An extrapolation approach for aeroengine's transient control law design

Transient control law ensures that the aeroengine transits to the command operating state rapidly and reliably. Most of the existing approaches for transient control law design have complicated principle and arithmetic. As a result, those approaches are not convenient for application. This paper proposes an extrapolation approach based on the set-point parameters to construct the transient control law, which has a good practicability. In this approach, the transient main fuel control law for acceleration and deceleration process is designed based on the main fuel flow on steady operating state. In order to analyze the designing feature of the extrapolation approach, the simulation results of several different transient control laws designed by the same approach are compared together. The analysis indicates that the aeroengine has a good performance in the transient process and the designing feature of the extrapolation approach conforms to the elements of the turbofan aeroengine.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm:PartⅡ,optimization in flight mission and controller gains correlation development

PartⅠhas illustrated the procedures to apply the Linkage Learning Genetic Algorithm(LLGA)in Gas Turbine Engine(GTE)controller gains tuning and generated the optimization results for runway conditions from idle to takeoff.However,the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number,which would lead to the variation in fuel flow supply regulation.As a result,the optimized gains in runway might not be suitable for other flight conditions.In order to maintain the optimal control performance,the GTE controller gains should be adjusted according to the flight conditions.This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations.For this purpose,the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude.Then,a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration.One representative point is selected from each segment as the datum point for optimization process.After this step,the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently.The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of(θδ)~(1/2)and the optimal K_(pla)in some flight conditions is approximately equal to p hd times of the Kplavalue in sea level standard condition.Finally,the complete flight mission is simulated with different gains and weather condition configurations.The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude conditions.In other steady states,the optimization effect is not very obvious.

Simplified procedure for controlling pressure distribution of a scramjet combustor

Scramjet engines are used at extreme temperatures and velocity. New control problems involving distributed parameter control have been found concerning investigations of the control of scramjet engines whose physical states are spatially interacted. Succeeding the existing theoretical studies on the distributed parameter control for scramjet engines, this paper puts forward a simplified distributed parameter control approach for scramjet engines aimed at engineering application. The simplified control procedure uses the classical proportional-integral（PI） compensation to control the target pressure distribution of scramjet engines, which is effective and applicable for practical implements. Simulation results show the validation of the simplified distributed parameter control procedure.

Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm:PartⅠ,building blocks detection and optimization in runway

This paper proposes a Linkage Learning Genetic Algorithm(LLGA)based on the messy Genetic Algorithm(mGA)to optimize the Min-Max fuel controller performance in Gas Turbine Engine(GTE).For this purpose,a GTE fuel controller Simulink model based on the Min-Max selection strategy is firstly built.Then,the objective function that considers both performance indices(response time and fuel consumption)and penalty items(fluctuation,tracking error,overspeed and acceleration/deceleration)is established to quantify the controller performance.Next,the task to optimize the fuel controller is converted to find the optimization gains combination that could minimize the objective function while satisfying constraints and limitations.In order to reduce the optimization time and to avoid trapping in the local optimums,two kinds of building block detection methods including lower fitness value method and bigger fitness value change method are proposed to determine the most important bits which have more contribution on fitness value of the chromosomes.Then the procedures to apply LLGA in controller gains tuning are specified stepwise and the optimization results in runway condition are depicted subsequently.Finally,the comparison is made between the LLGA and the simple GA in GTE controller optimization to confirm the effectiveness of the proposed approach.The results show that the LLGA method can get better solution than simple GA within the same iterations or optimization time.The extension applications of the LLGA method in other flight conditions and the complete flight mission simulation will be carried out in partⅡ.