Exhaust Gas Temperature Margin Research Articles

A deep learning-based prognostic framework for aeroengine exhaust gas temperature margin

The value of the gas-path parameter, exhaust gas temperature margin (EGTM), is the critical index for predicting aeroengine performance degradation. Accurate predictions help to improve engine maintenance, replacement schedules, and flight safety. The outside air temperature (OAT), altitude of the airport, the number of flight cycles, and water washing information were chosen as the sample input variables for the data-driven prognostic model for predicting the take-off EGTM of the on-wing engine. An attention-based deep learning framework was proposed for the aeroengine performance prediction model. Specifically, the multiscale convolutional neural network (CNN) structure is designed to initially learn sequential features from raw input data. Subsequently, the long short-term memory (LSTM) structure is employed to further extract the features processed by the multiscale CNN structure. Furthermore, the proposed attention mechanism is adopted to learn the influence of features and time steps, assigning different weights according to their importance. The actual operation data of the aeroengine are used to conduct experiments, where the experimental results verify the effectiveness of our proposed method in EGTM prediction.

A Novel Denoising Method for Retaining Data Characteristics Brought from Washing Aeroengines

Airlines evaluate the energy-saving and emission reduction effect of washing aeroengines by analyzing the exhaust gas temperature margin (EGTM) data of aeroengines so as to formulate a reasonable washing schedule. The noise in EGTM data must be reduced because they interfere with the analysis. EGTM data will show several step changes after cleaning the aeroengine. These step changes increase the difficulty of denoising because they will be smoothed in the denoising. A denoising method for aeroengine data based on a hybrid model is proposed to meet the needs of accurately evaluating the washing effect. Specifically, the aeroengine data is first decomposed into several components by time and frequency. The amplitude of the component containing the most noise is amplified, and Gaussian noise is added to generate noise-amplified data. Second, a Gated Recurrent Unit Autoencoder (GAE) model is proposed to capture engine data features. The GAE is trained to reconstruct the original data from the amplified noise data to develop its noise reduction ability. The experimental results show that, compared with the current popular algorithms, the proposed denoising method can achieve a better denoising effect, retaining the key characteristics of the aeroengine data.

A step parameters prediction model based on transfer process neural network for exhaust gas temperature estimation after washing aero-engines

The prediction of Exhaust Gas Temperature Margin (EGTM) after washing aero-engines can provide a theoretical basis for airlines not only to evaluate the energy-saving effect and emission reduction, but also to formulate reasonable maintenance plans. However, the EGTM encounters step changes after washing aeroengines, while, in the traditional models, a persistence tendency exists between the prediction results and the previous data, resulting in low accuracy in prediction. In order to solve the problem, this paper develops a step parameters prediction model based on Transfer Process Neural Networks (TPNN). Especially, “step parameters” represent the parameters that can reflect EGTM step changes. They are analyzed in this study, and thus the model concentrates on the prediction of step changes rather than the extension of data trends. Transfer learning is used to handle the problem that few cleaning records result in few step changes for model learning. In comparison with Long Short-Term Memory (LSTM) and Kernel Extreme Learning Machine (KELM) models, the effectiveness of the proposed method is verified on CFM56-5B engine data.

Analisis Pengaruh High Pressure Compressor Rotor Clearance Terhadap Exhaus Gas Temperature Margin pada CFM56-7

Exhaust Gas Temperatue is an parameter where the hot gases’s temperature leave the gas turbine. Exhaust gas temperature margin is the difference between highest temperature at take off phase with redline on indicator (???????????? ???????????????????????? °????=???????????? ????????????????????????????−???????????? ???????????????? ????????????). EGTM is one of any factor to determine engine performance. A good perfomance of an engine when it has a big margin (EGTM), during operation of an engine the EGTM could decrease untill 0 (zero). So many factors could affect EGTM deteroration there are: distress hardware such as airfoil erosion, leak of an airseals, and increase of clearance between tip balde and shroud. Increase of clearance happens in high pressure compressor rotor clearance. In CFM56-7 have 9 stage(s) of high pressure compressor and each stage give the EGT Loses. The calculation of EGT Effect/Losses is actual celarance – minimum clearance x 1000 x EGT Effect °C, where actual clearance define by the substraction of outside diameter’s rotor with inside diameter’s shroud, minimum clearance define in the manual, 1000 is adjustment from mils/microinch to inch, and EGT Effect is temperature that define in the manual. The analysist had done with 6 (six) engine serial number and proceed by corelation that shown linkage between clearance and EGT Effect, the corelation is strong shown the result of corelation (r) is 0.994275999 or nearest 1.

An Adaptive-Tunable-Based Hybrid RBF Network for EGTM Prediction

Aero-engine exhaust gas temperature margin (EGTM) is one of the main indexes of engine replacement; however, the application of existing methods in EGTM forecasting is restricted because of the limited prediction accuracy and many non-linearities. In this study, an adaptive-tunable-based hybrid radial basis function (RBF) network is proposed to improve the prediction accuracy of aero-engine EGTM. Firstly, a hybrid RBF network consisting of a RBF network and a linear regression model is built as a fundamental EGTM predictive algorithm. Secondly, to increase the network’s adaptation capabilities, the structural parameters of the proposed network are adaptively modulated by Brownian motion modeling and particle filter without physics-based models. Finally, multiple sets of EGTM data from a certain type aero-engines in an airline company is selected for engine removal time prediction. Experiment results demonstrate that the proposed adaptive-tunable-based hybrid RBF network with a high prediction accuracy, and can reflect the characteristics of EGTM well and truly, which can capture the dynamic nature of EGTM in time during the forecasting process.

Fuel and emission reduction assessment for civil aircraft engine fleet on-wing washing

On-wing washing for civil aviation engine is an important approach to improve the Exhaust Gas Temperature Margin (EGTM), and to reduce the fuel consumption as well as the resultant aviation emission. In view of the fuel flow reduction achieved by on-wing washing, this study proposes an estimation method of engine performance improvement due to washing according to fleet fuel consumption savings and engine washing costs. Finally, the engine washing interval of a fleet can be optimized based on the proposed performance assessment method of on-wing washing. A case study of one airline fleet of 200 CFM56 Engines demonstrates the effectiveness of the proposed method. The results show that the optimal washing interval is 3 times a year, and that an average annual cost savings of 20.15 million dollars can be achieved by reducing 5471 ton fuel consumption and 17,250 ton CO2 emission.

Surface Roughness Impact on Low-Pressure Turbine Performance Due to Operational Deterioration

The overall efficiency and operational behavior of aircraft engines are influenced by the surface finish of the airfoils. During operation, the surface roughness significantly increases due to erosion and deposition processes. The aim of this study is to analyze the influence of roughness on the aerodynamics of the low-pressure turbine (LPT) of a midsized high bypass turbofan. In order to gain a better insight into the operational roughness structures, a sample of new, used, cleaned, and reworked turbine blades and vanes are measured using the confocal laser scanning microscopy technique. The measurement results show local inhomogeneities. The roughness distributions measured are then converted into their equivalent sand grain roughness ks,eq to permit an evaluation of the impact on aerodynamic losses. The numerical study is performed using the computational fluid dynamics (CFD)-solver turbomachinery research aerodynamics computational environment (TRACE) which was validated before with the existing data from rig experiments. It is observed that the influence of the surface roughness on the turbine efficiency is significant at take-off but negligible at cruise. A detailed analysis on the aerodynamics at take-off shows that very rough airfoils lead to higher profile and secondary loss. Due to the higher disturbances present in flows circulating over rough walls, the transition occurs earlier, and the momentum thickness increases in the turbulent boundary layer. The service-induced roughness structures cause an efficiency drop in the LPT of ηT=−0.16% compared to new parts. A gas path analysis showed that this results in an increased fuel flow of Δm˙f=+0.06% and an exhaust gas temperature (EGT) rise of ΔEGT=+1.2K for fixed engine pressure ratio which is equivalent to roughly 4% of the typical EGT margin of a fully refurbished engine. This result stresses the importance of roughness-induced loss in LPTs.

Novel continuous function prediction model using an improved Takagi–Sugeno fuzzy rule and its application based on chaotic time series

A novel continuous function prediction model (CFPM) is proposed to resolve prediction problem whose input and output are both continuous functions (CFs). CFPM can simplify sample space reconstruction by using the coefficients of CFs, and use an improved Takagi–Sugeno (TS) fuzzy rule to predict output CF by optimizing the tendency of input CFs. The improved TS fuzzy rule handles each input CF as a consequent parameter and can obtain the nonlinear tendency. After learning process by using opinion-leader-based particle swarm optimization, output CF is determined. In the data prediction based on chaotic time series, CF can either be obtained directly or be fitted by discrete data points, thus the prediction range is enlarged because more discrete data points can be generated once output CF is determined.Two experiments and three cases based on chaotic time series are performed to validate CFPM. The Mackey–Glass chaotic time series is used to prove CFPM validation, while the NN3 time series is used to evaluate CFPM performance. The cases on exhaust gas temperature (EGT), EGT margin and delta EGT are used to show that CFPM is valuable in health status prediction for a particular aircraft engine in the practical engineering field.

Optimization of Aeroengine Shop Visit Decisions Based on Remaining Useful Life and Stochastic Repair Time

Considering the wide application of condition-based maintenance in aeroengine maintenance practice, it becomes possible for aeroengines to carry out their preventive maintenance in just-in-time (JIT) manner by reasonably planning their shop visits (SVs). In this study, an approach is proposed to make aeroengine SV decisions following the concept of JIT. Firstly, a state space model (SSM) for aeroengine based on exhaust gas temperature margin is developed to predict the remaining useful life (RUL) of aeroengine. Secondly, the effect of SV decisions on risk and service level (SL) is analyzed, and an optimization of the aeroengine SV decisions based on RUL and stochastic repair time is performed to carry out JIT manner with the requirement of safety and SL. Finally, a case study considering two CFM-56 aeroengines is presented to demonstrate the proposed approach. The results show that predictive accuracy of RUL with SSM is higher than with linear regression, and the process of SV decisions is simple and feasible for airlines to improve the inventory management level of their aeroengines.

Turbine Blade Tip Leakage Flow Control by Unsteady Periodic Wakes of Upstream Blade Row

An experimental and numerical study of tip leakage flow control in axial turbine was performed by means of unsteady periodic wakes of upstream blade row. The flow field was investigated respectively in linear turbine cascade and turbine rotor. Focus was placed on the analysis of unsteady interaction mechanism between upstream periodic wakes and tip leakage vortices. The influence of the upstream periodic wakes on turbine performance was also discussed. The results indicate that the upstream periodic wakes can effectively reduce the strength of the tip leakage vortices. The loss measurements and computations at the exit of the turbine blade showed a decrease in stagnation pressure loss or entropy rise and an increase in efficiency. It means the engine specific fuel consumption and the exhaust gas temperature margin can be improved as well as increased turbine total efficiency.

Wear Prognostic on Turbofan Engines

One of the most evident characteristic of wear for a turbofan engine is the exhaust gas temperature (EGT). It seems clear that this temperature increases when some carbon deposits on the turbine, when the compressor efficiency diminishes so the fuel flow should increase to produce the same amount of thrust, or even when some unbalance opens the spaces between the turbine and the casing. In any cases, an increase of the EGT should be analyzed because it is a wear symptom of the engine. It is mostly concluded by a water wash in the best case or a shop visit inspection and repair in the worst case. The engine manufacturer defines a schedule plan with its customer based on consumption of the EGT margin. This margin is the amount of available increase of the exhaust temperature before an inspection. Contractually, the engine is restored with a minimum EGT margin after each repair. Thus it is up to the manufacturer to understand how this margin is used to plan shop visits and to the company to estimate the current state of its engine. However, the EGT measurement is subject to a lot of noise and the company regularly washes their engines to increase randomly the margin and their capabilities. In this article we present a simple, automatic and embeddable algorithmic method to transform the successive EGT measurements in a delay indicator computed after each flight giving the amount of available use time. One challenge is to take care of the random wash or repair executed by the user. Finally this indicator may be transmitted automatically with the other data broadcasted by the aircraft computer (ACMS/ACARS) and it is used by the manufacturer to prepare his shop logistic.

High-Pressure-Turbine Clearance Control Systems: Current Practices and Future Directions

Improved blade-tip sealing in a high-pressure compressor and high-pressure turbine can provide dramatic improvements in specific fuel consumption, time on wing, compressor stall margin, and engine efficiency as well as increased payload and mission range capabilities. Maintenance costs to overhaul large commercial gas turbine engines can easily exceed $1 million. Removal of engines from service is primarily due to the spent exhaust gas temperature margin caused mainly by the deterioration of high-pressure-turbine components. Increased blade-tip clearance is a major factor in hot-section component degradation. As engine designs continue to push the performance envelope with fewer parts and the market drives manufacturers to increase service life, the need for advanced sealing continues to grow. A review of aero-gas-turbine engine high-pressure-turbine performance degradation and the mechanisms that promote these losses are presented. Benefits to the high-pressure turbine due to improved clearance management are identified. Past and present sealing technologies are presented along with specifications for next-generation engine clearance control systems.