulation
Real timeCFD based
in flightsimulation
x106
1 Zeta (1021)
1 Peta (1015)
1 Tera (1012)
1 Giga (109)
1 Exa (1018)
102
103
104
105
106
LES
CFD-basednoise
simulation
CFD-basednoise
simulation
RANS LowSpeed
RANS HighSpeed
HS Design
Data Set
Capability achieved during one night batchCapability achieved during one night batch
UnsteadyRANS
“Smart” use of HPC power:• Algorithms• Data mining• knowledge
1980 1990 2000 2010 2020 2030
CFD-basedLOADS
& HQ
CFD-basedLOADS
& HQ
Aero Optimisation& CFD-CSM
Aero Optimisation& CFD-CSM Full MDOFull MDO
Real timeCFD based
in flightsimulation
Real timeCFD based
in flightsimulation
x106
1 Zeta (1021)
1 Peta (1015)
1 Tera (1012)
1 Giga (109)
1 Exa (1018)
106
105
104
103
102
LES
CFD-basednoise
simulation
CFD-basednoise
simulation
RANS LowSpeed
RANS HighSpeed
HS Design
Data Set
UnsteadyRANS
“Smart” use of HPC power:• Algorithms• Big Data• Knowledge
# of Overnight Loads cases run
Capacity
Achieved during one night batchCapability
Available Computational
Capacity (Flops/s)
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Exaflops Challenges
Hardware challenges:• Billion of components• Energy consumption • Size of system memory• System resilience• Energy Efficiency• System resilience
Software Co-design challenges:• Extreme Parallelism• Compute, I/O & Storage Performance Balance• Simple rule of thumb design• Middleware aware of failure resilience
Optimize trade-off among• Supporting new workloads, e.g. Big Data• New HPC Delivery models, e.g. Cloud• applied mathematics, algorithms, computing challenges
Educational challenge
2018-2020 - CFD New Generation
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
CFD Technology Roadmap : NASA Vision 2030 CFD Code
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
High Fidelity CFD Strategy
Converge the current solutions
Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15 May 2015
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Evolution of CFD towards Exascale
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Knowledge & Visualization: POD RACER GappyPOD Unsteady Data extract.
// – parallel computingAMR – Automatic Mesh RefinementCFD – Computational Fluid DynamicsDG – Discontinuous Galerkin methodIGA – Iso-Geometric Analysis
LBM – Lattice Boltzmann MethodLES – Large-Eddy SimulationMB – Multi-BlockMDA/MDO – Multi-Disciplinary Analysis / Optimisation
POD – Proper Orthogonal DecompositionRANS –Reynolds Averaged Navier-StokesRSM – Reynolds Stress turbulence ModelSA – Spalart-Almarass turbulence modelSDM – Spectral Difference Method
SST – Shear Stress Transport turbulence model
Process Automation: CFD Manager, FlowSimulator Rapid CFD for MDO
Mesh Generation & Adaptation: MB, Hybrid Chimera, AMR Automatic Hyperflex, Uncertainty
Solver Algorithms: RANS 2nd Order // effectiveness Uncertainty High-Order (DG, SDM, IGA), LBM ; massively //
Hardware: TeraFlop/s 2004 PetaFlop/s 2014 ExaFlop/s 2024
Physical Modelling: RANS (SA, SST RSM) Unsteady RANS-LES, LBM LES
Multi-Disciplinary / Multiphysics: Parametric Shape, Adjoint-based Optimisation Coupling Validation & Verification MDA/MDO
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Challenge #1 – Visualization/Identification of aircraft shape in flight
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
•Design of Experiment of CFD-CSM computations•POD of surface pressure•Visualization of sensitivity to flight conditions
LES
Design of Experiments
Proper Orthogonal Decomposition
Non-Linear Interpolation
CFD – Computational Fluid DynamicsCSM – Computational Structural MechanicsDOE – Design of ExperimentsPOD – Proper Orthogonal Decomposition
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
•Derivative A/C design• Minimum change in geometry vs legacy• Capturing the flow physics (jet, ice shape…)
•New A/C design • Immersed boundary in LES
Challenge #2 – From Automated Chimera to Immersed CAD LES
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
LES
RANS
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
Challenge #3 – Multidisciplinary Design Optimization
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
MDOAdjoint-based Aero Optimization
Awareness of design change effect on other disciplines - Adjoint-based flexible shape Optimization- Sensitivity of Aero design on Structure Reserve Factor
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Direct Noise Computation
LES - 2x109 cells - HPC 8000 CPUcores – 20days
Challenge #4 – Flow control to reduce noise and vibration
RANS/LES - 7x107 cells - HPC 100 CPUcores – 15days
LES – Large-Eddy SimulationRANS –Reynolds Averaged Navier-Stokes
Constrained design
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
Conclusion
Exascale computing is seen as a key enabling technology for future aircraft design to be developed and optimised in a fully multi-disciplinary way, making a wide use of design systems that provide integrated analysis and optimisation capabilities which allow for a real-time/interactive way of working.The move from RANS to unsteady Navier-Stokes simulation, (ranging from current RANS-LES to full LES) and/or Lattice Boltzmann method will significantly improve predictions of complex flow phenomena around full aircraft configurations with advanced physical modelling. For instance moving LES capability from Petascale to Exascale computing will accelerate the understanding of noise generation mechanisms and will enable the elaboration of flow control strategy for noise reduction. Multi-disciplinary analysis and design, and real time simulation of aircraft manoeuver, supported by affordable CFD-based aerodynamic and aero elastic data prediction will be a significant change of paradigm in aeronautics industry.
© AIRBUS S.A.S. All rights reserved. Confidential and proprietary document.
May 2015Exascale Needs & Challenges for Aeronautics Industry - PRACEdays15
© Airbus S.A.S. All rights reserved. Confidential and proprietary document. This document and all information contained herein is the sole property of AIRBUS. No intellectual property rights are granted by the delivery of this document or the disclosure ofits content. This document shall not be reproduced or disclosed to a third party without the express written consent of AIRBUS S.A.S. This document and its content shall not be used for any purpose other than that for which it is supplied. The statementsmade herein do not constitute an offer. They are based on the mentioned assumptions and are expressed in good faith. Where the supporting grounds for these statements are not shown, AIRBUS S.A.S. will be pleased to explain the basis thereof.AIRBUS, its logo, A300, A310, A318, A319, A320, A321, A330, A340, A350, A380, A400M are registered trademarks.