Dynamic Systems & Control Group

Dynamic Systems & Control Group
July 1999 DS&C Recruiting

Contents UTC and UTRC Overview
United Technologies Corporation: Business Units and Products United Technologies Research Center - Organization and Core Capabilities Dynamic Systems and Control People - group member and university interactions Dynamic phenomena at UTC: Project organization Description of selected projects Specific features of research done at UTRC July 1999 DS&C Recruiting

Products and Organization
UTC and UTRC Overview Products and Organization July 1999 DS&C Recruiting

UNITED TECHNOLOGIES PRODUCTS AND BUSINESS UNITS
Pratt & Whitney Otis Carrier Hamilton Sundstrand Sikorsky Aircraft July 1999 DS&C Recruiting 4

UNITED TECHNOLOGIES FACT SHEET
MAJOR BUSINESSES Pratt & Whitney Aircraft engines, Carrier heating and air conditioning systems, Otis elevators and escalators, Sikorsky Helicopters, Hamilton Sundstrand aerospace systems. RANKINGS 41st largest U. S. corporation (1998, Fortune Magazine), 130th in the world (1998, Fortune Magazine, Global 500) EMPLOYEES 180,000 UTC employees, including approximately 105,700 outside the United states REVENUES $25.7 BILLION IN 1998, SALES TO U. S. GOVERNMENT $3.264 billion, or 12.7% of total sales (includes sales to NASA) R&D $1.31 billion in company-funded R&D in 1998 July 1999 DS&C Recruiting

UTRC: OUR VALUE TO UTC To provide technical leadership that increases the competitiveness of our business units. UTRC accomplishes this by integrating technical disciplines and expertise that have business unit applicability to create technology for the future needs of the corporation. July 1999 DS&C Recruiting 7

UTRC: MISSION STATEMENT
“See It First, Make It Happen” We team with UTC’s business units to foresee technological opportunities and create solutions that redefine marketplaces, increases competitiveness, better our society and leave a legacy of excellence. We aim to be a worldwide, diverse, and innovative community that is attractive to top talent and is recognized as a unique corporate resource. We strive for an environment of integrity, trust, mutual respect, fairness and learning in which we can all grow. July 1999 DS&C Recruiting

Office of the Director (UTRC)
UTRC: ORGANIZATION The Office of the Director provides the UTRC with leadership and strategic direction. A strong partnership exists between program planning and execution functions to ensure a clear focus on impacting the future of the business units. UTRC Director Leadership Strategic Direction Director, Research Programs Program Planning Director, Research Operations Program Execution Office of the Director (UTRC) July 1999 DS&C Recruiting 12

UTRC: SENIOR LEADERSHIP
The senior leaders at UTRC are organized to support the Research Center’s planning and execution efforts. Director Office of the UTRC Director Director, Research Programs Director, Research Operations Division Program Leaders P&W Sikorsky Carrier Hamilton Sundstrand Otis Int’l Fuel Cells Theme Leaders External Program Leader Disciplines: Mechatronic Systems ICCT Product Dev & Mfg Mat’ls & Structures Aeromechanical, Chemical & Fluid Sys International: Germany & China Services: Law, Finance, HR, Research Services The Knowledge Organization, Management of Technology July 1999 DS&C Recruiting 13

UTRC CORE CAPABILITIES
Aeromechanical, Chemical & Fluid Systems Acoustics Aerodynamics Heat Transfer Fluid Dynamics Combustion & Fuels Environmental Science Mechatronic Systems Dynamic Modeling & Analysis Controls Technology Controls Components Electronics Technology Advanced Embedded Systems Information, Computer & Communication Technology Advanced Digital Systems Diagnostic Technology Informistics Network Technology Systems & Software Materials & Structures Engineered Materials Material & Structural Modeling Materials Characterization Structural Integrity Surface Engineering Product Development & Manufacturing Product Innovation Methods Design for X Rapid Product Realization Nondestructive Evaluation Virtual Manufacturing Advanced Manufacturing Processes July 1999 DS&C Recruiting 9

UTRC: OUR EMPLOYEES The Research Center employs close to 800 scientists, engineers, technicians and support staff worldwide. 1997 DISTRIBUTION Administration 8% Facilities & Support 14% Technical Professionals & Support 78% July 1999 DS&C Recruiting 23

UTRC: TECHNICAL EMPLOYEES
The Center’s engineers and scientists form a diverse group of technical experts. Physics 7% Aeronautical 11% B.S. 21% Mechanical 28% Chemical 10% Ph.D. 41% M.S. 38% Materials 8% Computer Science/ Mathematics 12% Engineers - Other 10% Electrical 14% July 1999 DS&C Recruiting 24

UTRC: FUNDING SOURCES Financial support for the Research Center’s operations is provided through corporate, business unit sponsorship, and through contracts with industry and government. Sources of Funds 1998 TOTAL - $107.8 MILLION 14.7% Business Unit Technical Support 29.3% Business Unit Co-Planned Program $31.6 $15.8 28.5% Corporate Sponsored Research 12.6% Business Unit Subcontracts $13.6 $30.7 $16.1 July 1999 14.9% Direct Contracts DS&C Recruiting 25

Business Unit Relevance
UTRC: FUNDING USAGE Selection of technical programs is driven by the potential to create value for our six business units. Co-planning of program milestones with the business units is key to the planning and selection process. Business Unit Relevance 1998 TOTAL - $107.8 MILLION Sikorsky 6% HSD 5% Pratt & Whitney 42% UTA 5% Carrier 13% Generic (all Business Units) 19% July 1999 DS&C Recruiting Otis 10% 26

Dynamic Systems and Control People, Products, Problems, Solutions

Dynamic Systems and Control
Mission People and Skills University Teaming Publications Project Organization: Products, Problems, Solutions Selected Project Examples July 1999 DS&C Recruiting

MISSION STATEMENT We team with UTC’s business units to foresee
technological opportunities and create solutions that redefine marketplaces, increase competitiveness, leave a legacy of excellence. We provide world class technical expertise in the broad areas of dynamic systems and control including experimental programs, control system modeling, design, analysis and implementation and dynamic system analysis and computation. July 1999 DS&C Recruiting

Dynamic Systems and Control People
UTRC, University Partnering, Skills, Publications and Career Paths July 1999 DS&C Recruiting

People and Program Characteristics
Individual Metrics Technical depth - means demonstrated expertise in at least one area Technical breadth - means the ability to interact closely in several areas Communication - ability to present results to varied audiences Organization of Projects Business unit problem source Multidisciplinary teams for execution Intellectual property or competitive advantage as deliverables July 1999 DS&C Recruiting

Basic Research Areas of UTRC Interest
Methods for obtaining reduced order models for control of unsteady flow phenomena Methods of parameter identification of nonlinear dynamical models Methods for validation of nonlinear physics-based models against experimental data Computational tools for complex nonlinear dynamical systems Methods for on-line optimization of dynamical system behavior (e.g., reduce the magnitude of oscillations) with adaptive algorithms Observers for nonlinear and time-varying systems Generation of trajectories obeying state and actuator constraints for complex nonlinear systems (jet engine control, helicopter control) Control strategy for a complex dynamic system with redundant actuators of significantly different authority operating in the same bandwidth upon the multiple objectives of command following, disturbance rejection, and stability augmentation. Methods for optimization of actuator and sensor placement for control of complex systems Robust real-time model adaptation for a multivariable linear control system. July 1999 DS&C Recruiting

Group Members July 1999 DS&C Recruiting

Dynamic Systems and Control Group
Andrzej Banaszuk: has received Ph.D. in Electrical Engineering from Warsaw University of Technology in 1989, and Ph.D. in Mathematics from Georgia Institute of Technology in From 1989 to 1997 he has held various research and teaching positions at Warsaw University of Technology, Georgia Institute of Technology, University of Colorado at Boulder, and University of California at Davis. During that time he performed research in various areas of control theory including implicit systems, approximate feedback linearization of nonlinear systems, trajectory planning for nonlinear systems, nonlinear observers, feedback stabilization of periodic orbits, and control of surge and rotating stall in jet engines. He is an author or co-author of about 25 journal papers and numerous conference papers. Andrzej Banaszuk joined Controls Technology Group at United Technologies Research Center in April His work at UTRC has been focused on modeling and control of turbomachinery flutter, rotating stall, combustion instability, and flow separation. His current research interest is in reduced order modeling for control purposes of complex physical phenomena in turbomachinery, model validation and parameter identification for nonlinear systems using experimental data, and control of nonlinear systems in a neighborhood of non-equilibrium attractors. In 1998 Andrzej Banaszuk became an Associate Editor of IEEE Transactions on Control Systems Technology. Full CV and list of publication available at Jim Fuller: is a Senior Principal Engineer in Controls Technology and has 23 years of experience in modern control system design, analysis and development, the highlights of which include: development of multivariable, nonlinear and adaptive control and estimation algorithms for (1) controlling the flight of the RSRA/X-wing aircraft, (2) missile guidance, navigation, and control, (3) aided inertial navigation, (4) Propfan gas turbine engine, (5) air conditioner chillers and (6) improving ride and comfort of elevators. His experience also includes research into automated nap-of-the-earth helicopter flight, trajectory generation using optimal control theory, neural nets, fault tolerant and robust control algorithm synthesis, and passive and active ride control systems. Gonzalo Rey: has worked on theoretical studies of adaptive systems where he has applied nonlinear dynamical systems analysis tools such as bifurcation and averaging analysis. His competencies extend to servo control system design and control algorithms for aerospace and industrial motion control applications where he has acquired a broad experience base. He is skilled in the areas of robust adaptive control, linear system parameter identification, linear control and nonlinear system dynamics. His recent experience at UTRC includes research in the areas of active noise control and active control of flutter in turbo-machinery. July 1999 DS&C Recruiting

Dynamic Systems and Control Group (continued)
Chris Park: core competencies include structural dynamics, linear control theory, rotor dynamics, non-linear dynamic modeling, and experimental techniques. He is also competent in active materials, aerodynamics, servo control, and active noise control. His recent experiences at UTRC include active noise control system development and data analysis for enclosures, disturbance transmission path analysis, modeling rotor dynamics for active control system studies, and development of a real time active rotor control system for wind tunnel testing. Clas Jacobson: has worked for three years at UTRC (nine years in academia previously) in diverse areas of control systems design and implementation. He has contributed to programs in active noise control (duct and enclosure), combustion dynamics and control and compression system instabilities. His current interests are mainly in the identification and control of nonlinear systems for combustion and flow control applications. Danbing Seto: has worked in the areas of nonlinear adaptive control and control of complex mechanical systems, where he applied differential geometric tools to develop control algorithms for nonlinear systems in a triangular structure with or without unknown parameters. He also studied nonlinear vibrational control theory, from which he derived a mechanical model for laser cooling. His interdisciplinary experience include computer-controlled real-time systems, where he particularly focused on real-time scheduling, control system upgrade and software fault tolerance. His recent work at UTRC has been concentrated on 1) fault tolerance and 2) system identification. The former concerns the integrated fault management functionality in Otis elevator control systems with scalability, and the latter involves development of tools/methodologies for model validation of nonlinear systems as well as modeling jet engines using the state-of-the-art identification tools. His long-term technical goal at UTRC is to investigate estimation theory applied in integrated control systems, which unifies the research areas of model identification and state estimation together with control design. July 1999 DS&C Recruiting

Alexander Khibnik: has a background in analysis of nonlinear dynamical systems with an emphasis on analytical and numerical issues in bifurcation theory. He joined UTRC in 1997, after spending more than 20 years in academia. His experience with systems ranging from ecology to neurobiology to nonlinear physics is focused on the development and application of numerical tools for the analysis of their qualitative nonlinear dynamical behavior. His competencies extend to self-excited oscillations, coupled oscillators, resonance, fast-slow systems, continuation techniques, integrated with software and computer tool development. His recent experience at UTRC has focused on the analysis of compressor and combustion dynamics with an emphasis on modeling nonlinear dynamics from data. He is currently leading a team in flow control area which studies low dimensional dynamics of separation in diffuser flows and its utilization for model-based control of separation. Satish Narayanan: comes from an experimental fluid mechanics background and has applied the nonlinear dynamical systems approach to extract low-dimensional models of complex fluid flow phenomena. In doing so he has developed active nonlinear flow control strategies for turbulent flows of wide technological relevance such as jets and shear layers. His areas of expertise include nonlinear dynamics, reduced-order modeling, flow control, experimental fluid mechanics, turbulence, vortex dynamics and hydrodynamic stability. His current projects in UTRC involve dynamical modeling and active control of flow separation phenomena (experimental and numerical), the development and the implementation of a phased array – a new jet noise source localization technique, and the testing of new active control methods for jet noise reduction. Richard Murray: is an expert in the area of dynamical systems and nonlinear control, with applications to motion and flow control. His past work includes studies in geometric mechanics for Lagrangian systems with symmetries and nonholonomic constraints, real-time trajectory generation for motion control systems using differential flatness, and active control of compression, combustion, and cavity flow instabilities. Murray and his research group at Caltech have designed, built and operating a variety of experiments, including a thrust vectored flight control experiment, an axial flow compression system facility, and a cavity flow instability experiment. At UTRC, Murray is an active participant in programs relating to flow control, combustion dynamics and control, modeling and analysis, and smart products. July 1999 DS&C Recruiting

Leena Singh: has intensive experience in methods of motion control and trajectory generation of Lagrangian systems, specifically, articulated multi-link manipulators such as robot arms and hands. Key competencies and areas of interest are modern control theory, optimal control, passivity-based control, attitude control and exact, analytical algorithms for online trajectory generation in constraint-based spaces. She also has experience in modeling the spatial kinematics and dynamics of mechanical systems. At UTRC (since July 1997) she has worked on projects in the areas of kinematic modeling and control, and estimator design. Bernd R. Noack: has a fluid dynamics background. He has joined UTRC in December 1998 after 6 years in research institutes and academia. He has worked in the areas of wake flow, several open and confined flows, turbulence of superfluid helium, brain activity and time-signal analysis. He has experience with phenomenological modeling, Navier-Stokes simulation, Galerkin methods, linear and nonlinear stability analysis, Floquet theory, nonlinear dynamics, low-dimensional modeling, mean-field theories, center-manifold methods, harmonic balances, turbulence modeling and control. Particular UTRC applications include modeling and control of flow separation and mixing enhancement. Mike Dorobantu: is interested in the efficient computations of numerical solution to PDEs. In academia he focused on flow problems, such a flow through porous media, using multi-scale techniques, the application of wavelet-based preconditioning and homogenization, multi-grid preconditioning, and streamline diffusion stabilization methods. At UTRC he is developing classification algorithms based time-frequency analysis and multi-phase non-newtonian fluids mixing models. He is also involved in convergence acceleration and extracting spectral information from time-domain simulations of flow problems and deriving data-driven reduced order models for transient flows. July 1999 DS&C Recruiting

Dynamic Systems and Control External Collaborations

Modeling for Control of Mixing
Academic contacts: Professors Igor Mezic, University of California at Santa Barbara, Professor Luca Cortelezzi, McGill University UTRC Contacts: Dr. Bernd Noack, Dr Andrzej Banaszuk Project Goal:create a low order model and derive model-based control laws for mixing enhancement. Approach: vortex methods for modeling flow dynamics and dynamical system methods for control law derivation are investigated. Applications: modeling for control of combustion phenomena. Status: research in progress. Publications: Conference and journal submissions expected by mid 1999. July 1999 DS&C Recruiting

Model Validation for Nonlinear Systems
Academic contacts: Professors Igor Mezic and Roy Smith, University of California at Santa Barbara UTRC contact: Dr. Andrzej Banaszuk Project goal: create new methods for validation of nonlinear models with non-equilibrium behavior and stochastic disturbances against experimental data. Approach: methods from ergodic theory for comparison of behavior of dynamical systems and extensions of classical linear model validation concepts are investigated. Applications: modeling for control of combustion instability, flow separation, and rotating stall. Status: research in progress. Publications: conference and journal submissions expected by mid 1999. July 1999 DS&C Recruiting

Control Theory for Systems with Non-equilibrium Attractors
Academic Contact: Professor John Hauser, University of Colorado at Boulder UTRC Contact: Dr. Andrzej Banaszuk Project Goal: create methods and tools for control of models with non-equilibrium attractors, like periodic orbits. Typical goal is to achieve acceptable performance with limited actuator authority in the cases when stabilization of an equilibrium is not achievable or undesirable. Approach: dynamical system topological and Lyapunov function methods Applications: control of combustion instability, flow separation, and rotating stall. Status: preliminary results for shrinking of planar periodic orbits with saturated actuators available. Extensions to non-planar periodic orbits and to other type of attractors expected. Publication: “Control of planar periodic orbits”, accepted for 1999 CDC. Journal submission in progress. July 1999 DS&C Recruiting

Performance and Stability Analysis of Extremum Seeking Methods
Academic contacts: Professor Miroslav Krstic, University of California at San Diego, Mario Rotea, Purdue. UTRC contact: Dr. Andrzej Banaszuk Project goal: create methods and tools for performance and stability analysis for extremum-seeking algorithms. Approach: combination of methods from linear, nonlinear, and adaptive control Applications: adaptive control of combustion instability and flow separation Status: work in progress. Publication: conference and journal submission expected by late 1999. July 1999 DS&C Recruiting

Development of Parametric Analysis Techniques for Large Scale Systems
Academic Contact: Dr. Kurt Lust, Cornell University & Katholic University of Leuven ( UTRC Contact: Dr. Alexander I. Khibnik Project Goals: development of tools for parametric analysis that utilize existing CFD time simulation codes to compute and analyze steady-state solutions of large-scale models. Approach: acceleration of iterative methods (RPM, GMRES), effective spectral computations (Arnoldi, Jacobi-Davidson), continuation techniques Applications: large-scale models in fluid flows, combustion, acoustics, aeromechanics. Status: work in progress. Publication: conference and submission expected by late 1999. July 1999 DS&C Recruiting

Selected Recent Publications
System Identification for Limit Cycling Systems: A Case Study for Combustion Instabilities, R. M. Murray, C. A. Jacobson, R. Casas, A.I Khibnik, C.R. Johnson Jr., R. Bitmead, A.A. Peracchio, W.M. Proscia, 1998 American Control Conference Self-Tuning Control of a Nonlinear Model of Combustion Instabilities, M. Krstic, A. Krupadanam, C.A. Jacobson, 1997 IEEE Conference on Control Applications Active Control of Combustion Instability in a Liquid Fueled Low NOx Combustor, J. M. Cohen, N. M. Rey, C. A. Jacobson, T. J. Anderson, 1998 ASME Turbo Expo. Linear and Nonlinear Analysis of Controlled Combustion Processes. Part I: Linear Analysis. Part II: Nonlinear Analysis, A. Banaszuk, C.A. Jacobson, A.I. Khibnik, and P.G. Mehta, 1999 CCA, August 1999, Hawaii. - Active Control of Combustion Instability in a Liquid-Fueled Sector Combustor, J.R. Hibshman, J.M. Cohen, A. Banaszuk, T.J. Anderson, and H.A. Alholm, 1999 ASME Turbo Expo, 1999, Indianapolis. July 1999 DS&C Recruiting

Selected Recent Publications (continued)
- Adaptive detection of instabilities and nonlinear analysis of a reduced-order model for flutter and rotating stall in turbomachinery, G.S. Copeland, I.G. Kevrekidis, R. Rico-Martinez, 1999 CCA, Hawaii. A Backstepping Controller for a Nonlinear Partial Differential Equation Model of Compression System Instabilities, A. Banaszuk, H.A. Hauksson, and I. Mezic, SIAM Journal of Control and Optimization , 1999, to appear. - Design of Controllers for MG3 Compressor Models with General Characteristics Using Graph Backstepping, A. Banaszuk and A.J. Krener, Automatica , 35 (8) 1999, - On control of planar periodic orbits A. Banaszuk and J. Hauser, 1999 CDC, December 1999, Phoenix. - Analysis of low dimensional dynamics of flow separation. Khibnik, A.I, Narayanan, S., Jacobson, C.A. and Lust, K. Submitted to Notes in Computational Fluid Dynamics (Proceedings of Ercoftac and Euromech Colloqium 383 "Continuation Methods in Fluid Dynamics", Aussois, France, 6-9 September 1998). - Low-dimensional model for active control of flow separation. Narayanan, S., Khibnik, A.I. Jacobson, C.A., Kevrekidis, Y., Rico-Martinez, R. and Lust, K, CCA '99 (Hawai, August 1999). - Control of laminar mixing enhancement in a recirculation region, B.R. Noack, A. Banaszuk, and I. Mezic, to be submitted to “Physica D”, 1999. July 1999 DS&C Recruiting

UTRC Technical Career Path: Increasing Program Responsibility
Principal Research Engineer UTRC Technical Career Path: Increasing Program Responsibility Expert Senior Research Engineer Program manager (responsibility for technical direction and resourcing) Research Engineer Assistant Research Engineer Principal investigator (responsibility for technical direction) Individual contributor in single technical area July 1999 DS&C Recruiting

UTRC Career Paths Cover Technical and Management
Fellows Program Council OperatingCouncil Line Managers Program Managers Technical Track Technical Council 51 Line Management Track 50 49 Program Management Track 48 46 July 1999 DS&C Recruiting Common Competencies

Dynamic Systems and Control Organization, Content, Solutions
Projects Organization, Content, Solutions July 1999 DS&C Recruiting

UTRC research in dynamic modeling and control
UTC Business unit relevance drives the research research always tied to a product need emphasis on potential benefits to business units in either product or process full scale experimental rigs validate modeling and control concepts Ability to communicate with people of different background (coworkers, management, engineers in business units) is essential. Breadth of programs is typical Evaluation of problem Modeling at multiple time and spatial scales Control concepts evaluated to influence dynamics Proof of concept on full scale hardware July 1999 DS&C Recruiting

Dynamics and Control Approach
Phenomena Characterization Business case Risk assessment Product plan Business Unit Need Modeling Actuation limits Scaling laws Control Control design Actuation system Fundamental limits Demonstration Product or Process Improvement July 1999 DS&C Recruiting

Customer requirements
UTC cares for dynamic modeling, analysis, and control because dynamics (usually undesirable) affect UTC products. Customer requirements Product Problem Undesirable dynamics Solution Understand dynamics Change dynamics July 1999 DS&C Recruiting

Problems: undesirable dynamics affects UTC products
Carrier Noise (ducts, compressors, combustors) Compressor surge and stall Otis Elevator/cable dynamics Noise Power electronics dynamics Electric drives dynamics Pratt & Whitney Compressor stall and surge Fan flutter, turbine buffeting Compressor stator vortex shedding Blade cracks propagation Turbine blades temperature transients Diffuser/duct flow separation Inlet flow distortion Jet noise Combustor instability Sikorsky Structure noise and vibration Blades/structure interaction with air flow Generic 1. Noise and vibrations 2. Flow separation - efficiency loss 3. Flow/structure interaction - structural damage July 1999 DS&C Recruiting

Path to solutions: Understanding Dynamics
Basic understanding of physics Sensor selection Actuator selection Experimental data Physics-based modeling: Construction of dynamical system model Identification of model parameters Validation of models against data Model reduction (Galerkin, POD, …) Data-based modeling: Construction of dynamical system model: - linear: frequency response - nonlinear: embedding, neural nets, ... Study dynamical system properties (attractors, stability, bifurcations,...) Link model parameters to design parameters Identify sensor/actuator selection for active control July 1999 DS&C Recruiting

Short/mid term solutions: change dynamics (fix the problem)
Options: Issues, tradeoffs: Redesign product to avoid the undesired behavior Modify dynamics by passive fixes Modify dynamics by active control Can be impossible (product has to be shipped in 6 months ...) Can be expensive, difficult … What if the control system fails ... Long term solutions: design dynamics (prevent the problem) Incorporate dynamical system models early at the design process to avoid the undesired behavior Use the dynamical models to build the system with embedded sensors and actuators for active control Educate design engineers about dynamics July 1999 DS&C Recruiting

Dynamic Systems and Control Example: Combustion Instabilities

Performance Limitations in Aircraft Engines
Flutter and high cycle fatigue Aeromechanical instability Active Control a possibility Combustion instabilities Large oscillations cannot be tolerated Active control demonstrated at UTRC Jet noise and shear layer instabilities Government regulations driving new ideas Inlet separation Separation of flow from surface Possible use of flow control to modify Distortion Major cause of compressor disturbances Rotating stall and surge Control using BV, AI, IGVs demonstrated Increase pressure ratio Þ reduce stages Use NOLCOS slides here Set up basic problem in terms of concepts introduced before Label J as “size of stall limit cycle” Augment with slides from Caltech (reuse existing) Show results of air injection, including simulation Discuss the role of the unsteady part of the compressor characteristic Show preliminary results by Simon (do not include printouts in packet) July 1999 DS&C Recruiting

Combustion Dynamics & Control: Programs
PW/UTRC Joint Planned Programs Combustion Dynamic Modeling Active Instability Control (AIC) DARPA AIC - Liquid Fuel NASA Direct Injection Aeroengine AIC July 1999 DS&C Recruiting

Combustion Dynamics & Control: Capabilities
Experimental BFSC, ASDC High Pressure SNR Sector Rig Engine Modeling Unsteady CFD Euler Code Lumped/Linear Acoustics Reduced Order Heat Release Sensing & Actuation Pressure PMT 2D Flame Imaging Fuel Valves Solenoid PZT MOOG DDV; other Acoustic Forcing; Bleed Valve Dynamic Analysis & Control Model Analysis (Stability, Amplitude) System Identification Control Analysis & Design (Adaptive, Robustness) Control Implementation July 1999 DS&C Recruiting

Combustion Dynamics & Control: Team
Product Integration T. Rosfjord W. Proscia J. McVey W. Sowa J. Lovett (P&W) S. Syed (P&W) Modeling A. Peracchio G. Hendricks D. Choi A. Khibnik B. Wake Sensing & Actuation T. Anderson N. Rey J. Haley MOOG Experimental J. Cohen D. Kendrick H. Alholm R. Decker Control C. Jacobson A. Banaszuk Y. Zhang G. Rey R. Murray R. Bitmead M. Krstic July 1999 DS&C Recruiting

Example: industrial combustor design
Customer requirements low emission level lean mixture Product Undesirable dynamics: lean mixture violent pressure oscillations high cycle fatigue, combustor destruction Problem Solution Understand dynamics reduced order physics-based model model analysis predicts limit cycle model parameters linked to design parameters model allows to identify effective actuation mechanism Change dynamics. Options: 1. Redesign combustor 2. Use passive devices to reduce oscillations 3. Use active control to reduce oscillations July 1999 DS&C Recruiting

Combustion Instabilities Will Occur
Combustion Instabilities Limit Minimum Achievable NOx Emissions Goals: NOx/CO limits RMS pressure limits Wide range of operating conditions % power -40 to 120 F ambient temp. Instabilities inevitable combustion delay convective delay Passive design solution may be possible AIC can enable product Equivalence Ratio 10 20 30 40 50 15 % O2 Efficiency (%) 60 70 80 90 100 91 92 93 94 95 96 97 98 99 Efficiency 101 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 NOx LBO Combustion Instability Product Need “Stability boundary” defined as maximum allowable pressure fluctuation level July 1999 DS&C Recruiting

Combustors Experience Instabilities
Data obtained in single nozzle rig environment showing abrupt growth of oscillations as equivalence ratio is leaned out to obtain emissions benefit July 1999 DS&C Recruiting

Reduced Order Modeling
Combustion Dynamics & Control:Purpose of Modeling to Influence Product and Process Reduced Order Modeling Dynamic Analysis Sensing & Actuation Combustion Dynamics Active Control Development of dynamic models Improved acoustic models: 1D ® 3D Improved flame models Atomization & mixing models Development of prediction and analysis tools Predict stability boundaries reliably and early in the development process Development of design & test protocols Extract data from component tests Integrate physical understanding into design process Engine-ready sensing and actuation Modeling enables requirements specifications for vendors Modeling enables scaling effects to be understood Robust algorithms & architectures Modeling enables development of self-tuning algorithms for hands-off operation over long periods Modeling enables integrated diagnostics & prognostics Control at finer spatial scales Fuel/air ratio control for pattern factor Mixing control of higher power, lower emissions Process: Standard Work Product: Improved Engines July 1999 DS&C Recruiting

Combustion Dynamics & Control:Role of Dynamic Analysis in Modeling/Design Cycle
Observed Unacceptable Time Response Behavior Alter system dynamics to obtain acceptable behavior Model description capturing system dynamics 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 -10 -5 10 15 Second Order System - Low Damping Time Output 50 100 150 200 250 300 -15 20 25 30 Frequency Power Spectrum Magnitude (dB) System Level Model Showing Feedback Coupling Evaluation of Design Options Evaluation of model sensitivities Development of experimental protocols and model calibration Evaluation of paths to mitigate undesirable behavior Effects of Parameter Variation on Stability Boundary Enabling effective use of dynamic model Parametric analysis of system model July 1999 DS&C Recruiting

Combustion Dynamics & Control:Role of System Level Modeling
Air Feed System Combustion Response Combustor Acoustics Fuel Feed System System Level Model Captures Purpose of System Level Model - Key components and interactions System modifications - Preliminary design (& scaling) - Design optimization Active Control - Actuator authority - Control algorithm development - Experimentally obtained information - Lessons learned in transferable code System Level Model Analysis - Linear stability boundaries - Amplitude prediction - Closed-loop control performance July 1999 DS&C Recruiting

Heat Release subsystem
Thermoacoustic Modeling and Analysis Lean Premixed Combustion Instability Mechanism Thermoacoustic instability - feedback interconnection of acoustic and heat release component subsystems - instability of feedback system is mechanism of pressure oscillations Acoustic resonance sets the frequency of oscillation Heat release rate dependent on: Instantaneous equivalence ratio Instantaneous flame surface area Linear dynamics define system stability Nonlinear effects determine limit cycle amplitude Acoustic damping Heat release Fluctuating pressure driven by unsteady heat release Acoustic subsystem Heat Release subsystem Fluctuating heat release driven by unsteady velocity July 1999 DS&C Recruiting

Heat Release subsystem
Combustion Dynamics & Control: System Level Modeling and Analysis diagram Acoustic subsystem Heat Release subsystem Fluctuating heat release driven by unsteady velocity Fluctuating pressure driven by unsteady heat release Analysis shows model captures phenomenon Frequency varies with delay Amplitude vs Frequency varies with equivalence ratio Phenomena Increasing oscillations with decreasing mean equivalence ratio Mechanism System level model capturing phenomenon - 6th order nonlinear delay differential equation Key parameters are acoustic damping and mean equivalence ratio (heat release time delay is a function of mean equivalence ratio_ Analysis Linear stability boundaries Amplitudes of oscillation and character of loss of stability (bifurcation) July 1999 DS&C Recruiting

Evaluation of Mitigation Strategies
Combustion Dynamics & Control: Model Calibration and Use in Evaluation of System Modifications Coupled Resonator - Combustor System 50 100 150 200 250 300 350 400 450 0.005 0.01 0.015 Bode plots P4_2p over Vact and fits with 8 poles, 8 zeros: magnitude Magnitude Hz -1000 -800 -600 -400 -200 Bode plots P4_2p over Vact and fits with 8 poles, 8 zeros: phase Phase Files r60p14 and r60p29 Analysis allows calibration of model from data to enable quantitative studies Linear Acoustics G(s) d dt e s - t H(.) N p q pressure heat release rate c Feedback control modulating equivalence ratio Evaluation of Mitigation Strategies Evaluate passive design changes (resonators) for size, placement, prediction of performance Evaluate active control for actuation requirements (bandwidth) and prediction of performance Calibration System level model captures experimental data quantitatively Data Analysis Key parameters extracted from experiment (forced response tests) - trend in equivalence ratio (time delay) drives dynamical behavior July 1999 DS&C Recruiting

Examples July 1999 DS&C Recruiting

Flight Systems July 1999 DS&C Recruiting

Dynamics and Control Program: Constrained Multivariable Control

Many UTC products are multi-input, multi-output systems
Problem: Many UTC products are multi-input, multi-output systems but multivariable control theory is not useful for designing their control systems => difficult ad hoc designs Reason: Balancing performance against product cost and weight results in products operating near many physical constraints Popular control synthesis methods do not include constraints in their formulations Approach: Develop a multivariable control synthesis method that explicitly recognizes constraints in its formulation July 1999 DS&C Recruiting

The demand for efficiency pushes engine operation to the
physical limits => controller must must meet many constraints Thermal efficiency increases with burner temperature - nominal temperatures near melting point of parts - temperature overshoots rapidly degrade turbine life Propulsive Efficiency requires larger fans - structural constraints on fan speed Fan and compressor efficiency best near stall, surge, and flutter boundaries - operating constraints to avoid instabilities July 1999 DS&C Recruiting

Automatic Flight Control Systems Must Control 4 Degrees
of Freedom While Meeting Many Constraints Don’t strike fuselage Don’t stall rotor Actuator stroke and rate limits Limited engine power and responsiveness Pitch and roll limits July 1999 DS&C Recruiting

Approach: Optimization-Based Control Algorithm
Commands PLA Performance Index weights, timing parameters Constraints actuator limits actuator rate limits max T4 max,min N1,N2 compressor op lines Onboard, Constrained Actuator Time History Optimization Actuator Commands Onboard Model/Estimation continuously estimating state and uncertain parameters Sensor Signals Focus of control logic designer July 1999 DS&C Recruiting

Dynamic Systems and Controls: Jim Fuller, Leena Singh, Danbing Seto
UTRC Team Dynamic Systems and Controls: Jim Fuller, Leena Singh, Danbing Seto Informistics ( numerical algorithms ) Martin Appel Software Technology: William Weiss July 1999 DS&C Recruiting

Dynamics and Control Program:
Virtual Alignment Via Misalignment Estimation July 1999 DS&C Recruiting

The Commanche Alignment Algorithm Transforms Target
Location and Velocity from EOTAS to INS and Weapon Coordinates EOTAS Electro-optic Target Acquisition System NQIS Inertial Navigation System Gun The Alignment Algorithm Compensates for Bending, Installation Misalignments, and Sensor Errors for More Accurate Fire-control Goal: maintain alignment during even aggressive maneuvers July 1999 DS&C Recruiting

Key UTRC Team: Jim Fuller and Leena Singh
The Team Key UTRC Team: Jim Fuller and Leena Singh This activity is part of the Comanche development including: Sikorsky Aircraft, Comanche weapon system integrators Martin Marietta, Electro-optic Target Acquisition System Litton, NQIS and rate gyros General Electric, gun system July 1999 DS&C Recruiting

Red and Blue are part of the Kalman Filter Based Alignment Algorithm
EOTAS rate gyro frame Target search or tracking Maneuver stresses NQIS rate gyro frame Nominal rotation of seeker base wrt NQIS platform Tilting of gyro mount wrt EOTAS platform Rotation thru EOTAS gimbals to seeker base Rotation of seeker base wrt its nominal orientation due to fuselage bending Tilting of gyro mount wrt NQIS platform NQIS Mis-Mount Estimator Seeker Mis-Mount Estimator Gimbal angle resolvers Bending Estimator Aircraft Blueprints SG C SE SE C SB0 SB0 C SB SB C NB NB C NG Target position in seeker coordinates Target position in aircraft coordinates Red and Blue are part of the Kalman Filter Based Alignment Algorithm July 1999 DS&C Recruiting

Rationale and Approach
The inertial navigation, target acquisition, and gun systems each have a triad of rate gyros to support their operation The difference between the angular rates of two components - is primarily a measure of bending rate - secondarily a measure of mount errors, gyro biases, and bending - can be put in form of Kalman filter measurement equation Solution: Estimate the misalignment terms via a Kalman filter July 1999 DS&C Recruiting

-only the 2-DOF of bending orthogonal to the rotation are observable
Complicating Factors For any given rotation, -only the 2-DOF of bending orthogonal to the rotation are observable -degree of observability is proportional to rotation rate magnitude Rate gyros have slowly varying random biases - integral of bending rate measurement has large low frequency errors Need time varying filter gains, but covariance propagation requires too much computation Solution: Quasi-steady state Kalman filter July 1999 DS&C Recruiting

x x Bending Estimator Formulated as Kalman Filter IwSGSG qS SGwSBSB
y1 SBCSG 3 EOTAS rate gyros + + x Direction Cosine qS Rate/Position estimator + - 5 EOTAS gimbal resolvers Gimbal Kinematics 3 “measurements” SB0CNG SGwSBSB IwNGNG x y2 3 NQIS rate gyros Predicted measurement H F Z-1 - Estimate of fuselage bending between EOTAS base and NQIS base + + K + 15 states Kalman Gain Quasi-steady Kalman gains are scheduled analytically via an invented time varying transform July 1999 DS&C Recruiting

Dynamic Systems and Control Example: Control of Separated Flows

Subsonic Engine Flow Control
External Cowl Drag reduction Fan Nozzle Area control Subsonic Diffuser Separation control Jet Noise Community noise Fan and Compressor Separation control (fewer stages) Clearance (margins, performance) Noise Inlet Lip Separation control Combustion Mixing Dynamic mixing enhancement July 1999 DS&C Recruiting

Enabling process Diffuser rig DEMO Sikorsky, PW
Dynamic signature of separation Diffuser rig - subscale experiment - parametric studies - testbed for dynamic- model-based control - testbed for CFD-enabled model and control design Low order dynamic models (Galerkin, black box, phenom.) Model-based control (experiment, CFD) Sikorsky, PW - impact UTC products - implement and evaluate dynamic-model-based control design on real life applications DEMO July 1999 DS&C Recruiting

Methods and Issues Fluid dynamics Dynamic modeling
- diffuser geometry - boundary conditions - boundary layer - shear layer - onset of separation - flow transitions - hydrodynamic instabilities - large-scale structures and their temporal dynamics - turbulence and mixing - mechanics of actuation and affects on flow structures Dynamic modeling - phenomenological models - dimensional analysis - simplified NSE (integral eqs., parabolized eqs., self-similar solutions) - vortex methods - flow simulations (DNS, turbulence modeling) - POD methodology - Galerkin/POD models (analytical, solver-based) - black-box models (ANN) - CFD-based - based on experimental data - model analysis (ROM) - model analysis (tied to CFD models) Control of separation - control strategy - model-based control - actuators (local, distributed) - cost functional and actuation authority - observers (POD-based, NLPC based) - optimization of control parameters - design optimization Team: Satish Narayanan, Bernd Noack, Alexander Khibnik, Andrzej Banaszuk University connections: Princeton, Cornell, U.Houston, KIAM (Moscow), McGill, UCSD, UCSB, Max-Planck Inst., KU Leuven July 1999 DS&C Recruiting

Flow separation Motivation & objectives
predict dynamics of separated flows understand physics/dynamics of separation (low-D ?) develop dynamical models capturing essential dynamics enhance performance of devices involving flow separation design & demonstrate model/physics-based flow control strategies active control: stall in high-angle-of-attack airfoils, engine/axial fan inlet flows, thrust vectoring Approach (flow separation in 2D diffuser) Numerical (2D CFD: low Re, exact): spatiotemporal flow fields Dynamical analysis & modeling: identify dominant modes, low-D extract (parameter-dependent) dynamical models parametric/bifurcation analysis of models July 1999 DS&C Recruiting

2D DNS results (Rew1 ~ 30,000); N/w1~4; first transition; 6o < 2q< 8o
Snapshots of kinetic energy fields Longitudinal velocity traces (centerline of expansion exit) Appearance of low freq. oscillations Onset of asymmetry July 1999 DS&C Recruiting

Empirical eigenfunctions
Spatial patterns and temporal dynamics computed using POD (Karhunen-Loève) method POD modes used for Galerkin projection of governing equations POD coefficients used for training black-box models How POD is done? method of “snapshots” (equivalent to SVD) data mapped to standard rectangular domain (grid same for different angles) data symmetrized by adding “mirror image” data sparsed and mean subtracted (definition of mean?) scalar fields weighted & stacked together (scaling? choice of fields?) data for several angles stacked to form “representative” data set to span fields for parameter range of interest (“equal” representation?) July 1999 DS&C Recruiting

Cumulative POD energy spectrum
Flow reconstruction 2q~8.5o fi(x) uN(x,t) = Si ai(t) fi(x) i=1, …., N POD modes computed for ensemble of KE fields spanning 5.4o < 2q < 10o 1,5 & 20 modes capture 47%,76% & 95% energy Cumulative POD energy spectrum Notes: - 1 mode captures location of large structures - 5 modes capture asymmetry - 10 modes start capturing small scale details modes add very little to the picture of 10 modes July 1999 DS&C Recruiting

Galerkin solver-based model
Idea: Use CFD as a time-stepper and build a projection layer around it. Takes care of geometry automatically Parametric/bifurcation analysis feasible CFD time-stepper CFD/ Galerkin model U - POD modes Neural Network model NN model to predict & interpolate system dynamics NN model trained on limited temporal data set (POD coeffs.) goal: trace attractors (long term predictions) as parameters vary Discrete network: two-hidden-layer network for discrete time DS identification fitted function: X(n+1) = F( X(n), X(n-1), ... ; P) X - state variable, P - parameter July 1999 DS&C Recruiting

Bifurcation scenario 0o 2q 10o Asymmetric chaotic regime Unknown
Asymmetric invariant torus Asymmetric limit cycle/ chaotic regime Asymmetric limit cycle Secondary Hopf Asymmetric equilibrium Hopf Symmetry breaking Symmetric equilibrium Symmetric limit cycle 0o 2q 10o Multistability July 1999 DS&C Recruiting

Dynamic Systems and Control Example: Enclosure Noise Control

UTC Products Require Quiet Interiors
Similarities: Mechanisms: exterior excitation, structureborne and airborne paths; point and distributed sources Content: broadband and tonal, low to high frequencies Complicated subsystem coupling Goal: reduce cabin noise using active control July 1999 DS&C Recruiting

Product Requirements Drive Program Content
Division Product Requirements Helicopter Cabin Noise Automotive Interior Noise Elevator Interior Noise Equipment Room Noise Commuter Aircraft Noise Research Program Content Characterization & Modeling Requirements Control Arch. & Algorithms Sensors & Actuators Facilities & Demonstrations Technology Integrated System Design July 1999 DS&C Recruiting

Typical Helicopter Spectrum
500 1000 1500 2000 2500 Frequency (Hz) Cabin Acoustic Level (dB) 20 dB per division 1x Bull Gear Clash, 778 Hz 1x Bevel Gear Clash, 1140 Hz 2x Bull Gear Clash, 1556 Hz July 1999 DS&C Recruiting

Number of Acoustic Modes
600 500 400 Number of modes 300 200 100 200 400 600 800 Frequency For an acoustic space 5’x6’x9’ Global control with speakers and microphones is not feasible July 1999 DS&C Recruiting

Gear-Mesh Noise Control Architecture
Transmission Sensors Controller Noise Actuators source July 1999 DS&C Recruiting

Control System Schematic
Rotor Speed Reference Signal Adaptation h h Sensors y z u S x Controller x Sample Harmonic Estimator (Demodulate) Remodulate Plant T i,j July 1999 DS&C Recruiting

Performance Simultaneous performance at three tones
Optimized actuation configuration with minimum degrees of freedom Fundamental, f1 Fundamental, f2 Harmonic 2f1 100 100 100 90 90 90 80 80 80 Performance (dB) 70 70 70 60 60 60 50 50 50 5 10 15 20 25 30 5 10 15 20 25 30 5 10 15 20 25 30 Microphone number Microphone number Microphone number Overall July 1999 DS&C Recruiting 13

Adaptation Performance
Vary frequency by +/- 1% (10 seconds for full cycle) Adaptation maintains performance with adaptation adaptation off open loop 0.99 0.995 1 1.005 1.01 -25 -20 -15 -10 -5 5 10 15 Frequency (relative to nominal) Performance (dB relative to open loop) July 1999 DS&C Recruiting

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