July 1999DS&C Recruiting1 Dynamic Systems & Control Group

July 1999 DS&C Recruiting 1

Dynamic Systems &Control Group


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


UTC and UTRC Overview

Products and Organization


UNITED TECHNOLOGIES PRODUCTS AND BUSINESS UNITS

Pratt & Whitney Otis Carrier

Sikorsky AircraftHamilton Sundstrand


• 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

UNITED TECHNOLOGIES FACT SHEET


UTRC: OUR VALUE TO UTC

To provide technical leadership that increases thecompetitiveness of our business units.

UTRC accomplishes this by integrating technicaldisciplines and expertise that have business unitapplicability to create technology for the futureneeds of the corporation.


UTRC: MISSION STATEMENT

“See It First, Make It Happen”

We team with UTC’s business units to foreseetechnological 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 innovativecommunity that is attractive to top talent and isrecognized as a unique corporate resource. We strivefor an environment of integrity, trust, mutual respect,fairness and learning in which we can all grow.


UTRC: ORGANIZATIONThe Office of the Director provides the UTRC with leadership andstrategic direction. A strong partnership exists between program planningand execution functions to ensure a clear focus on impacting the future ofthe business units.

UTRC Director

• Leadership• Strategic Direction

Director,Research Programs• Program Planning

Director,Research Operations• Program Execution

Office of the Director (UTRC)


UTRC: SENIOR LEADERSHIPThe senior leaders at UTRC are organized to support the ResearchCenter’s planning and execution efforts.

Director, Research Operations

Disciplines:Mechatronic Systems ICCTProduct Dev & Mfg Mat’ls & StructuresAeromechanical, Chemical & Fluid Sys

International:Germany & China

Services:Law, Finance, HR, Research ServicesThe Knowledge Organization,Management of Technology

Director, Research Programs

Division Program Leaders P&W Sikorsky Carrier Hamilton Sundstrand Otis Int’l Fuel Cells

Theme Leaders

External Program Leader

Office of th

eDirector

UTRC Director


UTRC CORE CAPABILITIES

Aeromechanical, Chemical & Fluid SystemsAcousticsAerodynamicsHeat TransferFluid DynamicsCombustion & FuelsEnvironmental Science

Mechatronic SystemsDynamic Modeling & AnalysisControls TechnologyControls ComponentsElectronics TechnologyAdvanced Embedded Systems

Information, Computer & Communication Technology

Advanced Digital SystemsDiagnostic TechnologyInformisticsNetwork TechnologySystems & Software

Materials & StructuresEngineered MaterialsMaterial & Structural ModelingMaterials CharacterizationStructural IntegritySurface Engineering

Product Development & ManufacturingProduct Innovation MethodsDesign for XRapid Product RealizationNondestructive EvaluationVirtual ManufacturingAdvanced Manufacturing Processes


UTRC: OUR EMPLOYEESThe Research Center employs close to 800 scientists,engineers, technicians and support staff worldwide.

1997 DISTRIBUTION

Administration 8%

TechnicalProfessionals

&TechnicalSupport

78%

Facilities& Support

14%


UTRC: TECHNICAL EMPLOYEES

The Center’s engineers and scientists form a diverse group of technical experts.

Mechanical28%

Electrical14%

Aeronautical11%

Chemical10%

Physics 7%

Computer Science/

Mathematics 12%

Materials8%

Engineers - Other 10%

B.S. 21%

M.S. 38%

Ph.D. 41%


UTRC: FUNDING SOURCESFinancial support for the Research Center’s operations isprovided through corporate, business unit sponsorship, and through contracts with industry and government.

Sources of Funds1998 TOTAL - $107.8 MILLION

29.3% Business Unit Co-Planned Program

12.6% BusinessUnit Subcontracts

14.9% Direct Contracts

14.7% Business UnitTechnical Support

28.5% CorporateSponsored Research

$31.6 $15.8

$13.6

$16.1

$30.7


UTRC: FUNDING USAGESelection of technical programs is driven by the potential to createvalue for our six business units. Co-planning of program milestoneswith the business units is key to the planning and selection process.

Business Unit Relevance1998 TOTAL - $107.8 MILLION

Pratt & Whitney 42%

Generic(all Business Units) 19% Otis 10%

HSD 5%

Carrier 13%

Sikorsky 6%

UTA 5%


Dynamic Systems and Control

People, Products, Problems, Solutions


•Mission

•People and Skills

•University Teaming

•Publications

•Project Organization: Products, Problems, Solutions

•Selected Project Examples



MISSION STATEMENT

We team with UTC’s business units to foreseetechnological opportunities and create solutions that redefine marketplaces, increase competitiveness,leave a legacy of excellence.

We provide world class technical expertisein the broad areas of dynamic systems and control includingexperimental programs, control system modeling, design, analysis and implementation and dynamic system analysisand computation.



People

UTRC, University Partnering, Skills,

Publications and Career Paths


•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

People and Program Characteristics


• 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.

Basic Research Areas of UTRC Interest



Group Members


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 1995. 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 1997. 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 http://talon.colorado.edu/~banaszuk.

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.


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.


Dynamic Systems and Control Group (continued)

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.


Dynamic Systems and Control Group (continued)

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.



External Collaborations


•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.

Modeling for Control of Mixing


•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.

Model Validation for Nonlinear Systems


•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.

Control Theory for Systems with Non-equilibriumAttractors


•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.

Performance and Stability Analysis of ExtremumSeeking Methods


Academic Contact: Dr. Kurt Lust, Cornell University & Katholic University of Leuven (http://www.cs.kuleuven.ac.be/~kurt)

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.

Development of Parametric Analysis Techniquesfor Large Scale Systems


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.


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, 1343 -1368.

- 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.


Assistant Research Engineer

Research Engineer

Senior Research Engineer

Principal Research Engineer

Individual contributor in single technical area

Principal investigator (responsibility for technical direction)

Program manager (responsibility for technical direction and resourcing)

Expert

UTRC Technical Career Path: Increasing Program Responsibility


UTRC Career Paths Cover Technical and Management

Line Managers

Fellows Program Council OperatingCouncil

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Technical Council



Projects

Organization, Content, Solutions


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


Dynamics and Control ApproachPhenomena

Characterization

Modeling

Control

Business Unit Need

Demonstration Productor Process Improvement

• Business case• Risk assessment• Product plan

• Actuation limits• Scaling laws

• Control design• Actuation system• Fundamental limits


Problem

Solution

Product

Undesirable dynamics

Change dynamics

Understand dynamics

Customer requirements

UTC cares for dynamic modeling, analysis, and control because dynamics (usually undesirable) affect UTC products.


Problems: undesirable dynamics affects UTC products

• 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 interactionwith air flow

• Carrier

– Noise (ducts, compressors, combustors)

– Compressor surge and stall

• Otis

– Elevator/cable dynamics

– Noise

– Power electronics dynamics

– Electric drives dynamics

Generic1. Noise and vibrations2. Flow separation - efficiency loss3. Flow/structure interaction - structural damage


Path to solutions: Understanding Dynamics

Physics-based modeling:•Construction of dynamical system

model•Identification of model parameters•Validation of models against data•Model reduction (Galerkin, POD, …)

Basic understanding of physics

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

Experimental data

Sensor selection Actuator selection

),( uxfx


Short/mid term solutions: change dynamics (fix the problem)

•Redesign product to avoid the

undesired behavior•Modify dynamics by passive fixes •Modify dynamics by active control

•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

Long term solutions: design dynamics (prevent the problem)

•Can be impossible (product has to

be shipped in 6 months ...)

•Can be expensive, difficult …

•What if the control system fails ...

Issues, tradeoffs: Options:



Example: Combustion Instabilities


Performance Limitations in Aircraft Engines

•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 reduce stages

•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


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


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

Dynamic Analysis & Control• Model Analysis

(Stability, Amplitude)• System Identification• Control Analysis & Design

(Adaptive, Robustness)• Control Implementation

Sensing & Actuation• Pressure• PMT• 2D Flame Imaging• Fuel Valves

• Solenoid• PZT• MOOG DDV; other

• Acoustic Forcing; Bleed Valve


Combustion Dynamics & Control: Team

Experimental• J. Cohen• D. Kendrick• H. Alholm• R. Decker

Modeling• A. Peracchio• G. Hendricks• D. Choi• A. Khibnik• B. Wake

Sensing & Actuation• T. Anderson• N. Rey• J. Haley• MOOG

Control• C. Jacobson• A. Banaszuk• Y. Zhang• G. Rey• R. Murray• R. Bitmead• M. Krstic

Product Integration• T. Rosfjord• W. Proscia• J. McVey• W. Sowa• J. Lovett (P&W)• S. Syed (P&W)


Problem

Solution

ProductUndesirable dynamics: lean mixture violent pressure oscillations high cycle fatigue, combustor destruction

Change dynamics. Options: 1. Redesign combustor2. Use passive devices to reduce oscillations3. Use active control to reduce oscillations

Understand dynamics reduced order physics-based model model analysis predicts limit cyclemodel parameters linked to design parameters model allows to identify effective actuation mechanism

Customer requirementslow emission level lean mixture

Example: industrial combustor design


Combustion Instabilities Will Occur

Equivalence Ratio

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0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60

NOxLBO

CombustionInstability

Combustion Instabilities Limit Minimum Achievable NOx Emissions

• Goals: • NOx/CO limits• RMS pressure limits

• Wide range of operating conditions• 50 - 100% power• -40 to 120 F ambient temp.

•Instabilities inevitable•combustion delay•convective delay

• Passive design solution may be possible• AIC can enable product

“Stability boundary” definedas maximum allowable pressurefluctuation level

Product Need


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


Process: Standard Work

Combustion Dynamics Active ControlReduced Order Modeling

Dynamic AnalysisSensing & Actuation

•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

Product: Improved Engines

Combustion Dynamics & Control:Purpose of Modeling to Influence Product and Process


0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-10

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•Evaluation of model sensitivities

•Development of experimental protocols and model calibration

•Evaluation of paths to mitigate undesirable behavior

Observed Unacceptable Time Response Behavior

System Level Model Showing Feedback Coupling

Effects of Parameter Variation on Stability Boundary

Model description capturing system

dynamics

Parametric analysis of system model

Enabling effective use of dynamic

model

Alter system dynamics to obtain acceptable behavior

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Combustion Dynamics & Control:Role of Dynamic Analysis in Modeling/Design Cycle

Evaluation of Design Options


System Level Model

CombustorAcoustics

CombustionResponse

System modifications - Preliminary design (& scaling) - Design optimization

Active Control - Actuator authority - Control algorithm development

Combustion Dynamics & Control:Role of System Level Modeling

- Closed-loop control performance

- Linear stability boundaries- Amplitude prediction

System Level Model Analysis

- Lessons learned in transferable code

- Key components and interactions- Experimentally obtained information

System Level Model Captures Purpose of System Level Model

Fuel Feed System

Air Feed System


Thermoacoustic Modeling and AnalysisLean 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

Acoustic subsystem

Heat Release subsystem

Fluctuating heat release driven by unsteady velocity

Fluctuating pressure driven by unsteady heat release


0.540.56 0.58

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Heat Release subsystem

Fluctuating heat release driven by unsteady velocity

Fluctuating pressure driven by unsteady heat release

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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)

Combustion Dynamics & Control: System Level Modeling and Analysis

Analysis shows model captures phenomenon

N diagram

Frequency varies with delay

Amplitude vs

Frequency varies with equivalence ratio


Data Analysis

Key parameters extracted from experiment (forced response tests) - trend in equivalence ratio (time delay) drives dynamical behavior

Calibration

•System level model captures experimental data quantitatively

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

Combustion Dynamics & Control: Model Calibration and Use in Evaluation of System Modifications

0.45 0.5 0.55 0.6 0.65 0.70

0.5

1

1.5

2

2.5

3

3.5

4

4.5Amplitude of pressure oscillations

phibar

ampl

itude

(%

)

0.46 0.48 0.5 0.52 0.54 0.56 0.583.5

3.55

3.6

3.65

3.7

3.75

3.8

3.85

3.9

3.95

4x 10

-3 Variation of Time Delay with Equivalence Ratio - DARPA

Mean Equivalence Ratio

Tim

e D

elay

(se

cond

s)

0 50 100 150 200 250 300 350 400 4500

0.005

0.01

0.015Bode plots P4_2p over Vact and fits with 8 poles, 8 zeros: magnitude

Mag

nitu

de

Hz

0 50 100 150 200 250 300 350 400 450-1000

-800

-600

-400

-200

0Bode plots P4_2p over Vact and fits with 8 poles, 8 zeros: phase

Pha

se

Files r60p14 and r60p29

Fuel/airpremixing

nozzlem n m t

m i

Side branchresonator

Orifice(to turbine)

Combustor

Coupled Resonator - Combustor System

Linear Acoustics

G(s)ddt

e s H(.)

Nddt

p

q

pressure

heat release rate

e s c

Feedback control modulatingequivalence ratio

Analysis allows calibration of model from data to enable quantitative studies



Examples



Flight Systems


Dynamics and Control Program:

Constrained Multivariable Control


Problem: Many UTC products are multi-input, multi-output systems but multivariable control theory is not useful for designingtheir 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 constraintsin their formulations

Approach:Develop a multivariable control synthesis method that explicitly recognizes constraints in its formulation


Propulsive Efficiency requires larger fans - structural constraints on fan speed

Thermal efficiency increases with burner temperature - nominal temperatures near melting point of parts - temperature overshoots rapidly degrade turbine life

Fan and compressor efficiency best nearstall, surge, and flutter boundaries- operating constraints to avoid instabilities

The demand for efficiency pushes engine operation to thephysical limits => controller must must meet many constraints


Don’t stall rotor Don’t strike fuselage

Pitch and roll limitsLimited engine powerand responsiveness

Actuator strokeand rate limits

Automatic Flight Control Systems Must Control 4 Degreesof Freedom While Meeting Many Constraints


CommandsPLA

Onboard Model/Estimationcontinuously estimating

state and uncertain parameters

Performance Indexweights, timing parameters

Constraintsactuator limits

actuator rate limitsmax T4

max,min N1,N2compressor op lines

Onboard, ConstrainedActuator Time HistoryOptimization

Approach: Optimization-Based Control Algorithm

Actuator Commands

SensorSignals

Focus of control logic designer


UTRC Team

Dynamic Systems and Controls: Jim Fuller, Leena Singh, Danbing Seto

Informistics ( numerical algorithms ) Martin Appel

Software Technology: William Weiss


Dynamics and Control Program:

Virtual Alignment Via Misalignment Estimation


EOTASElectro-optic Target Acquisition System

NQISInertial Navigation System

The Commanche Alignment Algorithm Transforms TargetLocation and Velocity from EOTAS to INS and Weapon Coordinates

The Alignment Algorithm Compensates for Bending, Installation Misalignments, and Sensor Errors for More Accurate Fire-control

Goal: maintain alignment during even aggressive maneuvers

Gun


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

The Team


EOTASrate gyro

frame

Tilting of gyro mount wrt EOTAS platform

Rotation thru EOTAS gimbals toseeker base

Nominal rotation of seeker base wrt NQIS platform

Rotation of seeker base wrt its nominalorientation due to fuselage bending

Tilting of gyro mount wrt NQIS platform

NQIS rate gyro frame

SeekerMis-MountEstimator

Gimbalangleresolvers

AircraftBlueprints

BendingEstimator

NQISMis-MountEstimator

Maneuverstresses

Target search ortracking

SG C SE

Targetpositionin seekercoordinates

Target positionin aircraftcoordinates

SE C SB0 SB0 C SB SB C NB NB C NG

Red and Blue are part of the KalmanFilter Based Alignment Algorithm


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

The inertial navigation, target acquisition, and gun systemseach have a triad of rate gyros to support their operation

Solution: Estimate the misalignment terms via a Kalman filter

Rationale and Approach


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

Complicating Factors

Rate gyros have slowly varying random biases - integral of bending rate measurement has large low frequency errors

Need time varying filter gains, but covariance propagationrequires too much computation

Solution: Quasi-steady state Kalman filter


Rate/Positionestimator

Direction Cosine

x

x

GimbalKinematics

ISG

SG

ING

NG

SGSB

SB

S

SB0CNG

KalmanGain

H F

K

Z-1

+

+

++

+

+

-

-

SBCSGy1

y2

Estimate offuselage bendingbetween EOTASbase and NQIS base

15 states

3 “measurements”

Predictedmeasurement

5 EOTASgimbal resolvers

3 EOTAS rate gyros

3 NQISrate gyros

Quasi-steady Kalman gains are scheduledanalytically via an invented time varying transform

Bending Estimator Formulated as Kalman Filter



Example: Control of Separated Flows


Subsonic Engine Flow Control

Inlet Lip• Separation control

Subsonic Diffuser• Separation control

Fan and Compressor• Separation control (fewer stages)• Clearance (margins, performance)• Noise

External Cowl• Drag reduction Fan Nozzle

• Area control

Jet Noise• Community noise

Combustion Mixing• Dynamic mixing enhancement


Dynamic signature of separation

Low order dynamic models (Galerkin, black box, phenom.)

Model-based control (experiment, CFD)

DEMO

Enabling process

Diffuser rig

Sikorsky, PW

- subscale experiment- parametric studies- testbed for dynamic- model-based control- testbed for CFD-enabled model and control design

- impact UTC products- implement and evaluate dynamic-model-based control design on real life applications


Methods and Issues

Fluid dynamics- 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


Flow separationMotivation & 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 vectoringApproach (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


50 100 150

40

41

42

0 100 200 300 400 50041

42

43

0 100 200 300 400

38

40

42

44

Snapshots of kinetic energy fields Longitudinal velocity traces(centerline of expansion exit)•Appearance of low freq. oscillations

•Onset of asymmetry

2~6o

2~6.5o

2~8o

2D DNS results (Rew1 ~ 30,000); N/w1~4; first transition; 6o < 2< 8o


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?)


Flow reconstruction

Notes:

- 1 mode captures location of large structures - 5 modes capture asymmetry

- 10 modes start capturing small scale details - 20 modes add very little to the picture of 10 modes

• POD modes computed for ensemble of KE fields spanning 5.4o < 2 < 10o

• 1,5 & 20 modes capture 47%,76% & 95% energy

Cumulative POD energy spectrum

2~8.5o i(x) uN(x,t) = i ai(t) i(x)

i=1, …., N


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

)(ufdt

du

)()( tUyutu avg

)())((

Uyufdt

tUyudavg

avg )( UyufU

dt

dyavg

T

)(ygdt

dy

U - POD modes

CFDtime-stepper

CFD/Galerkinmodel

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


Symmetric equilibrium

Symmetric limit cycle

Asymmetric chaotic regime

Asymmetric equilibrium

Asymmetric invariant torus Asymmetric

limit cycle/ chaotic regime

Bifurcation scenario

0o 10o2

Multistability

Symmetry breaking

Hopf

Secondary Hopf

Unknown

Asymmetric limit cycle



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


Facilities &Demonstrations

Characterization &Modeling

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

Requirements

Technology

Control Arch.& Algorithms

Sensors &Actuators

Integrated System Design


Typical Helicopter Spectrum

0 500 1000 1500 2000 2500

Frequency (Hz)

Cab

in A

cous

tic L

evel

(dB

)20

dB

per

div

isio

n1x Bull Gear Clash, 778 Hz 1x Bevel Gear

Clash, 1140 Hz2x Bull Gear Clash, 1556 Hz


Number of Acoustic Modes

0 200 400 600 8000

100

200

300

400

500

600

Frequency

Num

ber

of m

odes

• For an acoustic space 5’x6’x9’• Global control with speakers and microphones is not feasible


Gear-Mesh Noise Control Architecture

Noisesource

Sensors

Actuators

Transmission

Controller


Control System Schematic

x x

Rotor Speed Reference Signal

ControllerSample

Harmonic Estimator(Demodulate)

Remodulate

h

Plant T

Adaptation

i,j

ySensors

h

z u


Performance

Overall

5 10 15 20 25 3050

60

70

80

90

100

Per

form

ance

(dB

)

Microphone number

Fundamental, f1

5 10 15 20 25 3050

60

70

80

90

100

Microphone number

Fundamental, f2

5 10 15 20 25 3050

60

70

80

90

100

Microphone number

Harmonic 2f1

• Simultaneous performance at three tones• Optimized actuation configuration with minimum degrees of freedom


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

0

5

10

15

Frequency (relative to nominal)

Per

form

ance

(dB

rel

ativ

e to

ope

n lo

op)

Documents

July 1999DS&C Recruiting1 Dynamic Systems & Control Group