APPLICATION OF HYBRID AERO-ENGINE MODEL FOR INTEGRATED FLIGHT/PROPULSION OPTIMAL CONTROL

The real-time capability of integrated flight/propulsion optimal control （IFPOC） is studied. An appli- cation is proposed for IFPOC by combining the onboard hybrid aero-engine model with sequential quadratic pro- gramming （SQP）. Firstly, a steady-state hybrid aero-engine model is designed in the whole flight envelope with a dramatic enhancement of real-time capability. Secondly, the aero-engine performance seeking control including the maximum thrust mode and the minimum fuel-consumption mode is performed by SQP. Finally, digital simu- lations for cruise and accelerating flight are carried out. Results show that the proposed method improves real- time capability considerably with satisfactory effectiveness of optimization.

Aero-engine Blade Fatigue Analysis Based on Nonlinear Continuum Damage Model Using Neural Networks

Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇s sum method is not suitable for aero-engine because of its low accuracy.A back propagation neutral network（BPNN） based on the combination of Levenberg-Marquardt（LM） and finite element method（FEM） is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade.Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve.A nonlinear continuum damage model（CDM）,based on the BPNN with one hidden layer and ten neurons,is built to investigate the nonlinear damage accumulation behavior,in which the results from the tests are used as training set.Comparing with linear models and previous nonlinear models,BPNN has the lowest calculation error in full load range.It has significant accuracy when the load is below 500 MPa.Especially,when the load is 350 MPa,the calculation error of the BPNN is only 0.4%.The accurate model of the blade is built by using 3D coordinate measurement technology.The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min,and the load history is obtained from finite element analysis（FEA）.Then the fatigue life of the compressor blade is predicted by using the BPNN model.The final fatigue life of the aero-engine blade is 6.55 104 cycles（10 916 h） based on the BPNN model,which is effective for the virtual design of aero-engine blade.

Application of multi-outputs LSSVR by PSO to the aero-engine model

Considering the modeling errors of on-board self-tuning model in the fault diagnosis of aero-engine, a new mechanism for compensating the model outputs is proposed. A discrete series predictor based on multi-outputs least square support vector regression （LSSVR） is applied to the compensation of on-board self-tuning model of aero-engine, and particle swarm optimization （PSO） is used to the kernels selection of multi-outputs LSSVR. The method need not reconstruct the model of aero-engine because of the differences in the individuals of the same type engines and engine degradation after use. The concrete steps for the application of the method are given, and the simulation results show the effectiveness of the algorithm.

Linear-in-Parameter Models Based on Parsimonious Genetic Programming Algorithm and Its Application to Aero-Engine Start Modeling

A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

New model-based method for aero-engine turbine blade tip clearance measurement

Active control of aero-engine turbine tip clearance is one of the best chances for engine performance uplift currently.To do that,the first requirement is real-time measurement of tip clearance in aero-engine working environment.However,turbine complexity makes it unlikely for tip clearance sensors to be loaded.In recognition of that,this paper proposed a model-based method for tip clearance measurement.Firstly,by considering previously wrongly neglected factors such as load deformation,a mathematical model to monitor dynamic tip clearance changes is built to improve calculation accuracy.Then,after clarifying the coupling relationship between engine models and tip clearance models,this paper builds a component-level mathematical model integrating dynamic characteristics of turbine tip clearance,which helps realize accurate measurement of tip clearance in working environment.How tip clearance affects turbine efficiency is studied afterwards and reported to aero-engine model,so as to mitigate performance difference between aero-engine model and real engines caused by turbine tip clearance.Lastly,by hardware-in-the-loop simulation,tip clearance model demonstrates 15.9%better accuracy than previously built models in terms of turbine centrifugal deformation calculation.As tip clearance measurement model takes averagely 0.34 ms in calculation,meeting the operation requirement,it proves to be an effective new way.

An improved nonlinear onboard adaptive model for aero-engine performance control

The onboard adaptive model can achieve the online real-time estimation of performance parameters that are difficult to measure in a real aero-engine,which is the key to realizing modelbased performance control.It must possess satisfactory numerical stability and estimation accuracy.However,the positive definiteness of the state covariance matrix may be destroyed in filter estimation because of the existence of some uncertain factors,such as the accumulated measurement error,noise,and disturbance in the strongly nonlinear engine system,inevitably causing divergence of estimates of Cholesky decomposition-based Spherical Unscented Kalman Filter(SUKF).Therefore,this paper proposes an improved SUKF algorithm(iSUKF)and applies it to the performance degradation estimation of the engine.Compared to SUKF,the iSUKF mainly replaces the Cholesky decomposition with the Singular Value Decomposition(SVD),which is numerically stable without any strict requirement for the state covariance matrix.Meanwhile,a correction factor is designed to assess the measurement deviation between the real engine and the nonlinear onboard model to correct the state covariance matrix,thus maintaining better numerical stability of parameters estimated by the filter.Then,an offline correction strategy is also proposed to eliminate the influence of the degradation of unestimated health parameters or the filter’s inadequate estimation of the coupled health parameters.This action effectively promotes the onboard adaptive model’s estimation accuracy concerning the degradation of the engine’real health parameters and its performance parameters.Finally,the simulation results show that the iSUKF can maintain the numerical stability of the filter’s estimation of health parameters.Compared with the existing methods,the offline correction strategy improves the estimation accuracy of the iSUKF-based nonlinear onboard adaptive model for the performance parameters of the real engine by more than 50%.The proposed method will provide feasible technical support for model-based aero-engine performance control.

Optimization of aero-engine combustion chambers with the assistance of Hierarchical-Kriging surrogate model based on POD downscaling method

In view of the long calculation cycle,high processing test and cost of the traditional aero-engine combustion chamber design process,which restricts the engine optimization design cycle,this paper innovatively proposes a surrogate model for the performance of aero-engine combustion chambers based on the POD-Hierarchical-Kriging method.Through experiments,the predicted results of the POD-Hierarchical-Kriging model are compared and analyzed with the calculated results of the one-dimensional program,and the root mean square error of the predicted values of combustion efficiency and total pressure loss is 0.0064%and 0.1995%,respectively.The accuracy of the POD-Hierarchical-Kriging model is compared with the cubic polynomial model,the basic Kriging model and the Hierarchical-Kriging model.It verifies the feasibility and accuracy of the POD-Hierarchical-Kriging model for the prediction of performance of aero-engine combustion chambers.The global sensitivity analysis method is applied to obtain the influence effect of design variables on the performance.Then,a multi-objective optimization method based on the NSGA-II algorithm is studied,and finally the optimal set of Pareto solutions is obtained and analyzed,which can be used to guide the optimal design of aero-engine combustion chambers and accelerate the progress of aero-engine development.

A tip clearance prediction model for multistage rotors and stators in aero-engines

Tip clearances of multistage rotors and stators greatly affect aero-engines’ aerodynamic efficiency, stability and safety. The inevitable machining and assembly errors, as well as the complicated error propagation mechanism, cause overproof or non-uniform tip clearances. However, it is generally accepted that tip clearances are difficult to predict, even under assembly state. In this paper, a tip clearance prediction model is proposed based on measured error data. Some 3 D error propagation sub-models, regarding rotors, supports and casings, are built and combined. The complex error coupling relationship is uncovered using mathematical methods. Rotor and stator tip clearances are predicted and analyzed in different phase angles. The maximum, minimum and average tip clearances can be calculated. The proposed model is implemented by a computer program,and a case study illustrates its performance and verifies its feasibility. The results can be referred by engineers in assembly quality judgement and decision-making.

Model parameters estimation of aero-engine based on hybrid optimization algorithm

A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.

Effect of Sealing Air on Oil Droplet and Oil Film Motions in Bearing Chamber

Beating chamber is one of important components that support aero-engine rotors and research on oil droplet and oil film motions is an important part of bearing chamber lubrication and heat transfer design. Consid- ering the pressure of sealing air is an important operating condition that affects the oil droplet and oil film mo- tions, the effect of sealing air pressure on airflow in bearing chamber is investigated in this paper firstly ; and then based on the air velocity and air/wall shear force, the oil droplet moving in core air, deposition of oil droplet im- pact on wall as well as velocity and thickness of oil film are analyzed secondly; the effect of sealing air pressure on oil droplet velocity and trajectory, deposition mass and momentum, as well as oil film velocity and thickness is discussed. The work presented in this paper is conducive to expose the oil/air two phase lubrication mechanism and has certain reference value to guide design of secondary air/oil system.