Simulation of Start-Up Process of Turbofan Engine Based on Full-State Characteristics of Fan

Difficulties in obtaining component characteristics in the sub-idle state of rotor constrain the simulation capabilities of ground and windmill start-up processes for turbofan engines.This paper proposes a backbone feature method based on conventional characteristics parameters to derive the full-state characteristics of fan.The application of the fan’s full-state characteristics in component-level model of turbofan engine enables zero-speed iterative simulation for ground start-up process and windmill simulation for windmill start-up process,thereby improving the simulation capability of sub-idle state during turbofan engine start-up.

MODEL REFERENCE ADAPTIVE CONTROL BASED ON NONLINEAR COMPENSATION FOR TURBOFAN ENGINE

The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.

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.

Modeling and simulation of turbofan engine based on equilibrium manifold

Because the normal operation of the engine is located near the equilibrium manifold, the method of equilibrium mani fold nonlinear dynamic modeling is adopted for turbofan engine more than the local train modeling. The method studies the sys tem characteristics near the equilibrium manifold. The modeling method can be realized through dynamic and static twostep, and for the specific parameter modeling steps and algorithm are given. The output of the test data is compared with the model output through numerical simulation, to check the model with an additional set of test data. The simulation results show that the model has reached the requirements of engineering accuracy.

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.

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.

Simplified Removable Ground Test-Bed for Testing Turbofan Engine

A new simplified removable ground test-bed was designed for testing a certainturbofan engine. The facilities are 5.5 m long, 1.5 m wide, 2.2 m high and not more than 4. 5 t ofits empty weight. There are four rubber wheels that could be towed. There is an independentelectrical measurement and control system to test the rotational speed of rotors, the gas pressureof the compressor, the exhaust gas temperature, etc. Cooperated with the oil truck and the electricpower supply truck, the turbofan engine could be preserved on the ground and started to the idlingregime. While running, the parameter of the engine could be recorded, disposed and displayed. Inaddition, the facilities were successfully applied to the plateau experiment in order to researchhow the atmosphere pressure affects the start of engines. Some data are given in the paper.

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.

Sustainable Design of Turbofan Engine： A Computer-Aided Design and Finite Element Analysis

A compressive design and analysis of a turbofan engine is presented in this paper. The components of jet engine have been analyzed based on mechanical design concept. An attempt has been to select materials based on sustainability and green design considerations. The energy content （e） of the materials has been one of the parameters for material selection. The choice of material has a substantial impact on cost, manuthcturing process, and the life cycle efficiency. All components nose cone, fan blade, inlet shaft, including compressor has been solid modeled using Siemens NX 11.0 CAD software. The finite element analysis of every component was performed and found safe. A tolerance analysis was performed before assembly of the turbofan engine. A numerical analysis was completed on blade and inlet geometries to determine a more efficient turbofan engine. Thermal analysis was executed oi1 the cone and suitable corrections were made. Finally, the cost and the total energy were estimated to show how much energy is needed to manufacture a turbofan jet engine.

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.

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.

Takagi-Sugeno fuzzy model identification for turbofan aero-engines with guaranteed stability

This paper is concerned with identifying a Takagi-Sugeno（TS） fuzzy model for turbofan aero-engines working under the maximum power status（non-afterburning）. To establish the fuzzy system, theoretical contributions are made as follows. First, by fixing antecedent parameters, the estimation of consequent parameters in state-space representations is formulated as minimizing a quadratic cost function. Second, to avoid obtaining unstable identified models, a new theorem is proposed to transform the prior-knowledge of stability into constraints. Then based on the aforementioned work, the identification problem is synthesized as a constrained quadratic optimization.By solving the constrained optimization, a TS fuzzy system is identified with guaranteed stability.Finally, the proposed method is applied to the turbofan aero-engine using simulation data generated from an aerothermodynamics component-level model. Results show the identified fuzzy model achieves a high fitting accuracy while stabilities of the overall fuzzy system and all its local models are also guaranteed.

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.

Flow Through Aerodynamic Torque Converter Installed in New Type Turbofan Engine

It is desired to increase the core engine speed of the turbofan, to get the best efficiency for the next leap of the engine technology. The conventional mechanism in which the front fan is directly connected to the output shaft of the core engine has 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 conventional speed while the core engine runs more efficiently at higher speed. Continuously, in this paper, the flow through the converter is simulated numerically by CFX-5 with the k-εturbulence model of the commercial CFD code. The secondary flow occurred on the hub wall affects markedly the flow condition on the blade surfaces, and the flow along the suction surface of the driver blade separates near the trailing edge, which is deviated to the blade tip by the centrifugal force due to the wheel rotation.

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.

Direct thrust control for multivariable turbofan approach

A novel turbofan Direct Thrust Control(DTC)architecture based on Linear ParameterVarying(LPV)approach for a two-spool turbofan engine thrust control is proposed in this paper.Instead of transforming thrust command to shaft speed command and pressure ratio command,the thrust will be directly controlled by an optimal controller with two control variables.LPV model of the engine is established for the designing of thrust estimator and controller.A robust LPV H∞filter is introduced to estimate the unmeasurable thrust according to measurable engine states.The thrust estimation error system is proved to be Affinely Quadratically Stable(AQS)in the whole parameter box with a prescribed H∞performance indexγ.Due to the existence of overdetermined equations,the solving of controller parameters is a multi-solution problem.Therefore,Particle Swarm Optimization(PSO)algorithm is used to optimize the controller parameters to obtain satisfactory control performance based on the engine’s LPV model.Numerical simulations show that the thrust estimator can acquire smooth and accurate estimating results when sensor noise exists.The optimal controller can receive desired control performance both in steady and transition control tasks within the engine working states above the idle,verifying the effectiveness of the proposed DTC architecture’s application in thrust direct control problem.

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。

NOx emissions of turbofan powered unmanned aerial vehicle for complete flight cycle

Unmanned Aerial Vehicles(UAVs) have been getting more and more popular in both civil and military arena. Similar to manned aircraft, their propulsion systems or engines emit harmful gases such as nitrogen oxides. Since UAVs have different mission profiles and operational parameters than manned aircraft, it is worthy to investigate their NOx emissions. Therefore, in this study, NOx emissions of a turbofan powered UAV for complete flight cycle was calculated and optimized within a range of altitude and speed parameters. NOx emissions were calculated based on ICAO ground test data and corrected to any speed and altitude during flight legs using both Boeing Fuel Flow Method 2 and DLR Fuel Flow Method. Total NOx emissions were calculated for complete flight cycles for different altitude and speed parameters. Numerical results were presented graphically and additionally optimization studies were conducted. Optimization studies include determination and comparison of speed and altitude for minimum NOx emissions by the two fuel flow methods and maximum loiter time achievable by UAV.

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.