Load Sharing Behavior of Star Gearing Reducer for Geared Turbofan Engine

Load sharing behavior is very important for power-split gearing system, star gearing reducer as a new type and special transmission system can be used in many industry fields. However, there is few literature regarding the key multiple-split load sharing issue in main gearbox used in new type geared turbofan engine. Further mechanism anal- ysis are made on load sharing behavior among star gears of star gearing reducer for geared turbofan engine. Compre- hensive meshing error analysis are conducted on eccentricity error, gear thickness error, base pitch error, assembly error, and bearing error of star gearing reducer respectively. Floating meshing error resulting from meshing clearance variation caused by the simultaneous floating of sun gear and annular gear are taken into account. A refined mathematical model for load sharing coefficient calculation is established in consideration of different meshing stiffness and support- ing stiffness for components. The regular curves of load sharing coefficient under the influence of interactions, single action and single variation of various component errors are obtained. The accurate sensitivity of load sharing coefficienttoward different errors is mastered. The load sharing coef- ficient of star gearing reducer is 1.033 and the maximum meshing force in gear tooth is about 3010 N. This paper provides scientific theory evidences for optimal parameter design and proper tolerance distribution in advanced devel- opment and manufacturing process, so as to achieve optimal effects in economy and technology.

MULTIVARIABLE MODEL REFERENCE ADAPTIVE CONTROL FOR A TURBOFAN ENGINE

A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.

Reliability Based Multi-Objective Thermodynamic Cycle Optimisation of Turbofan Engines Using Luus-Jaakola Algorithm

Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.

Cost Prediction of a Turbofan Engine Using the Robust Partial Least Squares Method

In recent years, the cost of engines has become increasingly important to engine manufacturers, who are consistently faced with major problems on how to reduce cost to a minimum. Cost has become a decisive factor for aircraft design. To control the continual rapid increased cost, engine cost prediction is indispensable early in the design phase. But the cost data of an aircraft engine is small; we introduce the Robust Partial Least Squares Method in solving this problem, and reducing or removing the effect of outlying data points, which is different from the Classical PLS. We use the MATLAB software doing several simulations; results and analysis of a real turbofan engine data set show the effectiveness and robustness of the Robust PLS method. The Robust PLS method can effectively be used to estimate Turbofan Engine cost with reasonable accuracy.

Component Matching Based on Low Bypass Ratio Mixed Exhaust Turbofan Engine

In order to study component matching which exists in off-design situation at the initial design stage of turbine engine,by establishing performance analysis model of low bypass ratio mixed flow turbofan engine and components characteristic data,and by applying Newton-Raphson method to solve the nonlinear equations of offdesign points in flying envelop,the factors which affect matching between engine components are studied.The results show that low pressure turbine(LPT)must not operate in a critical condition,and the partial derivative(slope)of pressure ratio to similitude mass flow ratio of working point in LPT characteristic map affects the stability of engine.The smaller the slope is,the more stable the engine is.In addition,the engine is more stable when the fan characteristic map is steep.

Simulation of the secondary air system of turbofan engines:Insights from 1D-3D modeling

Focusing on the internal flow and heat transfer analysis,a platform for the performance evaluation of the Secondary Air System(SAS)is developed.A multi-fidelity modeling technique has been developed in a turbofan engine model under different flight conditions.A turbine blade cool-ing model which integrates external heat transfer calculations and coolant side modeling with com-mon components is proposed.In addition,the Computational Fluid Dynamics(CFD)method is selected to capture the complex flow field structure in the preswirl system.The validity of the SAS models is compared with publicly available data.An elaborately designed cooling system for the AGTF30 engine is analyzed through three main branches.It is found that the 1D-3D mod-eling technique can provide more accurate predictions of the SAS for the AGTF30 engine.The results demonstrate the versatility and flexibility of the SAS models,thereby indicating the capacity of meeting most of the demands of flow and thermal analysis of the SAS.

Multivariable Decoupling Control of Civil Turbofan Engines Based on Fully Actuated System Approach

Gas turbine engines must be operated by means of control,and how to achieve multivariable control decoupling with aero-engine control constraints is an open thorny issue attracting increasingly more attention.The paper considers the multivariable decoupling problems of aero-engines by using a compound controller,which originates from the fact that it is impossible to eliminate all the nonlinear dynamics of system to obtain desired constant linear closed-loop system by using full actuated control because of modeling errors and some physical constraints.Two controllers are involved in the compound controller.One is a fully actuated controller and the other is classical feedback controller.In order to use fully actuated control and maintain the accuracy of engine model,a full state scheduling linear parameter-varying(LPV)modeling method is proposed based on fuzzy neural network weights.For a general input matrix of the system,its generalized inverse is applied to design fully actuated controller to result in a pseudolinear system.Combined with a feedback controller and control limiter,the control synthesis is achieved.The simulation shows that the proposed method is possessed of a better decoupling and tracking effect compared with traditional control approach.

Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear Component-Llevel Model(CLM) based on Long Short-Term Memory(LSTM) Neural Network(NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine under health condition. Then, a sensitivity analysis approach based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and the experimental results show the effectiveness of the proposed method.

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP)for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control.The designed control system includes four parts,namely a predictive model,rolling optimization,online correction,and feedback correction.Considering the strong nonlinearity of engine,a predictive model is established by Back Propagation(BP)neural network for the entire flight envelope,whose input and output are determined with random forest algorithm and actual situation analysis.Rolling optimization typically uses SQP as the optimization algorithm,but SQP algorithm is easy to trap into local optimization.Therefore,the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP.In addition to the traditional three parts of model predictive control,an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain.Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy,and the proposed control system can achieve good tracking performance with the limited parameters within the safe range。

Limit protection design in turbofan engine acceleration control based on scheduling command governor

A new limit protection method based on Scheduling Command Governor(SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller(GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities(LMIs). The SCG is an add-on control scheme which manages engine limits effectively based on reference trajectory optimization. Unlike the traditional min–max architecture with switching logic, the SCG method utilizes the Linear Parameter Varying(LPV) closed-loop model to form a prediction of future constraint violation and per instant solves a constraint-admissible reference within an approximate Maximal Output Admissible Set(MOAS).The influence of the variation of engine dynamic characteristics and equilibrium points during transient state control is handled by the design of contractive sets. Simulation results on a turbofan engine component-level model show the applicability and effectiveness of the SCG method. Compared to the traditional min–max method, the SCG method has less conservativeness. In addition,the design of contractive sets makes conservativeness tunable.

Fault diagnosis based on measurement reconstruction of HPT exit pressure for turbofan engine

Aero-engine gas path health monitoring plays a critical role in Engine Health Management(EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine(HPT) exit pressure, which is vital to distinguishing failure modes between different turbines. For the case of an abrupt failure occurring in a single turbine component, a model-based sensor measurement reconstruction method is proposed in this paper. First,to estimate the missing measurements, the forward algorithm and the backward algorithm are developed based on corresponding component models according to the failure hypotheses. Then,a new fault diagnosis logic is designed and the traditional nonlinear filter is improved by adding the measurement estimation module and the health parameter correction module, which uses the reconstructed measurement to complete the health parameters estimation. Simulation results show that the proposed method can well restore the desired measurement and the estimated measurement can be used in the turbofan engine gas path diagnosis. Compared with the diagnosis under the condition of missing sensors, this method can distinguish between different failure modes, quantify the variations of health parameters, and achieve good performance at multiple operating points in the flight envelope.

Non-affine parameter dependent LPV model and LMI based adaptive control for turbofan engines

The precise control of turbofan engines thrust is an important guarantee for an aircraft to obtain good flight performance and a challenge due to complex nonlinear dynamics of engines and time-varying parameters. The main difficulties lie in the following two aspects. Firstly, it is hard to obtain an accurate kinetic model for the turbofan engine. Secondly, some model parameters often change in different flight conditions and states and even fluctuate sharply in some cases. These variable parameters bring huge challenge for the turbofan engine control. To solve the turbofan engine control problem, this paper presents a non-affine parameter-dependent Linear Parameter Varying(LPV) model-based adaptive control approach. In this approach, polynomial-based LPV modeling method is firstly employed to obtain the basis matrices, and then the Radial Basis Function Neural Networks(RBFNN) is introduced for the online estimation of the non-affine model parameters to improve the simulation performance. LPV model-based Linear Matrix Inequality(LMI) control method is applied to derive the control law. A robust control term is introduced to fix the estimation error of the nonlinear time-varying model parameters for better control performance. Finally, the Lyapunov stability analysis is performed to ensure the asymptotical convergence of the closed loop system. The simulation results show that the states of the engine can change smoothly and the thrust of the engine can accurately follow the desired trajectory, indicating that the proposed control approach is effective. The contribution of this work lies in the combination of linear system control and nonlinear system control methods to design an effective controller for the turbofan engine and to provide a new way for turbofan engine control research.

Flow Through Aerodynamic Torque Converter Installed in New Type Turbofan Engine

增加扇涡轮的核心引擎速度被需要，到为引擎技术的下一跳跃得到最好的效率。因为扇子直径很大，前面扇子直接在被连接到核心引擎的产量柄的常规机制有增加线轴速度的限制。作者建议了前面扇子通过空气动力学的转矩变换器在被驾驶的一个新开车系统。当核心引擎以更高的速度更高效地跑时，前面扇子能以常规速度工作。在这份报纸，连续地，通过变换器的流动被 CFX-5 与商业 CFD 代码的 k- 蔚骚乱模型一起数字地模仿。第二等的流动发生在中心墙上显著地沿着司机片的吸表面在片表面，和流动上影响流动条件在后面的边附近分开，它被离心的力量由于车轮旋转背离到片尖端。

Model reference adaptive control with smooth switching scheme for piecewise linear systems and its application in turbofan engine control

A model reference adaptive control(MRAC)with smooth switching scheme was proposed for piecewise linear systems,and the method was utilized in turbofan engine control to avoid the discontinuity of control input.In this scheme,each sub-region of the operating envelope had its own MRAC controller,and smooth indicator function based smooth switching scheme was introduced to switch multiple controllers smoothly at the boundary of adjacent sub-regions.The Lyapunov stability analysis indicated that the proposed smooth switching scheme can guarantee the convergence of the closed-loop system during the controllers switching.The tracking error system was converted into a switched system to analyze the global stability of the closed-loop system.The advantage of the method was that the chattering of system output and instability caused by asynchronous switching can be eliminated.The simulation illustrates the effectiveness of the proposed control scheme in comparison with the existing MRAC controller with gain scheduling for turbofan engine.

Unsteady Operation of New Type Turbofan Engine with Aerodynamic Torque Converter Reducing Front Fan Speed

It is desired to increase the rotational speed of the core engine of the turbofan so as to get the best efficiency for the next leap of engine technology. The conventional mechanism in which the front fan is directly connected to the output shaft of a core engine, have a limit of increasing the spool speed, because the fan diameter is very large. The authors have proposed a new driving system in which the front fan is driven through the aerodynamic torque converter. The front fan can work at the best performance at slower speed while the core engine runs more efficiently at higher speed. Continuously, this paper discusses the response of the front fan in the unsteady operation of the core engine, accompanying with the internal flow. The system has the acceptable responsibility in the unsteady operation which is very important for the aircrafts.

基于冲击的涡扇发动机退化建模与下发预测

为解决多性能参数模型无法描述不同起飞推力下发动机性能退化的问题,利用单性能参数结合极端冲击模型描述起飞时发动机全功率运转对热端部件的热冲击影响,利用线性退化模型描述发动机自然退化过程,建立发动机性能可靠度退化模型。利用V2500发动机全寿命EGTM数据,结合最大似然估计给出性能可靠度表达式与模型参数,最后利用机队中同型V2500发动机非完整EGTM数据验证模型,预测下发时间,结果表明此模型预测下发时间和实际下发时间的误差小于3%,证明了模型的有效性。

涡扇发动机高原起动供油调整规律研究及故障分析

以某型涡扇发动机高原起动试验为研究对象,通过建立关键事件方式建立起动调整策略分析方法,总结分析了高原起动特点及起动供油量的调整规律,并针对发动机起动过程中出现的几种典型的失速、热悬挂失败案例,分析获得了起动失败的原因。结果表明:高原冷态起动失败主要为起动机脱开后发动机失速为主;热态起动失败主要为低压转子阻力矩增大导致热悬挂为主;起动供油调整规律为起动第二阶段减小供油量,起动第三阶段增加供油量。并提出了提升高原起动成功率的建议,可为发动机高原使用提供参考。

基于参数匹配的航空发动机飞行性能评估技术研究

本文提出了一种基于参数匹配的发动机性能评估模型建立方法,在不依赖部件特性的情况下,以发动机可测截面参数作为输入,建立影响系数矩阵,利用罚函数法求解非直接测量参数的极小二范数解,建立了考虑性能衰减影响的发动机性能评估模型。以某型大涵道比涡扇发动机仿真模型为应用对象,结果显示,在全包线内的典型工作状态下,仿真数据与评估模型计算数据相对误差小于4.5%,表明性能评估模型建立方法是正确的,所建立的性能评估模型可满足工程使用要求。

脉冲爆震外涵加力涡扇发动机总体性能研究

为研究外涵装有脉冲爆震燃烧室(PDC)的混合排气涡扇发动机性能,建立其性能模型。研究了隔离段总压恢复系数和外涵循环参数对PDC特性和整机性能的影响;分析了发动机性能参数对部件参数的敏感性;在相同设计循环参数下与传统加力涡扇发动机性能进行了对比。结果表明:提高隔离段总压恢复系数能够增大PDC增压比,提升发动机性能;风扇压比一定,涵道比增大,发动机耗油率和单位推力增大;风扇压比增大,涵道比在0.2~0.4时,单位推力先增大后减小,涵道比在0.4~0.5时,单位推力先增大后基本不变,涵道比在0.5~0.9时,单位推力一直增大,但增幅逐渐减小。不同涵道比下耗油率随风扇压比增大一直减小;发动机性能对直接影响外涵气流状态参数的部件参数敏感性高;由于PDC的增压特性,脉冲爆震外涵加力发动机仅利用外涵部分气流组织燃烧就可使单位推力与传统加力涡扇发动机相当,且耗油率在设计点降低27.7%,非设计点降低12.8%~26.8%。

机械剩余使用寿命预测模型的增量学习方法

机械剩余使用寿命预测模型依据设备状态监测数据样本进行寿命预测,当样本模式发生变化时,基于原模式样本训练好的模型在新模式样本上的预测表现往往较差。为使模型保留对原模式样本的处理能力,同时拓展出针对新模式样本的处理能力,提出了3种增量学习方法:第一种方法使用新模式样本与原模式样本的标签值构建损失函数;第二种方法在第一种方法基础上增加了模型参数初始化步骤;第三种方法在第一种方法的损失函数中增加了表示当前模型与原模型参数分布差异的正则化项。在涡扇发动机数据集上进行了不同学习方法的对比实验,结果表明所提方法实现了增量学习的目标,其中第三种方法在模型预测准确度和模型训练时长两方面取得了最优的综合表现。