Adaptive Engine Control

Challenges in Aircraft Engine Control and Gas Path Health Management

Dr. Sanjay Garg Donald L. Simon Chief, Controls and Dynamics Branch Controls and Dynamics Branch NASA Glenn Research Center NASA Glenn Research Center Ph: (216) 433-2685 Ph: (216) 433-3740 email: [email protected] email: [email protected] http://www.grc.nasa.gov/WWW/cdtb http://www.grc.nasa.gov/WWW/cdtb

Glenn Research Center Controls and Dynamics Branch at Lewis Field Challenges in Aircraft Engine Controls

Dr. Sanjay Garg Chief, Controls and Dynamics Branch NASA Glenn Research Center Ph: (216) 433-2685 email: [email protected] http://www.grc.nasa.gov/WWW/cdtb

Glenn Research Center Controls and Dynamics Branch at Lewis Field Outline

• Fundamentals of Aircraft Engine Control • Intelligent Engine Concept – from a controls perspective • Advanced Engine Control Logic • Active Component Control • Distributed Engine Control • Summary

Glenn Research Center Controls and Dynamics Branch at Lewis Field Turbofan Engine Basics N2 LPC - Low Pressure Compressor HPC - High Pressure Compressor HPT - High Pressure Turbine LPT - Low Pressure Turbine N1 - Fan Speed N2 - Core Speed

N1 • Dual Shaft – High Pressure and Low Pressure • Two flow paths – bypass and core • Most of the thrust generated through the bypass flow • Core compressed air mixed with fuel and ignited in the Combustor • Two turbines extract energy from the hot air to drive the compressors Glenn Research Center Controls and Dynamics Branch at Lewis Field Basic Engine Control Concept • Objective: Provide smooth, stable, and stall free operation of the engine via single input (PLA) with no throttle restrictions • Reliable and predictable throttle movement to thrust response

• Issues: • Thrust cannot be measured • Changes in ambient condition and aircraft maneuvers cause distortion into the fan/compressor • Harsh operating environment – high temperatures and large vibrations • Safe operation – avoid stall, combustor blow out etc. • Need to provide long operating life – 20,000 hours • Engine components degrade with usage – need to have reliable performance throughout the operating life

Glenn Research Center Controls and Dynamics Branch at Lewis Field Basic Engine Control Concept

• Since Thrust (T) cannot be measured, use Fuel Flow WF to Control shaft speed N • T = F(N) Pump fuel flow from Accessories fuel tank Control Sensor Throttle

Pilot’s Compute Meter the Inject fuel Measure power desired fuel computed flow into produced request flow fuel flow combustor power

Valve / Fuel nozzle Actuator Yes Determine No Power operating desired? condition

Control Logic

Glenn Research Center Controls and Dynamics Branch at Lewis Field Environment within a gas turbine

Aerodynamic 2000+ºC 20000+ hours Buffeting Flame temperature Cooling air Between service 120 dB/Hz to 10kHz - 40ºC ambient at 650+ºC

40+ Bar Gas pressures

Foreign objects Birds, Ice, stones Air mass flow ~2 tonne/sec 8mm+ Shaft movement

50 000g centrifugal 2.8m acceleration 1100+ºC Diameter >100g casing vibration Metal temperatures to beyond 20kHz 10 000rpm 0.75m diameter

Glenn Research Center Controls and Dynamics Branch at Lewis Field Operational Limits N2 LPC - Low Pressure Compressor HPC - High Pressure Compressor HPT - High Pressure Turbine LPT - Low Pressure Turbine N1 - Fan Speed N2 - Core Speed

N1 • Structural Limits: • Maximum Fan and Core Speeds – N1, N2 • Maximum Turbine Blade Temperature • Safety Limits: • Adequate Stall Margin – Compressor and Fan • Lean Burner Blowout – minimum fuel • Operational Limit: • Maximum Turbine Inlet Temperature – long life Glenn Research Center Controls and Dynamics Branch at Lewis Field Fuel flow rate (Wf) or fuel ratio Historical Engine Control unit (Wf/P3) Safe operating region

Max. flow limit Droop slope Proportional control gain or droop slope Required fuel flow @ steady state

Min. flow limit Idle Max. power power GE I-A (1942) Engine shaft speed • Fuel flow is the only controlled variable. - Hydro-mechanical governor. - Minimum-flow stop to prevent flame-out. - Maximum-flow schedule to prevent over-temperature

• Stall protection implemented by pilot following cue cards for throttle movement limitations Glenn Research Center Controls and Dynamics Branch at Lewis Field Typical Current Engine Control • Allows pilot to have full throttle movement throughout the flight envelope - There are many controlled variables – we will focus on fuel flow

• Engine control logic is developed using an engine model to provide guaranteed performance (minimum thrust for a throttle setting) throughout the life of the engine - FAA regulations provide a maximum allowable rise time of 5 sec to reach 95% and a maximum settling time for thrust from idle to max Glenn Research Center Controls and Dynamics Branch at Lewis Field Implementing Limits for Engine Control

Wf surge Ps30

blowout

R N2 • Limits are implemented by limiting fuel flow based on rotor speed • Maximum fuel limit protects against surge/stall, over-temp, over- speed and over-pressure • Minimum fuel limit protects against combustor blowout • Actual limit values are generated through simulation and analytical studies Glenn Research Center Controls and Dynamics Branch at Lewis Field Control Law Design Procedure • The various control gains K are determined using linear engine models and linear control theory • Proportional + Integral control provides good fan speed tracking • Control gains are scheduled based on PLA and Mach number • Control design evaluated throughout the envelope using a nonlinear engine simulation and implemented via software on FADEC processor • Control gains are adjusted to provide desired performance based on engine ground and altitude tests and finally flight tests

Math Prob Control Specs Eval Model Form Logic

Yes Spec Hardware Software Good to Go Met? Testing & V&V

No Adjust Control Gains Glenn Research Center Controls and Dynamics Branch at Lewis Field Outline

Glenn Research Center Controls and Dynamics Branch at Lewis Field Intelligent Engine Technologies - A Systems Viewpoint -

Engine System Controller Actuators PLA + -

DT Isolation Diagnostics N1 S1 O1 S2 Sensor O2 Sensor Readings Estimates &

S8 N6 O8 Weights Prognostics Information Information CompressionRegeneration Modeling Sensors

• Components such as actuators, • Simplified models are essential for sensors, control logic, & diagnostic controller design. Understanding systems have to be designed with the physics of the phenomena is overall system requirements in required to capture critical system mind. dynamics in these models.

Glenn Research Center Controls and Dynamics Branch at Lewis Field Intelligent Propulsion Systems Control System perspective Multifold increase in propulsion system Affordability, Capability Environmental Compatibility, Performance, Reliability and Safety Advanced Health Active Control Technologies Management technologies for enhanced performance for self diagnostic and and reliability, and reduced prognostic propulsion emissions system - active control of - Life usage monitoring and combustor, compressor, prediction vibration etc. - Data fusion from multiple - MEMS based control sensors and model based applications information Distributed, Fault-Tolerant Engine Control for enhanced reliability, reduced weight and optimal performance with system deterioration - Smart sensors and actuators - Robust, adaptive control Glenn Research Center Controls and Dynamics Branch at Lewis Field Modeling Engine Faults and Performance Deterioration*

A general influence coefficient matrix may be derived for any particular gas turbine cycle, defining the set of differential equations which interrelate the various dependent and independent engine performance parameters.

Physical Problems Degraded Component Changes in • Erosion Performance Measurable Result in Producing • Corrosion Parameters • Fouling • Flow capacities • Built up dirt • Efficiencies • Spool speeds • FOD • Effective nozzle • Fuel flow • Worn seals or Permitting areas • Temperatures excessive Allowing correction • Expansion • Pressures clearance isolation of of coefficients • Power output • Burned, bowed or missing blades • Plugged nozzles

* From ―Parameter Selection for Multiple Fault Diagnostics of Gas Turbine Engines‖ by Louis A. Urban, 1974 Glenn Research Center Controls and Dynamics Branch at Lewis Field Outline

Glenn Research Center Controls and Dynamics Branch at Lewis Field Advanced Engine Control Logic • Multi-variable Control (MVC) – extensive research on engine application in the mid1970s-90s • LQR based MVC demonstrated on F-100 engine at NASA GRC in 1979 • LQG/LTR based engine control studies in mid 1980s with engine test in UK • H-infinity based robust engine control studies at NASA GRC in mid 1990s • Life Extending Control demonstrated in simulation studies at GRC in early 2000s • Modify the acceleration logic to increase on-wing life while still meeting the performance requirements • Various research studies on Sensor Fault Detection, Isolation and Accommodation

Glenn Research Center Controls and Dynamics Branch at Lewis Field Engine Performance Deterioration Mitigation Control

• Motivation—Thrust-to-Throttle Relationship Changes with Degradation in Engines Under Fan Speed Control

Throttle Fan Speed Thrust

Degradation- induced shift

Glenn Research Center Controls and Dynamics Branch at Lewis Field EPDMC Architecture • The proposed retrofit architecture:

• Adds the following ―logic‖ elements to existing FADEC: • A model of the nominal throttle to desired thrust response • An estimator for engine thrust based on available measurements • A modifier to the Fan Speed Command based on the error between desired and estimated thrust - Since the modifier appears prior to the limit logic, the operational safety and life remains unchanged Glenn Research Center Controls and Dynamics Branch at Lewis Field EPDMC Evaluation Thrust response for Typical Mission With EPDMC • Throttle to thrust response is maintained – no “uncommanded” thrust asymmetry

Without EPDMC

Glenn Research Center Controls and Dynamics Branch at Lewis Field Commercial Modular Aero-Propulsion System Simulation 40k Simulation programmed in graphical language

C-MAPSS40k PAX200 Commercial Turbofan Engine and Controller Models

out_Nf Alt alt Alt altitude Nlp sens nf_sens Wf _act out_Nc Mach mach Nhp sens nc_sens

VSV_act out_T2 dTamb dTamb T2 sens T2_sens NcR NcR out_T24 VBV_act T24 sens T25_sens NfR Nf R Mach Engine flight data Mach out_T30 Controller T30 sens T30_sens used to tune out_T50 T50 sens egt_sens Nf_zro Nf _zro out_P2 physics-based P2 sens P2_sens out_P25 Nc_zro Nc_zro model P25 sens P25_sens out_P30 P30 sens P30_sens

Simulation Inputs corrected speeds f uel f low out_P50 out_Wf Plotting and graphical P50 sens P50_sens Fdrag out_Fdrag VSV out_VSV out_Fnet analysis capability Fnet Fnet Health Parameters Engine Outputs Displays

VBV Fgross out_Fgross out_VBV

Engine Model

GUI driven operation

C-MAPSS40k thrust and stall margin response to throttle movements Glenn Research Center at Lewis Field Model-Based Control and Diagnostics Concept

Actuator Engine Commands Instrumentation • Fuel Flow • Pressures • Variable Geometry Actuator • Fuel flow • Bleeds Positions • Temperatures • Rotor Speeds “Personalized” Engine Control On-Board Model Selected Sensors Component & Tracking Filter Performance • Efficiencies Estimates • Flow capacities Sensor Sensor Estimates • Stability margin Validation & • Thrust Fault Detection Sensor Measurements On Board

Ground Ground-Based Diagnostics Level • Fault Codes • Maintenance/Inspection Advisories

Glenn Research Center Controls and Dynamics Branch at Lewis Field Model-Based Engine Control Objective: Develop and demonstrate the capability to provide more efficient engine control using an on-board real-time model. Approach: - Develop a self-tuning engine model for the C-MAPSS40k engine simulation – using the optimal tuner approach - Validate the self-tuning model's ability to track changes in engine gas path performance parameters - Develop direct thrust and limited variable Self-tuning engine model vs. “un-tuned” control using model based estimated value piecewise linear model response (top), and corresponding model tuning parameter adjustments (bottom)

Tight control of Thrust achieved – preliminary linear design

Glenn Research Center at Lewis Field Adaptive Engine Control • The traditional engine control logic consists of a fixed set of control gains developed using an average model of the engine • Having an on-board engine model which “adapts” to the condition of the engine, opens up the possibility of adapting the control logic to maintain desired performance in the presence of engine degradation or to accommodate any faults while obtaining best achievable performance • An emerging technique for such an adaptive engine control is the Model Predictive Control (MPC)

Reference At each time step model is matched to measurements Prediction with impact of control horizon action • MPC solves a constrained (estimation) optimization problem online Prediction horizon Prediction with to obtain the “best” control Control horizon fixed control action at current value action - based on a tracked engine model, constraints, and Past Future the desired optimization Only first control action is implemented objective Glenn Research Center at Lewis Field Outline

Glenn Research Center Controls and Dynamics Branch at Lewis Field Separation Control in Intake Ducts

Glenn Research Center at Lewis Field Active Stall Control

• Detect stall precursive signals from

pressure measurements. Injector • Develop high frequency actuators and Intake Rotor injector designs. scoop • Actively stabilize rotating stall using high Compressor Stability Enhancement Using velocity air injection with robust control. Recirculated Flow • Demonstrated significant performance improvement with an advanced high speed compressor in a compressor rig with simulated recirculating flow Glenn Research Center Controls and Dynamics Branch at Lewis Field Active Flow Control - Compressors Compressor Stator Suction Surface Separation Control

49 50 51 52 47 48 1 46 2 45 3 44 4 43 5 42 6 Flow Delivery 41 7

r 40 8 r o o t 9 t a 39 System a l l u 38 10 u m m

u Flow Control u

c 11 37 c 35% c Stator Annulus c A A

36 12

t t s s

e 35 13 a

E Chord W 34 14 33 15 32 16 31 17 30 18 29 19 28 20 27 21 26 25 24 23 22 30º Flow Injection

Mass Flow Control Multistage Axial Compressor Filter Meter Valve 75

+ (Non-dimensional

F Injection frequency) 0

50 0.47

(%)

c 0.56

 1.12

25 1.68

Installed Smart Vane Stators 0

within the vane, thewithin Net total pressure loss reduction loss totalpressure Net

Rapid Prototype -25 Flow Control Vane 0 10 20 30 40 50 3 Injected momentum coefficient, Cμ x 10 Active Combustion Controls

Downstream Dynamic Pressure Trace P3=97 psi, T3=561F, f/a=.040, Wair=3.71lbs/sec

-8 -6 -4 -2

0 psi 2 4 6 8 0.00 0.08 0.17 0.25 0.33

time (sec)

Pattern Factor Combustion Control Instability Control Objective: Actively reduce combustor Objective: actively pattern factor suppress thermo-acoustic driven pressure Status: Concept Emission Minimizing Control oscillations demonstrated in Status: Concept collaboration with Objective: Actively reduce NOx demonstrated on a single Honeywell Engines production combustion rig in 2003. under the AST Status: Fuel actuation concept and Continuing research under program - 2000. hardware developed under AST program. current projects. Preliminary low order emission models developed under the HSR program 2000. Glenn Research Center at Lewis Field edmond wong 970728 Active Control of Combustion Instability High-frequency fuel valve Advanced Control Methods NL Acoustics

White Noise Instability Pressure

+ Pressure from + Fuel Fuel Modulation + Combustor Pressure Fuel lines, Injector Flame Valve & Combustion 

Phase Shift Controller

Filter

Fuel delivery system model and hardware

Accumulator

Modulated Combustor Instrumentation Modulating Fuel Flow (pressures, temp’s) Fuel Valve In

Fuel Fuel Injector Nozzle Assy Emissions Probe

Research combustor rig

Glenn Research Center Controls and Dynamics Branch at Lewis Field Active Instability Control on a Low Emission Combustor Prototype

Pressure Results from testing Oct-Nov 2011 Sensor Closed-Loop Self-Excited System Combustion Combustor Combustor Φ’ Process Acoustics P’ + Fuel-air Natural feed-back process Mixture system Artificial control process Actuator Controller Sensor Fuel Actuator (other side) Run 1018, Date 101123, Variable filtered_plant Page 1/1 0.7 2 Time RMS=1.61, Mean=0.33 2.5 0.6 Max=0.6 @ 626Hz Time RMS=0.54, Mean=0 1 2 0.5 1.5 0.4 Controller Off

0 064

1 0.3 plant psi

0.5 0.2 -1 0 0.1

-0.5 0 -2

-1 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 Time, sec Instability Prevention 0.5 0.05 2 Time RMS=1.3, Mean=0 0 Max=0.05 @ 631Hz Time RMS=0.11, Mean=0 0.04 -0.5 1

-1 0.03 Controller On 0

-1.5 067 psi 0.02 plant -2 -1 -2.5 0.01

-3 0 -2 1 2 3 0 5 10 15 20 25 30 35 40 Instability SuppressionFrequency, Hz Time, sec Intelligent Management of Turbine Tip Clearance

Time Scales: Flights Minutes Seconds Milliseconds Problem: Engine Cruise Pinch Eccentric Wear Clearance Points Shaft Motion Approach: Regen. Case Case Magnetic Seals Cooling Actuation Bearings

Notional Mission Profile

Take-off Decel Re-Accel

Cruise Turbine Tip Tip Clearance Turbine

Pinch Points

Time Glenn Research Center Controls and Dynamics Technology Branch at Lewis Field Outline

Glenn Research Center Controls and Dynamics Branch at Lewis Field Distributed Engine Control

Objectives: • Enable new engine concepts • Enable new engine performance enhancing technologies • Improve reliability • Reduce overall cost • Reduce control system weight

Challenges: • High temperature electronics • Communications based on open system standards • Control function distribution Government – Industry Partnership Distributed Engine Control Working Group

Glenn Research Center Controls and Dynamics Branch at Lewis Field Distributed Control Technology Roadmap T=0 years 5 10 15 20

CORE I/O . Core-Mounted : Data Concentrator Digital Communications Distributed Power SOI μP, logic, analog SiC power

NETWORKED CONTROL . Engine Network Smart System Devices >300 Celsius Electronics SOI μP, logic, analog Medium Scale Integration SiC μP, logic, analog SiC power

FULLY DISTIBUTED Common Network Communications (Wireless) Embedded Control Law Embedded Power Harvesting

SOI μP, logic, analog Large Scale Integration SiC μP, logic, analog SiC power Summary • There are tremendous opportunities to improve and revolutionize aircraft engine performance through ―proper‖ use of advanced control technologies – Intelligent engine control integrated with reliable condition monitoring and fault diagnostics to extend on-wing operating life, maintain performance with aging, safely accommodate faults while maintaining best achievable performance etc. – Active control of engine components to provide the desired performance characteristics throughout the flight envelope and enable low emission higher performance components – Distributed engine control to enable new engine concepts, reduce ―control system‖ weight, increase operational reliability, and flexibility to easily incorporate new and improved capabilities

Glenn Research Center Controls and Dynamics Branch at Lewis Field References • H. Austin Spang III and Harold Brown, ―Control of Jet Engines‖, Control Engineering Practice, Vo. 7, 1999, pp. 1043-1059 • Link Jaw and Jack D. Mattingly, ―Aircraft Engine Controls,‖ AIAA Education Series Book • Jonathan A. DeCastro, Jonathan S. Litt, and Dean K. Frederick, ―A Modular Aero-Propulsion System Simulation of a Large Commercial Aircraft Engine‖, NASA TM 2008-215303. • Jeffrey Csank, Ryan D. May, Jonathan S. Litt, and Ten-Huei Guo, ―Control Design for a Generic Commercial Aircraft Engine‖, NASA TM-2010-216811 • Sanjay Garg, ―Propulsion Controls and Diagnostics Research in Support of NASA Aeronautics and Exploration Mission Programs,‖ NASA TM 2011-216939. NASA TMs are available for free download at: http://ntrs.nasa.gov/search.jsp

Engine Simulation Software C-MAPSS40k – available to U.S. citizens http://sr.grc.nasa.gov/public/project/77/

Glenn Research Center Controls and Dynamics Branch at Lewis Field