Aero-engine Performance Research Articles

A multi-rate sensor fusion approach using information filters for estimating aero-engine performance degradation

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter (KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However, it is hard to achieve state estimation using various kinds of sensor measurements at the same sampling rate due to a complex network and physical characteristic differences between sensors, especially in an advanced multi-sensor architecture. For this purpose, a multi-rate sensor fusion using the information filtering approach is proposed based on the square-root cubature rule, which is called Multi-rate Square-root Cubature Information Filter (MSCIF) to track engine performance degradation. Soft measurement synchronization of the MSCIF is designed to provide a sensor fusion condition for multiple sampling rates of measurement, and a fault sensor is isolated by maximum likelihood validation before state estimation. The contribution of this paper is to supply a novel multi-rate information filter approach for sensor fault tolerant health estimation of an aero-engine in a multi-sensor system. Tests are conducted for aero-engine performance degradation estimation with multiple sampling rates of sensor measurement on both digital simulation and semi-physical experiment. Experimental results illustrate the superiority of the proposed algorithm in terms of degradation estimation accuracy and robustness to sensor failure in a multi-sensor system.

Analysis of vibration from low-rigidity contact in belt grinding of blisk blade

A blisk is one of the key parts of an aero-engine, whose surface processing quality directly affects aero-engine performance. Different degrees of vibration occur during the process of new open belt grinding which seriously affect the precision of the dimensions and the surface quality of the entire blade profile. With the aim of addressing this problem, this study constructed a physical model of blisk belt grinding, analysed the low-rigidity characteristics of the grinding system, and researched the vibratory mechanism of the blisk belt grinding system based on a dynamic analysis method. In addition, the factors affecting the stability of the grinding process and the stability conditions of the grinding were considered. Then, the belt grinding process of a blade surface was simulated through a numerical method. The technological parameters were quantified for different conditions of the blisk belt grinding vibration. The optimal combination of process parameters was obtained. Finally, the optimised process parameters were validated experimentally. The research demonstrates that vibration from blisk belt grinding is related to the process parameters as follows, in the order of the greatest influence: the grinding pressure, belt velocity, feed speed, and contact wheel hardness. After optimisation, the cross-sectional profile is 0.031–0.041 mm and the surface roughness is 0.1–0.2 μm; the surface is smoother and has better consistency.

Effect of Inlet Air Pre-Cooling of Water Injection on Compressor Performance at High Flight Mach

In high altitude and Mach number, the inflow air with the high temperature will influence on the aero-engine performance while the mass injection pre-compressor cooling (MIPCC) technology is one of the problem-solving ways to reduce high temperature. To explore the convection coupling process between droplet and inflow air, the compressible Reynolds average N-S equations in the compressor coupled with the pre-cooling section is solved by the finite volume method to analyze its performance changes at different water injection rates and droplet sizes. Results show that, in the flight of 3.5 Mach number, the larger water injection rate easily form the shock wave due to the disturbance of droplets in the pre-cooling section. Furthermore, the temperature on the pressure surface near the trailing edge of the rotor blade aggravates along the radial migration, leading to uneven temperature distribution in the radial direction. Within the water injection rates of 0-8% and the particle sizes of 10-20 µm, the inflow mass flow of air improves by 15.3-31.4%; the temperature ratio of compressor drops by 3.6-16.14%, which results in the decrease of specific compression work of the compressor and the changing trend from “increasing” to “decreasing” for the compressor efficiency.

Evaluation of mass injection cooling on flow and heat transfer characteristics for high-temperature inlet air in a MIPCC engine

Thermal management on high-temperature inlet air cooling is one crucial factor to improve the aero-engine performance. In this study, Mass Injection and Pre-compressor Cooling (MIPCC) technology is applied to a problem-solving way to reduce inlet air temperature. Numerical simulations are employed to investigate and predict the relationship between flow field characteristics and mass injection cooling at high altitude simulated pre-compressor section of MIPCC engine, which is based on the regression orthogonal analysis. The solution accuracy of numerical procedure is qualified with the experimental data. Results show that the flow loss and temperature reduction in the pre-compressor section are of non-linear and linear relationships with spray cooling conditions, respectively. Then, the dominant factors of influencing on the temperature reduction are droplet size and spray flow rate, and that of influencing on total-pressure reduction is droplet size. Especially, the effect of each spray factor on the flow field characteristics is of independence. According to the characteristic prediction in given spray conditions, the total-temperature drop and total-pressure drop coefficients after mist injection cooling are 1.57–29.13% and 0.461–1.683%, respectively. These observations signify that, by comparison with the dry air condition, a reduction of the temperature and flow loss in the pre-compressor section are correspondingly in the range of 7.3–135.5 K and 33.3–81.73% after mist injection. Thus, an appropriate mist injection condition can improve flow field characteristics at the most extent, and then heat management of inlet air can be controlled within a certain range.

Performance evaluation of a turbojet engine integrated with interstage turbine burner and solid oxide fuel cell

More/all-electric aircraft is one of the most promising development directions for aircraft. However, the performance of the turbine engine will be strongly affected by the high electric power fraction when the electric power is extracted from the main engine. In order to improve aero-engine performance and electric power fraction, the turbojet engine integrated with an interstage turbine burner (ITB) and solid oxide fuel cell (ITB-SOFC engine) is proposed in this paper. Turbojet engine provides thrust for propulsion and SOFC provides electric power for electrical equipment. In order to evaluate the performance of the ITB-SOFC engine, the thermodynamic analysis model of the engine performance is developed. Analysis results show that the ITB-SOFC engine has advantages in thermal efficiency and specific thrust over the turbojet engine, the increments of these two parameters can reach 2.94% and 23.87% respectively. There is a limitation in pressure ratio for the ITB-SOFC engine, which is 24. The electric power fraction of the ITB-SOFC engine can vary in a wide range. The specific thrust increment over a turbojet engine is high up to 24.07% if the electric power fraction is high up to 0.2.

A diagnostics tool for aero-engines health monitoring using machine learning technique

In this work an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. The aim is to link engine measurable data to its health status.A numerical tool has been implemented in order to calculate engine performance in design condition and to create a database of expected vales. Then different degradation levels have been introduced in the two main components, compressor and turbine of a single spool turbojet and the diagnostics instruments have been trained to detect the component fault.In order to evaluate the performance prediction two different machine learning based techniques, namely, artificial neural network (ANN) and support vector machine (SVM) have been compared.Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The two main degradation causes considered are the compressor fouling and turbine erosion.The machine learning techniques were applied with two aims: aero-engine performance prediction and health diagnostics. The study was carried out based on three samples flights, whose data were used for the training and testing process of the prediction and diagnostics tools.The knowledge and the continuous monitoring of the engine health status can be crucial for maintenance and fleet management operations.

Development of a simple tool able to study flight cycles gas turbine performance and emissions

During flight cycles, engine conditions may vary drastically from the design ones. Particularly, the high-security standard and the necessity to reduce emissions, have led to a continuous attention to the efficiency in all flight conditions. The objective of the present study is to develop a quick and easy way to analyze and investigate an aero-engine performance during different flight cycle conditions. Firstly, taking into consideration the GE 90 engine and its design characteristics, with the help of the in-house software ESMS (Energy System Modular Solver), many different flight conditions are tested, varying TIT, altitude, and Mach number independently. A big effort has been dedicated to this preliminary phase in order to get a perfect convergence for all the circumstances. Secondly, the aim was to adopt a wide-ranging approach to the problem: results have been stored and a sort of mapping has been created for that particular engine. This has allowed the final user to reproduce any flight cycle and to get the results in terms of thermodynamic parameters, as well as power and fuel consumption, without the necessity to run the code for each scenario. Time is drastically reduced and this lets the user quickly change input parameters to obtain the desired results. Thirdly, using a correlation, emissions, and in particular the emission index parameter of NOx, has been evaluated for all the flight point simulated. This procedure has been applied to a common flight path for the GE 90 engine and results have been compared to the nominal ones, proving the robustness of the software and the solidity of the procedure.

A comprehensive approach to understanding irreversibility in a turbojet

Regarding interest in and concerns about high efficiency in recent times, an irreversibility assessment of energy conversion systems is significant. Turbojets, a type of energy conversion system, are widely used to provide thrust for aerial vehicles, such as military aircraft missiles, commercial aircraft and so on. From this point of view, the current study aims to introduce a comprehensive irreversibility assessment methodology exemplification for a turbojet. First of all, a basic irreversibility assessment methodology is explained with an application. Following this, a comprehensive assessment is performed. Within this framework, a number of a novel measures are defined by derivations in addition to previously well-known indicators. These measures are beneficial for the decomposition of the irreversibility in a turbojet and its components. At the end of the study, the highest endogenous irreversibility is determined to be in the turbine component of the turbojet engine whereas the highest avoidable irreversibility is found to be in the compressor component of the turbojet engine. The current paper is considered to be of use for researchers and scientists interested in aero-engine performance, thermal engineering and aerospace engineering.

Aeroengine After-Maintenance Performance Prediction Based on Simplified Mixed Takagi—Sugeno Model

An aeroengine is a type of complex equipment, for which reasonable maintenance is essential to ensure the reliability of its life-cycle operation. The after-maintenance performance prediction method can provide infrastructure supports for aeroengine maintenance optimization. The aeroengine after-maintenance performance is affected by the before-maintenance performance and maintenance levels. The aeroengine performance is represented by a time series; however, the maintenance-level data are regarded as discrete variables. For these characteristics, a simplified structure identification and mixed variables Takagi–Sugeno (SMTS) model, which can simultaneously address time series and discrete variables, is proposed. The proposed model can predict the after-maintenance performance time series for the condition of a small sample set. The effectiveness of the proposed SMTS model is demonstrated by the real-valued data of an aeroengine fleet. The traditional neural network, process neural network, support vector regression, and deep learning network are adopted as comparison models. The results validate that the proposed SMTS model has the distinct advantages of prediction accuracy and stability.

基于量子粒子群和支持向量机的航空发动机性能监控研究

基于量子粒子群和支持向量机的航空发动机性能监控研究

A Service-Oriented Tool-Chain for Model-Based Systems Engineering of Aero-Engines

This paper proposes a service-oriented tool-chain with an emphasis on domain-specific views, simulation automation, and process management to support model-based system engineering of aero-engines. In the tool-chain, a domain-specific modeling approach is adopted to facilitate the descriptions of co-design workflows and the related development information. The relevant domain-specific models are the basis for automated creation of a Web-based process management system consolidating and controlling service-oriented technical resources (models, data, and tools). In particular, the system also provides support for automated orchestration of tool operations, model, and simulation configurations. In order to promote the model and tool interoperability, the tool-chain adopts open standards for integrations, including open services for lifecycle collaboration and functional mock-up interface. Finally, through a case study of simulation-based aero-engine performance analysis, we evaluate the flexibility and efficiency of this tool chain by comparing it with a traditional simulation process both qualitatively and quantitatively.

Aeroengine Performance Prediction Based on Double-Extremum Learning Particle Swarm Optimization

AbstractTo predict the performance parameter changing trend of an aeroengine, a novel double-extremum learning particle swarm optimization (DELPSO) algorithm is proposed. Inspired by human learning behavior, this algorithm simulates this behavior, including the strategies of collective learning, private tutoring, and research behavior, so that obtained final solutions would be in the global optimal area or its neighbor area as close as possible. Meanwhile, to improve the prediction performance, a nonlinear mapping function is designed to describe the feature relationship between inputs and outputs of historical data. Based on the DELPSO, the fitness function synthetically considers the changing trend and the prediction error and can adaptively select optimal parameters of the nonlinear mapping function. The experimental results demonstrate that the DELPSO has globally stable and reliable performance. To validate the prediction performance of the proposed DELPSO, it is also applied to an aeroengine. Its good prediction performance indicates that the proposed DELPSO is an important reference for maintenance decision-making of aeroengines.

An Onboard Aeroengine Model-Tuning System

AbstractThis study investigates an onboard aeroengine model (OBEM)-tuning system (OTS) based on a hybrid Kalman filter, which is mostly used to estimate the aeroengine performance. The OTS structur...

Analysis of Aero-engine Performance and Selection Based on Fuzzy Comprehensive Evaluation

Selecting an aero-engine which can effectively meet the design requirements of aircraft is the first step in the overall program design of aircraft. The common judgment is fuzzy. The current judgment studies of aero-engine performance often limited to a particular aspect and can’t reflect the overall performance. So we can’t accurately select and use. This paper aims to solve this problem. Taking two aero-engines as examples, the related parameters are quantified depending on the design requirements for the aircraft. A fuzzy comprehensive evaluation model is established based on fuzzy mathematical theory. Then according to the principle of maximum membership degree, the paper selects the most suitable engine. Meanwhile the applicability of the theory in the field of engineering is verified and provides guidance for other engineering fields.

A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

Aero-engine Compressor Performance Analysis Based on Deterioration Impact Factors

Aero-engine Compressor Performance Analysis Based on Deterioration Impact Factors

A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models.

Aero engine compressor fouling effects for short- and long-haul missions

The impact of compressor fouling on civil aero engines unlike the industrial stationary application has not been widely investigated or available in open literature. There are questions about the impact of fouling for short- and long-haul missions comparatively, given their unique operational requirements and market. The aim of this study is to quantify the effects of different levels of fouling degradation on the fan, for two different aircraft with different two-spool engine models for their respective typical missions. Firstly, the study shows the increase in turbine entry temperature for both aircraft engines, to maintain the same level of thrust as their clean condition. The highest penalty observed is during take-off and climb, when the thrust setting is the highest. Despite take-off and climb segment being a larger proportion in the short-haul mission compared to the long-haul mission, the percentage increase in fuel burn due to fouling are similar, except in the worst case fouling level were the former is higher by 0.8% points. In addition to this, for all the cases, the additional fuel burn due to fouling and its cost is shown to be small. Likewise, the increase in turbine entry temperature for both missions at take-off are similar, except in the worst case fouling level for the short-haul mission were the turbine entry temperature is 7 K higher than the corresponding long-haul mission for the same level of degradation. The study infers that the penalty due to rise in temperature is of more concern than the additional fuel burn. Hence the blade technology (cooling and material) and engine thrust rating are key factors in determining the extent to which blade fouling would affect aero engine performance in short- and long-haul missions.

Thermodynamics Cycle Analysis, Pressure Loss, and Heat Transfer Assessment of a Recuperative System for Aero-Engines

Abstract Aiming in the direction of designing more efficient aero-engines, various concepts have been developed in recent years, among which is the concept of an intercooled and recuperative aero-engine. Particularly, in the area of recuperation, MTU Aero Engines has been driving research activities in the last decade. This concept is based on the use of a system of heat exchangers (HEXs) mounted inside the hot-gas exhaust nozzle (recuperator). Through the operation of the system of HEXs, the heat from the exhaust gas downstream the LP turbine of the jet engine is driven back to the combustion chamber. Thus, the preheated air enters the engine combustion chamber with increased enthalpy, providing improved combustion and by consequence, increased fuel economy and low-level emissions. If additionally an intercooler is placed between the compressor stages of the aero-engine, the compressed air is then cooled by the intercooler; thus, less compression work is required to reach the compressor target pressure. In this paper, an overall assessment of the system is presented with particular focus on the recuperative system and the HEXs mounted into the aero-engine's exhaust nozzle. The herein presented results were based on the combined use of CFD computations, experimental measurements, and thermodynamic cycle analysis. They focus on the effects of total pressure losses and HEX efficiency on the aero-engine performance especially the engine's overall efficiency and the specific fuel consumption (SFC). More specifically, two different hot-gas exhaust nozzle configurations incorporating modifications in the system of HEXs are examined. The results show that significant improvements can be achieved in overall efficiency and SFC, hence contributing to the reduction of CO2 and NOx emissions. The design of a more sophisticated recuperation system can lead to further improvements in the aero-engine efficiency in the reduction of fuel consumption. This work is part of the European funded research program Low Emissions Core engine Technologies (LEMCOTEC).

A GM (1, 1) Markov Chain-Based Aeroengine Performance Degradation Forecast Approach Using Exhaust Gas Temperature

Performance degradation forecast technology for quantitatively assessing degradation states of aeroengine using exhaust gas temperature is an important technology in the aeroengine health management. In this paper, a GM (1, 1) Markov chain-based approach is introduced to forecast exhaust gas temperature by taking the advantages of GM (1, 1) model in time series and the advantages of Markov chain model in dealing with highly nonlinear and stochastic data caused by uncertain factors. In this approach, firstly, the GM (1, 1) model is used to forecast the trend by using limited data samples. Then, Markov chain model is integrated into GM (1, 1) model in order to enhance the forecast performance, which can solve the influence of random fluctuation data on forecasting accuracy and achieving an accurate estimate of the nonlinear forecast. As an example, the historical monitoring data of exhaust gas temperature from CFM56 aeroengine of China Southern is used to verify the forecast performance of the GM (1, 1) Markov chain model. The results show that the GM (1, 1) Markov chain model is able to forecast exhaust gas temperature accurately, which can effectively reflect the random fluctuation characteristics of exhaust gas temperature changes over time.