Baseline design of the KunLun Turbulence Profiler instrument

Adaptive optics systems are the most powerful tools to counteract the image blurring caused by atmospheric turbulence,allowing ground-based telescopes to capture high-resolution images.A critical parameter influencing adaptive optics system performance is the atmospheric refractive index structure constant,C_(n)^(2),which characterizes the intensity of atmospheric optical turbulence as a function of altitude.Given its simplicity,the lunar scintillometer is the preferred method for detecting atmospheric turbulence in challenging environments like Dome A in Antarctica,where sites are still in the developmental stages and local environmental conditions are extremely harsh.However,optimizing the performance of such instruments requires carefully determining the baseline configuration of photon sensors according to each site's specific optical turbulence profile characteristics.This study uses a Monte Carlo method to identify the optimal configuration for the KunLun Turbulence Profiler(KLTP),an instrument comparable to the lunar scintillometer,developed for use at Dome A.Simulations conducted using the obtained optimal baseline configuration recovered three different model optical turbulence profiles,demonstrating the effectiveness of our method in obtaining an optimal baseline configuration.Our approach can be easily applied to baseline design for similar turbulence profilers at other sites.

Research on the Application of Electromagnetic Method in the Exploration of Altered Rock-type Gold Deposits in the East Kunlun Metallogenic Belt

Introduction The East Kunlun Orogenic Belt is located in the northeastern part of the Qinghai–Tibet Plateau(Li et al.,2007),stretching from the East Kunlun to the Elashan area in an east–west direction(Guo et al.,2018).It is an important part of the Central Orogenic Belt(Xiong et al.,2023).It is considered one of the important gold mineralization regions in the Tethys tectonic domain(Norbu et al.,2023)and an essential potential base for mineral resources in China.Wulonggou and Gouli gold mines have been discovered successively,earning the reputation of the"Golden Belt of Qinghai Province"(Feng et al.,2004;He et al.,2023).

Spatial differences of Sustainable Development Goals(SDGs)among counties(cities)on the northern slope of the Kunlun Mountains

The county(city)located on the northern slope of the Kunlun Mountains is the primary area to solidify and extend the success of Xinjiang Uygur Autonomous Region,China in poverty alleviation.Its Sustainable Development Goals(SDGs)are intertwined with the concerted economic and social development of Xinjiang and the objective of achieving shared prosperity within the region.This study established a sustainable development evaluation framework by selecting 15 SDGs and 20 secondary indicators from the United Nations’SDGs.The aim of this study is to quantitatively assess the progress of SDGs at the county(city)level on the northern slope of the Kunlun Mountains.The results indicate that there are substantial variations in the scores of SDGs among the nine counties and one city located on the northern slope of the Kunlun Mountains.Notable high scores of SDGs are observed in the central and eastern regions,whereas lower scores are prevalent in the western areas.The scores of SDGs,in descending order,are as follows:62.22 for Minfeng County,54.22 for Hotan City,50.21 for Qiemo County,42.54 for Moyu County,41.56 for Ruoqiang County,41.39 for Qira County,39.86 for Lop County,38.25 for Yutian County,38.10 for Pishan County,and 36.87 for Hotan County.The performances of SDGs reveal that Hotan City,Lop County,Minfeng County,and Ruoqiang County have significant sustainable development capacity because they have three or more SDGs ranked as green color.However,Hotan County,Moyu County,Qira County,and Yutian County show the poorest performance,as they lack SDGs with green color.It is important to establish and enhance mechanisms that can ensure sustained income growth among poverty alleviation beneficiaries,sustained improvement in the capacity of rural governance,and the gradual improvement of social security system.These measures will facilitate the effective implementation of SDGs.Finally,this study offers a valuable support for governmental authorities and relevant departments in their decision-making processes.In addition,these results hold significant reference value for assessing SDGs at the county(city)level,particularly in areas characterized by low levels of economic development.

SIMULATION INVESTIGATION OF AEROENGINE FAULT DIAGNOSIS USING NEURAL NETWORKS

Traditional scheduled maintenance systems are costly, labor intensive, and typically provide noncomprehensive detection and diagnosis of engine faults. The engine monitoring system (EMS) on modern aircrafts has the potential to provide maintenance personnel with valuable information for detecting and diagnosing engine faults. In this paper, an RBF neural network approach is applied to aeroengine gas path fault diagnosis. It can detect multiple faults and quantify the amount of deterioration of the various engine components as a function of measured parameters. The results obtained demonstrate that the accuracy of diagnosis is consistent with practical requirements. The approach takes advantage of the nonlinear mapping feature of neural networks to capture the appropriate characteristics of an aeroengine. The methodology is generic and applicable to other similar plants having high complexity.

INTELLIGENT FUSION FOR AEROENGINE WEAR FAULT DIAGNOSIS

Four common oil analysis techniques, including the ferrography analysis （FA）, the spectrometric oil analysis （SOA）, the particle count analysis （PCA）, and the oil quality testing （OQT）, are used to implement the military aeroengine wear fault diagnosis during the test drive process. To improve the precision and the reliability of the diagnosis, the aeroengine wear fault fusion diagnosis method based on the neural networks （NN） and the Dempster-Shafter （D-S） evidence theory is proposed. Firstly, according to the standard value of the wear limit, original data are pre-processed into Boolean values. Secondly, sub-NNs are established to perform the single diagnosis, and their training samples are dependent on experiences from experts. After each sub-NN is trained, diagnosis results are obtained. Thirdly, the diagnosis results of each sub-NN are considered as the basic probability allocation value to faults. The improved D-S evidence theory is applied to the fusion diagnosis, and the final fusion results are obtained. Finally, the method is verified by a diagnosis example.

Application of Genetic Algorithm to Solving Nonlinear Model of Aeroengines

Solving the nonlinear model of an aeroengine is converted to an optimization problem, and thus some optimization search methods can be used. An approach to solving the nonlinear model of an aeroengine by use of the genetic algorithm (GA) is developed. By comparison with N R algorithm, the accuracy of the values of initial guesses is not required for GA. Especially, the approach developed can be used when no priori knowledges of the values of initial guesses are availabe, and the convergence is improved significantly. GA properly combined with N R algorithm can increase the convergence speed.

3D Measurement and Stereo Reconstruction for Aeroengine Interior Damage

The borescopy inspection problem of aeroengine interior important partdamages such as firebox's burn and corruption, vane' s crack, bump, abrade and concave pit, is aimedat. A new system is developed to carry out 3D measurement and stereo reconstruction of engineinterior damage, in which the borescope of Japanese OLYMPUS Corporation is used as hardware. In thesystem, functions are implemented, such as image collection, camera calibration, imagepreprocessing, stereo matching, 3D measurement and stereo reconstruction. It can provide moredetailed inspection and more accurate estimation of engine interior damages. Finally, an example isused to verify the effectivity of the new method.

Application of Small Sample Analysis in Life Estimation of Aeroengine Components

The samples of fatigue life tests for aeroengine components are usually less than 5,so the evaluation of these samples belongs to small sample analysis. The Weibull distribution is known to describe the life data accurately,and the Weibayes method （developed from Bayesian method） expands on the experiential data in the small sample analysis of fatigue life in aeroengine. Based on the Weibull analysis,a program was developed to improve the efficiency of the reliability analysis for aeroengine compgnents. This program has complete functions and offers highly accurate results. A particular turbine disk＇s low cycle fatigue life was evaluated by this program. From the results,the following conclusions were drawn：（a） that this program could be used for the engineering applications,and （b） while a lack of former test data lowered the validity of evaluation results,the Weibayes method ensured the results of small sample analysis did not deviate from the truth.

Aeroengine Nonlinear Sliding Mode Control Based on Artificial Bee Colony Algorithm

For a class of aeroengine nonlinear systems,a novel nonlinear sliding mode controller(SMC)design method based on artificial bee colony(ABC)algorithm is proposed.In view of the strong nonlinearity and uncertainty of aeroengines,sliding mode control strategy is adopted to design controller for the aeroengine.On basis of exact linearization approach,the nonlinear sliding mode controller is obtained conveniently.By using ABC algorithm,the parameters in the designed controller can be tuned to achieve optimal performance,resulting in a closedloop system with satisfactory dynamic performance and high steady accuracy.Simulation on an aeroengine verifies the effectiveness of the presented method.

Prediction Approach to Life on Wing for Civil Aeroengine

To reduce engine maintenance cost and support safe operation, a prediction method of engine life on wing was proposed. This method is a kind of regression model which is a function of the condition monitoring and failure data. Key causes of engine removals were analyzed, and the life limit due to performance deterioration was predicted by proportional hazards model. Then the scheduled removal causes were considered as constraints of engine life to predicte the finai life on wing. Application of the proposed prediction method to the case of CF6-80C2A5 engine fleet in an airline proved its effectiveness.

Remaining Useful Life Prediction of Aeroengine Based on Principal Component Analysis and One-Dimensional Convolutional Neural Network

In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.

A GPA diagnostic system foraeroengine applications

In this research,a GPA(Gas Path Analysis) diagnostic system enhanced with GPA Index is described for gas path sensor and component fault diagnosis.A method of measurement correction is used in order that the measurement data obtained at un-standard ambient and operating conditions can be used for diagnostic analysis.The developed diagnostic system has been implemented into a Cranfield University gas turbine performance and diagnostic analysis software PYTHIA for gas turbine performance degradation analysis.The developed method and software have been applied to a model aero gas turbine engine to test the effectiveness of the system.The analysis shows that the developed diagnostic system can diagnose degraded sensor and components effectively using performance deviation measured at un-standard ambient and operational conditions.Theoretically,the idea of the diagnostic approach can be applied to different gas turbine engines.

SHRIMP zircon U-Pb age and O isotopic analysis of the dunite from Kudi ophiolite in the West Kunlun,China

1.Objective The West Kunlun in Xinjiang is located on the northwestern margin of the Qinghai-Tibet Plateau(Fig.1a)and at the junction of the Paleo-Asian tectonic domain and the Tethys tectonic domain.It serves as an important area for the study on the geologic evolution of the Karakorum-West Kunlun due to its special tectonic position.

Fault diagnosis method for an Aeroengine Based on Independent Component Analysis and the Discrete Hidden Markov Model

The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.

Application of Interval Multi-attribute Decision-Making Method to Aeroengine Performance Ranking

In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored infurmation during a period is taken as interval numbers, and the interval multi-attribute decision-making method is employed to predict the performance of aeroengine, The synthetic weights of interval numbers are obtained by calculating deviation degree and possibility degree. As an example of application, 5 performance parameters monitored on 10 CF6 aeroengines of China Eastern Airlines Co., Ltd are adopted as decision attributes to verify the algorithm. The obtained synthetic ranking result shows the effectiveness and rationality of the proposed method in reflecting the performance stares of aeroengins.

Application study on plasma ignition in aeroengine strut–cavity–injector integrated afterburner

To increase the thrust-weight ratio in next-generation military aeroengines,a new integrated afterburner was designed in this study.The integrated structure of a combined strut–cavity–injector was applied to the afterburner.To improve ignition characteristics in the afterburner,a new method using a plasma jet igniter was developed and optimized for application in the integrated afterburner.The effects of traditional spark igniters and plasma jet igniters on ignition processes and ignition characteristics of afterburners were studied and compared with the proposed design.The experimental results show that the strut–cavity–injector combination can achieve stable combustion,and plasma ignition can improve ignition characteristics.Compared with conventional spark ignition,plasma ignition reduced the ignition delay time by 67 ms.Additionally,the ignition delay time was reduced by increasing the inlet velocity and reducing the excess air coefficient.This investigation provides an effective and feasible method to apply plasma ignition in aeroengine afterburners and has potential engineering applications.

Extrapolation for Aeroengine Gas Path Faults with SVM Bases on Genetic Algorithm

Mining aeroengine operational data and developing fault diagnosis models for aeroengines are to avoid running aeroengines under undesired conditions.Because of the complexity of working environment and faults of aeroengines,it is unavoidable that the monitored parameters vary widely and possess larger noise levels.This paper reports the extrapolation of a diagnosis model for 20 gas path faults of a double-spool turbofan civil aeroengine.By applying support vector machine(SVM)algorithm together with genetic algorithm(GA),the fault diagnosis model is obtained from the training set that was based on the deviations of the monitored parameters superimposed with the noise level of 10%.The SVM model(C=24.7034;γ=179.835)was extrapolated for the samples whose noise levels were larger than 10%.The accuracies of extrapolation for samples with the noise levels of 20%and 30%are 97%and 94%,respectively.Compared with the models reported on the same faults,the extrapolation results of the GASVM model are accurate.

Aeroengine Performance Parameter Prediction Based on Improved Regularization Extreme Learning Machine

Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.

High Cycle Fatigue Life Prediction and Reliability Analysis of Aeroengine Blades

Based on the nonlinear continuum damage model （CDM） developed by Chaboehe, a modified model for high cycle fatigue of TC4 alloy was proposed. Unsymmetrical cycle fatigue tests were conducted on rod specimens at room temperature. Then the material parameters needed in the CDM were obtained by the fatigue tests, and the stress distribution of the specimen was calculated by FE method. Compared with the linear damage model （LDM）, the dam- age results and the life prediction of the CDM show a better agreement with the test and they are more precise than the LDM. By applying the CDM developed in this study to the life prediction of aeroengine blades, it is concluded that the root is the most dangerous region of the whole blade and the shortest life is 58 211 cycles. Finally, the Cox propor- tional hazard model of survival analysis was applied to the analysis of the fatigue reliability. The Cox model takes the covariates into consideration, which include diameter, weight, mean stress and tensile strength. The result shows that the mean stress is the only factor that accelerates the fracture process.

Research on Time-Synchronization-Based Test and Control System of Aeroengine

This paper presents a method of NTP-based time synchronization and a strategy of master-slave server structured time synchronization to ensure the test and control system of aeroengine to be time-synchronized. Based on time synchronization, the hierarchy and the integration of the measurement and control system of aeroengine are investigated. In result, our method is successfully applied for multiple front-end tests in a simulative altitude test facility of aeroengine.