Gas turbine diagnostics has a history almost as long as gas turbine development itself. Early engine fault diagnosis was carried out based on manufacturer information supplied in a technical manual combined with maintenance experience. In the late 1960s, when L. A. Urban introduced gas path analysis, gas turbine diagnostics made a big breakthrough. Since then different methods have been developed and used in both aerospace and industrial applications. To date, a substantial number of papers have been published in this area. This paper intends to give a comprehensive review of performance-analysis-based methods available thus far for gas turbine fault diagnosis in the open literature.
Pollutant emissions from aircraft in the vicinity of airports and at altitude are of great public concern due to their impact on environment and human health. The legislations aimed at limiting aircraft emissions have become more stringent over the past few decades. This has resulted in an urgent need to develop low emissions combustors in order to meet legislative requirements and reduce the impact of civil aviation on the environment. This article provides a comprehensive review of low emissions combustion technologies for modern aero gas turbines. The review considers current high Technologies Readiness Level (TRL) technologies including Rich-Burn Quick-quench Lean-burn (RQL), Double Annular Combustor (DAC), Twin Annular Premixing Swirler combustors (TAPS), Lean Direct Injection (LDI). It further reviews some of the advanced technologies at lower TRL. These include NASA multi-point LDI, Lean Premixed Prevaporised (LPP), Axially Staged Combustors (ASC) and Variable Geometry Combustors (VGC). The focus of review is placed on working principles, a review of the key technologies (includes the key technology features, methods of realising the technology, associated technology advantages and design challenges, progress in development), technology application and emissions mitigation potential. The article concludes the technology review by providing a technology evaluation matrix based on a number of combustion performance criteria including altitude relight auto-ignition flashback, combustion stability, combustion efficiency, pressure loss, size and weight, liner life and exit temperature distribution.
Accurate gas turbine performance models are crucial in many gas turbine performance analysis and gas path diagnostic applications. With current thermodynamic performance modeling techniques, the accuracy of gas turbine performance models at off-design conditions is determined by engine component characteristic maps obtained in rig tests and these maps may not be available to gas turbine users or may not be accurate for individual engines. In this paper, a nonlinear multiple point performance adaptation approach using a genetic algorithm is introduced with the aim to improve the performance prediction accuracy of gas turbine engines at different off-design conditions by calibrating the engine performance models against available test data. Such calibration is carried out with introduced nonlinear map scaling factor functions by “modifying” initially implemented component characteristic maps in the gas turbine thermodynamic performance models. A genetic algorithm is used to search for an optimal set of nonlinear scaling factor functions for the maps via an objective function that measures the difference between the simulated and actual gas path measurements. The developed off-design performance adaptation approach has been applied to a model single spool turbo-shaft aero gas turbine engine and has demonstrated a significant improvement in the performance model accuracy at off-design operating conditions.
The results indicate that increased awareness of the importance of pre-travel vaccination is needed among the travellers in order to improve their KAP.
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