Kenneth J. Semega

Sensor Needs for Control and Health Management of Intelligent Aircraft Engines

ABSTRACT NASA and the U.S. Department of Defense are conducting programs which support the future vision of “intelligent” aircraft engines for enhancing the affordability, performance, operability, safety, and reliability of aircraft propulsion systems. Intelligent engines will have advanced control and health management capabilities enabling these engines to be self-diagnostic, self-prognostic, and adaptive to optimize performance based upon the current condition of the engine or the current mission of the vehicle. Sensors are a critical technology necessary to enable the intelligent engine vision as they are relied upon to accurately collect the data required for engine control and health management. This paper reviews the anticipated sensor requirements to support the future vision of intelligent engines from a control and health management perspective. Propulsion control and health management technologies are discussed in the broad areas of active component controls, propulsion health management and distributed controls. In each of these three areas individual technologies will be described, input parameters necessary for control feedback or health management will be discussed, and sensor performance specifications for measuring these parameters will be summarized.

Sensing Challenges for Controls and PHM in the Hostile Operating Conditions of Modern Turbine Engine (Postprint)

Abstract : Advanced gas turbine engines have evolved over the last several decades to dominate aviations propulsion, commercial and the military market. Continuing engine performance and reliability advances will require sensor components that operate reliably under extreme engine operating conditions (e.g., takeoff, max thrust) and in harsh environments (e.g., high temperature and radiation). The design of advanced controls and Propulsion Health Management (PHM) will also depend on the use of components with increased susceptibility to atmospheric radiation. This paper will discuss the current and future engine operating temperature environment that provides major challenges in sensor design for control and propulsion health management. Atmospheric radiation effects on the design and operation of engine electronics and PHM systems will be discussed. Methods to mitigate deleterious effects on system safety and performance will also be discussed. Finally, expected changes in the engine operating conditions over the next several decades will be discussed along with solutions for sensing and control.

IR STRUCTURED EMISSION-BASED SPECIATION, THERMOMETRY, AND TOMOGRAPHY OF CO AND H2O IN HIGH-PRESSURE COMBUSTORS

Passive optical probes and high-resolution emission spectroscopy are used to provide a general-purpose real-time temperature and chemical species sensing capability. Probes can be inserted in the combustor, at the turbine inlet, in the augmenter, or at the engine exit with application as an engine development diagnostic tool that provides spatially resolved measurements of the key combustion parameters: temperature, CO concentration, and H2O concentration Multiple probes are arrayed to collect the emitted infrared radiation over different views of the hot gas path. Line-of-sight averaged concentrations and temperatures are determined by spectral analysis of the emitted radiation along each line of sight (LOS). Spatial profiles may also be determined by simultaneous analysis of overlapping lines of sight. The collected infrared spectra contain optically thin and optically thick features that reflect the effects of emission and absorption within the combustion region. The known spectral structure of the component spectra can be used for the automated interpretation of the observed radiance spectra in terms of concentrations and temperatures along the line of sight, and in specific volume elements of overlapping lines of sight. In this work, we present measurements of atmosphericpressure flames and high-pressure combustors and describe the formalism for fitting the observed spectra to a basis of simulated spectra to extract estimates of concentrations and temperatures. The spectral basis is constructed using a multilayer radiation transport model, in which each line-of-sight or measurement volume is divided into segments of uniform concentration and temperature. The observed radiance emanating from each segment is calculated as a function of the local physical variables. The collection of observed data, which contains a highly structured emission spectrum over each line of sight, is fit to the spectral basis to extract line-ofsight averaged physical properties, or in the case of spatial reconstruction, volume-averaged properties for each of the overlap regions.

Prognostics and Health Management of Aircraft Engine EMA Systems

In this study, the authors conducted a model-based, engine system analysis of Electro-Mechanical Actuators (EMAs). This effort employed an existing, NASA developed, aircraft engine model. A critical engine actuator within the model was replaced by a dynamic, physics based EMA model that includes: controller, motor, drivetrain and feedback sensor sub-models. The actuator model includes simulation of the electrical, mechanical and thermal response of the system. The resulting platform was used to simulate a range of critical actuator fault conditions including: feedback resolver fault, ball-screw degradation, motor winding short, and LVDT non-linearity. Since the available experimental data from propulsion system EMAs is very limited, this platform provides an ideal opportunity to evaluate and enhance prognostic capability for critical engine applications. The model fault tests were used to demonstrate a prototype prognostics and health management (PHM) system for engine EMAs. First, the system response was used to develop an appropriate mode detection algorithm to identify the ideal system conditions for collection of diagnostic evidence. Then, using the acquired transient and steady-state system response, diagnostic data features were derived from EMA related sensors and engine performance parameters. Using these features as a starting point, a system level reasoner was created using multiple classification techniques including LDA, QDA and SVM. Using model generated data with simulated system variance, it was demonstrated that the reasoner provides excellent fault detection, isolation and severity assessment capability for all considered fault modes. Finally, a suitable actuator life model was developed and a probabilistic prognostic approach was used to determine the remaining useful life of the system. The demonstrated PHM system will significantly enhance the ability to safely operate aircraft, schedule maintenance activities, optimize operational life cycles, and reduce support costs.© 2011 ASME

Improved Turbine Engine Performance, Responsiveness, and Prognostics Using Model-Based Control in a Hardware-in-the-Loop Simulation

Maintaining continuous performance in the event of failures from aircraft propulsion engines is critical for mission readiness, mission completion, and maintenance cost. In this paper, a Model-Based Control (MBC) approach was developed and demonstrated for a turbine engine application that significantly improved the performance and fault recovery capability of the engine system. In addition, model-based health management techniques were demonstrated and showed encouraging results towards a real-time implementation. A Full Authority Digital Engine Controller (FADEC) with model-based control was successfully integrated with the high performance Turbine Engine Dynamic Simulator (TEDS). This environment was composed of a complete high fidelity Hardware-In- the- Loop (HIL) simulation of an engine with MBC system that provided both pre-flight and post-flight data results for evaluation. This paper will document and discuss key technologies demonstrated with the hardware in the loop simulation, which are: enhanced fault detection, real-time fault accommodation, model optimization for real-time execution, and integration of the control estimator with the high fidelity thermodynamic engine model. The results of this effort show a model-based controller can significantly improve engine transient response and performance.