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 tak...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.展开更多
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 control...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.展开更多
The major problem with the Scramjet engine is the starting and unstarting conditions,which depend on intake performance.Although the engine starts efficiently at designed conditions without any problem,at off-design c...The major problem with the Scramjet engine is the starting and unstarting conditions,which depend on intake performance.Although the engine starts efficiently at designed conditions without any problem,at off-design conditions,due to misalignment of shocks,not satisfying either shock on the lip or shock on the shoulder and this leads to spillage flowand loss of total pressure.The intake design is modified to satisfy shock on shoulder condition to improve the operating range of the scramjet engine.Using ANSYS Fluent,Inviscid flowsimulations are carried on modified design,it satisfied the shock-on-shoulder requirement,but in viscous simulations,the flow leads to unstarting conditions because of shock wave interaction with the boundary layer.Thus,shock on shoulder can be neglected in the design of hypersonic intake for scramjet engines.This paper analyzes a two-ramp,two-dimensional scramjet intake design using ANSYS Fluent.An elaborative CFD analysis was performed to estimate the efficiency of the hypersonic intake isolator because of changes in the flight conditions concerning free-stream conditions such as Mach number,angle of attack,and real-flow atmospheric conditions concerning altitude.This analysis shows that performance parameters such as total pressure recovery decreases during off design conditions.However,the normalized pressure ratio increases from 19 at Mach 4 to 72 at Mach 8.Due to an increase in the angle of attack,there is a increase in the pressure ratio and decrease in total pressure recovery.The flow separation bubble size increases as the Mach number increases leading to unstarting condition and increases as the angle of attack increases.An injection technique is used to suppress the flow separation.Out of the various orifices analysed the research concludes diamond shape injectors at 45°angle with total Nine injectors for mass flow rate not greater than 4% of intake mass flow satisfying all the performance parameters has reduced the flow separation bubble size from 4 mm to 0.95 mm in hypersonic intakes of Scramjet Engine at Mach 5.展开更多
文摘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.
文摘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.
文摘The major problem with the Scramjet engine is the starting and unstarting conditions,which depend on intake performance.Although the engine starts efficiently at designed conditions without any problem,at off-design conditions,due to misalignment of shocks,not satisfying either shock on the lip or shock on the shoulder and this leads to spillage flowand loss of total pressure.The intake design is modified to satisfy shock on shoulder condition to improve the operating range of the scramjet engine.Using ANSYS Fluent,Inviscid flowsimulations are carried on modified design,it satisfied the shock-on-shoulder requirement,but in viscous simulations,the flow leads to unstarting conditions because of shock wave interaction with the boundary layer.Thus,shock on shoulder can be neglected in the design of hypersonic intake for scramjet engines.This paper analyzes a two-ramp,two-dimensional scramjet intake design using ANSYS Fluent.An elaborative CFD analysis was performed to estimate the efficiency of the hypersonic intake isolator because of changes in the flight conditions concerning free-stream conditions such as Mach number,angle of attack,and real-flow atmospheric conditions concerning altitude.This analysis shows that performance parameters such as total pressure recovery decreases during off design conditions.However,the normalized pressure ratio increases from 19 at Mach 4 to 72 at Mach 8.Due to an increase in the angle of attack,there is a increase in the pressure ratio and decrease in total pressure recovery.The flow separation bubble size increases as the Mach number increases leading to unstarting condition and increases as the angle of attack increases.An injection technique is used to suppress the flow separation.Out of the various orifices analysed the research concludes diamond shape injectors at 45°angle with total Nine injectors for mass flow rate not greater than 4% of intake mass flow satisfying all the performance parameters has reduced the flow separation bubble size from 4 mm to 0.95 mm in hypersonic intakes of Scramjet Engine at Mach 5.
