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

1. 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

2. Workshops • 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

3. Outline Introduction Dr. Jeff Tromp 1330 – 1335 Operator's View of Air Mr. Dave Leggett 1335 – 1420 Vehicles Future Technologies Air Vehicles MDT Research 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

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

5. 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

6. 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

7. MultiDisciplinary Technology The 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

8. Challenges • 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

9. 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

10. MDT Center Vision, Payoffs and Approach 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

11. Research Focus TasksMD Center 2002 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)

12. 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

13. 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

14. Exergy- Based Methods for Design of Aerospace Vehicles David J Moorhousedavid.moorhouse@wpafb.af.mil

15. 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

16. Exergy-Based Design MethodsCurrent 6.1 Task • 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

17. System 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 must equal the customer + overhead work done through the mission: H is energy content of the fuel/weight,  is overall efficiency, dW/dt < 0.

18. Aerospace Vehicle DesignExergy as a System-Level Metric 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

19. Exergy-Based Design Methods Current 6.1 Task • 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

20. Implementing Exergy Analysis Capability into Cobalt CFD Solver • 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.

21. Exergy/Entropy Analysiswith Cobalt CFD 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.

22. 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

23. 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

24. 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

25. Outline • Relationship to VA Goals • Challenges • Adaptive Structure Design

26. 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

27. 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

28. The ChallengeA Multidisciplinary Design Task Design of an structurally integrated adaptive wing from an energy formulation Structural Design Actuator Integration Mechanism Design + + + + Control Laws Power Electronics +

29. 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 Adaptive 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 Total input energy = stored energy + transferred energy

30. Our Approach Mission Identification & Vehicle Configuration Structural Design & Integration Energy Based Design

31. kd1o V kd1o d5o ri I ro kd1o d5i d3o d1o fi III IV II d3i d1i fi fi Efficiency of Mechanisms III • Efficiency with external load (variable force)

32. 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 (AFG) Efficiency III Input port • Stored energy inside of body • Total energy • Loaded efficiency Output port

33. 25% 10% Morphing Airfoils What is the Right Shape? What is the minimum energy input? Sanders, Eastep,& Forster, J of Aircraft, 2002 Henderson, Weisshaar & Sanders, AIAA 2001-1428

34. Contributions from MD Community • Development of methodologies for diverse technology systems

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

36. 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

37. Objectives 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

38. Projected 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

39. Current UQ Efforts

40. Span of UQ In-House Activities

41. Random Vibrations Non-ideal BCs Stochastic FEM AFOSR Lab Task: Quantifying Uncertainty 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

42. Sub-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

43. Monte Carlo Simulation Limit-Cycle Oscillation of Uncertain Panels Young’s modulus modeled as a random field Nonlinear Isotropic Plate Property variability impacts character and severity of response

44. Intersection of FTW Challenges and UQ Research

45. {FTW Challenges}  {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)

46. {FTW Challenges}  {UQ} • 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

47. {FTW Challenges}  {UQ} • 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

48. {FTW Challenges}  {UQ} • 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?)

49. {FTW Challenges}  {UQ} • 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?

50. 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