Aero Gas Turbine Flight Performance Estimation Using Engine Gas Path Measurements

Abstract

Mature gas turbine performance simulation technology has been developed in the past decades and, therefore, gas turbine performance at different ambient and operating conditions can be well predicted if good thermodynamic performance software and necessary engine performance information are available. However, the performance of gas turbine engines of the same fleet may be slightly different from engine to engine due to manufacturing and assembly tolerance and may change over time due to engine degradation. Therefore, it is necessary to monitor and track important performance parameters of gas turbine engines, particularly those that cannot be directly measured, to ensure safe operation of the engines. For that reason, a novel gas turbine performance estimation method using engine gas path measurements has been developed to predict and track engine performance parameters at different ambient, flight, degraded, and part-load operating conditions. The method is based on the influence coefficient matrix of thermodynamic performance parameters of gas turbine engines and the Newton-Raphson mathematical algorithm. Contrary to the conventional gas turbine off-design performance predictions where component characteristic maps are essential, it has the advantage that no component characteristic maps are required for the predictions and, therefore, it is relatively simple thermodynamically, fast in calculation, and desirable in engineering applications. It is able to make important invisible performance parameters visible to gas turbine users, which is a useful complement to current engine condition monitoring techniques. The developed method was applied to the performance prediction of a model gas turbine engine similar to EJ200 low-bypass turbofan engine running at different altitudes, Mach numbers, and part load, with and without degradation, by using simulated gas path measurements to test the effectiveness of the method. The results show that the method is able to predict the engine performance with good accuracy without the consideration of measurement noise and with slightly lower accuracy when measurement noise is included. It takes about 30 s for a typical prediction point, which is suitable for offline performance tracking and condition monitoring. Theoretically, the method can be applied to the performance estimation of any types of gas turbine engines.

Gradient-like minimization methods for aeroengines diagnosis and control

“…Working with measurements from installed sensors and with some strong simplifying assumptions in their models to alleviate the computational load (no use of the turbomachinery component maps is made of, for instance), which limit the accuracy of the predictions to some extent, the researchers make use of several Kalman filter techniques to achieve real-time update of the fault thresholds and to diagnose all sensors installed within the engine. A similar approach to diagnose aeroengine components was also presented by Lu et al 19 In addition, Li et al 17 presented a new version of the GPA method for the calculation of the performance of an engine, to predict and control its behavior. The aim is to be able to fine-tune an engine fleet model to accurately represent each individual engine within the fleet, by means of model adaption.…”

“…At intermediate timescales, GPA can be used for the fine adjustment of the engine models installed on-board. Lu et al., 16 Li et al., 17 and Volponi et al., 18 for instance, are good examples of the use of different approaches, all based upon the GPA technique, to achieve slightly different goals in terms of models update.…”

“…In addition, Li et al. 17 presented a new version of the GPA method for the calculation of the performance of an engine, to predict and control its behavior. The aim is to be able to fine-tune an engine fleet model to accurately represent each individual engine within the fleet, by means of model adaption.…”

Performance simulation of a parallel dual-pressure once-through steam generator

“…To satisfy the above convergence criteria, Newton Raphson (NR) iteration method [22] was used to search for an optimal solution of the variables ( / and / ) by Eq. (13) until the RMS of the errors defined by Eq.…”

Performance Estimation and Fault Diagnosis Based on Levenberg–Marquardt Algorithm for a Turbofan Engine

“…These physical failures in each gas path component would result in degradation of the thermodynamic efficiency and the flow capacity, which are called health parameters. The degradation of health parameters leads to the variation of measurements, including the spool speeds, temperatures and pressures [11]. Accurate estimation of the performance parameters of engine components over engine lifetime and reliably detecting the gradual degeneration and abrupt fault are expected to play a critical role in effective engine diagnostics and maintenance [12,13].…”