Sanjay Garg

Glenn Research Center, Cleveland, Ohio

Air Breathing Propulsion Controls and Diagnostics 

Research at NASA Glenn Under NASA Aeronautics 

Research Mission Programs

NASA/TM—2015-218744

June 2015
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Sanjay Garg

Glenn Research Center, Cleveland, Ohio

Air Breathing Propulsion Controls and Diagnostics 

Research at NASA Glenn Under NASA Aeronautics 

Research Mission Programs

NASA/TM—2015-218744

June 2015

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135
Acknowledgments

The author will like to thank all the members of the Intelligent Control and Autonomy Branch for their enthusiasm and initiative 

in performing the research documented in this paper, and for providing the graphics and information for the paper. The Branch 

members including civil servants and contractors are: Eliot Aretskin-Hariton, Rachael Bis, Jeff Chapman, Amy Chicatelli, Joseph 

Connolly, Jeff Csank, Dennis Culley, Christopher Fulton, Ten-Huei Guo, Joe Hemminger, George Kopasakis, Dzu K. Le, Jonathan 

Litt, James Liu, William Maul, Ryan May, Kevin Melcher, Daniel Paxson, Aidan Rinehart, Joseph Saus, Donald Simon, Shane 

Sowers, Thomas Stueber, Randy Thomas, Dan Vrnak, Edmond Wong and Alicia Zinnecker. The author will also like to thank the 

various NASA program/project managers who have supported these research efforts.

Available from

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Space Administration.

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at Lewis Field

Glenn Research Center

Intelligent Control and Autonomy Branch

Air Breathing Propulsion Controls and 

Diagnostics Research at NASA Glenn Under 

NASA Aeronautics Research Mission Programs

Dr. Sanjay Garg

Chief, Intelligent Control and Autonomy Branch

Ph: (216) 433-2685

FAX: (216) 433-8990

email: sanjay.garg@nasa.gov

http://www.grc.nasa.gov/WWW/cdtb

Abstract

The Intelligent Control and Autonomy Branch (ICA) at NASA (National Aeronautics and Space 

Administration) Glenn Research Center (GRC) in Cleveland, Ohio, is leading and participating in various 

projects in partnership with other organizations within GRC and across NASA, the U.S. aerospace 

industry, and academia to develop advanced controls and health management technologies that will help 

meet the goals of the NASA Aeronautics Research Mission Directorate (ARMD) Programs. These efforts 

are primarily under the various projects under the Advanced Air Vehicles Program (AAVP), Airspace 

Operations and Safety Program (AOSP) and Transformative Aeronautics Concepts Program (TAC). The 

ICA Branch is focused on advancing the state-of-the-art of aero-engine control and diagnostics 

technologies to help improve aviation safety, increase efficiency, and enable operation with reduced 

emissions. This paper describes the various ICA research efforts under the NASA Aeronautics Research 

Mission Programs with a summary of motivation, background, technical approach, and recent 

accomplishments for each of the research tasks.

NASA/TM—2015-218744 1
Aeronautics Research Mission Directorate

Advanced Air 

Transport Technology

AATT - (GRC)

Advanced Air 

Vehicles (AAVP)

Airspace Operations

And Safety (AOSP)

Integrated Aviation 

Systems (IASP)

NASA Aeronautics Program Structure

Effective FY15

Transformative Aeronautics

Concept (TACP)

Revolutionary Vertical

Lift Technology

RVLT - (LaRC)

Commercial Supersonic

Technology

CST - (LaRC)

Advanced Composites

AC - (LaRC)

Aeronautics Evaluation

and Test Capabilities

AETC - (ARMD)

Airspace Technology

Demonstration

ATD - (ARC)

SMART NAS – Testbed

for Safe Trajectory

Operations (ARC)

Safe Autonomous

System Operations

SASO - (ARC)

Environmentally

Responsive

Aviation

ERA - (LaRC)

UAS Integration

in the NAS

(AFRC)

Flight Demonstration

and Capabilities

FDC - (AFRC)

Cross Program

Operations

CPO - (ARMD)

Leading Edge

Aeronautics Research

for NASA

LEARN - (ARMD)

Transformative Tools

and Technologies

TTT – (GRC)

Convergent Aeronautics

Solutions

CAS - (ARMD)

--------------------- Mission Programs ------------------- Seedling Program

This chart provides an overview of the NASA Aeronautics Research Mission program structure.

The mission programs are meant to be Technology Readiness Level 2-6, while the seedling program 

is meant for investigating new innovative ideas. The Leading Edge Aeronautics Research for NASA 

(LEARN) program is for funding proposals from industry and academia. Each of the projects listed under 

the Mission programs typically has NRAs (NASA Research Announcements) requests focused on very 

specific technology needs to meet the project goals.

Go to http://www.aeronautics.nasa.gov/nra.htm to see the list of open NRAs.

The red ovals around the projects represents projects where Intelligent Control and Autonomy Branch 

has research tasks associated with propulsion controls and diagnostics technologies. 

NASA/TM—2015-218744 2
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Intelligent Control and Autonomy Branch

Advanced Air Vehicles Program

• AATT – Dynamic Systems Analysis Tools and Methods

• CST – Aero-Propulso-Servo-Elasticity

• CST (AFRL) – Hypersonic Propulsion System Control

Airspace Operations and Safety Program

• ATD – Propulsion Simulation for Enhanced Simulator Fidelity

• SMART NAS – Runtime Assurance of Complex Systems

Transformative Aeronautics Concept

• TTT – Distributed Engine Control Tools and Technologies

• TTT – Model Based Engine Control

• TTT – Active Combustion Control

• TTT – Pressure Gain Combustion

• CAS – Gas Path Health Management

Other

• Enhanced Engine Response Control – discontinued after VSST

• Engine Simulations: C-MAPSS, C-MAPSS40k, T-MATS

• Engine Icing – detection and mitigation

GRC “Aero Controls” Tasks

ICA Tasks Under NASA Aeronautics Research

The Intelligent Control and Autonomy Branch (ICA) tasks under the NASA Aeronautics Research 

Mission Directorate programs, and the program/project that support these tasks, are listed above. The 

tasks are described in the rest of the paper in terms of task objectives and current progress.

The tasks listed under “Other” was recent work done under projects that was discontinued after the 

Fiscal Year 2015 replanning of the NASA Aeronautics Research Programs.

NASA/TM—2015-218744 3
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Dynamic Systems Analysis

Project Goals Project Status

• Developed the Tool for Turbine Engine 

Closed-loop Transient Analysis (TTECTrA) 

Expected to be released summer 2014.

• Proposed a metric to analyze the transient 

capability of an engine design using 

TTECTrA and will be presented at the 

2014 Propulsion and Energy Forum in 

Cleveland, OH.

• Enable assessment of dynamic issues 

associated with performance and 

operability of subsonic turbofan engines 

in the early conceptual design stages. 

• Develop a generic engine control 

module which can be integrated with 

existing tools to provide dynamic 

systems analysis for conceptual engine 

designs.

• TTECTrA automatically designs a 

controller and provides an estimate 

of the closed-loop capability of the 

conceptual design while meeting 

operability constraints such as 

avoiding surge

• Allows for tradeoff between transient 

performance (response time) and 

operability margins (Surge Margin)

Thrust 

Command

Power Management Protection Logic Engine 

Model

10 12 14 16 18 0

2

4

6 x 104

Time,s

Thrust,lbf

10 12 14 16 18 0

10

20

30

40

Time,s

HPC SM, %

SM17

SM14

SM8

SM6

Download TTECTrA from Github:

https://github.com/nasa/TTECTrA

Increased 

Performance

Intelligent Control and Autonomy Branch

Dynamic Systems Analysis

Systems Analysis is typically done with steady state performance in mind. However, for complex 

systems such as aircraft engines, the capability to meet transient performance requirements over a wide 

operating envelope and a long operating life is critical. The steady-state performance based system 

analysis approaches do not capture the capability of a system to meet such requirements. When assessing 

technologies for system performance improvement, it is quite possible that a configuration that looks 

good from a steady-state performance perspective might be more challenging to control to meet transient 

and safety requirements. The objective of the Dynamic Systems Analysis (DSA) task is to develop tools 

and techniques that can be used to evaluate competing configurations and technologies from the 

perspective of being able to meet transient performance, operational life and safety requirements. The 

initial focus of this work is to develop the tools for DSA of traditional turbine engine configurations, with 

a goal to extend these tools to alternative innovative propulsion concepts. 

The Tool for Turbine Engine Closed-loop Transient Analysis (TTECTrA) was developed as an initial 

step for DSA of turbofan engines. It is a semi-automated control design tool for subsonic aircraft engine 

simulations. The purpose of this tool is to provide the user a preliminary estimate of the transient 

performance of an engine model without the need to design a full nonlinear controller. TTECTrA has 

been developed in the MATLAB/Simulink environment, which allows users to access a standard library 

of functions and to add on toolboxes such as the Control System Toolbox, which can be used to simplify 

the control design process (and is required to use TTECTrA). The user’s guide (Ref. 1) provides 

additional information and the software is expected to be publicly available in July 2014.

Industry members who face challenges in developing systems analysis tools for their applications 

which can consider dynamic performance requirements in early stages of assessing various competing 

technologies, can partner with us to leverage our experience in developing such tools for turbomachinery 

systems (see Ref. 1).

NASA/TM—2015-218744 4
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Aero-Propulso-Servo-Elasticity (APSE)

Goals

Develop dynamic propulsion system models, aero-servo-elastic & aerodynamic 

models, and integrate them in closed loop together with atmospheric turbulence 

to study the dynamic performance of supersonic vehicles for ride quality, vehicle 

stability, and aerodynamic efficiency.

Approach

• At NASA GRC the propulsion system models 

are developed for a Variable Cycle Engine 

(VCE) based on 1D gas dynamics for engine 

and quasi-1D CFD for the inlet and nozzle. 

• Alternatively, parallel flow path modeling is 

developed that includes rotational flow to 

study dynamic performance of flow distortion.

Accomplishments

• Developed atmospheric turbulence models, 

propulsion system 1D gas dynamics models, 

and quasi 1D inlet and nozzle models.

• Developed first VCE dynamic model with 

feedback controls and schedules to operate 

continuously with varying power level.

• Developed first closed loop APSE system.

Variable cycle engine thrust response Nozzle frequency domain response

Inlet axial 

pressure 

distribution

Inlet CFD – 2D flow field

Closed loop APSE Model

Aero-Servo-Elastic wing displacements w/ 

and without propulsion 

system coupling

Supersonic Concept Vehicle

Aero-Propulso-Servo-Elasticity (APSE)

For the NASA High Speed Project, the overall objective is to perform the research to advance the 

technology so that the industry will be in a position to develop supersonic cruise civil transport aircraft. 

There are many technical challenges remaining for supersonic vehicle technology development, such as 

sonic boom reduction for overland flight, emissions (NOx ) reduction, fuel efficiency, materials, control 

and handling qualities, etc. 

The objective of the Aero-Servo-Elasticity (ASE) task and specifically the APSE) task is to develop 

dynamic models for integrated propulsion and airframe systems and associated control designs to study 

overall vehicle performance, such as vehicle stability and vehicle ride quality as well as aerodynamic 

efficiency. The future supersonic transport vehicles are expected to be slender body with a highly flexible 

structure. Initial studies have shown that there is a potential for significant coupling between the vehicle 

flexible modes and the propulsion dynamics under atmospheric turbulence, which can result in 

unacceptable ride qualities. Therefore the objective of the overall APSE effort is to develop higher fidelity 

propulsion system dynamic models, understand the coupling issues with vehicle flexible modes, and 

design conceptual control logic to mitigate the effects of such coupling. The types of modeling employed 

are volume gas dynamics with component performance characteristics - lumped and stage-by-stage, 

Computational Fluid Dynamics (CFD), and parallel flow path modeling. Control designs involve 

feedback controls loop shaping, and engine schedules for compressor Inlet Guide Vanes, fan bypass, and 

exit nozzle area.

Shown in the chart above are: simulated thrust responses for various Mach numbers; a frequency 

domain response of the nozzle; 2D flow field for a CFD model developed for the inlet dynamics; the 

steady state static pressure distribution across the inlet; and the first preliminary APSE model developed 

with associated wing displacements response - with and without the propulsion system (see Ref. 2).

NASA/TM—2015-218744 5
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Intelligent Control and Autonomy Branch

Hypersonic Propulsion System Control – Overview

Pre-compression

forebody plate

Isolator

Low-speed plug

High-speed plug

Variable ramp

Bleed ducting

High speed cowl

splitter

Pivot for AoA

Overboard 

Bypass

Low-speed 

flow path

High-speed 

flow path

Low-speed 

flow path

AIP

High-speed 

flow path

AIP

GRC 10-foot x 10-foot

Combined Cycle Engine (CCE)

Testbed

• Low to high speed flowpath 

transition control

• Shock positioning

• Fuel flow

Σ Set

Point Controller

Shock 

Position

Estimator

• AOA

P2

e u

Process

Geometry

Free-stream

• Pt0

• M0

• Tt0

Hypersonic Propulsion System Control - Overview

Hypersonic air breathing propulsion is a technology of interest to NASA for more routine, safe, and 

affordable access to space. To enable this technology, NASA has conducted fundamental research on 

turbine based combined cycle (TBCC) propulsion systems. The current activity addresses the challenge of 

safe and efficient transition from a turbine engine to a dual-mode scramjet (DMSJ) combustor while 

cruising at Mach 3. This event is identified as an inlet mode transition. To experimentally investigate an 

inlet mode transition, a combined cycle engine (CCE) inlet system was designed, fabricated, and tested in 

the NASA Glenn Research Center 10- x 10-foot supersonic wind tunnel. The inlet system is called the 

CCE large scale inlet for mode transition experiments (CCE-LIMX). Previous activity with the CCELIMX in the wind tunnel included characterization testing, Phase 1, and system identification 

experiments for control studies, Phase 2. The Phase 1 experiments defined steady-state operating points 

that can be linked together to form mode transition schedules. Likewise, the Phase 2 experiments included 

dynamic perturbations of the inlet system to acquire data for reducing to control design models. The goal 

of this research effort is to conduct mode transition experiments in the wind tunnel with an operating 

turbine engine mounted at the aft end of the CCE-LIMX - Phase 4. To prepare for Phase 4, CCE-LIMX 

wind tunnel tests are needed to develop and refine bypass door control algorithms that will allow closedloop control of the normal shock position in the turbine engine low-speed flow path - Phase 3. Current 

activities are focused on reinstalling the inlet into the wind-tunnel and performing controls experiments to 

make ready a system for testing with a turbine engine (see Ref. 3).

NASA/TM—2015-218744 6
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Partial annulus inlet/fan CFD model displaying static 

pressure contours calculated at zero AOA in 

FINE/Turbo. Engine face sectored into equal quadrants 

Total thrust using different controllers at increasing 

AOA (0° to 21 °). Thrust was determined as a function 

of AOA using the parallel version of C-MAPSS40k

20 30 40 50 60 70 80 90 100 2.4

2.5

2.6

2.7

2.8

2.9 x 104

Total Thrust, lbf

 

Open Loop

Fan Speed Control

EPR Control

High Angle of Attack Propulsion System Modeling

Parallel Compressor Theory approach converted 

CFD results into modified performance maps in CMAPSS40k. Four parallel quadrants were modeled

Thrust variation was converted to a set of scale 

factors. The scale factors are functions of AOA and 

normalized thrust

• Study the impact on thrust for operation beyond “normal” AOA

Intelligent Control and Autonomy Branch

High Angle of Attack Propulsion System Modeling

Commercial engines are designed to operate over a limited range of angles of attack (AOA). As AOA 

gets larger, the airflow into the engine is reduced, resulting in reduced thrust and stability. The thrust 

depends on the controlled variable (fan speed or engine pressure ratio (EPR)). NASA Aviation Safety 

Program is interested in developing aircraft simulations that can provide realistic training for pilots in 

handling Loss of Control (LoC) situations where the aircraft has reached an abnormal flight state. Part of 

this effort involves developing realistic engine performance simulations in high AOA situations.

The high AOA engine modeling was performed using CFD (Computational Fluid Dynamics) by 

sectoring the inlet and fan into four equal quadrants. The C-MAPSS40k engine simulation was sectored 

into four corresponding parallel streams using Parallel Compressor Theory. This is a well-established 

technique for modeling the effects of inlet distortion on compressor performance. The CFD provided 

performance map adjustments at various AOAs for each of four parallel versions of C-MAPSS40k. 

Running the parallel version of C-MAPSS40k provided thrust and stall margin reduction information, 

which was subsequently converted to tabular form to serve as an adjustment to the output of the baseline 

single stream C-MAPSS40k. This allows much faster execution of the simulation while producing results 

similar to those of the parallel compressor approach. Currently, full annulus CFD modeling at high AOA 

is being pursued to validate these results (see Ref. 4).

NASA/TM—2015-218744 7
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Run-time Assurance of Advanced Propulsion Algorithms

Intelligent Control and Autonomy Branch

Advanced 

Model-Based

Engine Controller

Run-time 

System Monitor

State

Control

Trigger

Baseline

Certifiable

Backup Controller

Engine

Transition Control –

transfer control to backup 

system when safety 

violations are detected

Safe

Operating 

Region

Run-time Monitor checks system 

states

• Advanced aircraft engine controllers are 

becoming increasingly difficult to certify using 

current verification practices. 

• Run-time assurance (RTA) methods may 

hold the promise of certifying these advanced 

controllers by continuously monitoring the 

state of the feedback system during 

operation. 

• In the event that anomalous behavior is 

detected, control is automatically reverted to 

a baseline certified backup controller that 

assures continued safe operation of the 

engine.

Recovery 

Achievability 

Envelope (RAE)

Notional Safe Operating Region

Run-time Assurance for Advanced Propulsion Algorithms

Safety and operational requirements for modern aircraft systems call for increasingly advanced 

control capabilities. Flight-certification of such systems will require that they undergo thorough 

verification and validation (V&V) to achieve high confidence in their safety. Unfortunately, rigorous 

certification of such systems has proven to be challenging and costly using current V&V practices. One 

potential approach to addressing this shortfall in V&V capability is the use of run-time assurance (RTA) 

methods. RTA methods hold the promise of certifying these advanced controllers by continuously 

monitoring the state of the feedback system during operation. In the event that anomalous behavior is 

detected, control is automatically reverted to a baseline certified controller that assures continued safe 

operation of the engine. 

A preliminary study was undertaken to investigate the potential ability of a RTA framework to 

guarantee the safe closed-loop operation of the NASA-developed model-based engine controller (MBEC). 

Key system variables were identified and continually monitored to ensure that the controller did not 

violate the safety and operational limits of the engine during simulated take-off and cruise operations. In 

addition, simulated sensor faults and code errors were introduced to confirm the RTA system’s ability to 

effectively switch from the MBEC to the baseline controller. These initial experimental results illustrate 

the potential benefits of using RTA to safely operate the engine with advanced control systems. Research 

is ongoing to investigate the capability of the RTA system to safely operate the engine throughout the 

operating envelope (see Ref. 5).

NASA/TM—2015-218744 8
Distributed Engine Control

Modeling – Simulation – Hardware-in-the-Loop

Collaborative Environment for 

Development of Distributed Control 

Applications on the Turbine Engine

Real-Time Target

HIGH 

TEMPERATURE 

HARDWARE

SIMULATED 

HARDWARE

Stimulus Control 

Network

Concept

High-Fidelity 

Real-Time Modeling

Hardware-in-the-Loop 

Validation

Revolutionary 

Controls 

Technology

Development, 

Evolution, & 

Sustainment

Distributed Engine Control

Modeling – Simulation – Hardware-in-the-Loop

Turbine engine control technology is poised to make the first revolutionary leap forward since the 

advent of full authority digital engine control in the mid-1980s. This change aims squarely at overcoming 

the physical constraints that have historically limited control system hardware on aero-engines to a 

federated architecture. A distributed control architecture, enabled by high temperature electronics, allows 

the complex analog interfaces between system elements and the control unit to be replaced by 

standardized interfaces with signals that have been digitized at the source. These effects additionally 

include embedded processing, and common network interfaces that modularize the system at a hardware 

level. While this scheme simplifies the physical integration of the system, its complexity appears in other 

ways. In fact, integration now becomes a shared responsibility among suppliers and system integrators. 

While these are the most obvious changes, there are additional concerns about performance, reliability, 

and failure modes due to distributed architecture that warrant detailed study. NASA Glenn Research 

Center is in the process of developing a new facility intended to address the many challenges of the 

underlying technologies of distributed control. The facility is capable of performing both simulation and 

hardware studies ranging from component to system level complexity. Its modular and hierarchical 

structure allows the users to focus their interaction on specific areas of interest (see Ref. 6).

NASA/TM—2015-218744 9
DEC - Smart Transducer Modeling Efforts

Actuator and Sensor Models in C-MAPSS40k

• Actuator models: transfer 

function + nonlinearities

• Sensor models: transfer function 

+ noise

• IEEE 1451 standards describe modular smart 

transducers • Smart Transducer Interface Module 

(STIM) • Network-Capable Application Processor 

(NCAP) • Simulink® (The MathWorks, Inc.) Smart 

Transducers library under development

Smart Transducer Models (using library)

DEC—Smart Transducer Modeling Efforts

Progress toward the implementation of distributed engine control (DEC) in an aerospace application 

may be accelerated through the development of a hardware-in-the-loop (HIL) system for testing new 

control architectures and hardware outside of a physical test cell environment. One component required in 

an HIL simulation system is a high-fidelity model of the control platform: sensors, actuators, and the 

control law. The control system developed for the Commercial Modular Aero-Propulsion System 

Simulation 40k (C-MAPSS40k) provides a baseline for development of a model for simulating a 

distributed control architecture with a high degree of accuracy. This distributed controller model will

contain enhanced hardware models, capturing the dynamics of the transducer and the effects of data 

processing, and a model of the controller network. 

A multilevel framework has been developed that establishes three sets of interfaces in the control 

platform: communication with the engine (through sensors and actuators), communication between 

hardware and controller (over a network), and the physical connections within individual pieces of 

hardware. This introduces modularity at each level of the model, encouraging collaboration in the 

development and testing of various control schemes or hardware designs. At the hardware level, this 

modularity is leveraged through the creation of a Simulink library containing blocks for constructing 

smart transducer models complying with the IEEE 1451 specification. The complexity added to the 

control platform model when such hardware models are incorporated introduces realistic effects, related 

to quantization of feedback measurements, in the controller response while still allowing simulation in 

real-time, as required for HIL simulation (see Ref. 7).

NASA/TM—2015-218744 10
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Model Based Engine Control

Goals

• Use an on-board “self-tuning” model of the engine to provide accurate estimates 

of unmeasured parameters for control design as the engine ages

• Allow for the engine to serve as a backup to the flight control system during 

emergency scenarios by improving the transient response time of the engines

Approach

• CMAPSS40k simulation as baseline engine

• Integrate engine with Optimal Tuner Kalman 

Filter to get estimates of unmeasured 

parameters

• Replace current control architecture with a 

Thrust controller and Stall Margin limit 

protection

Results

Thrust Control

response over

engine life 

compared to 

baseline EPR 

control

Intelligent Control and Autonomy Branch

Stall Margin 

limiter over 

engine life cycle 

compared to 

baseline 

acceleration 

limiter 

Model Based Engine Control

Model- based engine control (MBEC) is being developed as one of the advanced engine control 

system methodologies to improve turbofan engine efficiency as well as transient performance. The 

MBEC concept is to use an on-board engine model to generate estimates of quantities that cannot be 

measured or are difficult to measure, such as Thrust and Compressor Stall Margin, and use these 

estimates to provide direct control of the parameters of interest. Such an approach will result in 

consistency of engine throttle to thrust response as the engine degrades with usage, and will also enable 

the capability to design engines with a reduced stall margin for transient operation. The challenge in 

MBEC is to ensure that the on-board model provides good estimates of the parameters of interest.

An MBEC architecture has been developed and is comprised of two components; an on-board model 

designed to provide real-time estimates of desired unmeasured parameters, and a controller with limit 

protection logic. Simulation studies have been conducted with MBEC designed for the Commercial 

Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) engine model. Results are shown for 

a large thrust transient at low altitude, which could simulate a takeoff scenario or emergency operations 

during a landing. The plots compare the MBEC and baseline controller. The bottom plot shows that using 

the MBEC approach, the minimum Stall Margin reached during acceleration transients can be tightly 

controlled to a preset value throughout the operational life of the engine. This allows the capability to set 

the minimum Stall Margin limit to a lower value and hence provide a faster engine response. Current 

research work is focusing on how to leverage the capability provided by MBEC to design the overall 

engine system for higher efficiency operation (see Ref. 8).

NASA/TM—2015-218744 11
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JASC Device Drive 

Electronics Jansen Aircraft Systems 

Controls, Inc. (JASC)

SBIR Phase II 

Fuel-Flow Modulator 

Active Combustion Control - Fuel 

Modulator Development

WASK Engineering, Inc.

SBIR Phase I 

Prototype Model 

Fuel-Flow Modulator

Georgia Tech

Fuel-Flow 

Modulator 

Active Signal 

Technologies, Inc.,

Fuel-Flow 

Modulator 

Intelligent Control and Autonomy Branch

Phase Shift

Controller

Fuel 

Valve

Fuel lines, Injector

& Combustion Σ

NL Acoustics

Flame

White Noise

+ +

+

Filter

Pressure from

Fuel Modulation Combustor Pressure

Instability Pressure

High Bandwidth fuel flow modulation 

is essential for suppression of thermo- acoustic instabilities

Active Combustion Control—Fuel Modulator Development

Lean combustion concepts for aircraft engines are prone to combustion instabilities. Mitigation of 

these instabilities is an enabling technology for low-emissions combustors. Prior research at NASA Glenn 

Research Center has demonstrated active control to suppress a high-frequency combustion instability in a 

combustor rig designed to emulate an actual aircraft engine instability experience with a conventional, 

rich-front-end combustor. The current effort is structured to develop further understanding of the problem 

as applied to future lean-burning, very low-emissions combustors. See Reference 9 for an overview of 

GRC accomplishments in active combustion control for low emission combustors.

Current research is focused on suppressing the thermo-acoustic instabilities related to increasing fuel 

flow that may prevent full-power operation of a combustor. Active combustion instability suppression 

using the Adaptive Sliding Phasor Average Control (ASPAC) method has been successfully demonstrated 

experimentally in a NASA combustion test cell operating at engine pressures, temperatures, and flows. A 

critical component in the control loop using the ASPAC method is a high-frequency fuel valve used to 

perturb the combustor fuel flow.

To meet the challenge of thermo-acoustic instability suppression using ASPAC, to further investigate 

applicability of the ASPAC method, and to investigate other control methods, actuators with the ability to 

modulate fuel flow frequencies as high as 1 kHz are required. For practical application, these modulators 

will be small, fast, and tolerant to high temperatures. Devices to meet these requirements require custom 

design since they are not off-the-shelf commercially available. To this end, NASA GRC is partnering 

with various small businesses to develop actuators that can be used for active combustion control in the 

GRC combustion research facilities. The concepts shown in the chart are at various stages of 

development.

NASA/TM—2015-218744 12
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Graphical User interface 

(GUI) for initiating one- dimensional combustion 

simulation process

• Reduced order combustor dynamic simulation 

developed earlier for instability analysis:

− Physics-based Sectored 1-D, reacting, time- accurate with computationally efficient areas 

transitions

− Simulation model verified against available 

experimental data

− However, simulation is in Fortran code making it 

difficult to use for control design development and 

evaluation

• Simulation re-coded in Matlab to allow ease of advanced control algorithm development:

− Code streamlines simulation setup with graphical 

user interface (GUI) dialog boxes to acquire user 

defined simulation parameters.

− Designed to accommodate combustor design 

changes without the need for editing and 

recompiling the source code.

− Packaged in an executable file that can be initiated 

in a Command Window without requiring the 

presence of a licensed MATLAB seat. 

− Report generating code has also been packaged in 

an executable file that is capable of running in 

Command Window and in the MATLAB 

environment.

Example report of one- dimensional combustion 

simulation process. Left 

illustrates pressure 

fluctuations. Right 

illustrates velocity 

fluctuations.

Active Combustion Control – Combustor 

Dynamic Simulation Development 

Intelligent Control and Autonomy Branch

Active Combustion Control—Combustor Dynamic

Simulation Development

As discussed previously, NASA Active Combustion Control (ACC) research considers modulating 

the fuel flow into the combustor to assert pressure oscillations out-of-phase with the thermo-acoustic 

instability; thus, suppressing the thermo-acoustic instability. Such a control approach employs a dynamic 

pressure sensor for feedback, an actuator to provide high bandwidth fuel flow modulation, and a control 

algorithm to command the actuator with feedback signals from the sensor. To streamline development of 

closed-loop control algorithms, a combustor simulation, the simulated one-dimensional MATLABlanguage-based (S1D_ML) code, has been developed. This is a MATLAB based version for the sectoredone-dimensional combustor simulation process, developed earlier in Fortran—see Reference 10. 

Source code modification can be made to the S1D_ML simulation using MATLAB programming 

tools. This code can be compiled to an executable that reads an input setup file to define combustor 

geometry and boundary conditions. This alleviates the need to own a copy of MATLAB to make changes 

to the process-defining source code and for running the S1D_ML simulation.

The process is designed to periodically save information for post processing report generation. The 

data saved can be reduced to create the following two types of graphical reports: Power Spectral Density 

(PSD) and a Wave pattern analysis (Wave). Illustrated in the lower right corner of the chart above is an 

example of a wave pattern analysis report. The Wave illustrates pressure and velocity on contour plots 

with respect to location and time. The Wave report enables recognition of resonance patterns in the data 

set.

NASA/TM—2015-218744 13
Dynamic Modeling of Pressure Gain

Combustion (PGC) Systems

Goals

– Capture fundamental unsteadiness and essential gasdynamic physics

– Model critical losses

– Obtain performance, emissions, and sizing information

– Optimize performance

– Assess controls requirements

Approach

– Use simplest CFD possible to achieve goals

Compressor Turbine

Fan ∆Pt

>0.0, Pt4/Pt3>1

PG

C

2D axis-symmetric simulation (with kinetics) of a

shrouded, valved, resonant pulse combustion system.

The contours of temperature (and NOx) represent a

moment in time during the ≈300 Hz. cycle .

• Inlet P, and T are gas turbine representative

• Overall temperature ratio is gas turbine

representative

• Overall pressure ratio=1.012

• Competitive emissions

Example: Resonant Pulsed PGC Example: Rotating Detonation Engine RDE Nozzle

A

B

C

D

E

F

G

y 

Comparison of 

measurements 

from and 

Simplified Q2D 

simulation of a 

Rotating 

Detonation 

Engine

• Mass flow 

matched within 

2%

• Thrust 

matched within 

2%

• Simulation 

converges in 

1-2 minutes on 

a laptop

Dynamic Modeling of Pressure Gain Combustion (PGC) Systems

Pressure gain combustion (PGC) is under investigation for the full range of air breathing flight 

applications. Of particular interest is the potential application to gas turbines. Here, the pressure rise (as 

opposed to a conventional combustor loss) yields higher engine thermal efficiency and specific power.

All PGC systems are fundamentally unsteady. They achieve limit cycle, not steady-state operation. 

Furthermore, they are fluidically (though not mechanically) complex. As such, there is a certain level of 

fidelity that models require in order to capture the physics and provide utility as design and optimization 

tools. The current PGC modeling effort is focused on providing such fidelity, but not more than is 

necessary. This effort has led to the use of simplified CFD approaches, two of which are illustrated above.

The left side shows output from a 2D axis-symmetric simulation of a so-called resonant pulse 

combustor. These are among the simplest of the PGC systems. They provide the least amount of pressure 

gain, but their simplicity, robust operation, and low emissions potential are attractive. They have also 

been successfully integrated and operated in gas turbine systems.

The right side compares computed and measured (CTAP) results from a rotating detonation engine 

(RDE). These PGC devices produce some of the highest pressure gains, but also produce high heat loads, 

substantial flow non-uniformities, and valving challenges. The flow in an RDE is nominally axial; 

however, the detonation travels circumferentially and continuously (Ref. 11).

NASA/TM—2015-218744 14
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Propulsion Diagnostic Method Evaluation Strategy 

(ProDiMES): Public Benchmarking Results

• ProDiMES provides a standard benchmarking 

problem to enable the comparison of candidate 

aircraft engine gas path diagnostic methods

• ProDiMES was successfully used by 

researchers from NASA, University of Liege, 

and Wright State University to benchmark four 

gas path diagnostic methods

Intelligent Control and Autonomy Branch

Engine Fleet

Simulator User’s

Diagnostic

Solutions

Evaluation

Metrics

Results

ProDiMES Architecture

Fan LPC HPC HPT LPT VSV VBV Nf Nc P24 Ps30 T24 T30 T48 Wf P2 T2 Pamb 0

20

40

60

80

100

CCR (%)

Fault Type

Diagnostic method #1

Diagnostic method #2

Diagnostic method #3

Diagnostic method #4

Blind-Test-Case Metric Results: Classification Performance

Public Benchmarking Results

– The benchmarking comparison results 

highlighted the relative strengths of the four 

diagnostic methods – Analytical and empirical classification 

approaches were found to provide similar 

diagnostic performance – Combining the best performing detection 

approach with the best performing isolation 

approach gave best overall diagnostic results. 

• ProDiMES was found to provide a suitably challenging problem and an effective 

means for conducting an initial evaluation of gas path diagnostic methods

Propulsion Diagnostic Method Evaluation Strategy (ProDiMES)

Public Benchmarking Results

Recent technology reviews have identified the need for objective assessments of aircraft engine 

health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark 

problem was created and made publicly available through the NASA Software Catalog. This software 

tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been 

constructed based on feedback provided by the aircraft EHM community. It provides a standard 

benchmark problem enabling users to develop, evaluate and compare gas path diagnostic methods. 

The availability of ProDiMES was publicized to the aircraft engine health management community. 

Interested participants were invited to apply their diagnostic methods to the provided benchmark problem, 

and participate in a follow-on workshop to share results and lessons learned. At this workshop, four 

diagnostic methods that had been applied to the ProDiMES blind-test-case data set were presented. These 

methods, which were developed by NASA, the University of Liege, and Wright State University, 

included: Weighted Least Squares Single Fault Isolation; Probabilistic Neural Network Single Fault 

Isolation; Performance Analysis Tool; and Generalized Observer/Estimator for Single Fault Isolation

The blind test comparison results highlighted the relative strengths of the four diagnostic methods. 

The study revealed that the applied detection strategy had a significant impact on overall diagnostic 

performance, and pairing the best fault detection and fault isolation approaches gave the best overall 

diagnostic results. Also, it was found that analytical (model-based) and empirical (data-driven) diagnostic 

approaches gave similar results when applied to ProDiMES. Overall participant feedback on the 

ProDiMES benchmarking tool and process was generally positive. ProDiMES was found to provide a 

suitably challenging problem and an effective means for conducting an initial evaluation of gas path 

diagnostic methods (see Ref. 12).

NASA/TM—2015-218744 15
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An Integrated Architecture for Aircraft 

Engine Performance Monitoring and Fault 

Diagnostics: Engine Test Results

Model-based gas path health 

management architecture

– Designed for processing continuous 

engine measurement data 

– Provides performance estimation and 

gas path fault diagnostics

Intelligent Control and Autonomy Branch

Fault Diagnostics

Real-Time Self Tuning Model

(Performance Trend Monitoring)

Performance 

Baseline Model

Engine

Periodic update of model tuning parameters

+

-

u

y

yˆ

Estimated 

performance 

parameters

y

yr

y

~

Performance Trend Monitoring and Gas 

Path Fault Diagnostic Architecture 

Architecture applied for processing data 

acquired during Vehicle Integrated 

Propulsion Research (VIPR) engine testing

– VIPR is an on-wing engine ground-test 

conducted on a C-17 aircraft

– VIPR includes both nominal and seeded-fault 

engine operating scenarios.

VIPR Results: Architecture was found to ...

– Avoid false alarms

– Correctly detect faults

– Correctly isolate faults during 

steady-state operating conditions

• Work is ongoing to improve transient 

diagnostic performance

Pratt & Whitney 

F117 Turbofan Engine

Boeing C-17 Globemaster III

VIPR Test Aircraft and Engine

An Integrated Architecture for Aircraft Engine Performance

Monitoring and Fault Diagnostics: Engine Test Results

Conventional aircraft engine gas path diagnostic approaches are designed for ground-based postflight processing of “snapshot” measurement data collected at a limited quantity of operating points each 

flight. However, advances in onboard processing and flight data acquisition capabilities are providing 

access to increased quantities of flight data and enabling new diagnostic approaches. Analyzing full-flight 

streaming measurement data, either onboard in real-time or post-flight, can help reduce diagnostic latency 

and improve overall engine safety and reliability. In response to this need, NASA has developed a modelbased performance trend monitoring and gas path diagnostic architecture designed to process streaming 

full-flight aircraft engine measurement data. 

Recently, this architecture has been applied for the processing of data collected during the NASA 

Vehicle Integrated Propulsion Research (VIPR) engine tests. The VIPR program is a series of groundbased engine tests conducted to mature aircraft engine health management technologies. These tests are 

ongoing at the NASA Armstrong Flight Research Center/Edwards Air Force Base on a C-17 aircraft 

equipped with Pratt & Whitney F117 turbofan engines. The VIPR tests include “baseline” runs, where the 

test engine is operating normally without faults, as well as non-damaging fault cases consisting of misscheduled actuators in two bleed valves. 

Analysis of the VIPR engine test data marks the first time the model-based performance trend 

monitoring and gas path fault diagnostic architecture was applied for processing real engine data. Overall, 

the results are encouraging. The technique was shown to avoid false alarms when presented nominal data, 

and to correctly detect anomalies when presented faulty data. Furthermore, the technique was found to 

correctly isolate the correct fault type during steady-state engine operating conditions although it did 

experience fault misclassifications during engine transients. Future work will focus on improving the 

model’s dynamic accuracy (see Ref. 13).

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Model-Predictive Automatic Recovery System

(MPARS)

Throttle Position

Altitude

Air Speed

Sink Rate

Angle of Attack

Flap Position

etc. are used to predict if a go-around 

must be initiated.

• An automated system to recover from unstable approaches which can 

result in ground collision

Intelligent Control and Autonomy Branch

Model Predictive Automatic Recovery System (MPARS)

The Model-Predictive Automatic Recovery System (MPARS) is a proof-of-concept algorithm that 

monitors an aircraft’s flight condition during the approach to landing phase. It continually estimates the 

minimum altitude that would be reached if the pilot were to initiate a go-around maneuver at any point 

during the landing approach. When it determines that the expected minimum altitude reached during a goaround is getting close to a safe threshold, then it initiates an immediate go-around maneuver by 

increasing thrust and pitching up the aircraft. The concept relies on a simplified on-board model of the 

flight dynamics that determines when the aircraft operation is unsafe as defined by its inability to perform 

a go-around without coming dangerously close to the ground when not above the runway. If it would 

result in a minimum altitude threshold violation when the aircraft is not over the runway, the go-around is 

initiated. If not, normal operation continues. MPARS shuts off once the runway is reached. MPARS was 

developed and tested in NASA Glenn’s flight simulator using the NASA-developed Transport Class 

Model (TCM), an aircraft simulation, integrated with two copies of the NASA-developed C-MAPSS40k 

model, a turbofan engine simulation. Piloted simulations using MPARS showed that it was able to 

intervene safely whenever the pilot was in an unstable approach mode, but did not interfere when the pilot 

was maintaining a safe approach for landing. MPARS research is motivated by the multiple reports of 

pilot errors during the landing phase which have resulted in ground collisions (see Ref. 14).

NASA/TM—2015-218744 17
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Glenn Research Center

Enhanced Engine Response Control

Higher risk for enhanced engine performance

FAA mandated maximum risk of engine failure

Enhanced operation provides up to 30% faster 

response to throttle changes and up to 35% 

higher maximum thrust. 1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

0 20 40 60 80 100

Failure Probability

Operating Point

Current

Elevated

Baseline

Enhanced

0 100 200 300 400 500

2000

4000

6000

Altitude, ft

0 100 200 300 400 500 150

200

250

300

Air Speed, kts

Time, s

PCA, Baseline (3 runs)

PCA, Enhanced (2 runs)

With the flight control system inoperable, the 

flight path angle (γ) and roll angle (φ) were used to 

command throttle in a Propulsion Controlled 

Aircraft (PCA) type arrangement.

Here the pilot attempted to fly an altitude profile 

and maintain airspeed.

• With baseline engines it was difficult, often 

resulting in large oscillations

• With enhanced engine response,

it was simple

• Risk-based approach to enhanced engine performance 

- Modification of controller limits enables faster response and extra thrust

Intelligent Control and Autonomy Branch

Enhanced Engine Response Control

In this risk-based approach to enhanced engine performance, modification of controller limits enables 

improved responsiveness and thrust. However, these limits are in place to protect the engine against 

stalling, or to achieve a minimum time between overhauls by protecting component life. These standard 

controller limits result in very safe, very reliable engine operation.

The FAA requires a risk of engine failure of 10-5

/flight hour or below, but in an emergency, the 

chance of saving the aircraft might increase through improved engine performance, outweighing a higher 

risk of engine failure. Enhanced operation provides faster response to throttle changes and higher 

maximum thrust. The improvement that is achievable depends on how much additional risk is acceptable. 

In this case, a risk of engine failure of 10-3

/flight hour was chosen. Since use of the enhanced modes is 

restricted to emergency situations only, the duration of their use is short, presumably justifying the higher 

risk.

These enhanced control features were evaluated in a piloted flight simulator using the NASAdeveloped Transport Class Model (TCM), an aircraft simulation, integrated with two copies of the 

NASA-developed C-MAPSS40k model, a turbofan engine simulation. The testing demonstrated potential 

safety improvements enabled by the enhanced control modes. For example, using an aircraft that has lost 

its flight control system, but with the pilot commands mapped into the throttles in an arrangement similar 

to Propulsion Controlled Aircraft (PCA), the pilot attempted to fly an altitude profile while maintaining 

airspeed. With the baseline engine control, the pilot over-controlled, causing large oscillations. However, 

with the enhanced control, the pilot was able to maintain precise control of speed and flight path. It took 

several tries before the pilot learned how to perform the task successfully (see Ref. 15).

NASA/TM—2015-218744 18
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T-MATS

Toolbox for the Modeling and Analysis of Thermodynamic Systems

• MATLAB/Simulink plug-in providing generic building blocks that may be 

combined to create complex thermodynamic simulations (dynamic and steady- state).

• T-MATS abilities: • Iterative solving capability • Generic thermodynamic component models 

– Turbomachinery components (compressor, turbine, burner, 

nozzle, etc.) • Control system modeling (controller, actuator, sensor, etc.)

• License allows for open collaboration • Apache 2.0, open source license(free of proprietary and export 

restriction) • Download for free: https://github.com/nasa/T-MATS/releases

Intelligent Control and Autonomy Branch

T-MATS

The Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) is a Simulink 

toolbox for modeling and simulation of thermodynamic systems and their controls. The package contains 

generic thermodynamic and controls components that may be combined with a variable input iterative 

solver and optimization algorithm to create complex systems to meet the needs of a developer. The TMATS software contains a simulation framework, multi-loop solver techniques, and modular 

thermodynamic simulation blocks. While much of the capability in T-MATS is in transient 

thermodynamic simulation, the developer’s main interests are in aero-thermal applications; as such, one 

highlight of the T-MATS software package is the turbomachinery block set. This set of Simulink blocks 

gives a developer the tools required to create virtually any steady-state or dynamic turbomachinery 

simulation, e.g., a gas turbine simulation. In systems where the control or other related systems are 

modeled in Simulink, the T-MATS user has the ability to create the complete system in a single tool.

T-MATS turbomachinery blocks were created using a philosophy that combines the understandability 

and logic of physics-based models with the accuracy and tunability of empirically-developed models. The 

user’s guide (Ref. 16) is written with the assumption that the user is familiar with modeling 

thermodynamic systems. T-MATS is written in MATLAB/Simulink v2012b (The Mathworks, Inc.). It is 

open source, which enables collaboration without limitation. The T-MATS software comes with an 

example application for modeling a turbojet engine, data for which is available in published literature. TMATS has been used to recreate the NASA developed C-MAPSS (Commercial Modular Aero Propulsion 

System Simulation) model of a 90,000lb thrust class engine, and also a turbofan engine developed earlier 

in the NPSS (Numerical Propulsion System Simulator) format.

NASA/TM—2015-218744 19
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Intelligent Control and Autonomy Branch

Engine Ice Crystal Accretion Effect: 

Simulation, Detection, and Mitigation

• Ice crystals have been found to accrete in engine 

compression system. Accretion can lead to engine 

power-loss:

–Ice ingestion into combustor causing flameout

–Compressor surge

–Engine rollback

• 153 power-loss events identified from 1988-2010

• Previously developed detection algorithm, using only Nf & Nc for steady-throttle flight, 

being applied to the new engine model

Modeling, Detection & Mitigation 

The Engine Icing Problem

• Previously developed simple model in C-MAPSS40k 

engine simulation and developed steady-state 

detection algorithm

• Developed new model using T-MATS based on test 

data from PSL on a Honeywell ALF502-R5 engine:

–Uses stacked maps to model impact of blockage on LPC

–Includes effect of heat loss due to melting ice crystals in 

HPC

• PSL results confirm that rollback is due to action of 

control limit regulators

Engine Icing Modeling based on PSL data

Engine Ice Crystal Accretion Effect: Simulation, Detection and Mitigation

Over the past 20 years, there have been over 150 reported cases of aircraft engine power loss due to 

the accretion of ice crystal particles in the compression system of commercial turbofan engines. The 

majority of the work in response to this aviation safety concern has focused on understanding the 

mechanism by which particles in high ice-water content (HIWC) conditions can accrete on compressor 

stator blades, understanding the environmental conditions in which accretion can occur, and related 

regulatory guidance. While avoidance of HIWC conditions and compressor redesigns are the ideal longterm solutions, a systems level analysis highlights some near-term solutions.

To this end, Low Pressure Compressor (LPC) maps gave been generated that include the effect of 

blockage due to ice accretion. These compressor maps have been integrated into the C-MAPSS40k engine 

simulation to model the change in engine performance during ice accretion to be simulated. Using this 

capability in conjunction with the realistic engine controller of C-MAPSS40k, previous research has 

shown that the engine rollback phenomenon is caused by the normal behavior of the engine controller 

responding to operational limits being encountered. It was then shown that the change in engine 

performance associated with ice accretion can be detected using the shaft speed sensors during steadythrottle flight.

Recently, testing of an obsolete Honeywell ALF502-R5 engine has been conducted in the NASA 

GRC Propulsion Systems Lab (PSL) where an ice crystal cloud is generated at the inlet to the engine. 

Based on data from this testing, an engine and ice accretion model has been developed using the T-MATS 

package. The effects of ice crystal accretion are accounted for by using “degraded” LPC maps and the 

effect of the ice crystal cloud is accounted for by introducing heat loss terms into the HPC to model the 

vaporization of the crystals. Future work will be based on this model and use the PSL data for 

validation/verification of both detection algorithms and mitigation strategies (see Ref. 17).

NASA/TM—2015-218744 20
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Intelligent Control and Autonomy Branch

Reducing Conservatism in Aircraft Engine Response Using 

Conditionally Active Min-Max Limit Regulators

• Conditionally Active (CA) limit regulation makes the limit 

regulator active only if i) the regulated variable is “close” 

to the specified limit; and ii) the rate of change of the 

regulated variable is such that the regulated variable will 

reach the limit within a specified number of control 

update time steps

• Typical modern engine control logic consists of a Min-Max selection to limit the fuel flow 

command for engine operational safety

• This approach is inherently conservative as a limit regulator may become active even when 

there is no danger of a limit being violated

y1

t

(1-α1)y1max

y1max

A

B

t

A tB

t

A

+β1*ΔT

t

B

+β1 t *ΔT A'

t

B

'

14 15 16 17 18 19 20

0

1

time [s]

Accel Limit Active

Nominal

CA Accel

• Application demonstrated on the C-MAPSS40k engine simulation – much faster engine 

response with CA limit regulator while maintaining safe operation

14 15 16 17 18 19 20

1.1

1.2

1.3

1.4

1.5

EPR

14 15 16 17 18 19 20 0

1

2

3

4 x 104

time [s]

Fnet [lbf]

Command

Baseline

CA Accel

Reducing Conservatism in Aircraft Engine Response Using 

Conditionally Active Min-Max Limit Regulators

Current aircraft engine control logic uses a Min-Max control selection structure to prevent the engine 

from exceeding any safety or operational limits during transients due to throttle commands. This structure 

is inherently conservative and produces transient responses that are slower than necessary. In order to 

utilize the existing safety margins more effectively, a modification to this architecture is proposed, 

referred to as a Conditionally Active (CA) limit regulator. This concept uses the existing Min-Max 

architecture with the modification that limit regulators are active only when the operating point is close to 

a particular limit and is approaching the limit fast enough to exceed it within a certain number of 

controller updates if corrective action is not taken. The CA limit regulator approach requires selection of 

two parameters—one defining closeness to the limit and the other defining the number of controller 

update steps it will take to reach the limit at the current rate of change of the variable, α and β, 

respectively, in the sketch shown in the chart.

The CA limit regulator approach was applied to the C-MAPSS40k engine simulation. There is no 

analytical approach to selecting the parameters α and β, so an empirical approach was investigated using 

knowledge of engine operation. The results of the application are summarized in the plots on the right in 

the chart. For a throttle command from idle to full, the acceleration limit regulator in the baseline engine 

control becomes active right when the command is issued and stays active for a long period, thus resulting 

in a slow engine response. With the CA limit regulator, the acceleration limit regulator is activated a little 

after the command is issued, and remains active only for a short period during the transient, thus resulting 

in a much faster response (see Ref. 18).

NASA/TM—2015-218744 21
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Glenn Research Center

4th GRC Propulsion Control and Diagnostics Workshop

Dec. 11-12, 2013, Cleveland, OH.

• Workshop Objectives:

– Disseminate information to the research community about the propulsion control 

and diagnostics research being done at NASA GRC in support of various projects 

under the NASA Aeronautics Research Mission Directorate (ARMD) programs. 

– Get feedback on value of the research and validity of technical approach. 

– Identify opportunities for potential collaboration and sharing of tools and methods. 

• Workshop Content:

– Detailed presentations on the GRC PCD research efforts – progress to date and 

future plans, and tools and simulations available for public use.

– DoD panel and industry panel to discuss ongoing research in various organizations 

and future vision for engine control

– Poster session with demonstration of GRC developed software packages, and 

poster presentations by partners and other research community members.

– One-on-one discussions between NASA researchers and attendees

• Presentations available at:

http://www.grc.nasa.gov/WWW/cdtb/aboutus/workshop2013/2013_PCD_Workshop_Agenda.pdf

– Over 60 attendees with significant participation from Industry. Overall extremely 

positive feedback.

Intelligent Control and Autonomy Branch

4th GRC Propulsion Control and Diagnostics Workshop

The 4th GRC PCD (Propulsion Control and Diagnostics) Workshop was held on December 11 and 

12, 2013 at the Ohio Aerospace Institute, Cleveland, OH. The objectives of the workshop were to: 

Disseminate information to the research community about the propulsion control and diagnostics research 

being done by the Controls group in support of various projects under the NASA Aeronautics Research 

Mission Directorate (ARMD) programs; get feedback from peers on the value of the research and validity 

of the technical approach; and identify opportunities for potential collaboration and sharing of tools and 

methods. 

The workshop consisted of: detailed presentation on ongoing research in aircraft engine control and 

diagnostics covering progress to date, future plans, and tools and simulations available for public use; 

DoD (Air Force, Army and Navy) panel and industry panel to discuss ongoing research in various 

organizations and future vision for engine control; poster session with demonstration of GRC developed 

software packages for engine simulation, control design and evaluation, and poster presentations by our 

partners and select research community members; and one-on-one discussions between NASA 

researchers and attendees to answer any questions and identify potential collaboration opportunities. 

There were over 60 attendees from academia, industry and government with a large portion being from 

the industry. The feedback from the attendees was extremely positive in terms of the workshop meeting 

the stated objectives and the opportunity it provides for networking within the aero-propulsion control 

and diagnostics research community.

The presentations from the workshop are available at the ICA Branch website as indicated in the 

chart above. Below is a sample of overall feedback provided by some of the attendees:

- “Well organized. Appreciate the hand out package.” - Industry Participant

- “Presentations were brief and there was sufficient opportunity to interact during breaks. Providing the 

presentations on the CD is also very useful.” - Industry Participant

- “Very worthwhile workshop both for the tech content and the interaction. The format is very 

convivial. I will always make every effort to attend future events; thanks for the effort and thought 

your team and you put into getting a quality event.” - Small Business Participant

NASA/TM—2015-218744 22
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Glenn Research Center

Intelligent Control and Autonomy Branch

• The Intelligent Control and Autonomy (ICA) Branch at NASA 

Glenn Research Center, Cleveland OH, is conducting cutting 

edge research in aero-propulsion control and diagnostics in 

support of NASA Aeronautics Research Mission Programs

• The various controls and health management technologies being 

developed by ICA in collaboration with industry and academia 

partners will help improve aviation safety and increase fuel 

efficiency while helping meet challenging emission reduction 

goals. Additionally the technologies being developed will help 

enable more autonomous operation of propulsion systems.

• ICA has developed many engine simulation software packages 

as well as other tools which are helping the research community 

advance the state-of-the-art for engine control and diagnostics

• Multidisciplinary cross-organizational collaboration and a system 

level approach are essential for successful development and 

transition of Intelligent Propulsion System technologies.

Concluding Remarks

Concluding Remarks

The Intelligent Control and Autonomy Branch (ICA) at NASA GRC (Glenn Research Center) is 

working in strong partnership with industry, academia and other government agencies to develop the 

propulsion control and health management technologies that will help meet NASA’s Aeronautics 

Research Mission goals. Our aim is to use the public resources in a most efficient manner to make a 

significant contribution to the aggressive goals that have been set by the administrator in the latest 

strategic plan for NASA, and to ensure that our activities are aligned with the goals of the NASA 

Missions that we participate in. We take a systems level approach to ensure that the various components 

of a control or diagnostic system work together as an integrated system to achieve the desired objectives. 

We also actively pursue opportunities to disseminate information on our technology development efforts 

to the aerospace research community by presenting papers at technical conferences, holding the GRC 

Propulsion Control and Diagnostics Workshop on a bi-annual basis, and making available various 

software tools for enabling advanced research in propulsion control and diagnostics.

References

1. Csank, J.T., and Zinnecker, A.M., “Tool for Turbine Engine Closed-loop Transient Analysis 

(TTECTrA) Users’ Guide,” NASA/TM—2014-216663, June 2014.

2. Kopasakis, G. et al. “Propulsion System Dynamic Modeling of the NASA Supersonic Concept 

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