Asbestos Exposure during Gasket Removal from Aircraft Engine

We have previously evaluated asbestos exposure associated with various maintenance procedures on light aircraft. The purpose of this study was to evaluate asbestos exposure during engine maintenance on light aircraft. This test was designed to evaluate the potential for asbestos exposure to mechanics and others who remove asbestos-containing engine gaskets from reciprocating style aircraft engines. Utilized in this test was an air cooled, horizontally opposed, aviation gasoline burning engine, assembled during 1986 and operated intermittently up into 2015, having accumulated 1680 hours run time. Nearly 75% of the asbestos-containing gaskets installed during 1986 were still in place at the time of testing. Chrysotile asbestos contents of such gaskets ranged from 55% to 60% by area, for those of sheet style and 5% by area, for the spiral wound metal/asbestos style. Despite the levels of effort required to effect gasket removals, the professional aircraft mechanic was not exposed to airborne asbestos fibers at the lower limits of sampling and analytical detection achieved;all of which were substantially less than the current Occupational Safety and Health Administration Permissible Exposure Limits for asbestos. The results of this testing indicate an absence of gasket related asbestos exposure risk to mechanics who work with light aircraft engines, including those having asbestos-containing gaskets. These results are consistent with the findings of Mlyarek and Van Orden who studied the asbestos exposure risk occasioned during overhaul of larger radial style reciprocating aircraft engines [1].

GRADE-LIFE PROGNOSTIC MODEL OF AIRCRAFT ENGINE BEARING

Research on practical and verifiable prediction methods for the service life of bearings plays a critical role in improving the reliability and safety of aircraft engines. The concept of grade-life （GL） is introduced to de- scribe the service life of bearings. A GL prognostic model for aircraft engine bearings is proposed based on sup- port vector machine （SVM） and fuzzy logic inference. Firstly, the mathematical model is discussed to predict the physics-based GL （PGL）. Then, the diagnostic estimation model based on SVM is presented in detail to predict the empirical GL （EPL）. Thirdly, a fuzzy logic inference is adopted to fuse two GL predicted results. Finally, the GL prognostic model is verified by the run-to-failure data acquired from an accelerated life test of an aircraft bearing. The results show that the model provides a more practical and reliable prediction for the service life of bearings.

RELIABILITY EVALUATION MODEL BASED ON DATA FUSION FOR AIRCRAFT ENGINES

Reliability evaluation for aircraft engines is difficult because of the scarcity of failure data. But aircraft engine data are available from a variety of sources. Data fusion has the function of maximizing the amount of valu- able information extracted from disparate data sources to obtain the comprehensive reliability knowledge. Consid- ering the degradation failure and the catastrophic failure simultaneously, which are competing risks and can affect the reliability, a reliability evaluation model based on data fusion for aircraft engines is developed, Above the characteristics of the proposed model, reliability evaluation is more feasible than that by only utilizing failure data alone, and is also more accurate than that by only considering single failure mode. Example shows the effective- ness of the proposed model.

Aircraft Engine Gas Path Fault Diagnosis Based on Hybrid PSO-TWSVM

Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.

Numerical Analysis and Verification of the Gas Jet from Aircraft Engines Impacting a Jet Blast Deflector

The process of the gas jet from aircraft engines impacting a jet blast deflector is not only a complex fluid–solid coupling problem that is not easy to compute, but also a safety issue that seriously interferes with flight deck envi?ronment. The computational fluid dynamics(CFD) method is used to simulate numerically the impact e ect of gas jet from aircraft engines on a jet blast deflector by using the Reynolds?averaged Navier?Stokes(RANS) equations and turbulence models. First of all, during the pre?processing of numerical computation, a sub?domains hybrid meshing scheme is adopted to reduce mesh number and improve mesh quality. Then, four di erent turbulence models includ?ing shear?stress transport(SST) k-w, standard k-w, standard k-ε and Reynolds stress model(RSM) are used to compare and verify the correctness of numerical methods for gas jet from a single aircraft engine. The predicted values are in good agreement with the experimental data, and the distribution and regularity of shock wave, velocity, pressure and temperature of a single aircraft engine are got. The results show that SST k?w turbulence model is more suitable for the numerical simulation of compressible viscous gas jet with high prediction accuracy. Finally, the impact e ect of gas jet from two aircraft engines on a jet blast deflector is analyzed based on the above numerical method, not only the flow parameters of gas jet and the interaction regularity between gas jet and the jet blast deflector are got, but also the thermal shock properties and dynamic impact characteristics of gas jet impacting the jet blast deflector are got. So the dangerous activity area of crew and equipments on the flight deck can be predicted qualitatively and quantitatively. The proposed research explores out a correct numerical method for the fluid–solid interaction during the impact process of supersonic gas jet, which provides an e ective technical support for design, thermal ablation and structural damage analysis of a new jet blast deflector.

Prediction of Aircraft Engine Health Condition Parameters Based on Ensemble ELM

In view of aircraft engine health condition parameters prediction,an ensemble ELM based prediction approach is proposed in this paper. In the approach,the AdaBoost. RT algorithm is improved to adjust its threshold adaptively,and is utilized as the basic framework to establish the ensemble learning model using ELM as weak learners. The proposed approach is evaluated through the prediction of the actual engine fuel flow deviation time series,and the results demonstrate that this approach is feasible for the prediction of aircraft engine health condition parameters. The performance of the proposed approach is compared with single ELM, single process neural network ( PNN) ,and a similar ensemble ELM based approach using AdaBoost. RT as basic framework. The results show that,the proposed approach is more accurate than single ELM and single PNN,and no worse than the ensemble prediction approach for contrast,furthermore,the given approach is more convenient for practical application. Therefore,the proposed approach is better suited to the prediction of aircraft engine health parameters.

A Generative Adversarial Nets Method for Monitoring Data Generation on Aircraft Engines

A sufficient sample size of monitoring data becomes a key factor for describing aircraft engines state.Generative adversarial nets(GAN)can be used to expand the sample size based on the existing state monitoring information.In the paper,a GAN model is introduced to design an algorithm for generating the monitoring data of aircraft engines.This feasibility of the method is illustrated by an example.The experimental results demonstrate that the probability density distribution of generated data after a large number of network training iterations is consistent with the probability density distribution of monitoring data.The proposed method also effectively demonstrates the generated monitoring data of aircraft engine are in a reasonable range.The method can effectively solve the problem of inaccurate performance degradation evaluation caused by the small amount of aero?engine condition monitoring data.

THE ENGINEERING PREDICTION FOR AIRCRAFT NOISE

The acoustic emission from an aircraft during the flight is a dynamic process. The emitting acoustic power and received mean square acoustic pressure are functions of time. The paper deals with this problem as a quasi-steady one. The whole process is resolved into several elementary procedures, for example, the flight trajectory, the geometric relation between the noise sources and observers, the noise source characteristics, the air propagation and ground effect. Two calculation examples are given in the paper, one for a long-range passenger with high bypass-ratio turbofan engine, and the other for a 3-leaved propeller. The composition of aircraft noise, its time history, frequency spectrum and directivity can be clearly described by these curves. It may be useful for the designer to perform the acoustic design of his aircraft layout.

Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

Residual useful life(RUL)prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost.Owing to various failure mechanism and operating environment,the application of classical models in RUL prediction of aircraft engines is fairly difficult.In this study,a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed.First of all,sensor data obtained from the aircraft engines are preprocessed to eliminate singular values,reduce random fluctuation and preserve degradation trend of the raw sensor data.Secondly,three kinds of recurrent neural networks(RNN),including ordinary RNN,long shortterm memory(LSTM),and gated recurrent unit(GRU),are individually constructed.Thirdly,ensemble learning mechanism is designed to merge the above RNNs for producing a more accurate RUL prediction.The effectiveness of the proposed method is validated using two characteristically different turbofan engine datasets.Experimental results show a competitive performance of the proposed method in comparison with typical methods reported in literatures.

Engine-Out Takeoff Path Optimization for Large Civil Aircraft

Maximum regulated takeoff weights and hence payloads of large commercial jets are limited by government regulations which take into account local airport conditions as well as a variety of safety factors. One of the challenging conditions that must be met is linked to a minimum obstacle clearance in the unlikely event of an engine failure on the runway at the worst possible time. This requirement becomes an overriding factor for airports surrounded by challenging terrain, and therefore a well defined takeoff path out of these airports has the potential to transform a financially unsustainable operation into a commercially viable one. The research described in this paper represents an ongoing attempt to resolve this important problem and makes use of recent advances in robot path planning techniques.

The Effect of Ignition Parameters on the Combustion Characteristics of an Aviation Piston Engine

A cylinder combustion simulation model was established for a two-stroke aviation piston engine used in a small unmanned aerial vehicle. The influence of different ignition system parameters on the combustion process of aviation kerosene was studied using this model. The research results showed that under the working conditions of 5500 r/min and 50% throttle opening, as the ignition energy increased, the peak values of average cylinder pressure and average temperature increased, and the combustion duration shortened, The advance of the combustion center of gravity increases the tendency of the engine to knock. Under the same operating conditions, as the ignition timing advances, the peak values of average pressure and average temperature in the cylinder increase, gradually approaching the top dead center, and the tendency of engine detonation increases more significantly.

面向对象的航空发动机总体仿真程序Digital Engine

基于面向对象的程序设计思路,设计开发了一套航空发动机总体性能仿真程序Digital Engine,并就其稳态和过渡态计算能力,与业内知名软件Gas Turb进行了初步对比。对比结果表明,Digital Engine与Gas Turb软件的计算结果基本吻合,能够满足实际工程中对航空发动机工作特性及性能的仿真与预测需求。

CONDITION-BASED MAINTENANCE DECISION MAKING METHODS FOR CIVIL AEROENGINE

To provide some feasible condition-based maintenance （CBM） decision making methods for civil aeroengine, firstly, the theory of aeroengine CBM decision making is described. The proportional intensity（PI） model is established based on the reliability and condition monitoring data. According to the model, the decision making methods are proposed for the optimal preventive maintenance（PM） interval and removal. Then, the time on wing （TOW） is predicted by collecting actual data based on the engine age and operating conditions. Finally, an example of a fleet for CF6-80C2 engines is illustrated. It shows that sufficient engine operation data are the key of accurate decision making. Results indicate that the CBM decision making methods are helpful for engineers in airlines to control engine maintenance actions and TOW, thus decreasing risks and maintenance costs.

航空发动机EngineCAD微机集成系统的开发与研究

本文介绍在航空发动机研制与改型设计阶段中采用最新技术的EngineCAD微机集成设计系统。该系统可以实现对涡喷、涡扇、涡轴和涡桨航空发动机部件图和总图的参数化实时设计,在参数化设计过程中提供发动机信息数据库、结构数据库的交互式查询,为设计提供所需的信息、数据和结构图形支持,可达到快速地生成供方案论证应用的各种航空发动机部件图、总图及各种信息,能快速实现多方案对比设计。具体应用表明,使用该系统大大缩短了设计周期,提高了设计的一次成功率。

基于ArcGIS Engine的飞机人工增雨航线自动化设计算法研究

人工增雨作为应急抗旱和缓解水资源紧张的重要手段,与社会经济可持续发展、生态安全和人民生活密切相关。航线设计是飞机人工增雨作业方案设计的一项重要内容,基于GIS平台自动化设计飞行航线将成为趋势。在传统飞行航线设计方案的基础上,研究提出了基于ArcGIS Engine的半圆弧过渡条播航线、"弓"字形条播航线和"之"字形条播航线三种层状云作业"穿云"航线与一种对流云或层积混合云作业"绕云"航线的自动化设计算法。应用结果显示,四种自动化算法均能够快速有效地设计出飞机航线,取得很好的效果。

Engine Selection Based on Utility Theory

Since an engine is seen as the″heart″of an airplane,the objective and scientific evaluation of it is significant to ensure normal operation of airlines.Aiming at the limitations of current studies on selecting engines,a quantitative comprehensive evaluation system of engine options was established and an optimization model based on the utility theory and analytic hierarchy process(AHP)was proposed.Considering the judgement of different customers on the balance between income and risk,the utility of each evaluation index was determined by utilizing the piecewise utility function.The AHP was used to analyze individual demands of customers.Finally,the optimal scheme was selected through calculating the weighted utility value.According to the actual needs of a domestic airline,the utility of three engine options was calculated.The results showed that the value of risk factor can be set to determine the selection scheme based on the degree of preferences(conservative type,neutral type or adventurous type).

Luster Polish Strengthening Treatment for Raceway Surface of Aeroengine Bearings

A new surface strengthening technology, luster polish strengthening treatment, was proposed to treat the raceway surface of aeroengine bearings （9Cr18Mo） with the centrifugal strengthening machine exclusively designed for luster polish strengthening treatment. The experimental results showed that luster polish strengthening treatment produced a compressive residual stress layer with a depth of over 80 μm below the surface of the bearing raceway, and thus effectively removed the metamorphic layer in the raceway surface. After luster polish strengthening treatment, the average surface hardness of the aeroengine bearing raceway was increased from 61.02 HRC to 63.01 HRC, the surface roughness was reduced from 0.06 μm to 0.03 μm, and the contact fatigue life of the aeroengine bearings was improved by about 90%, with the dispersion of fatigue life being reduced remarkably. Theoretical calculation result agrees with that obtained by experiment.

Technologies and studies of gas exchange in two-stroke aircraft piston engine:A review

The in-cylinder gas exchange process is crucial to the power performance of two-stroke aircraft piston engines,which is easily influenced by complex factors such as high-altitude performance variation and in-cylinder flow characteristics.This paper reviews the development history and characteristics of gas exchange types,as well as the current state of theory and the validation methods of gas exchange technology,while also discusses the trends of cutting-edge technologies in the field.This paper provides a theoretical foundation for the optimization and engineering design of gas exchange systems and,more importantly,points out that the innovation of gas exchange types,the modification of theoretical models,and the technology of variable airflow organization are the key future research directions in this field.

Gas exchange optimization in aircraft engines using sustainable aviation fuel:A design of experiment and genetic algorithm approach

The poppet valves two-stroke(PV2S)aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio,uniform torque output,and flexible valve timings.However,its high-altitude gas exchange performance remains unexplored,presenting new opportunities for optimization through artificial intelligence(AI)technology.This study uses validated 1D+3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines.The valve timings of the PV2S engine exhibit considerable flexibility,thus the Latin hypercube design of experiments(DoE)methodology is employed to fit a response surface model.A genetic algorithm(GA)is applied to iteratively optimize valve timings for varying altitudes.The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance.The optimal valve overlap angle emerged as 93°CA at sea level and 82°CA at 4000 m altitude.The effects of operating parameters,including engine speed,load,and exhaust back pressure,on the gas exchange process at varying altitudes are further investigated.The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes.This effect is especially pronounced at elevated altitudes.The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency.This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines,serving as a valuable reference for further optimization efforts.

Dynamic Simulation of the Aircraft Environmental Control System

The dynamic research of aircraft environmental control system (ECS) is an important step in the advanced ECS design process. Based on the thermodynamics theory, mathematical models for the dynamic performance simulating of aircraft ECS were set up and an ECS simulation toolbox (ECS_1.0) was created with MATLAB language. It consists of main component modules (ducts, valves, heat exchangers, compressor, turbine, etc.). An aircraft environmental control system computer model was developed to assist engineers with the design and development of ECS dynamic optimization. An example simulating an existing ECS was given which shows the satisfactory effects.