Adaptive modeling of jet engine performance with application to condition monitoring

Adaptive simulation technology enables the calibration of a performance simulation code to a given in-service gas turbine and provides correct prediction of its performance. This is a fundamental prerequisite for reliable gas-path diagnostics and performance health monitoring. In this paper, a new offdesign performance adaption algorithm is introduced. Cranfield University's consolidated engine performance simulation code PYTHIA is enhanced with the capability of offdesign performance adaptation to model available field data. The software minimizes, via a genetic algorithm, an objective function that measures the error between an initial engine model output and the real engine data by varying some characteristics' scaling factors. In this study, a multiple-point adaptation procedure was applied to a two-shaft aeroengine. This generated an optimized engine model that minimized its deviations from a set of test-bed data. The adapted model was then tested against different real data, resulting in an average error, over 8 measured parameters, of less than 0.35%. Nomenclaturea = weighting factor ETA = isentropic efficiency K = number of measurement N1 = relative low-pressure shaft speed, % N2 = relative high-pressure shaft speed, % n = number of offdesign points OF = objective function P = pressure, atm P = measurable performance-parameter vector PR = pressure ratio SF = scaling factor T = temperature, K u = ambient and operating-condition vector WAC = corrected mass flow rate, kg=s X = component-characteristics vector Subscripts amb = ambient DP = design point ETA = isentropic efficiency N = relative shaft speed OD = offdesign PR = pressure ratio WAC = corrected mass flow rate, flow capacity 0 = design point 6 = low-pressure compressor exit 8 = high-pressure compressor exit 11 = high-pressure turbine exit 15 = outlet fan turbine exit Superscript def = default

Section: Analysis Of Resultsmentioning

confidence: 99%

“…Nevertheless, an adaptation at the design point does not guarantee a simulation model adapted in a relevant offdesign range. Therefore, other methods based on offdesign adaptation were implemented and described by Stamatis et al [6], Lambiris et al [7], and Kong et al [8].…”

mentioning

confidence: 99%

Multiple-Point Adaptive Performance Simulation Tuned to Aeroengine Test-Bed Data

Marinai

Gatto

et al. 2009

“…Lambiris et al 100 present a method of performance simulation of jet engines, with the possibility of adapting to engine particularities. It employs an adaptationprocedurecoupled to a performancemodel solving the component matching problem.…”

Section: Typical Design Applicationsmentioning

confidence: 99%

Survey of Advancements in Jet-Engine Thermodynamic Simulation

Sanghi

Lakshmanan

Sundararajan

2000

The art of engine simulation has come into existence with the advent of analog and digital computers and has become an integral part of any engine development program. It has attracted considerable attention from researchers because of the tremendous bene ts it offers not only in terms of cost reduction, but also because of the con dence it induces into the behavior of gas turbine being designed. A substantial number of works have been carried out in the eld of engine simulation and its design applications during the past 50 years. The present paper intends to give a comprehensive overview of the attempts that have been made by the earlier researchers, and the considerable amount of knowledge, database, and the expertise that has been made available. Nomenclature= mass ow rate W F = fuel ow rate c = ratio of speci c heats D = change/error in a quantity g m = spool mechanical ef ciency r = total pressure loss coef cient Subscripts ACC = engine accessories BP = bypass duct H, HP = high-pressurespool HPC = high-pressurecompressor HPT = high-pressureturbine L, LP = low-pressure spool LPC = low-pressure compressor LPT = low-pressure turbine nz = nozzle a = ambient conditions 1, 2, 3, = engine stations 4, 5, 6, . . .

“…Stamatis et al [5] introduced a sensitivity analysis and a fast selection procedure to optimize the modification factors in 1992. Lambiris et al [6] further improved the method in 1994 by introducing a weighted error function and a polytope algorithm to optimize modification factors. Roth et al [7] introduced an optimization concept for engine cycle model matching and a minimum variance estimator algorithm [8] for performance matching of a turbofan engine.…”

Section: Introductionmentioning

confidence: 99%

Aero Gas Turbine Flight Performance Estimation Using Engine Gas Path Measurements

2015

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.