To reduce specific fuel consumption (SFC) of aero-engine, increase the combat radius of fighter jets, improve economic performance, and contribute to energy conservation and emission reduction, this paper proposes a hybrid optimization algorithm combining the chimp optimization algorithm (ChOA) and sequential quadratic programming (SQP). The algorithm ensures effective optimization while significantly reducing solving time. Focusing on a double bypass variable cycle engine, a sensitivity analysis is conducted to select optimal control variables. An intelligent optimization control system integrates ChOA-SQP with active disturbance rejection control (ADRC) for minimum SFC optimization. Results indicate that ChOA achieves a 7.28 % average SFC reduction, SQP achieves 6.71 %, and ChOA-SQP achieves 7.47 %, with ChOA-SQP reducing SFC an additional 0.57 % compared to SQP and 0.19 % compared to ChOA. Convergence time is only 24.25 % of ChOA and 109.75 % of SQP, with a convergence speed faster than ChOA and similar to SQP. The system transitions seamlessly to the minimum SFC point while ensuring safe engine operation, reducing SFC by 7.58 % at the subsonic cruise design point and 5.69 % at the supersonic cruise design point, with maximum thrust fluctuations of 0.87 % and 0.95 %, respectively.

This paper investigates the best control method of the lowest specific fuel consumption (SFC) to reduce the specific fuel consumption of the triple-bypass variable cycle engine. Specific fuel consumption is the ratio of fuel flow to thrust. First, the Kriging model of the engine near the supersonic cruise and subsonic cruise state points was extracted using the component-level model of the triple-bypass variable cycle engine, and the PSM was obtained close to the steady-state point. The contribution of each control variable to the engine’s specific fuel consumption was computed using the PSM and, at the same time, due to the linear characteristics of the PSM, it was easy to deal with various constrained linear optimization problems, and the steady-state points with the smallest specific fuel consumption under the constraints could be obtained through the linear optimization algorithm; however, the surge margin and pre-turbine temperature of the optimized point were limited in the optimization process, the method of direct switching inevitably brought the problem of overshoot of the controlled quantity, and the actual controlled quantity could still exceed the safe operation boundary of the engine in the process of change. Moreover, the performance optimization control itself is premised on sacrificing the surge margin of the engine, and its operating boundary is closer to the surge line, so the limitation protection problem in the transition state cannot be ignored in the process of performance optimization control. In this paper, a multivariable steady-state controller was designed based on Model Predictive Control (MPC) to meet the needs of engine optimization control mode switching. The simulation results of the supersonic cruise mode show that the minimum fuel consumption control can reduce the fuel consumption of the engine by 2.6% while the thrust remains constant.

Almost all of the wear debris generated during the operation of the machine is suspended in the circulating lubricating oil. The analysis of the wear debris in the lubricating oil can effectively monitor the wear state of the machine and provide early warning of failures. An overview on inductive sensors for measuring wear debris in lubrication is introduced. To begin with, the significance of analyzing the wear debris in lubricating oil is explained and the working principle of the inductive wear sensors is illustrated. Furthermore, the development of inductive wear sensors and the key limitations are summarized. Finally, some rest factors affecting the sensor and the processing method of the induction signal aliasing are discussed, and the future development trend is prospected. It is pointed out that developing high sensitivity wear debris inductive sensors, increasing sensor throughput, and solving the problem of aliasing of detection signals are the following issues that should be further studied in the future.

基于"光伏原理"课程,构建了太阳能光—热—电综合利用虚拟仿真实验系统,将其用于学生在线自学.通过开展线上线下混合式教学、翻转课堂等学习,教学实践取得了良好的效果.结果表明,基于虚拟仿真的线上线下混合课程构建对人才培养具有很好的促进作用.

Inductive oil debris monitors can detect wear debris in lubricating oil in real-time, which has great potential for monitoring the working conditions of mechanical systems. However, the superimposition of the induced voltages when multiple debris particles pass through a sensor at a close distance may lead to an erroneous estimation of the peak-to-peak value of the wear debris waveforms. A complete implementation framework is proposed to separate the aliasing signals based on fully convolutional neural networks, which includes a segmented fractional calculus filtering technique and a semi-simulated training dataset generation method. The results of physical experiments indicate that the proposed method can reduce the average error rate of the peak-to-peak value from 15.36% to 3.96% and the maximum error rate from 56.33% to 9.27% compared with those before separation. The stability and computing time of this method are also evaluated through physical experiments.

To perform transient state control of an aero-engine, a structure that combines linear controller and min–max selector is widely adopted, which is inherently conservative and therefore limits the fulfillment of the engine potential. Model predictive control is a new control method that has vast application prospects in the field of aero-engine control. Therefore, this paper proposes a wide-range model predictive controller that can control the engine over a wide range within the flight envelope. This paper first introduces the engine parameters and the model prediction algorithm used by the controller. Then a wide-range model prediction controller with a three-layer nested structure is presented. These three layers of the structure are univariate controller, nominal point controller, and wide-range controller from inside to outside. Finally, by analyzing and verifying the effectiveness of the univariate controller for small-range variations and the wide-range model predictive controller for large-range parameter variations, it is demonstrated that the controller can schedule the controller’s output based on inlet altitude, Mach number, and low-pressure shaft corrected speed, and ensure that the limits are not exceeded. It is concluded that the designed wide-range model predictive controller has good dynamic effect and safety.

On line lubricating oil debris measurement is an efficient way to judge the operating status of a machine, and the inductive oil debris sensor is widely adopted. Considering the debris sensor works in a harsh environment, it is a challenge work to reduce or separate the noise via signal processing, especially for the requirements of detecting small-sized wear debris. To overcome the limitations of existing methods, a novel signal decomposition method called symplectic geometry mode decomposition (SGMD) is proposed to extract the signature of oil debris. SGMD can remodel the state and eliminating noise adaptively, and the simulation results manifest that SGMD can extract the signature of debris accurately and effectively. When analyzing the experimental signal, the SGMD and EMD are combined, and the results show that it has a better decomposition ability than EMD or wavelet decomposition.

A new measuring system for the engine tip clearance based on alternate current discharge is proposed in this article. Firstly, theoretical analysis and numerical simulation of the system, as well as the experiment of gas discharge are introduced to prove the feasibility of the method. Then a validation platform is built to study the effects of different probe materials and structures, as well as changes in temperature and humidity under atmospheric pressure on the system. Finally, the system calibration and tip clearance measurement are carried out. The results show that tungsten copper probe with planar structure is more suitable for measuring the tip clearance. When the tip clearance is between 0-6mm, the designed system has high measurement accuracy and its measurement error within 0.05mm. Compared with the traditional spark discharge method, the proposed method not only has the similar advantages, but also overcomes many defects, and has a broader application prospect.

针对某型航空发动机热源模拟台的温度调控需求,设计一种以STM32低功耗微处理器为控制核心,LabView为上位机软件开发平台的分布式多路温度调控系统.系统主要分为4大子系统,分别为以铠装K型热电偶和DAM-3038热电偶模拟量输入模块为主要组成部件的温度采集系统;基于LabView2015开发的上位机软件;以一块STM32F407为核心的微处理器部分以及以固态继电器(SSR)与电热丝为主要部件的温度控制部分.经过实际试验验证,该系统能够在保证设计的多路温度控制功能实现的情况下稳定运行,具有较强的实用性.

Blade tip clearance is one of the important parameters affecting the performance, safety and stability of a gas turbine engine. However, it is difficult to measure the tip clearance in real time and accurately during the development and test process of an engine. In order to promote the development of tip clearance–measuring technology and the optimal design of the gas turbine engine, some typical measuring methods of tip clearance and a novel measuring method based on AC discharge are introduced. In this article, the significance for measuring tip clearance of an engine is illustrated first. Then, operating principles, characteristics and developments of those typical measurement approaches are introduced. After that, these methods are analyzed, and the particular characteristic of each measuring approach is summarized.

To provide the desired thrust and prevent the engine from exceeding any safety or operational limits, a min–max selector with linear limiters is widely employed in current aircraft engine control logic. However, with the further requirements of engine performance, the traditional linear limiters should be improved. Though there are many researchers working on the development of improvement methods, none of those methods consider the limitation of core shaft acceleration. In this paper, a novel control scheme for aircraft engine based on sliding mode control with acceleration/deceleration limiter is proposed. Above all, the controller construction process is introduced, and the asymptotic stability of the whole controller is given. Then, with linearized model of JT9D turbofan engine, the control performance of the new approach is presented, which is also compared with the traditional methods. The simulation results show that the proposed method is efficient, and it can ensure all outputs of the controller, including the core shaft acceleration $\dot {N}_{c}$ , high-pressure turbine outlet temperature increment $\Delta T_{48}$ , high-pressure compressor stall margin increment $\Delta SmHPC$ , and so on, are well controlled.

Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data sample for engine fault diagnosis is established. Finally, the important parameters of PNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and compared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively diagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results are significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM is up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston engine control system.

To solve the shortcomings of existing control methods for an electromagnetic direct drive vehicle robot driver, including large speed tracking error and large mileage deviation, a new adaptive speed control method for the electromagnetic direct drive vehicle robot driver based on fuzzy logic is proposed in this paper. The electromagnetic direct drive vehicle robot driver adapts an electromagnetic linear motor as its drive mechanism. The control system structure is designed. The coordinated controller for multiple manipulators is presented. Moreover, an adaptive speed controller for the electromagnetic direct drive vehicle robot driver is proposed to achieve the accurate tracking of desired speed. Experiments are conducted using a Ford FOCUS car. Performances of the proposed method, proportional–integral–derivative, and fuzzy neural network are compared and analyzed. Experimental results demonstrate that the proposed control method can accurately track the target speed, and it can inhabit the change of speed caused by interference under different test conditions, and it has small mileage deviation, which can meet the requirements of national vehicle test standards.

The authors wish to make the following corrections to the published paper [...]

The variable cycle engines (VCE) that combine the advantages of turbofan and turbojet engines, are widely considered to be the next generation aircraft engines. However, developing VCE requires high costs. Thus, it is essential to build a mathematical model when developing an aircraft engine, which may avoid a large number of real tests and reduce the cost dramatically. Modeling is also crucial in control law development. In this article, based on a graphical simulation environment, a rapid method for modeling a double bypass variable cycle engine using object-oriented modeling technology and modular hierarchical architecture is described. Firstly, the mathematical model of each component is built based on the thermodynamic calculation. Then, a hierarchical engine model is built via the combination of each component mathematical model and the N-R solver module. Finally, the static and dynamic simulations are carried out in the model and the simulation results prove the effectiveness of the modeling method. The VCE model built through this method has the advantages of clear structure and real-time observation.

A novel wireless power transfer approach for the rotary parts telemetry of a gas turbine engine is proposed. The advantages of a wireless power transfer (WPT) system in the power supply for the rotary parts telemetry of a gas turbine engine are introduced. By simplifying the circuit of the inductively-coupled WPT system and developing its equivalent circuit model, the mathematical expressions of transfer efficiency and transfer power of the system are derived. A mutual inductance model between receiving and transmitting coils of the WPT system is presented and studied. According to this model, the mutual inductance between the receiving and the transmitting coils can be calculated at different axial distances. Then, the transfer efficiency and transfer power can be calculated as well. Based on the test data, the relationship of the different distances between the two coils, the transfer efficiency, and transfer power is derived. The proper positions where the receiving and transmitting coils are installed in a gas turbine engine are determined under conditions of satisfying the transfer efficiency and transfer power that the telemetry system required.

With the increasing demand for maneuverability of special aircrafts,such as missiles and rockets,thrust vector control technology has become more and more important.However,the thrust vector electromechanical actuation system is a high order object with large inertia,so the tuning of the parameters of its controller is very difficult,and the control effect is often not ideal.In view of the problems above,considering the particle swarm optimization(PSO)algorithm features strong capability of global optimization, simple and easier to implement,thus the PSO algorithm is adopted to optimize parameters of controller(control gain).Firstly,the typical thrustvectorsystem with electromechanical actuator is researched,and its mathematical model is setup.Then,the control gain tuning is equivalent to optimization problem,the control parameters are optimized by using PSO algorithm.Simulation of the optimized thrust vector controlsystem is carried out,and the results show that settling time of unit step response is less than 1 s,with 0.505% overshoot,while nozzle position tracks the command signal quickly and accurately.This indicates the good control effect of optimized controller and verifies the feasibility of applying PSO to solve the problem of controller optimization.

The hardware design of the high pressure fuel injection controller can be simplified by using MC33816 new programmable solenoid valve controller.The design of high pressure fuel injector based on MC33816 is discussed. The PC software which is developed by LabVIEW,can be related to the parameters of MC33816 which are adjusted flexibly,so as to calibrate the fuel injection quantity GDI nozzle under different parameters to meet the supply of aviation piston engine under different working conditions. The hardware and software design of high pressure fuel injection controller and related design details have good reference value,while the experiment finally verifies the performance of the high pressure fuel injection controller based on MC33816 design to injector control.

A new approach to build the numerical modeling of AC (alternating current) plasma anemometer is proposed. Firstly, the plasma model and gas flow model utilized in the proposed method are introduced. The plasma model (xpdp2) is built by PIC/MCC modeling method, while gas flow field model is the fluid model. By combining the flow field model and plasma model, the proposed anemometer model could be obtained. Then the effects of flow velocity on the ion density distribution, electron density distribution and electric potential distribution are studied from micro perspective, and the results show that charged particles move towards the direction of flow velocity. Another facts can also be observed, the movement of electron is not obvious, and flow velocity has no effect on the electronic potential. Finally, the effects of supply voltage, discharge frequency and electrode spacing on the discharge characteristics are investigated from macro perspective, and the results show that there is a nearly linear relationship between flow velocity and gap voltage, which indicate that the plasma anemometer could be applied for flow velocity measurement. The simulation result shows that linear relationships are pretty good when the frequencies are 2 MHz and 3.65 MHz. In addition, the result also shows that, within our chosen distance, small spacing is more suitable for high frequency plasma anemometer.

Min–Max selector structure is widely employed in current aircraft engine control logic. And the structure must provide desired thrust and prevent the engine from exceeding any safety or operational limits. In this paper, a new control scheme, that is Min–Max selector structure composed of Sliding Mode (SM) regulator and linear regulator, is presented. The main regulator is a linear regulator and all limit regulators are SM regulators. It could overcome the possibility of limit violation for the traditional Min-Max, and don’ t need the augmented state references that is one drawback of SMC Min-Max(all regulators are SM regulator). The simulation results show that the proposed approach could effectively prevent limit violation and can improve Min–Max limit protection for aircraft engine control.