Dr Jeff Tromp Air Vehicles Directorate AFRL/VA Air Force Research Laboratory

Air Vehicles Multidisciplinary Air Vehicles Multidisciplinary Technology Research & Capability Technology Research & Capability

Needs: A Top-down Air Force Needs: A Top-down Air Force Research Laboratory ForecastResearch Laboratory Forecast

6 September 20026 September 2002

Dr Jeff TrompAir Vehicles Directorate

AFRL/VA

Air Force Research Laboratory

WorkshopsWorkshops

• Multidisciplinary Technologies– 9th AIAA MAO Conference, 4-6 Sept, Atlanta GA

• Air Vehicles Controls Technologies– 2002 IEEE Conference on Decision & Control, 10-13 Dec, Las Vegas

NV

• Aeronautical Sciences Technologies– 41st Aerospace Sciences Meeting, 6-9 Jan 2003, Reno NV

• Air Vehicles Structures Technologies– 44th Structures, Structural Dynamics, and Materials Conference, 7-

10 April 2003, Norfolk VA

OutlineOutline

Introduction Dr. Jeff Tromp 1330 – 1335

Operator's View of AirMr. Dave Leggett 1335 – 1420Vehicles Future Technologies

Air Vehicles MDTResearch Needs Dr. Dave Moorhouse 1420 –

1450 Dr. Brian Sanders 1450 – 1520 Dr. Chris Pettit 1520 – 1550 Dr. Phil Beran 1550 – 1620

Group Discussion Dr. Dave Moorhouse 1620 – 1700

AFRL Air Vehicles DirectorateAFRL Air Vehicles Directorate

Center ofCenter ofMultidisciplinary TechnologiesMultidisciplinary Technologies

Current Research Tasks Current Research Tasks andand

Relation to the Air Vehicles Future Relation to the Air Vehicles Future Technology Workshop ConceptsTechnology Workshop Concepts

AIAA MA&O Conference, Sept 2002AIAA MA&O Conference, Sept 2002

Center of MD Technologies Center of MD Technologies

Purpose of the Workshop:

• Introduce the AFRL Center

• Show the MD Technical Challenges for Air Vehicles

• Summarize the Current MD Technology Center Research Tasks

• What is & is Not Being Done at Present

• Discuss Opportunities

• Answer Your Questions

Center of MD Technologies Center of MD Technologies Legacy ---> Stand Up ---> Vision

MDO 1999 FUTURE

Structuraloptimization

Servoelasticity

Aero/structuresoptimization

Aero/servoelastic control

New designapplications

Revolutionaryconcepts &innovative

optimizationalgorithms

TODAY

MultiDisciplinary Technology MultiDisciplinary Technology The ForecastThe Forecast

• Conceptual Analyses Will Need Higher Fidelity -- conceptual design with detailed analysis

• Non-Linear Effects May Start to Dominate Solutions -- full nonlinear design and analysis

• Technologies Will Have to be Assessed in the Context of the Complete System

• Analysis Tools Will Be Needed with High-Order Coupling Between Disciplines

ChallengesChallenges

• Physics based (non-historical) design

• Efficient computational tools to predict flight vehicle responses, mission performance, etc

• Quantification and mitigation of modeling uncertainties

• Integration of technical disciplines

• Acceptance of computational culture

• System-Level Optimization

The AFRL VA OperationThe AFRL VA Operation

•Robust Efficient Design•Synergistic Interactions•Energy-Based Design•System Optimization•Morphing Aircraft•Flight Experimentation

ComputationalModeling &Simulation

ModernControl

Concepts

Inter- & Multi- DisciplinaryTechnologies

•High-Order Physics•CFD & CEM•Nonlinear Aero-Structures and Aeroacoustics

•Transition & turbulence•Numerical Experiments

•Robust Design Methods•Reconfiguration Strategies•Adaptive/Intelligent Control •Tailless Aircraft Control •Man/machine Modeling•Uninhabited Vehicle Control

A Theoretical Basis for Innovative Fully-Integrated Vehicles

MDT Center MDT Center Vision, Payoffs and ApproachVision, Payoffs and Approach

VisionVision

Develop and validate comprehensive analysis, modeling and simulation, and design techniques for

complex engineering systems

PayoffPayoff

Reduce cost and acquisition time of weapon systemsReduce developmental risk thru increased fidelity in design process Enable invention in aerospace vehicle

concepts

Enable revolutionary aerospace vehicle design and innovation

through multidisciplinary technology integration

ApproachApproach

Research Focus TasksResearch Focus TasksMD Center 2002MD Center 2002

Efficient Design & Analysis Tools

Uncertainty Quantification

Physics-based modeling tools and processes for design, analysis, and increased analytical certification of aerospace vehicles (current focus - reduced order methods for aeroelastic analysis)

Rules and tools for understanding variability in system properties and operating environment on air vehicle response (current focus - uncertainties in structural response)

Methodology for system-level design using exergy as common currency (current focus – system-level framework for multidisciplinary design of subsystems with computation of entropy generation rate)

Incr

easi

ng R

isk

Methodologies to support design and invention of adaptive structures for air vehicles (current focus – structural design with integration of mechanization and actuation)

Morphing Aircraft Structures

Energy Based Design

Center of MD Technologies Center of MD Technologies F T W Technical Challenges

High Altitude Long Endurance UAV:• High aspect ratio, low drag aerodynamics• Integrated structural sensor integrity, durability, damage

tolerance increased • 360-degree aperture integration ~~ joined wing

aero/structures

Unattended Battlespace Sensors:• Lightweight low-cost airframe • Energy management• Intelligent vehicles ~~ morphing aircraft ??

Space Operations Vehicles:• Hot integrated structures and reusable cryogenic tanks

Center of MD Technologies Center of MD Technologies F T W Technical Challenges

Long Range Strike Aircraft:•Reduced structural mass fraction, aeroelastic control

Directed Energy Tactical Aircraft: •Control of vehicle structural vibrations and acoustics

Strategic Airlifters:•Design Integration•Unconventional structures

Exergy- Based Methods for Exergy- Based Methods for Design of Aerospace Vehicles Design of Aerospace Vehicles

David J MoorhouseDavid J Moorhouse

[email protected]@wpafb.af.mil

Why Exergy-Based Methods ???Fully-Integrated Aircraft Design

Technology Challenges:• Accurate prediction/design tools• Plasma generation at reasonable

energy levels• Control of plasma fields• Flight weight/small volume

magnetic systems• Integration of airframe &

propulsion • Energy extraction/power

distribution• Energy conservation only

1st Law Principles • Exergy/entropy for design &

analysis of entire vehicle 2nd Law Principles

• Exergy equals available work from an energy source

Technology Payoffs:• Economical high speed• Significantly lower structure temp• More efficient combustion• Innovative control• Extended aircraft range

CFS3/29/00-19/27

Design Integration Framework: • Vehicle design requirements specified as an energy system

• Mission is work to be done by the exergy available from the fuel

• Every system is a component in minimizing the exergy consumed

• Provide the necessary understanding to allow decomposition into appropriate energy systems together with appropriate interactions

Exergy-Based Design MethodsExergy-Based Design MethodsCurrent 6.1 TaskCurrent 6.1 Task

Exergy-Based Framework to Facilitate the Design & System Optimization of Efficient Systems

System Level Exergy MethodsSystem Level Exergy Methods

Define specific energy as total energy per unit weight:

Then at each point in the mission:

customer work, which is a requirement.

overhead work, which should be minimized !

And the system equation is that the Exergy of the fuel burned mustequal the customer + overhead work done through the mission:

H is energy content of the fuel/weight, is overall efficiency, dW/dt < 0.

221 VhE g

Pdt

dEW

dt

dwp

c

DVdt

dEW

dt

dwo

dt

dw

dt

dw

dt

dWH oc

Design Mission Stated in Terms of Work to be DoneHow precise does this need to be ?

Comes From the Exergy of the Fuel Consumed Propulsion System Converts Fuel Into: Mission Work, Including Power to Drive Mission Equipment Mission Overhead - Overcome Vehicle Drag - Power for Other Subsystems - Power to Lift Itself and Required Fuel Waste due to Inefficiencies in Operation & Thermal Performance

Aerospace Vehicle DesignAerospace Vehicle DesignExergy as a System-Level MetricExergy as a System-Level Metric

It has been shown that an explicit calculation of the entropyin the wake yields a different solution for the lift distribution that provides minimum induced drag {depends on assumptions}. A more advanced method for computing the entropy generated in the vehicle flow field is a necessary part of the design process.

Flow Field Computation of Entropy Generation Rate:

• Develop theoretical framework for Exergy Analysis.

• Implement analysis capability into CFD computer program.

Exergy-Based Design Methods Exergy-Based Design Methods Current 6.1 TaskCurrent 6.1 Task

Developed the Computational Methods to Compute the Flow Characteristics of Energy Systems

Objective

• Develop the theoretical framework for calculating the entropy generation rate, entropy-based residuals, and entropy-based numerical metrics.

• Implement exergy analysis capability into the Unstructured Euler/Navier-Stokes Flow Solver Cobalt-60.

• Validate computational capability by computing the induced drag on selected airplane wing plan-forms, using both classical and exergy methods.

Implementing Exergy Analysis Implementing Exergy Analysis Capability into Capability into Cobalt Cobalt CFD SolverCFD Solver

Accomplishments

• Formalized entropy and entropy generation formula appropriate for Euler/Navier-Stokes Equations.

• Developed Entropy/2nd Law-Based Residuals and numerical metrics (exergy) appropriate for Euler/Navier-Stokes.

• Implemented Computational Algorithm in the Code.

• Tested Computational Capability with Boundary-Layer and Shock Jump Comparisons.

Exergy/Entropy AnalysisExergy/Entropy Analysiswith with Cobalt Cobalt CFD SolverCFD Solver

FY02 FY03 FY04

Exergy framework for any vehicle as a system of energy systems

Needed: Control, Scaling Laws and Optimization Methods for Integrated Energy-Based Vehicles

systemintegration

computationComputation of entropygenerated in wake. Liftdistribution for min. drag

Computation of entropy generated by structural shapes

Computation of unsteadygeneration of wake entropydue to use of adaptive structureson a vehicle concept

structuresDefinition of adaptivestructure as an energy system

Optimized design of anadaptive structure

Exergy-Based Design MethodsExergy-Based Design Methods The Short RoadmapThe Short Roadmap RangeRange

Uncertainty Analysis & Reduced-Order Modelling

CurrentAFOSR task

Center of MD Technologies Center of MD Technologies Exergy-Based Design MethodsExergy-Based Design Methods

Integrating Concept Technical Challenges/EXERGY

• High aspect ratio, low drag aerodynamics• Integrated structural sensor integrity, durability, damage tolerance

increased 4X • Lightweight low-cost airframe • Energy management ~~~ in general• Intelligent vehicles - Ranges from collaborative “swarm” control techniques

to near-sentient individual and teaming capabilities • Hot integrated structures and reusable cryogenic tanks ~~~ cooling heat

exchangers?• Reduced structural mass fraction, aeroelastic control • Control of vehicle structural vibrations and acoustics • Design Integration ~~~ for unconventional vehicles• Unconventional structures

This Task May be Too Long Term


Morphing Aircraft Structures Morphing Aircraft Structures

MULTIFUNCTIONAL & MULTIFUNCTIONAL & ADAPTIVEADAPTIVE STRUCTURES TEAM STRUCTURES TEAM(MAST)(MAST)

AFRLBowman, Forster, Garner, Joo, Keihl, Reich, Sanders, Cannon (VACC)

External CollaboratorsWashington, Ohio State UniversityWeisshaar, PurdueMurray, University of DaytonInman, VPI

OutlineOutline

• Relationship to VA Goals

• Challenges

• Adaptive Structure Design

Relationship to VA Goals Relationship to VA Goals

Adaptive Structures Application to UAV’s and SOV’s:

Flow managementThermal load managementPointing devicesStealth

•Adaptive structures required for design of sensorcraft and multimission vehicles

•Multimission capability emphasized in VA workshop

Variable Geometry Wings

DARPADARPA Morphing Aircraft StructuresMorphing Aircraft Structures

Fuselage & Propulsion System

• Aircraft are currently designed around specific missions

• Can we develop aircraft capable of multiple missions?

e.g., reconnaissance air vehicles transform into effective ground attack vehicles

- dihedral- wing - wing planform

From fixed platforms to commanded, time variant, variable geometry, load-bearing structures

- sweep- aspect ratio- twist

First challenge: Morph the wing

The ChallengeThe Challenge A Multidisciplinary Design Task

Design of an structurally integrated adaptive wing from an energy formulation

Mechanism Design

Actuator Integration

Structural Design

Control Laws

B

A

xx C

G

Power Electronics

Actuator+

Systemcontrol (input)

signalamplifier /

power conditioner

EnergySource

low powerhigher power

+

-V

XF=0

+

++

+

+

fi

ri

d1i d3

i d2i

A

B

C

F

H

G

Utr

i

fi

D

Ui

E

Adaptive Structure DesignAdaptive Structure Design

Approach

• Develop a theoretical framework to identify energy flow inside of the body (input energy, transferred energy, stored energy and etc.) for efficiency calculation

• Exergy-based framework to facilitate the design & system optimization of efficient systems

Actual shape change

Aerodynamic force

Desired shape change

Actuation force

Body

Total input energy = stored energy + transferred energy

energyInput

energyUsefulloaded

Mission Identification &

Vehicle Configuration

Energy Based Design

Structural Design & Integration

Our ApproachOur Approach

9 5 -2 0

KU B AND ANT AN/AR A-63

KA B AND ANT

EC M LO/HI B AND R EC IEVE FWD ANT ALQ-165

LOW B AND EC M FWD TR ANSM IT ANT ALQ-165 LH&R H SINGLE PLAC E

GPS

WING M ISSLE ILLUM INATOR UNDER SIDE L/RIFF/UHF/VHF C OM M ANT

AR C -210 HAVE QUIC K

X-B AND ANT APH-202

ADF ANT

TAC TS ANT

EC M FWD LO-B AND TR ANSM IT ANT ALQ-165

UHF/TAC AN DATA LINK AR C -210 HAVE QUIC K

M SL ILLUM ANT

WDL AFT

RWR ANT ALR -67

EC M LO B AND TR ANSM IT ANT ALQ-165

EC M HI B AND TR ANSM IT ANT ALQ-165

FLTSATC OM

LOW B AND SEAD HIGH B AND SEAD

RWR ANT ALR -67

EC M FWD HI-B AND ANT ALQ-165

IFF INT WDL FWD

EC M HI-B AND TR ANSM IT ANT LH & R H ALQ-165

RWR LOW B AND ANT ALR -67

TAC AN ANT

UHF/VHF DATA LINK/IFF ANT AR C -216 HAVE QUIC K

R DR ALTM ANT APN-194

# 1( T OP)

# 6, 7( RE AR T OP)

# 3( B OT T O M)

# 2( BOT T O M)

#4, 5( L EF T/ RI GHT)

# 8( T OP WI N G)

C onventional antenna installation schem es com prom ise structural and antenna perform ance

• N on-loadbearing cavity installations require support structure

• A dded cost and w eight• B lade antennas not acceptable for LO vehicles • A ntenna size and installation locations com prom ised

C LA S T echnology B enefits• Single function antennas replaced by large

m ultifunction antennas

• G row th Potential - A void C ostly R etrofits

• 75% R eduction in Structural C ut-O uts

• $0.5 - 3 .3 M C ost Savings Per A ircraft

• 260 - 1000 Lbs. W eight Savings Per A ircraft

• E nhanced A vionic Voice and E W C om m

• Im proved Low O bservable Perform ance

• Supportability and D rag R eduction

C onventional single function antenna suite C L A S m ultifunction antenna suite

C onform al L oadbearing A ntenna Structures (C L A S)

Efficiency of Mechanisms IIIEfficiency of Mechanisms III

• Efficiency with external load (variable force)

d3i

d3o

fi

II

kd1o

V

ri

fi

III

ro

d1i

kd1o

d1o

IV

d5i

d5o

fi

I

kd1o

Efficiency IIIEfficiency III

• Stored energy inside of body

• Total energy

• Loaded efficiency

ri

D

C

B

E

fi

d4i

d1i d2

i

A

Utri

Ui

kd1o

Dkd1

o

C

B

E

ro

d4o

d1o d2

oA

Utro

Uo

Uacto (AFG)

F

G

Input port

Output port

BDEEDBCBAABC

loadexternalbyeStoredactuationbyeStoredU stored

..

oi

total

UU

EDBABC

BDECBA

BDEEDBCBAABC

energyTransferedenergyStoredU

ABC

GFAU

U

energyInput

workUseful

i

oact

loaded

What is the Right Shape?What is the Right Shape?

0 100 200 300 400 500 600 700 8000

2

4

6

8

10

ConventionalConformal

75%

q (psf)

25%

50%

P(deg/sec)

Frame 001 ½ 04 Dec 2001 ½Frame 001 ½ 04 Dec 2001 ½

25%

10%

x/c0.25 0.5 0.75 1

0

0.5

1

1.5

2

2.5

3

3.5

4

CP

Articulated Surface

Conformal Surface - 2nd Order

Conformal Surface - 4th OrderConformal Surface - 3rd Order

Frame 001 ½ 17 Feb 2002 ½ Conformal Control SurfacesFrame 001 ½ 17 Feb 2002 ½ Conformal Control Surfaces

Sanders, Eastep,& Forster, J of Aircraft, 2002 Henderson, Weisshaar & Sanders, AIAA 2001-1428

What is the minimum energy input?What is the minimum energy input?

Morphing AirfoilsMorphing Airfoils

Contributions from MD Contributions from MD CommunityCommunity

• Development of methodologies for diverse technology systems


Uncertainty Quantification (UQ) Uncertainty Quantification (UQ)

Chris L. Pettit, Ph.D., P.E. Chris L. Pettit, Ph.D., P.E.

Terminology *Terminology *

• Uncertainty: A potential deficiency in any phase or activity of the modeling process that results from lack of knowledge

• Error: A recognizable deficiency in any phase or activity of the modeling process that is not due to lack of knowledge

• Sensitivity Analysis: Multiple simulations to determine the effect of varying some input parameter or model assumption

• Uncertainty Analysis: Like sensitivity analysis, but explicitly includes likely range of variability, interaction between sources of uncertainty, and levels of confidence associated with ranges of input variability

* AIAA Guide for Verification and Validation of Computational Fluid Dynamics Simulations

ObjectivesObjectives

• Enable more efficient and robust implementation of innovative concepts and technologies

• Support the Air Force goal of reducing life-cycle costs by increasing reliance on analysis in the design and certification of aircraft structures

Develop and demonstrate methods for validating physics-based models designed to mimic stochastic response variability, especially in nonlinear systems

Formulate guidelines for constructing minimally-complex analytical models that capture variability in system properties

» Predict response variability! Develop and refine uncertainty quantification (UQ) methods,

and demonstrate their applicability to the design of robust systems of Air Force relevance

Support development of a UQ-informed certification framework

Develop and demonstrate uncertainty quantification (UQ) methods to quantify and improve the robustness of computational models in multidisciplinary design

Projected Long-Term ImpactsProjected Long-Term Impacts

• Risk quantification for performance and certification metrics– Rational basis for making decisions– Cost-effective risk mitigation depends on risk quantification

… we can’t know how far to go if we don’t know where we are

• Fewer test failures and redesigns– More efficient RDT&E program– Certification cost savings

• Robust designs with fewer operational problems– O&S savings– Better models to facilitate future expansion of system

capabilities

• Capability enhancement– Pervasive UQ expected to enhance robust implementation of

innovative design concepts» Sensorcraft» Multifunctional structures Make certification robust and lean

Current UQ EffortsCurrent UQ Efforts

Span of UQ In-House ActivitiesSpan of UQ In-House Activities

UQ AreaLab Task: Bolted Joint UQ

Lab Task: Bonded Joint UQ Panel LCO

Reliability-Based Design

Proposed Lab Task: UQ for Nonlinear Aerospace

Physics (except BC's)AlgorithmDiscretizationBC's and/or IC'sLoadsMaterial PropertiesStatisticsDesign ProcessesDesign CertificationCostHuman Variability and Error

Color Meaning Should address if time and resources were available Partially or indirectly addressed Directly addressed

AFOSR Lab Task: AFOSR Lab Task: Quantifying Quantifying Uncertainty in Structural ResponseUncertainty in Structural Response

• Research Objectives– Isolate and quantify specific elements of model and property

uncertainty to define their contribution to errors and variability in response prediction

» Focus on poorly-modeled (e.g. BC’s and joints) or often ignored factors (e.g., damping)

– Demonstrate validation of structural component models through reproduction of response variability

– Develop guidelines for modeling BC and material uncertainties in design-level models

k, c

kw, cw

m(x), c(x), E(x)

p(x,t)

Stochastic FEM

Non-ideal BCs

Random Vibrations

Sub-TasksSub-Tasks

• Experimental and Analytical Study of Uncertainty in Bolted Joints

• Energy dissipation in mechanical joints• Sensitivity to parametric and epistemic uncertainty• Suggest minimum-complexity modeling for design analyses• Validation vs. calibration

• Uncertainty in Strength of Composite Bonded Joints• Define and prioritize sources of uncertainty in joint strength• Develop and validate physics-based models• Provide guidance to experimentalists to ensure future

studies provide sufficient data to support UQ

• Limit-Cycle Oscillations of Uncertain Panels• Role of system variability (e.g., constitutive properties and

boundary conditions) in the long term response of a nonlinear aeroelastic system

Limit-Cycle Oscillation of Uncertain Limit-Cycle Oscillation of Uncertain PanelsPanels

Young’s modulus modeled as a random field

Monte Carlo Simulation

Nonlinear Isotropic Plate

Property variability impacts character and severity of response

Intersection of FTW Challenges Intersection of FTW Challenges and UQ Researchand UQ Research

{FTW Challenges} {FTW Challenges} {UQ} {UQ}

• Organized by FTW-identified vehicle concepts

• Not addressing UQ for identified technical challenges in control or information processing systems unless they influence airframe questions (e.g., aeroservoelasticity)


HALE/ISR• Substantial increases in durability and damage

tolerance

• Robust implementation of low drag through loiter

• High temperature engine materials

• Accelerated introduction of new materialsRecce/Strike UAV’s • Proactive/predictive health management

• Low-cost composites manufacturing

• Reliable bonded joints in composite structures

• High-accuracy autonomous warheads

In General …• Lightweight, low-cost everything


Space Operations Vehicles• Real-time, integrated health management

• Sensors and NDE

• Durable, damage tolerant TPS, structures, propulsion

• Hot integrated structures and reusable cryogenic tanks

• Manufacturability and producibility

Long-Range Strike• Reduced structural mass fraction

• Aeroelastic control (AAW?)

• Supersonic weapons carriage and release

• Proactive/predictive health management

• High T supportable (???) LO materials and composites insertion


Directed Energy Tactical

• Modeling and simulation

• Effects testing

• Thermal management

• Hardening flight-critical hardware to EMI

• Stealthy, conformable RF transparent structural apertures

• Control of vehicle vibrations/acoustics

• Random eigenvalue problem???

• Beam propagation through near-field flow (boundary layer?)


What UQ-related issues are missing from the FTW-identified challenges???

• How to design (optimize) integrated health management systems? Must balance weight, system complexity, probability of detecting damage (e.g., number of sensors and their spatial density), cost, survivability of IVHM system, etc.

• Mission- or system-specific risk requirements and risk-based certification

• Manned vs. unmanned? Allocating risk in complex systems? Decision theory?

Strategic Airlifters

• Vehicle design integration

• UQ-based design?

• Survivable high-lift systems

• Unconventional structures

• QRA to compare with conventional design concepts?

• Durable LO Structures

Design Efficient Analysis MethodsDesign Efficient Analysis MethodsPhilip S. Beran, Ph.D.Philip S. Beran, Ph.D.

Principal Research Aerospace EngineerPrincipal Research Aerospace Engineer

Multidiscplinary Technologies CenterMultidiscplinary Technologies Center

[email protected]@wpafb.af.mil

99thth AIAA/ISSMO MA&O Symposium, Sept 2002 AIAA/ISSMO MA&O Symposium, Sept 2002

Mission StatementMission Statement

Develop and validate new computational Develop and validate new computational methods for the design and analysis of methods for the design and analysis of

revolutionary air vehicle conceptsrevolutionary air vehicle concepts

The Challenges of Design Efficient, The Challenges of Design Efficient, Multidisciplinary AnalysisMultidisciplinary Analysis

Murray didn't feel the first pangs of real panicuntil he pulled the emergency cord.

• Nonlinear physics– Steady and unsteady

• Large dimensionality of discrete, PDE-based models

– Time-domain approach not an advantage

• Large parameter spaces

• Communication between models: complex and iterative

– Frameworks– Interpolation

• Sensitivity computation

• People

ApproachApproach

• Focus on aeroelastic interactions, with longer term goal of aerothermoelastic interactions

– Maintain a general framework while studying interaction physics

• Explore techniques for lowering system order, suitable for integration with current high-fidelity, physics-based methodologies

– Focus on proper orthogonal decomposition but examine other methods

• Study the physical phenomenon of store-induced limit-cycle oscillation (LCO): understand mechanisms and required physics

• Merge reduced order modeling work with limit-cycle analysis (transonic regime)

– Develop fast methodology for evaluating bifurcation structure/location– Cast analysis in form suitable for structural optimization with dynamical

stability constraint

Develop a methodology to determine rapidly the linear and nonlinear (aeroelastic) stability of large, multidisciplinary

systems for application to design

Established LinkagesEstablished Linkages

Design Efficient Analysis Methods

AFOSR (6.1)Computational Nonlinear

Aeroelasticity for MD Analysis and Design of Flexible Air Vehicles

Collaborations with Government

and Academia

• Dr. F. Eastep (NRC/UD) • Drs. K. & U. Ghia (UC)• Dr. J. Scott (OSU) • Dr. T. Strganac (IPA/TA&M)• Dr. W. Silva (NASA LaRC) • Drs. Thornburg & Soni (MSU/UAB)• Drs. Cornelius & Slater, Mr. Anderson (WSU)• Dr. King & Maj Millman (Air Force Institute of Tech.)

Existing

Lab Task

Computational Algorithms for Quantification of Uncertainties in Nonlinear Aerospace Systems

Proposed Lab Task w/Pettit

(UQ)

• Dr. Grandhi (WSU; AFOSR-Funded Collaborative Task)

Order Reduction with Proper Order Reduction with Proper Orthogonal Decomposition (POD)Orthogonal Decomposition (POD)

Full-Order Analysis Sample System Physics(Snapshots, S)

POD is used to Identify Modes

Project Equationsto Compute

Modal Amplitudes

Expand to EstimateFull-Order Solution

STS

Solve ReducedOrder Problem

Research

General framework for reduced order modeling of large systems of nonlinear, discrete equations: modal integrity (phase 1);

shocks (phase 2); complete projections (phase 3)

HF AeroelasticSimulationProgram

HF AeroelasticSimulationProgram

New POD ToolRAPOD

New POD ToolRAPOD

EvaluateF(wf,ws)

Snapshots

POD analysis

New Framework

Introduce new physics1 2 3

4 5 6

Design Analysis FunctionsDesign Analysis Functions

• First-order discrete form– dw/dt = F(w;)

– Free parameter,

• Compute POD modes, – w = w0 + r

– dr/dt = T F(w0+r ; )

– Jacobian: J d(T F)/dr

• Analysis functions– Numerical evaluation of J

– Static analysis: T F = 0

– Bifurcation analysis: static or Hopf

– Implicit time integration (2nd-order predictor-corrector)

– Sensitivity derivatives

fluid-fluid sensitivity

fluid-structure sensitivity

structure-structure sensitivity

structure-fluid

sensitivity

J() =

Jacobian Rank: Full=O(106), Reduced=O(101)

Im(J-Eigenvalue)

Hopf bifurcation:conjugate pair

Static bifurcation Increasing

Re(J-Eigenvalue)

Functional analysis approach to nonlinear reduced order equations

Modal Integrity: Panel LCO Modal Integrity: Panel LCO Response (Mach 1.2)Response (Mach 1.2)

Nondimensional Dynamic Pressure,

Pea

kP

anel

Def

lect

ion

,wd/h

(3/4

Ch

ord

)

10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Beran and Pettit (2001)Gordnier and Visbal (2000)Full Order: Current10-Mode ROM: LCO10-Mode ROM: Bifurcation

Time, t

Pan

elD

efle

ctio

n,w

d/h

(3/4

Chord

)

0 100 200 300 400-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

10-Mode ROMFull

ROMTraining

Euler Equations

CFD Domain: 50L x 25L 141 x 116 grid points (stretched) 65K DOFs

Von Karman Equation

Deformed Panel: Length L Modal formulation or finite difference 53 grid points

U

• Train ROM at = 25 (nondim. dynamic pressure)

• 10 modes determined from short training cycle

• Full-order: 3 CPU hours to compute LCO

• Reduced order (beyond training time):

– 3+ orders of magnitude DOF reduction

– 3 minutes (bifurcation point): compute J()

– 45 minutes (synchronized, implicit time integration with large time steps)

Shocks and Efficient ProjectionsShocks and Efficient Projections

POD

Full/POD Shock

Domain decompositioninvolving 6-fold order reduction

Continuity constraints

• Project continuum equations (Galerkin): damping?

• Project discrete equations: subspace extension

– [T F(w0+r)]i Aij rj + Bijk rj rk + Cijkl rj rk rl

• POD/Volterra synthesis: nonlinearity

– Full (310 sec); subspace projection (78 sec); POD/Volterra (0.08 sec)

Pressure Response to (t)

Full-Order Analysis of Store-Induced Full-Order Analysis of Store-Induced Limit-Cycle Oscillation (LCO)Limit-Cycle Oscillation (LCO)

I II

III IV

y

x

z

4 ft

20 ft

1/3 ft

Structural Grid Point

x ls

ycoff

U

cs

ea

lw

cw

Adjust streamwise position of store CM

to achieve LCO• Simple configuration

(rectangular/parabolic-arc)• Transonic small-disturbance

theory (TSDT) with and without interactive boundary layer modeling (CAPTSDv)

• Store position: NASTRAN• Validation with Euler equations• Flutter and LCO solutions

SI-LCO: Flutter and LCOSI-LCO: Flutter and LCO

Mach

Vel

oci

ty(F

t/S

ec)

0.65 0.7 0.75 0.8 0.85 0.9 0.95300

350

400

450

500

550

600

650

700

750

800

850

900 Clean Wing (CAPTSDv)Clean Wing (NASTRAN)Wing + Store (CAPTSDv)Wing + Store (NASTRAN)LCO: Wing + Store (CAPTSDv)

LCO

Time, t

CL

0 25 50-0.3

-0.2

-0.1

0

0.1

0.2

0.3

• Mach/velocity not matched• Higher flutter speeds with

store prior to LCO• LCO over restricted Mach

range at much lower speeds than flutter

• LCO response dominated by modes 1 and 2

SI-LCO: LCO Response SI-LCO: LCO Response

Flutter: Mach 0.84

U = 750 ft/sec

Mode 1 (1.90 Hz)

Flutter: Mach 0.90

U = 850 ft/sec

Modes 3,4 (9.52 Hz)

LCO: Mach 0.92

U = 410 ft/sec

Modes 1,2 (2.92 Hz)

• LCO: Unsteady surface mesh • Mach 0.92 and U = 410 ft/sec• Similar deflection behavior reported by

Pitt and Yurkovich (Boeing experiment – 1991): Coupling of modes 1 & 2

Aeroelastic Analysis of High-Aeroelastic Analysis of High-Altitude Long-Endurance UAVAltitude Long-Endurance UAV

MDICE

FASTRANENS3DAECOBALTCAPTSD

.

.

FEMSTRESSNASTRAN

ANSYS

.

.

6-DOF

RAPOD (ROM)

CONTROLS

MESH

DEFORMATION

USER Fcns

Fluid-

Structure

Interpolation

Overset Tools

FLOW STRUCTURE DYNAMICS OTHER

GUI

DAGSI/AFRL Investigation Nonlinear Aeroelastic Analysis of the SensorCraft

Joined-Wing ConfigurationK. & U. Ghia (UC), Scott (OSU), Thornburg (MSU), Huttsell (AFRL/PM), Beran (Co-PI)

Slice Locations on the Joined-Wing

Negative Cp vs. normalized chord at the join (V-section)

Design Efficient Analysis MethodsDesign Efficient Analysis MethodsNear-Term RoadmapNear-Term Roadmap

CY02 CY03 CY04

Analysis of store-induced LCO

Needed: Physics-based methods for large-amplitude AE oscillations and aerothermoelastic interactions

LCO

Reduced Order Modeling

Efficient ROMs for 2-D AE systems in viscous flow

ROM techniques for UQ of 2-D AE systems

Application of ROM to EBD

Aerothermoelastic analysis of 2-D scramjet configuration (TBD)

Efficient ROMs for 3-D AE systems in viscous flow: SensorCraft

Optimization of structural sizes for 3-D AE system: nonlinear stability constraints

CY05

AerothermoelasticHeated panel: PT w/ROM

Design

Reflection on Integrating Reflection on Integrating Concept Technical ChallengesConcept Technical Challenges

• HALE UAVs: High-aspect-ratio, low-drag aerodynamics– Fluid/Structure interaction physics for high AR and joined-wing configurations

» Moderate structural deflections and potentially separated flows» Minimized weight to enhance endurance» Robustness?

– Go beyond static problem and examine the dynamic problem– Design integration?

• Battlefield Sensors: Intelligent vehicles– Potentially large structural deformations and shape changes– Potentially nonlinear, separated, low-Re flows– How to build nonlinear ASE models?– Design integration?

• Space Operations Vehicles: Damage tolerant structures– Physics of aerothermoelastic interactions

• Long-Range Strike: Reduced structural mass fraction– Structural optimization accounting for shock/viscous effects (Mach 5?)

Workshop SummaryWorkshop Summary

Center of MD TechnologiesCenter of MD TechnologiesWorkshop SummaryWorkshop Summary

• Brief Discussion of Technical Challenges -- high level Air Force needs -- interpreted for MD issues

• Summary of Current Research Tasks -- not put together for the challenges -- work in progress

• What is Next?

Center of MD TechnologiesCenter of MD TechnologiesWorkshop SummaryWorkshop Summary

QUESTIONS:

What is the Science in Design Integration ??

How should quantitative risk analysis be employed in design and certification?

How to Include EVERYTHING in System-Level Optimization ? -- Connections between all disciplines

How can the MD community contribute to the development of current Air Force goals ???

Backup SlidesBackup Slides

Why Do UQ for Aircraft Why Do UQ for Aircraft Structures?Structures?

• Air Vehicles Directorate of AFRL needs to understand the proper role for UQ in airframe design and certification– Philosophy: Certification should be a process of managing risk from conception

to retirement– Risk management is difficult or haphazard when the risks are not adequately

identified and quantified» Safety factors account for uncertainties indiscriminately» We need to scrutinize all stages of conceptual, preliminary, and detailed

design» Need a closer relationship between testing and model validation

• Validation should include mean behavior and its variability as much as possible. Otherwise, it’s just calibration or tuning.

• Traditional methods and historical databases can be inadequate for unique structural concepts, extreme environments, and new materials– Many new structural technologies and concepts being developed, but little or

no usage experience– How can we rationally assess the risk and return-on-investment for new

technologies?

Why Do UQ for Aircraft Why Do UQ for Aircraft Structures? (cont.)Structures? (cont.)

• Lag between development and confident use of new materials, connection methods (e.g., bonded joints), and inspection methods is an expensive bottleneck

• Lack of predictive capability in design leads to test failures, missed performance goals, and expensive certification processes– Inadequate and poorly validated analytical models of aircraft structures

and their operating environments– No rigorous means to evaluate confidence in computational predictions– Safety factors hide the sources of uncertainty and error

• As in many other engineering fields, UQ for airframes appears to be the best bet for tackling these tough issues

Uncertainty Quantification (UQ)Uncertainty Quantification (UQ)

Technical Challenge

Lack of predictive capability in design leads to test failures, missed performance goals, and expensive certification processes

Goal

Promote better decisions through increased confidence in model-based predictions by providing methods to quantify variability and validate physics-based models of aerospace structures

Long-Term Impact• Certification cost savings• Robust designs with fewer operational problems• Risk quantification for certification metrics

Material Properties- manufacturing variability- statistical uncertainty

Physics Model- linear vs. nonlinear- constitutive relations

Substructure Boundary Conditions- joint flexibility

Heterogeneous Environment- gust loads- exhaust-washed structures- survivability

Current Status of Sub-Task 2 Current Status of Sub-Task 2 (Bonded Joints)(Bonded Joints)

• Participants identified– Dr. Steve Clay (AFRL/VASD)– Dr. Roger Ghanem (JHU)

• Gathering existing data to support probabilistic modeling of material and adhesive properties

– Facilitated through Dr. Clay’s participation in Composites Affordability Initiative (CAI)

• Initial model will be of beam on nonlinear elastic foundation with uncertain constitutive properties

– Shear and bending– Simpler problems first … more realism

later (e.g., double lap joint in tension)

“Pi” bonded joint

Proper Orthogonal Decomposition of Proper Orthogonal Decomposition of Young’s Modulus Field – Mode 2 Young’s Modulus Field – Mode 2

/ M 860; 47x47 grid; COV = 0.01; CL/x = 4.8

Non-Ideal Boundary FixityNon-Ideal Boundary Fixity

• Boundary not perfectly clamped– 0.85 1

• At / M = 850– No LCO in the deterministic

case– Softening the boundary

slightly induces LCO

Current Issues in Uncertainty Current Issues in Uncertainty Quantification for AirframesQuantification for Airframes

Technical Challenges and NeedsTechnical Challenges and Needs

• Deterministic models are not “done” yet, especially for multidisciplinary and nonlinear systems– Physics of extreme environments and multi-scale phenomena

» e.g., epistemic uncertainties in corrosion, damage, and aerothermoacoustic loads

– Execution of high fidelity models is often still prohibitively slow for UQ– Models take a long time to develop and debug

» Nonlinear, multidisciplinary simulations are immature and not robust• UQ applications can amplify these shortcomings, but might also

reveal hidden problems– Modeling of often ignored or idealized features is perhaps a relatively

bigger problem now than in the past» Many of the “algorithm” and “discretization” difficulties have been

resolved» UQ demands better understanding of model shortcomings

• e.g., effects of joints in energy dissipation of built-up structures» Need to understand inputs better!

• e.g., damping, BC’s and IC’s, uncertain environments (e.g., corrosion)

UQ requires good physics models and robust algorithms

Technical Challenges and Technical Challenges and Needs (cont.)Needs (cont.)

• Model Validation and Model Uncertainty– Separation of measurement uncertainty from property variability between

samples– How many measurements and tests are “enough”?

» Better use of modeling to plan test and measurement regimen• Unique issues for aircraft structures due to intermediate size of

production runs– Sounds like a good applications for Bayesian updating!

» Information frameworks for integrating analyses and test data• How do we extract and combine the information we really need?

» Coping with limited data of poor resolution• Getting sufficient information for minimal investment

» Ultimate question: How much confidence do we require in our predictions? How much risk is acceptable? This is not just a technical problem.

– Developing modeling guidelines based on UQ needs in addition to deterministic considerations» Should promote early recognition, characterization, and prioritization

of UQ sources– SAB recommended more formal UQ integration with current activities!

Technical Challenges and Technical Challenges and Needs (cont.)Needs (cont.)

• Life Prediction– Loads, nonlinear response, fatigue, corrosion

» SAB recommended more VA work in structural reliability analysis for life prediction!

– Models and health monitoring data for remaining life of existing structures» Direct impact on sustainment research in VA» What level of data and integrated health monitoring is required if

validated analytical models are available?• How to optimize design of health monitoring system to robustly

detect changes in the system’s health?» Load-path dependence for time-dependent reliability assessment

– “Robust” usage projections (mission analyses)» Operational needs often change during design’s lifetime. Can we use

UQ to estimate the required room for growth? A role for non-probabilistic methods?

• UQ for CFD– Model validation– Better understanding of turbulence-induced loads

UQ Transition Challenges and UQ Transition Challenges and NeedsNeeds

• How should risk-informed certification of airframes be done?

• If rigorous UQ is to supersede safety factors, it must produce meaningful and intuitive information

– Technical and managerial uses of information– “Acceptable risk” as a basis for design requirements

» Who should establish them? What are the consequences of being wrong?

• Educating and convincing management, industry, certification officials, and other engineers to accept an “uncertain” approach

– Most aerospace engineers lack formal risk analysis training!– The current design and certification philosophy:

» Enforces safety indiscriminately» Hides the sources of uncertainty» Can provide a false sense of security

• Only get qualitative indications of system’s robustness• Tests cannot cover many operational conditions. We need validated

analytical models to demonstrate safety in non-test conditions!

UQ Transition Challenges and UQ Transition Challenges and Needs (cont.)Needs (cont.)

• How to quantify the benefits of avoiding problems through better analysis?– We only know how much a problem costs after it occurs– If good analysis prevents a problem from ever occurring, how

much is saved? What is the ROI? Where are the benefits? Are they monetary?

» Lower RDT&E costs» Lower O&S costs» Higher availability» Expanded capability

• Can we use non-probabilistic UQ methods to quantify variability in schedule and cost models?– Future designs are expected to differ significantly from

traditional designs» More unique technology and greater system complexity» Cost and schedule risk will be higher. Can we anticipate

them better?

A role for non-

probabilistic methods?

UQ Transition Challenges and UQ Transition Challenges and Needs (cont.)Needs (cont.)

• Cost-Benefit and Trade-Off Studies– Can the required structural safety levels for UAV’s be relaxed

from those imposed on manned aircraft?– What is the risk in trading structural weight for improved

capacity or performance?

• Compliance with Acquisition Regulations– DoD system acquisition directives are full of phrases like “… risk

must be managed …” and “ … risks should be acceptable …”– At best, risk is assessed qualitatively in current practice– Qualitative risk assessment is risky …

» Experts are notorious for their tendency to underestimate uncertainty!

– How can we make the risk management process more objective?

Dr Jeff Tromp Air Vehicles Directorate AFRL/VA Air Force Research Laboratory

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Transcript of Dr Jeff Tromp Air Vehicles Directorate AFRL/VA Air Force Research Laboratory