Dynamic Data-driven

Feedback Control of Combustion Instabilities

Principal Investigator: Dr. Asok Ray

Senior Research Personnel: Dr. Shashi Phoha

Graduate Students: Devesh Jha, Sihan Xiong, Sudeepta Mondal

Pennsylvania State University, University Park, PA

DDDAS Review Meeting, Jan 27th -29th , 2016

Project Start Date: Sept 30, 2015

INSTABILITIES IN LEAN PREMIXED COMBUSTION SYSTEMS

LIM

ITA

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OF

STA

TE-O

F-TH

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TEC

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IQU

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Nonlinear Complex Dynamic Models of the Combustion Process

Difficulties for Real-time Event Detection and Accurate Prediction

Lack of Mathematical Formulation for Dynamic Data Driven Models

Conservative Control System for Mitigation of Instabilities

Incompatibility of Existing Control Techniques with Statistical Estimators

Low NOx emission regulation Low equivalence ratio combustion Prone to instabilities

Heat Release Rate

Oscillations

Flow Oscillations

Acoustic Oscillations

COMPLEX HYDRODYNAMIC,

THERMAL & ACOUSTIC COUPLING

Motivation for Use of Dynamic Data Driven Techniques

COMBUSTION INSTABILITY DYNAMICS

STABLE COMBUSTION UNSTABLE COMBUSTION

STRUCTURAL DAMAGE AS A CONSEQUENCE OF COMBUSTION INSTABILITY

Normal Condition Damaged Condition

Picture Credit: “Y. Huang and V. Yang: Dynamics and stability of lean-premixed swirl-stabilized combustion, Progress in Energy and Combustion Science 35 (2009) 293–364”

Development of a unified dynamic data-driven methodology for applications to gas turbine engines for tactical and transport aircraft

Adaptive statistical learning for diagnosis and prognosis of combustion instabilities Identification of incipient sources of combustion instabilities

Online adaptation of critical measurement system parameters Placement and re-allocation of pertinent measurement resources among

appropriate nodes of the aircraft’s sensor network

Analysis and synthesis of real-time active combustion control algorithms for flight and propulsion control systems Resilient control (finite-time horizon) under both anticipated and

unanticipated emergency situations for fast recovery of the aircraft by DDDAS-based augmentation of the existing flight-propulsion control

Experimental validation of the theoretical research in DDDAS with applications to

aircraft gas turbine engines on existing and new laboratory apparatuses

OBJECTIVES OF THE DDDAS RESEARCH PROJECT

INITIAL WORK ON DATA-DRIVEN DETECTION AND ESTIMATION OF INSTABILITIES

Collaborative Effort for Domain Expertise and Different Sources of Experimental Data

PENN STATE CENTER FOR COMBUSTION, POWER AND PROPULSION Prof. Dom Santavicca and coworkers Test Apparatus for Methane Gas Combustion

PENN STATE CENTER FOR COMBUSTION, POWER AND PROPULSION Prof. Dominic Santavicca and coworkers Test Apparatus for Methane Gas Combustion

Collaborative Effort for Domain Expertise and Different Sources of Experimental Data

SCHEMATIC VIEW OF THE COMBUSTOR APPARATUS AT PENN STATE

INITIAL WORK ON DATA-DRIVEN DETECTION AND ESTIMATION OF INSTABILITIES

Collaborative Effort for Domain Expertise and Different Sources of Experimental Data

PENN STATE CENTER FOR COMBUSTION, POWER AND PROPULSION Prof. Dominic Santavicca and coworkers Test Apparatus for Methane Gas Combustion

AEROSPACE ENGINEERING DEPARTMENT, Indian Institute of Technology (IIT), Madras Prof. S.R. Chakravarthy and coworkers Test Apparatus for Methane Gas Combustion

SCHEMATIC VIEW OF THE COMBUSTOR APPARATUS AT IIT MADRAS, INDIA

Collaborative Effort for Domain Expertise and Different Sources of Experimental Data

AEROSPACE ENGINEERING DEPARTMENT, Indian Institute of Technology (IIT), Madras Prof. S.R. Chakravarthy and coworkers Test Apparatus for Methane Gas Combustion

Note: All dimensions are in millimeters

Small extension ducts

|

200 145 695

273

250

1167

Fuel

Test section

Optical access

Swirler Pressure

transducers Inlet air Settling

chamber

|

| | | | Inlet duct

|

Big extension duct

120120

120/90

60 Air 25 Fuel

12

_

_

_

_ _

_

| |

Note: All dimensions are in millimeters

Air slits

Mixing tube

Swirler Plate

Fuel inlet manifold

Fuel Injection tube Swirler

Perforated holes Fuel mixing

holes

Details of Fuel Injection System Details of Swirler System

INITIAL WORK ON DATA-DRIVEN DETECTION AND ESTIMATION OF INSTABILITIES

Collaborative Effort for Domain Expertise and Different Sources of Experimental Data

PENN STATE CENTER FOR COMBUSTION, POWER AND PROPULSION Prof. Dom Santavicca and coworkers Test Apparatus for Methane Gas Combustion

AEROSPACE ENGINEERING DEPARTMENT, Indian Institute of Technology(IIT), Madras Prof. S.R. Chakravarthy and coworkers Test Apparatus for Methane Gas Combustion

MECHANICAL ENGINEERING DEPARTMENT, Jadavpur University, Kolkata, India Prof. A. Mukhopadhyay and coworkers Test Apparatus for LP Gas Combustion

Data Acquisition System (Signal Conditioner, NI PXI, PC)

Photo Multiplier Tube (PMT)

Premixing Tube

Fuel Ports

Swirler

Fuel In

let

Air in

let

Combustor Quartz Tube

Int Dia = 60 mm

L=200 mm

L=350 mm

Mass Flow Controller (MFC)

1 6 2 3 4 5 Flame

SCHEMATIC VIEW OF THE COMBUSTOR APPARATUS AT JADAVPUR UNIVERSITY, INDIA

MECHANICAL ENGINEERING DEPARTMENT, Jadavpur University, Kolkata, India Prof. A. Mukhopadhyay and coworkers Test Apparatus for Liquid Petroleum (LP) Gas Combustion

Collaborative Effort for Domain Expertise and Different Sources of Experimental Data

CURRENT PLAN OF AN ELECTRICALLY HEATED RIJKE TUBE FOR TESTING DYNAMIC DATA-DRIVEN ACTIVE CONTROL OF INSTABILITIES

Control & monitoring of critical phenomena in actual gas turbine engines

Active control by changing the acoustic properties of the combustion chamber upon detection and estimation of instability precursors

Safer option to test controller performance in the laboratory environment

Schematic View of the Planned Electrically Heated Rijke Tube Apparatus at Penn State

PUBLICATIONS

Refereed Journal Publications

1. S. Sarkar, V. Ramanan, S. Chakravarthy and A. Ray, ``Dynamic Data-driven Prediction of Instability in a Swirl-stabilized Combustor,” International Journal of Spray and Combustion Dynamics, under review.

2. S. Sarkar, P. Chattopadhyay and A. Ray, ``Symbolization and Information-theoretic Analysis of Dynamic Data-driven Systems for Feature Extraction and Pattern Classification,“ Pattern Recognition, under review.

3. Y. Li, D. K. Jha, A. Ray and T.A. Wettergren, ``Information Fusion of Passive Sensors for Detection of Moving Targets in Dynamic Environments,” IEEE Transactions on Cybernetics, in press.

Refereed Conference Publications

1. S. Xiong, A. Ray and S. Phoha, ``Complex Hilbert Space Modeling of Machine Intelligence for Integration with Cognition & Decision Models,” June 2016 DDDAS Workshop, San Diego, CA, under review. 2. D.K. Jha, N.N. Virani, S. Xiong and A. Ray, ``Markov Modeling of Time Series via Spectral Analysis in Dynamic Data-driven Application Systems,” June 2016 DDDAS Workshop, San Diego, CA, under review.

3. S. Sarkar, D. K. Jha, K.G. Lore, S. Sarkar and A. Ray, ``Multimodal Spatiotemporal Information Fusion using Neural-Symbolic Modeling for Early Detection of Combustion Instabilities,” 2016 American Control Conference, Boston, MA, under review. 4. M. Hauser, Y. Li, J. Li and A. Ray, ``Real-time Combustion State Identification via Image Processing: A Dynamic Data-Driven approach,” 2016 American Control Conference, Boston, MA, under review.

POTENTIAL OUTCOMES OF THE RESEARCH PROJECT

Development of monitoring & control algorithms and the associated software to accommodate adverse and emergency situations:

Dynamic data-driven algorithms resulting from synergistic combinations of multimodal (e.g., acoustic and optical) sensing for aircraft engine monitoring & control.

Future programs requiring the advanced DDDAS-based technology include:

• Joint Strike Fighter (JSF)

• Unmanned Compact Air System (UCAS)

• Versatile Affordable Advanced Turbine Engines (VAATE)

• Next Generation Product Family commercial engines.

SCIENTIFIC/TECHNICAL PLAN AND ANTICIPATED ACCOMPLISHMENTS

Information Modeling Innovation

Innovation: Symbolic Dynamic Data Modeling of Combustion Process using multimodal sensing

(e.g., Chemiluminescence & Acoustic Sensing and Flame Imaging)

Applications: Monitoring & Control of Combustion Instabilities in Tactical and Transport Aircraft

New Capabilities: Extension of Aircraft’s Operational Envelopes under Diverse Flight Conditions

Algorithmic Innovation Innovation: Compression, Fusion, and Quantification of Forward and Feedback Information for

• Feature Extraction and Pattern Classification from Spatially Distributed Time Series and

Images

• Real-time Robust Control of Nonlinear Dynamical Systems at Multiple Spatio-temporal Scales

Applications: Process Monitoring & Control for both Military and Commercial Applications

New Capabilities: Real-time Execution of Low-complexity Algorithms on In-situ Platforms

Measurement/Instrumentation Innovation Innovation: Dynamic Adaptation of Measurement Parameters and Re-allocation of Measurement

Resources

Applications: Computer-instrumented Monitoring & Control of Combustion for Integrated Flight-

propulsion

New Capabilities: Sensor Network-based Online Monitoring & Control of Combustion

Instabilities

Heat Release Rate Fluctuation

(Combustion), q

Pressure Fluctuation (Combustor acoustics), p

Velocity Fluctuation (Flow Dynamics), u

OVERALL MECHANISM OF THERMO-ACOUSTIC INSTABILITY IN COMBUSTOR CLOSED-LOOP SELF EXCITED SYSTEM

HIGHLY NONLINEAR COUPLED

DYNAMICS

COMBUSTION INSTABILITY DYNAMICS THERMO-ACOUSTIC FEEDBACK CYCLE

DATA-DRIVEN FEEDBACK CONTROL OF COMBUSTION INSTABILITY

CHANGE IN OPERATING CONDITION

Prior Information • Available Nonlinear Models • Expected Operating Conditions

Propulsion System Controller Design

Combustion Process

Data

Multimodal Sensing

Adaptive Statistical Estimation and Prediction

STABLE

UNSTABLE

Sensing System Adaptation

DATA-DRIVEN FEEDBACK CONTROL OF COMBUSTION INSTABILITY

MULTIMODAL SENSING

STABLE

UNSTABLE

HI-SPEED CAMERA: Flame Characteristics

STABLE UNSTABLE

MULTIPLE PRESSURE SENSORS: Pressure Fluctuations

DATA-DRIVEN FEEDBACK CONTROL OF COMBUSTION INSTABILITY

CHANGE IN OPERATING CONDITION

Prior Information • Available Non-linear Models • Expected Operating Conditions

Propulsion System Controller Design

Combustion Process

Data

Multi-Modal Sensing

Adaptive Statistical Estimation and Prediction

STABLE

UNSTABLE

Sensing System Adaptation

DYNAMIC DATA-DRIVEN FEEDBACK CONTROL OF COMBUSTION INSTABILITIES

ADAPTIVE STATISTICAL ESTIMATION AND PREDICTION

MULTIMODAL SENSING

DYNAMIC DATA-DRIVEN MODELING FOR PREDICTING

TRANSIENT BEHAVIOR BETWEEN STABLE REGIONS OF

PHASE SPACE

Anomaly Detection

Model Correction

System State Identification

CAUSAL PRECURSORS TO INSTABILITY FOR ACTIVE

CONTROL

Dynamic Sensor Selection for Behavior

Characterization

STATISTICAL MARGINS FOR STABLE OPERATION

+ Statistical Learning-based

DIFFICULT TO ESTIMATE PRECURSORS USING MODEL-BASED APPROACHES FOR INHERENT COMPLEXITY

OF THE COMBUSTION PROCESS

WIDE RANGE OF UNCERTAIN OPERATING CONDITIONS

Statistical Root-Cause Analysis

Representation Learning

Transfer Learning

HIGH-SPEED DYNAMICS

Requirements of Fast Detection and Estimation of Events

Detection of Pre-cursors to Unstable Behavior

ACCURATE PREDICTIVE MODELING FROM A DATA-DRIVEN PERSPECTIVE

Presence of Hidden Variables

Uncertainties in Model Parameters

Capturing All Pertinent Uncertainties in the Statistical Model

Construction of Conditionally Independent Statistical Models

DYNAMIC DATA-DRIVEN FEEDBACK CONTROL OF COMBUSTION INSTABILITIES

RESEARCH CHALLENGES

INITIAL WORK ON DATA-DRIVEN DETECTION AND ESTIMATION OF INSTABILITIES

Pressure Measurements to Identify Precursors to Instabilities

Markov Modeling of Data for Representation and Learning

1. Pre-Processing 2. Finite-order

Markov Modeling

Stochastic Approximation of

Data

Change in Model Parameters

0.6 0.8 1 1.2 1.4 1.6 1.8

x 104

0

0.2

0.4

0.6

0.8

1

Re

Insta

bility In

de

x

Transient

Unstable

Stable

INITIAL WORK ON DATA-DRIVEN DETECTION AND ESTIMATION OF INSTABILITIES

t v h1 h2 hn

W1

W2

Wn

Symbolic Tim

e Series

Analysi

s

Input video

Time series (Pressure)

xD-Markov Machine to model

video and time series

D-Markov Machines of output

hidden units From video

Fusi

on

of

Hi-

Spee

d V

ideo

s an

d P

ress

ure

Tim

e-S

erie

s D

ata Data Representation using Deep Learning and Markov Models

Temporal Features

Sym

bo

lic T

ime

Seri

es A

nal

ysis

(ST

SA)

INITIAL WORK ON DATA-DRIVEN DETECTION AND ESTIMATION OF INSTABILITIES

10th Hidden Unit 5th Hidden Unit

Fusion of Hi-Speed Videos and Pressure Time-Series Data

S. Sarkar, D. K. Jha, K.G. Lore, S. Sarkar and A. Ray, ``Multimodal Spatiotemporal Information Fusion using Neural-Symbolic Modeling for Early Detection of Combustion Instabilities,” 2016 American Control Conference, Boston, MA, under review.

Thank You