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

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

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

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Air Vehicles Multidisciplinary Technology Research & Capability Needs: A Top-down Air Force Research Laboratory Forecast6 September 2002

Dr Jeff Tromp

Air Vehicles Directorate

AFRL/VA

Air Force Research Laboratory

- 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

Introduction Dr. Jeff Tromp1330 – 1335

Operator's View of AirMr. Dave Leggett1335 – 1420

Vehicles Future

Technologies

Air Vehicles MDT

Research NeedsDr. Dave Moorhouse 1420 – 1450

Dr. Brian Sanders1450 – 1520

Dr. Chris Pettit1520 – 1550

Dr. Phil Beran1550 – 1620

Group DiscussionDr. Dave Moorhouse1620 – 1700

AFRL Air Vehicles DirectorateCenter ofMultidisciplinary TechnologiesCurrent Research Tasks andRelation to the Air Vehicles Future Technology Workshop ConceptsAIAA MA&O Conference, Sept 2002

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

Legacy ---> Stand Up ---> Vision

MDO 1999 FUTURE

Structural

optimization

Aero/structures

optimization

Revolutionary

concepts &

innovative

optimization

algorithms

New design

applications

Aero/servo

elastic control

Servo

elasticity

TODAY

- 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

- 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 Operation

Inter- & Multi-

Disciplinary

Technologies

Computational

Modeling &

Simulation

Modern

Control

Concepts

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

- High-Order Physics
- CFD & CEM
- Nonlinear Aero-Structures and Aeroacoustics
- Transition & turbulence
- Numerical Experiments

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

A Theoretical Basis for Innovative Fully-Integrated Vehicles

Vision

Enable revolutionary aerospace vehicle design and innovation

through multidisciplinary technology integration

Payoff

Reduce cost and acquisition time of weapon systems

Reduce developmental risk thru increased fidelity in design process Enable invention in aerospace vehicle concepts

Approach

Develop and validate comprehensive analysis, modeling and simulation, and design techniques for complex engineering systems

Efficient Design & Analysis Tools

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

Uncertainty Quantification

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

Increasing Risk

Morphing Aircraft Structures

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

Energy Based Design

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)

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 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 Design of Aerospace Vehicles David J Moorhousedavid.moorhouse@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 Framework to Facilitate the Design

& System Optimization of Efficient Systems

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 must

equal the customer + overhead work done through the mission:

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

Design Mission Stated in Terms of Work to be Done

How 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

- It has been shown that an explicit calculation of the entropy
- in 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.

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.

- 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-Based Design Methods

The Short RoadmapRange

FY02

FY03

FY04

Computation of entropy

generated in wake. Lift

distribution for min. drag

computation

Current

AFOSR task

Computation of entropy

generated by structural shapes

Computation of unsteady

generation of wake entropy

due to use of adaptive structures

on a vehicle concept

system

integration

Exergy framework for any

vehicle as a system of

energy systems

Optimized design of an

adaptive structure

structures

Definition of adaptive

structure as an energy system

Uncertainty Analysis & Reduced-Order Modelling

Needed: Control, Scaling Laws and Optimization Methods for

Integrated Energy-Based Vehicles

Center of MD Technologies Exergy-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

Center ofMultidisciplinary Technologies Morphing Aircraft Structures

AFRL

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

MULTIFUNCTIONAL & ADAPTIVE STRUCTURES TEAM

(MAST)

External Collaborators

Washington, Ohio State University

Weisshaar, Purdue

Murray, University of Dayton

Inman, VPI

Outline

- Relationship to VA Goals
- Challenges
- Adaptive Structure Design

Relationship to VA Goals

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

Flow management

Thermal load management

Pointing devices

Stealth

- Adaptive structures required for design of sensorcraft and multimission vehicles
- Multimission capability emphasized in VA workshop

DARPA

Morphing Aircraft Structures

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

Variable Geometry Wings

- 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 a

- wing planform

- sweep

- aspect ratio

- twist

Fuselage & Propulsion System

First challenge: Morph the wing

Design of an structurally integrated adaptive wing from an energy formulation

Structural Design

Actuator Integration

Mechanism Design

+

+

+

+

Control Laws

Power Electronics

+

Aerodynamic force

Desired shape change

Body

Actual shape change

Actuation force

fi

G

E

A

H

fi

C

Ui

Utri

d1i

d2i

d3i

B

D

F

ri

- 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

Total input energy = stored energy + transferred energy

Our Approach

Mission Identification &

Vehicle Configuration

Structural Design & Integration

Energy Based Design

kd1o

V

kd1o

d5o

ri

I

ro

kd1o

d5i

d3o

d1o

fi

III

IV

II

d3i

d1i

fi

fi

- Efficiency with external load (variable force)

d4i

B

fi

ri

Utri

D

E

Ui

A

C

d1i

d2i

d4o

B

ro

kd1o

Uo

D

E

F

kd1o

Utro

C

G

A

d1o

d2o

Uacto (AFG)

Input port

- Stored energy inside of body
- Total energy
- Loaded efficiency

Output port

25%

10%

What is the Right Shape?

What is the minimum energy input?

Sanders, Eastep,& Forster, J of Aircraft, 2002

Henderson, Weisshaar & Sanders, AIAA 2001-1428

- Development of methodologies for diverse technology systems

Center ofMultidisciplinary Technologies Uncertainty Quantification (UQ) Chris L. Pettit, Ph.D., P.E.

- 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

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

- 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 analyticalmodels 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

- 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

- Pervasive UQ expected to enhance robust implementation of innovative design concepts

Make certification robust and lean

Current UQ Efforts

Random Vibrations

Non-ideal BCs

Stochastic FEM

- 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

- Isolate and quantify specific elements of model and property uncertainty to define their contribution to errors and variability in response prediction

- 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

Monte Carlo Simulation

Young’s modulus modeled as a random field

Nonlinear Isotropic Plate

Property variability impacts character and severity of response

Intersection of FTW Challenges and UQ Research

- 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)

- In General …
- Lightweight, low-cost everything

- HALE/ISR
- Substantial increases in durability and damage tolerance
- Robust implementation of low drag through loiter
- High temperature engine materials
- Accelerated introduction of new materials

- Recce/Strike UAV’s
- Proactive/predictive health management
- Low-cost composites manufacturing
- Reliable bonded joints in composite structures
- High-accuracy autonomous warheads

- 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?)

- Strategic Airlifters
- Vehicle design integration
- UQ-based design?

- Survivable high-lift systems
- Unconventional structures
- QRA to compare with conventional design concepts?

- Durable LO Structures

- 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?

Design Efficient Analysis Methods

Philip S. Beran, Ph.D.

Principal Research Aerospace Engineer

Multidiscplinary Technologies Center

philip.beran@wpafb.af.mil

9th AIAA/ISSMO MA&O Symposium, Sept 2002

Develop and validate new computational methods for the design and analysis of revolutionary air vehicle concepts

- 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

Murray didn't feel the first pangs of real panic

until he pulled the emergency cord.

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

- 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

Design Efficient Analysis Methods

Computational Nonlinear Aeroelasticity for MD Analysis and Design of Flexible Air Vehicles

AFOSR (6.1)

Existing

Lab Task

Proposed Lab Task w/Pettit (UQ)

Computational Algorithms for Quantification of Uncertainties in Nonlinear Aerospace Systems

- 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.)

Collaborations with Government and Academia

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

Introduce new physics

HF Aeroelastic

Simulation

Program

New

Framework

Evaluate

F(wf,ws)

POD analysis

Snapshots

New POD Tool

RAPOD

1

2

3

STS

Full-Order Analysis

Sample System Physics

(Snapshots, S)

POD is used

to Identify Modes

Research

4

5

6

Project Equations

to Compute

Modal Amplitudes

Solve Reduced

Order Problem

Expand to Estimate

Full-Order Solution

General framework for reduced order modeling of large systems of nonlinear, discrete equations: modal integrity (phase 1); shocks (phase 2); complete projections (phase 3)

fluid-structure sensitivity

fluid-fluid sensitivity

J() =

structure-structure sensitivity

structure-fluid sensitivity

- 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

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

Im(J-Eigenvalue)

Hopf bifurcation:

conjugate pair

Re(J-Eigenvalue)

Functional analysis approach to nonlinear reduced order equations

Static bifurcation

Increasing

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)

Pressure Response to (t)

- 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)

Domain decomposition

involving 6-fold order reduction

Shock

Continuity constraints

Full/POD

POD

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

I

II

III

IV

- 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

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

DAGSI/AFRL Investigation Nonlinear Aeroelastic Analysis of the SensorCraft Joined-Wing Configuration

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

Fluid-

Structure

Interpolation

Overset Tools

GUI

MDICE

FASTRAN

ENS3DAE

COBALT

CAPTSD

.

.

FEMSTRESS

NASTRAN

ANSYS

.

.

6-DOF

RAPOD (ROM)

CONTROLS

MESH

DEFORMATION

USER Fcns

STRUCTURE

DYNAMICS

OTHER

FLOW

Efficient ROMs for 2-D AE systems in viscous flow

CY02

CY03

CY04

CY05

Reduced Order

Modeling

Application of ROM to EBD

ROM techniques for UQ of 2-D AE systems

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

Analysis of store-induced LCO

LCO

Aerothermoelastic analysis of 2-D scramjet configuration (TBD)

Heated panel: PT w/ROM

Aerothermoelastic

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

Design

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

- 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?

- Fluid/Structure interaction physics for high AR and joined-wing configurations
- 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 Summary

Center of MD Technologies

Workshop 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 Technologies

Workshop 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 Slides

- 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?

- 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

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

- 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

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

- Boundary not perfectly clamped
- 0.85 b 1

- At l / M= 850
- No LCO in the deterministic case
- Softening the boundary slightly induces LCO

Current Issues in Uncertainty Quantification for Airframes

- 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

- Nonlinear, multidisciplinary simulations are immature and not robust
- 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)

- Physics of extreme environments and multi-scale phenomena

UQ requires good physics models and robust algorithms

- 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!

- Unique issues for aircraft structures due to intermediate size of production runs
- 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.

- Better use of modeling to plan test and measurement regimen
- 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!

- 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?

- Loads, nonlinear response, fatigue, corrosion
- UQ for CFD
- Model validation
- Better understanding of turbulence-induced loads

- 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!

- 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?

- Future designs are expected to differ significantly from traditional designs

A role for non-probabilistic methods?

- 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?