George Kopasakis

TL;DR: A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, defendable sensor suite to address system health assessment requirements.

TL;DR: In this article, a sensor selection architecture is proposed that will provide a justifiable, dependable sensor suite to address system health assessment requirements, and the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic, rather than any systematic approach.

TL;DR: In this article, the adaptive sliding phase averaged control (ASPAC) algorithm is used to suppress high frequency (500 Hz) instability in a liquid-fueled combustor with a control phase that continuously slides back and forth within the phase region.

Abstract: The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

TL;DR: The propulsion system component volume dynamics modeling of a turbojet engine that will be used for an integrated vehicle Aero- Propulso-Servo-Elastic model and for propulsion efficiency studies are covered.