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Electrified aircraft propulsion (EAP) systems hold great potential for the reduction of aircraft fuel burn, emissions, and noise. Currently, NASA and other organizations are actively working to identify and mature technologies necessary to bring EAP designs to reality. A requirement for the development of any civil aircraft and its systems is to ensure that potential hazards in the design are identified and appropriately mitigated to ensure that the system is safe. During aircraft development, a system safety assessment that consists of a functional hazard assessment is conducted to identify all potential failure conditions of each function, and classify those failures according to the severity of their effects on the aircraft or its occupants. The more severe a function's failure condition classification, the greater the development assurance level required for the function to ensure that the probability of the hazard is acceptably low. Today, aircraft engines and their control systems receive type certificate approval as a stand-alone system to signify their airworthiness. However, the complex coupling and distributed nature of EAP designs are expected to place added challenges on the certification of these systems. This presentation will provide an initial high-level review of the potential failure modes and hazards posed by a generic EAP system along with potential mitigation strategies for those failures. The EAP system is assumed to be a hybrid design consisting of gas turbine engines, mechanical drives, electric machines, power electronics and distribution systems, energy storage devices, and motor driven propulsors. The functionality provided by each of these EAP subsystems will be discussed along with the potential failure modes they may encounter. This will include a discussion of coupled failure effects, where a fault in one EAP subsystem effects the operation of other subsystems in the architecture. Next, potential failure mitigation strategies are discussed including both software-based and hardware-based mitigation strategies. The presentation will conclude with an example evaluation of the potential failure modes and mitigation strategies for a concept EAP system proposed by NASA.

This paper explores the novel Strayton engine concept. This engine combines the cycles of a Brayton engine with that of a Stirling engine to create a highly efficient recuperating gas turbine engine. In the explored case, both Brayton cycle and Stirling cycle engines are used to generate electrical power. Additionally, the Stirling engine is used to draw heat out of the Brayton turbine (acting to cool the turbine blades), while also pumping heat into Brayton cycle just before combustion occurs (acting as the mechanism for recuperation). The purpose of this paper is to detail the system level modeling techniques used to generate the simulation, perform a cycle analysis of the combined cycle engine, identify key technologies and challenges associated with the concept, and compare potential performance gains with existing gas turbine engines and internal combustion engines. Topics such as controls, blade cooling effects, engine weight, and heat transfer using heat pipe are also explored. Results from this work show potential architectures that could provide the required heat transfer rates, potential control strategies, and performance benefits, including efficiency gains between 10% and 3% on engines ranging from 200HP to 670HP with the combined cycle engine.

Electrified aircraft propulsion (EAP) systems hold potential for the reduction of aircraft fuel burn, emissions, and noise. Currently, NASA and other organizations are actively working to identify and mature technologies necessary to bring EAP designs to reality. This paper specifically focuses on the envisioned control technology challenges associated with EAP designs that include gas turbine technology. Topics discussed include analytical tools for the dynamic modeling and analysis of EAP systems, and control design strategies at the propulsion and component levels. This includes integrated supervisory control facilitating the coordinated operation of turbine and electrical components, control strategies that seek to minimize fuel consumption and lessen the challenges associated with thermal management, and dynamic control to ensure engine operability during system transients. These dynamic control strategies include innovative control approaches that either extract or supply power to engine shafts dependent upon operating phase, which may improve performance and reduced gas turbine engine weight. Finally, a discussion of control architecture design considerations to help alleviate the propulsion/aircraft integration and certification challenges associated with EAP systems is provided.

The Commercial Aero-Propulsion Control Working Group (CAPCWG), consisting of propulsion control technology leads from The Boeing Company, GE Aviation, Honeywell, Pratt & Whitney, Rolls-Royce, and NASA (National Aeronautics and Space Administration) Glenn Research Center, has been working together over the past year to identify propulsion control technology areas of common interest that we believe are critical to achieving the challenging NASA Aeronautics Research goals for Thrust 3a: Ultra-Efficient Commercial Vehicles - Subsonic Transports, and Thrust 4: Transition to Alternative Propulsion and Energy. This paper describes the various propulsion control technology development areas identified by CAPCWG as most critical for NASA to invest in. For Thrust 3a these are: i) Integrated On-Board Model Based Engine Control and Health Management; ii) Flexible and Modular Networked Control Hardware and Software Architecture; iii) Intelligent Air/Fuel Control for Low Emissions Combustion; and iv) Active Clearance Control. For Thrust 4a, the focus is on Hybrid Electric Propulsion (HEP) for single aisle commercial aircraft. The specific technology development areas include: i) Integrated Power and Propulsion System Dynamic Modeling for Control; ii) Control Architectures for HEP; iii) HEP Control Verification and Validation; and iv) Engine/Airplane Control Integration. For each of the technology areas, the discussion includes: problem to be solved and how it relates to NASA goals, and the challenges to be addressed in reducing risk.

This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.

Aircraft flying in regions of high ice crystal concentrations are susceptible to the buildup of ice within the compression system of their gas turbine engines. This ice buildup can restrict engine airflow and cause an uncommanded loss of thrust, also known as engine rollback, which poses a potential safety hazard. The aviation community is conducting research to understand this phenomena, and to identify avoidance and mitigation strategies to address the concern. To support this research, a dynamic turbofan engine model has been created to enable the development and evaluation of engine icing detection and control-based mitigation strategies. This model captures the dynamic engine response due to high ice water ingestion and the buildup of ice blockage in the engines low pressure compressor. It includes a fuel control system allowing engine closed-loop control effects during engine icing events to be emulated. The model also includes bleed air valve and horsepower extraction actuators that, when modulated, change overall engine operating performance. This system-level model has been developed and compared against test data acquired from an aircraft turbofan engine undergoing engine icing studies in an altitude test facility and also against outputs from the manufacturers customer deck. This paper will describe the model and show results of its dynamic response under open-loop and closed-loop control operating scenarios in the presence of ice blockage buildup compared against engine test cell data. Planned follow-on use of the model for the development and evaluation of icing detection and control-based mitigation strategies will also be discussed. The intent is to combine the model and control mitigation logic with an engine icing risk calculation tool capable of predicting the risk of engine icing based on current operating conditions. Upon detection of an operating region of risk for engine icing events, the control mitigation logic will seek to change the engines operating point to a region of lower risk through the modulation of available control actuators while maintaining the desired engine thrust output. Follow-on work will assess the feasibility and effectiveness of such control-based mitigation strategies.

This paper describes an intelligent propulsion control architecture that coordinates with the flight control to reduce the amount of pilot intervention required to operate the vehicle. Objectives of the architecture include the ability to: automatically recognize the aircraft operating state and flight phase; configure engine control to optimize performance with knowledge of engine condition and capability; enhance aircraft performance by coordinating propulsion control with flight control; and recognize off-nominal propulsion situations and to respond to them autonomously. The hierarchical intelligent propulsion system control can be decomposed into a propulsion system level and an individual engine level. The architecture is designed to be flexible to accommodate evolving requirements, adapt to technology improvements, and maintain safety.

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

The purpose of this effort is to develop, demonstrate, and evaluate three asymmetric thrust detection approaches to aid in the reduction of asymmetric thrust-induced aviation accidents. This paper presents the results from that effort and their evaluation in simulation studies, including those from a real-time flight simulation testbed. Asymmetric thrust is recognized as a contributing factor in several Propulsion System Malfunction plus Inappropriate Crew Response (PSM+ICR) aviation accidents. As an improvement over the state-of-the-art, providing annunciation of asymmetric thrust to alert the crew may hold safety benefits. For this, the reliable detection and confirmation of asymmetric thrust conditions is required. For this work, three asymmetric thrust detection methods are presented along with their results obtained through simulation studies. Representative asymmetric thrust conditions are modeled in simulation based on failure scenarios similar to those reported in aviation incident and accident descriptions. These simulated asymmetric thrust scenarios, combined with actual aircraft operational flight data, are then used to conduct a sensitivity study regarding the detection capabilities of the three methods. Additional evaluation results are presented based on pilot-in-the-loop simulation studies conducted in the NASA Glenn Research Center (GRC) flight simulation testbed. Data obtained from this flight simulation facility are used to further evaluate the effectiveness and accuracy of the asymmetric thrust detection approaches. Generally, the asymmetric thrust conditions are correctly detected and confirmed.

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

The accretion of ice in the compression system of commercial gas turbine engines operating in high ice water content conditions is a safety issue being studied by the aviation community. While most of the research focuses on the underlying physics of ice accretion and the meteorological conditions in which accretion can occur, a systems-level perspective on the topic lends itself to potential near-term operational improvements. Here a detection algorithm is developed which has the capability to detect the impact of ice accretion in the Low Pressure Compressor of an aircraft engine during steady flight as well as during changes in altitude. Unfortunately, the algorithm as implemented was not able to distinguish throttle changes from ice accretion and thus more work remains to be done.

This paper applies a previously developed sensor data qualification technique to a commercial aircraft engine simulation known as the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k). The sensor data qualification technique is designed to detect, isolate, and accommodate faulty sensor measurements. It features sensor networks, which group various sensors together and relies on an empirically derived analytical model to relate the sensor measurements. Relationships between all member sensors of the network are analyzed to detect and isolate any faulty sensor within the network.

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 has been created and made publicly available. 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 diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

The accretion of ice in the compression system of commercial gas turbine engines operating in high ice water content conditions is a safety issue being studied by the aviation sector. While most of the research focuses on the underlying physics of ice accretion and the meteorological conditions in which accretion can occur, a systems-level perspective on the topic lends itself to potential near-term operational improvements. This work focuses on developing an accurate and reliable algorithm for detecting the accretion of ice in the low pressure compressor of a generic 40,000 lbf thrust class engine. The algorithm uses only the two shaft speed sensors and works regardless of engine age, operating condition, and power level. In a 10,000-case Monte Carlo simulation, the detection approach was found to have excellent capability at determining ice accretion from sensor noise with detection occurring when ice blocks an average of 6.8% of the low pressure compressor area. Finally, an initial study highlights a potential mitigation strategy that uses the existing engine actuators to raise the temperature in the low pressure compressor in an effort to reduce the rate at which ice accretes.

The accretion of ice in the compression system of commercial gas turbine engines operating in high ice water content conditions is a safety issue being studied by the aviation sector. While most of the research focuses on the underlying physics of ice accretion and the meteorological conditions in which accretion can occur, a systems-level perspective on the topic lends itself to potential near-term operational improvements. This work focuses on developing an accurate and reliable algorithm for detecting the accretion of ice in the low pressure compressor of a generic 40,000 lbf thrust class engine. The algorithm uses only the two shaft speed sensors and works regardless of engine age, operating condition, and power level. In a 10,000-case Monte Carlo simulation, the detection approach was found to have excellent capability at determining ice accretion from sensor noise with detection occurring when ice blocks an average of 6.8% of the low pressure compressor area. Finally, an initial study highlights a potential mitigation strategy that uses the existing engine actuators to raise the temperature in the low pressure compressor in an effort to reduce the rate at which ice accretes.

This document describes a Concept of Operations (ConOps) for an Integrated Vehicle Health Assurance System (IVHAS). This ConOps is associated with the Maintain Vehicle Safety (MVS) between Major Inspections Technical Challenge in the Vehicle Systems Safety Technologies (VSST) Project within NASA s Aviation Safety Program. In particular, this document seeks to describe an integrated system concept for vehicle health assurance that integrates ground-based inspection and repair information with in-flight measurement data for airframe, propulsion, and avionics subsystems. The MVS Technical Challenge intends to maintain vehicle safety between major inspections by developing and demonstrating new integrated health management and failure prevention technologies to assure the integrity of vehicle systems between major inspection intervals and maintain vehicle state awareness during flight. The approach provided by this ConOps is intended to help optimize technology selection and development, as well as allow the initial integration and demonstration of these subsystem technologies over the 5 year span of the VSST program, and serve as a guideline for developing IVHAS technologies under the Aviation Safety Program within the next 5 to 15 years. A long-term vision of IVHAS is provided to describe a basic roadmap for more intelligent and autonomous vehicle systems.

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

The accretion of ice particles in the core of commercial aircraft engines has been an ongoing aviation safety challenge. While no accidents have resulted from this phenomenon to date, numerous engine power loss events ranging from uneventful recoveries to forced landings have been recorded. As a first step to enabling mitigation strategies during ice accretion, a detection scheme must be developed that is capable of being implemented on board modern engines. In this paper, a simple detection scheme is developed and tested using a realistic engine simulation with approximate ice accretion models based on data from a compressor design tool. These accretion models are implemented as modified Low Pressure Compressor maps and have the capability to shift engine performance based on a specified level of ice blockage. Based on results from this model, it is possible to detect the accretion of ice in the engine core by observing shifts in the typical sensed engine outputs. Results are presented in which, for a 0.1 percent false positive rate, a true positive detection rate of 98 percent is achieved.

Talk covers: (1) Problem of Engine Power Loss;(2) Modeling Engine Icing Effects; (3) Simulation of Engine Rollback; (4) Icing/Engine Control System Interaction; (5) Detection of Ice Accretion; (6) Potential Mitigation Strategies.