towards virtual aircraft design and testing based on...

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Towards Virtual Aircraft Design and Testingbased on High-Fidelity Methods- Recent Developments at DLR -

O. Brodersen, C.-C. Rossow, N. KrollDLR Institute of Aerodynamics and Flow Technology

Digital-X

Prof. A. Jameson 80th

Symposium Mathematics, Computing & Design– Where Analysis and Creativity Combine20-21 Nov. 2014, Stanford, USA

Society: European Aviation Vision “Flightpath 2050” & Aeronautics Strategy Germany

Environment: 75% CO2, 90% NOx, 65% noiseIndustrial Competiveness: innovation process frombasic research to full-scale demonstrators

Towards the Virtual AircraftMotivation & Strategic Objectives


Industry:Robust & efficient design process with all disciplinesMore knowledge into processes

Need for DLR Virtual Aircraft Software PlatformSupport industrial and research activities in GermanyIdentify future options for HPC based aircraft designSimulate all Flight Physics aircraft propertiesrelevant for design & certificationTrade studies for technology evaluationEnhance and maintain aircraft design capabilities in DLR

Towards the Virtual AircraftChallenges & Capability Needs

Multi-DisciplinesLink of preliminary overall aircraft and “detailed” MDA / MDO

High-fidelity for relevant disciplines

Large number of design parameters

Identification of realistic & relevantload scenarios for structurallay-outs (metal, CFRP)

Representation of relevant systemproperties (mass, volume,performance, energy)


DLR LamAir

AerodynamicsSeparated flowsTransition laminar/turbulentUnsteady effects


AIAA CFDDrag/High Lift Prediction

WorkshopsAirbus


Software & HardwareMassively parallel computer hardwareWorkflow-management environment with “automatic” processesConsistent and comprehensive data formats



Reliable Multi-Disciplinary Simulation and Optimization require:Comprehensive verification & validation efforts

Towards the Virtual AircraftVerification & Validation Needs

Wind Tunnel (ETW) Flight Test A/C DLR A320-ATRACFD

HINVA (High Lift Inflight Validation)German Aeronautics Research Program (LuFo) project1st flight tests performed in August 2012 in Toulouse2nd flight tests planned of end of 2014 in Braunschweig


Previous WorkHistory

1982-1992

Digital-X

CevcatsCode

Next Generation Code


Reynolds-averaged Navier Stokes CodeUnstructured grids, overlapping, adaptation Finite Volume method 2nd orderAdvanced turbulence models, e.g. RSMHybrid RANS/LESInterfaces for multidisciplinary coupling,e.g. FlowSimulatorContinuous verification & validation efforts

Applied in European aircraft industry,e.g. Airbus, Airbus D&S, Airbus Helicopter,RRD)Research platform for European universitiesand research organizations

Previous WorkTAU CFD Solver


Courtesy: Airbus D&S

Previous WorkCFD-CSM Coupling

Flight test of high lift configurationMa=0.204, Re=~25M, = 10°

• Trimmed aircraft• Nastran-in-the-loop

Symbols = Flight test data

Wing bending

Simulation

Simulation@Front Spar

Structural Model

CFD grid

Simulation@Rear Spar

Flap bending


Previous WorkCFD-FM Coupling

Air DropTime-consuming certification process of newairdrop systems or loads (costs/risks)Generic, blunt cargo bodies and parachutesAircraft wake characterized by strong vorticesRelative motion of cargo and parachute→ coupling with multi-body simulationExperimental validation in wind tunnel


Preliminary Aircraft DesignDevelopment of a system forpreliminary aircraft design, CPACS

Previous WorkAerodynamic/Multidisciplinary Design & Optimization

RANS-based Design & OptimizationGradient-free:

High-fidelity methods (aerodynamics & structure)Low number of parameter and load casesHigh manual setup time &computational costs


RANS-based Design & OptimizationGradient-based:

1st adjoint code bought from Prof. Jameson ca. 2000Flower adjoint developments (discr.)Development of adjoint in TAU (discr.) ca. 2004Laminar airfoils:Cp on upper side & CD min. @ CL=const





1st adjoint code bought from Prof. Jameson ca. 2000Flower adjoint developments (discr.)Development of adjoint in TAU (discr.) ca. 2004Laminar airfoils:Cp on upper side & CD min. @ CL=constBWB optimization, 110k param., Euler, 2011






1st adjoint code bought from Prof. Jameson ca. 2000Flower adjoint developments (discr.)Development of adjoint in TAU (discr.) ca. 2004Laminar airfoils:Cp on upper side & CD min. @ CL=constBWB optimization, 110k param., Euler, 2011Multipoint shape optimization, 75 param., 2011Aero-elastic adjoint (RANS), deformation includedStructural thickness constant



Long TermDevelopment of an integrated software platform for multidisciplinaryanalysis and optimization based on high-fidelity methods

Disciplines: aerodynamics, structures, flight mechanics & control

Development of a next generation CFD solver with innovative solutionalgorithms and high parallel performance on future HPC architectures

DLR Project Digital-XTowards Virtual Aircraft Design and Flight Testing

Short Term (2015)Realistic maneuver simulationsIntegrated aero/structural designDemonstration of new capabilities usingconfigurations relevant for industry & researchFirst release of next generation solver

DLR LamAir

Airbus XRF-1


Simulations of the Flight EnvelopeCoupling of relevant disciplinesMoving control surfacesEfficient prediction of static and dynamic loads

CFD SolverImproved modelling of physicsRobust and efficient algorithmsSoftware design of next generation solver (HPC)

Multi-Disciplinary Optimization Parametric representation of structureIdentification of critical loads for structural sizingPlanform and airfoil shape optimizationIntegrated process of preliminary and “detailed” design

Simulation EnvironmentIntegration of tools/methods into a single platformEfficient usage of HPC resources

DLR Project Digital-XObjectives 2015


DLR Project Digital-XContent and Partner

HAP 2CFD

Solver

Digital-X

HAP 3Reduced Order

Methods

HAP 4Multidisciplinary

Optimization

HAP 5Maneuver at Flight

Envelope Limits

HAP 6Numerical

Uncertainties

HAP 7HelicopterSimulation

HAP 8Automatic Processes

HAP 1Management

Partner / LinksAirbus Industrial boundary conditions Airbus research configuration

XRF-1 Test of methods/processes Research Center Jülich High Performance Computing

Project AeroStrukt (universities)

DLR Institutes Aerodynamics and Flow TechnologyAeroelasticityPropulsion TechnologyStructures and DesignComposite Structures and Adaptive SystemsFlight SystemsAir Transportation SystemsSystem Dynamics and ControlSimulation and Software Technology

Duration: 2/2012 – 12/2015Efforts: 120 PY


Improved Modelling of PhysicsReynolds stress models (RSM)

As standard RANS method for all configurationsScale resolving simulations (SRS)

Targeted application for specific components of aircraftTransition prediction and modeling

Necessary for accurate results of turbulence modelsTurbulence modeling improvements

Targeted experimental investigations

DLR Project Digital-XCFD Solver TAU

Improved Robustness and EfficiencyAgglomerated multigrid for complex applicationsImplicit algorithms (hierarchy of pre-cond.)

Integration of advanced turbulence modelsAdaptive local grid refinement

Best practice guideAdjoint solver for efficient sensitivity analysis


Future CFD

Current TAU not sufficiently suitedfor strong scaling on future HPC

Full exploitation of future HPC

Consolidation of current DLR CFD solvers

Basis for innovative algorithms & concepts,e.g. higher order finite element

Integration into multidisciplinary simulation

State-of-the-art software engineering methods,C++11, templates, multi-level parallelization

DLR Project Digital-XNext Generation Solver

Shared Memory &Communication Overlap


DLR Project Digital-XReduced Order Models

Motivation & ActivitiesHuge aero & aeroelasticity data required for aircraft loads analysis

RANS computations for all cases of the flight envelope is still not feasible

Development of parametric models for static & dynamic aero-loads prediction

Development of correction methods for aeroelastic applications


DLR Project Digital-XMDA – Gust Loads

Ma = 0.836 m = 150 to, Re = 77 x 106 , H = 8.2 km gust = 213.36 m, vgust = 10.52 m/s

ObjectiveAnalysis of gust load based on different methods and coupled disciplines

TasksCoupling of disciplines (CFD, CSM, FM)Integration of flight controlModelling / meshing of control surfacesIntegration into parallel simulation backbone FlowSimulator


Need for High-Fidelity SimulationsPotential for weight reduction?

ObjectiveMDO of full aircraft based on multi-fidelity

DLR Project Digital-XMDO

MethodLow fidelity methods for preliminary designFast methods for identification of critical load casesRANS methods for aerodynamics and structures for selected load casesParallel software platform for high-fidelityInteractive workflow managementDemonstration case: Airbus XRF-1


DLR Project Digital-XMDO – Implementation


1st Test of Implemented ProcessOptimize wing with min. fuel burn (cruise) Wing-body geometry, XRF-1 as baselineRANS for aerodynamics, FEM for structureFully-stressed design for structural thickness, 2.5g, -1g loads5 parameters: AR, sweep, twistTarget lift, wing area=const. (parameterization)

No explicit constraintsCruise-climb: 3 altitude segmentsComparison based on ODE integration & Breguet range eq.

DLR Project Digital-XMDO – Implementation


Project continues until end of 2015

Progress achieved on disciplinary level as well as in linking all aeronautical institutes of DLR

“Virtual Product” is one of DLR “Leading/Key Concepts”

Follow-on project/s planning process started

DLR Project Digital-XFuture


www.DLR.de • DLR-AS Chart 28

Professor Jamesonhas significantly influenced our research!

Thank you very much!

DLR Institute of Aerodynamics and Flow Technology

towards virtual aircraft design and testing based on...

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