Parallel combustion aero-engine optimization design method based on NSGA-II

Abstract

The application provides a parallel combustion aero-engine optimization design method based on NSGA-II, first, performance indexes of the parallel combustion aero-engine are derived based on aerodynamic thermal calculation; then design parameters affecting the performance indexes are determined, and a multi-objective mathematical model of engine design parameter optimization is constructed; then an evaluation function nondim_multiGA is constructed, flight conditions such as flight height and Mach number are considered, and fitness is calculated; finally, the gamultiobj function is solved to obtain a Pareto optimal solution set that meets the main bypass flow path flow distribution principle under the corresponding flight conditions. The application provides an efficient optimization design method for the parallel combustion aero-engine, which can greatly reduce the design cost and provide a reference for the trial production of a prototype.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aero-engine design, in particular to a parallel combustion aero-engine optimization design method. BACKGROUND

[0002] In order to obtain good aircraft performance, aerodynamic and thermodynamic parameters need to be optimized according to the requirements of integrated design, and multiple parameters need to be considered for optimization, which is a typical multi-objective optimization design problem. For example, when a hypersonic aircraft is designed by using multi-disciplinary integrated optimization, the performance of the inlet, nozzle and combustion chamber in terms of propulsion and aerodynamics needs to be considered, and there are as many as ten design parameters. It is insufficient to use single target optimization of only thrust performance or only aerodynamic performance.

[0003] The design scheme of the parallel combustion aero-engine has a specific double combustion chamber working mode, that is, two combustion chambers work at the same time, and the airflow after the compressor is divided for processing. At this time, the work amount of the turbine only needs to meet the power consumption of the compressor, so the ideal cycle thermal efficiency will be higher than that of the traditional turbofan engine cycle efficiency, and basically equivalent to the main combustion chamber cycle efficiency of the traditional turbofan engine, thereby reducing the fuel consumption of the engine. However, the number of engine design parameters increases, and different parameters may be coupled with each other. The use of traditional design methods has a large time cost, and the use of the established thermodynamic mathematical model can preliminarily find the values of the main design parameters after the trade-off of different performance indicators in a short time, which can greatly reduce the design cost and provide a reference for the trial production of the prototype. SUMMARY

[0004] The technical problem to be solved by the present application is to solve the problems in the background art. A parallel combustion aero-engine optimization design method based on NSGA-Ⅱ is provided. Parallel combustion improves engine thrust level, reduces fuel consumption, and has a more compact structure. The application of the improved NSGA-Ⅱ algorithm is more targeted in combination with the established thermodynamic mathematical model, which greatly reduces the design time and cost.

[0005] The present application adopts the following technical solutions to solve the above technical problems:

[0006] The parallel combustion aero-engine optimization design method based on NSGA-Ⅱ comprises the following steps:

[0007] Step 1), based on aerodynamic and thermodynamic calculation, the performance indicators of the parallel combustion aero-engine are derived;

[0008] Step 1.1), divide the engine typical section according to the main combustion flow path and the outer bypass flow path;

[0009] Step 1.2), based on aerodynamic thermodynamics, the pressure and temperature of the typical section are obtained by considering the parameters of the engine involving the inlet, compressor, combustion chamber, turbine and nozzle;

[0010] Step 1.3), the engine performance index is derived from the pressure and temperature of the typical section and the engine component parameters;

[0011] Step 2), the design parameters affecting the engine performance index and their ranges are determined, and a multi-objective mathematical model for optimizing the engine design parameters is constructed;

[0012] Step 2.1), a number of values of the pressure and temperature of the typical section of the engine, component parameters and performance index are obtained by orthogonal test;

[0013] Step 2.2), variance analysis is performed to analyze the influence of each factor on the engine performance index, and the design parameters affecting the engine performance index and their ranges are determined;

[0014] Step 2.3), a multi-objective optimization model of the engine performance index is established;

[0015] Step 3), an evaluation function nondim_multiGA is constructed for calculating and outputting the performance index of each population and the fitness of each individual of the population according to the input Mach number, flight altitude and population design parameter set in accordance with the main bypass flow path flow distribution principle;

[0016] Step 3.1), the input Mach number, flight altitude and design parameters of each individual in the population design parameter set are sequentially substituted into the thermodynamic calculation program to obtain the engine performance index and the value of the ratio of the main combustion flow path to the total gas flow, wherein the calculation formula of the ratio x of the main combustion flow path to the total gas flow is as follows:

[0017]

[0018] In the formula, T t3 is the total temperature at the outlet section of the engine compressor, T t2 is the total temperature at the inlet section of the compressor, T t4 is the total temperature at the inlet section of the turbine, T t5 is the total temperature at the outlet section;

[0019] Step 3.2), it is judged whether the value of x is greater than or equal to 0 and less than or equal to 1;

[0020] Step 3.2.1), if x is greater than or equal to 0 and less than or equal to 1, it meets the main bypass flow path flow distribution principle, and the values of the individual design parameters and performance index are stored;

[0021] Step 3.2.2), if x is less than 0 or greater than 1, which does not conform to the main outer envelope flow path flow distribution principle, then eliminate the individual design parameters and performance indicators;

[0022] Step 3.3), according to the main outer envelope flow path flow distribution principle, the remaining design parameters and their corresponding performance indicators are combined with the multi-objective optimization model to calculate the performance indicators of each population and the fitness of each population individual;

[0023] Step 4), the gamultiobj function is used to solve and obtain the Pareto front solution set of the multi-objective optimization of the engine performance indicators;

[0024] Step 4.1), the function gacommon is called to determine the constraint type of the optimization problem;

[0025] Step 4.2), the function gamultiobjsolve is called to solve the multi-objective optimization problem;

[0026] Step 4.2.1), the function gamultiobjMakeState is called to generate the initial population, i.e. the initial design parameters of the engine population;

[0027] Step 4.2.2), let P be the initial design parameters of the population, G be the preset Mach number, and Q be the preset flight height;

[0028] Step 4.2.3), input P, G, and Q into the evaluation function nondim_multiGA to obtain the performance indicators of each population and the fitness of each population individual that conform to the main outer envelope flow path flow distribution principle;

[0029] Step 4.2.4), determine whether the value of the fitness of the optimal individual of the population is greater than the preset fitness threshold, and whether the fitness of the optimal individual and the population fitness is increasing;

[0030] Step 4.2.4.1), if the calculated fitness of the optimal individual of the population is greater than the preset fitness threshold or the fitness of the optimal individual and the population fitness is not increasing, the performance indicators of each population and the corresponding engine design parameters at this time are taken as the Pareto optimal solution;

[0031] Step 4.2.4.2), if the calculated fitness is less than or equal to the preset fitness threshold and the fitness of the optimal individual and the population fitness is increasing, the function stepgamultiobj is called to evolve the population for one generation, update the design parameters P, and call the function gamultiobjConvergrd to determine whether the iteration number is greater than the preset maximum evolution threshold;

[0032] Step 4.2.4.2.1), if the iteration number is greater than the preset maximum evolution threshold, calling the gaplotpareto function and the gaplotparetodistance function to draw a Pareto optimal solution graph and a Pareto optimal distance graph;

[0033] Step 4.2.4.2.2), if the iteration number is less than or equal to the preset maximum evolution threshold, jumping to Step 4.2.2).

[0034] As a further optimization scheme of the parallel combustion aero-engine optimization design method based on NSGA-II of the application, the engine performance indicators in the step 1) include: cycle thermal efficiency η, power W, thrust F and specific fuel consumption sfc.

[0035] As a further optimization scheme of the parallel combustion aero-engine optimization design method based on NSGA-II of the application, the design parameters affecting the engine performance indicators in the step 2) include: main combustion chamber cycle temperature ratio τ main , bypass combustion chamber cycle temperature ratio τ outer , compressor pressure ratio π C , turbine pressure ratio π T , compressor adiabatic efficiency η C , turbine internal efficiency η T , main combustion chamber loss and bypass combustion chamber loss

[0036] As a further optimization scheme of the parallel combustion aero-engine optimization design method based on NSGA-II of the application, the mathematical expression of the multi-objective optimization model in the step 2) is:

[0037] Find:min F1(x),F2(x)

[0038] F1(x)=[-η(x),-W(x)] T ,F2(x)=[-F(x),sfc(x)] T

[0039]

[0040] subject to:x i ∈M,i=1,2,…,8

[0041] Wherein, F1(x), F2(x) represent the objective function vector, min represents the vector minimization; X is a design variable vector with a dimension of 8, x1, x2, x3, x4, x5, x6, x7, x8 are eight vectors of X, respectively corresponding to τ main , τ outer , πC ,π T ,η C ,η T , Corresponding; M represents the value space of the design variable.

[0042] Compared with the prior art, the above technical scheme has the following technical effects: the parallel combustion aero-engine has higher thrust and lower fuel consumption, the improved NSGA-II algorithm is used to optimize and analyze the screened engine design parameters, the parameter optimization design is more accurate, the design cost and design time can be greatly reduced, and the reference for prototype manufacturing of the new heat cycle aero-engine with parallel combustion is provided. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The flow chart of the NSGA-II-based parallel combustion aero-engine optimization design method of the application;

[0044] Figure 2 The structure schematic diagram of the parallel combustion aero-engine in the application;

[0045] Figure 3 The Pareto frontier solution set diagram obtained by the application under the consideration of flight conditions and with thermal efficiency-power as the optimization target;

[0046] Figure 4 The Pareto frontier solution set diagram obtained by the application under the consideration of flight conditions and with thrust-fuel consumption as the optimization target. DETAILED DESCRIPTION

[0047] The technical scheme of the application will be further described in detail below with reference to the drawings:

[0048] The application is further explained in combination with specific embodiments, but the application is not limited by the specific embodiments.

[0049] As shown in Figure 1 , the application discloses a NSGA-II-based parallel combustion aero-engine optimization design method, comprising the following steps.

[0050] Step 1), the performance index of the parallel combustion aero-engine is derived based on aerodynamic thermodynamic calculation;

[0051] The parallel combustion adds an outer bypass combustion chamber outside the main combustion chamber and designs an adjustable nozzle, establishing a new heat cycle different from the single Brayton cycle used in the traditional turbojet engine. Through thermodynamic formula derivation and calculation, the pressure and temperature of the typical interface along the way are first determined, and then the parameters of the components involved in the engine, such as the inlet, compressor, combustion chamber, turbine and nozzle, are considered to determine the performance index and write the thermodynamic calculation program.

[0052] Assume in ideal cycle, specific heat capacity of working medium C p and specific heat ratio k are constant. Given ambient pressure p0, ambient temperature T0, far coming flow Mach number Ma0. c π T is compressor pressure ratio, π n is turbine pressure ratio, τ main is main combustion chamber cycle temperature ratio, τ outer is outer bypass combustion chamber cycle temperature ratio.

[0053] Then for main combustion chamber one way:

[0054] Total pressure and total temperature of far coming flow:

[0055]

[0056]

[0057] Total pressure and total temperature of inlet duct outlet airflow:

[0058] p t2 = σ in p t0

[0059] Where total pressure recovery coefficient σ in can be approximately calculated by the following formula:

[0060] 0<Ma0≤1 σ in = 0.97

[0061] Ma0> 1 σ in = 0.97 [1-0.075 (Ma0-1) 1.35 ]

[0062] T t2 = T t0

[0063] Total pressure and total temperature of compressor outlet airflow:

[0064] p t3 = π c p t2

[0065]

[0066] Total pressure and total temperature of main combustion chamber outlet airflow:

[0067]

[0068] T t4 = τ main T0

[0069] Total pressure, total temperature of turbine exit flow:

[0070]

[0071]

[0072] Total pressure, static pressure, total temperature and static temperature of inner fixed nozzle exit flow (assuming total pressure recovery coefficient of tail nozzle is 1, full expansion):

[0073] p t6 = p t5

[0074] p6≈p0

[0075] T t6 = T t5

[0076]

[0077] wherein η c is the adiabatic efficiency of the compressor, η is the primary combustion chamber loss, η T is the internal efficiency of the turbine.

[0078] Similarly, for the outer combustion chamber one way:

[0079] Total pressure, total temperature of outer combustion chamber inlet flow:

[0080] p t7 = p t3

[0081]

[0082] Total pressure, total temperature of outer combustion chamber exit flow:

[0083]

[0084] T t8 = τ outer T0

[0085] wherein η is the outer stage combustion chamber loss.

[0086] Static pressure, total temperature and static temperature of outer adjustable nozzle exit flow (assuming nozzle full expansion):

[0087] p9≈p0

[0088] T t9 = T t8

[0089]

[0090] Assume that the turbine work is just equal to the compressor work and the intake compression is included in the compressor work, and assume that the specific heat capacity of the working medium C p is constant, the ratio x of the main combustion chamber flow path to the total airflow mass is:

[0091]

[0092] The cycle heat absorption amount is:

[0093] Q1=m air [xC p (T t4 -T t3 )+(1-x)C p (T t8 -T t3 )]

[0094] Wherein, m air is the total mass flow of the incoming flow;

[0095] The cycle heat release amount is:

[0096] Q2=m air [xC p (T6-T0)+(1-x)C p (T9-T0)]

[0097] The cycle net work amount is:

[0098] W=Q1-Q2

[0099] The cycle thermal efficiency is:

[0100]

[0101] The thrust is:

[0102]

[0103] The specific fuel consumption is:

[0104]

[0105] In the formula, H u is the heat of combustion of aviation kerosene;

[0106] Step 2), determine the design parameters affecting the engine performance index, and construct a multi-objective mathematical model of the engine design parameter optimization design.

[0107] The performance indexes of the parallel combustion aero-engine include: cycle thermal efficiency η, work W, thrust F and specific fuel consumption sfc.

[0108] After orthogonal test and variance analysis of many aerodynamic and thermal parameters of the parallel combustion aero-engine, the design parameters affecting the engine performance index are determined to include: main combustion chamber cycle temperature ratio τ main , outer combustion chamber cycle temperature ratio τ outer , compressor pressure ratio π C , turbine pressure ratio π T , compressor adiabatic efficiency η C , turbine internal efficiency η T , main combustion chamber loss and outer combustion chamber loss

[0109] Table 1 upper and lower limits of the optimized design parameters

[0110]

[0111] The total inlet flow rate of the parallel combustion aero-engine satisfies:

[0112]

[0113] In the formula, is the total inlet flow rate, R is the gas constant, k is the specific heat ratio, p t0 is the total inlet pressure, T t0 is the total inlet temperature, A is the engine inlet area, and q(Ma) is the flow rate function expressed in Mach number, which is calculated by the following formula:

[0114]

[0115] In the formula, Ma is the Mach number.

[0116] The mathematical expression of the multi-objective optimization model is:

[0117] Find:min F1(x),F2(x)

[0118]

[0119] subject to:x i ∈M, i = 1, 2, …, 8

[0120] Wherein, F1(x), F2(x) represent the objective function vector, min represents the vector minimization, X is a design variable vector with a dimension of 8, x1, x2, x3, x4, x5, x6, x7, x8 are respectively eight vectors of X, which are respectively τ main , τ outer , π C , π T , η C , η T , Correspondingly, M represents the design variable value space.

[0121] Step 3), constructing an evaluation function nondim_multiGA for calculating and outputting the performance indicators of each population and the fitness of each individual of the population in accordance with the input Mach number, flight height and population design parameter set in accordance with the main outer flow path flow distribution principle;

[0122] As shown in Figure 2 , the main outer flow path performs flow splitting, and the main outer flow path flow distribution principle is as follows: the value of the ratio x of the main combustion flow path to the total airflow mass is greater than or equal to 0 and less than or equal to 1, and the value is calculated according to the established parallel combustion aero-engine mathematical model by the following formula:

[0123]

[0124] In the formula, T t3 is the total temperature at the outlet section of the engine compressor, T t2 is the total temperature at the inlet section of the compressor, T t4 is the total temperature at the inlet section of the turbine, and T t5 is the total temperature at the outlet section. The formula is converted from enthalpy change based on the condition that the power consumption of the compressor is equal to the work done by the turbine.

[0125] The flight height and the incoming flow Mach number form the inlet flight condition, the flight height takes three working conditions of 8 km, 11 km and 15 km, and the incoming flow Mach number takes three working conditions of 0.7, 1.5 and 2.5, which are combined to form six calculation working conditions suitable for the operation of the aero-engine.

[0126] Table 2 Optimization Calculation Working Condition

[0127]

[0128] Step 4), using the gamultiobj function to solve and obtain the Pareto front solution set of the multi-objective optimization of the engine performance indicators, including the following steps:

[0129] Step 4.1), calling the function gacommon to determine the constraint type of the optimization problem;

[0130] Step 4.2), calling the function gamultiobjsolve to solve the multi-objective optimization problem;

[0131] Step 4.2.1), first calling the function gamultiobjMakeState to generate an initial population, i.e., the initial design parameters of the engine population;

[0132] Step 4.2.2), letting P be the initial design parameters of the population, G be the preset Mach number, and Q be the preset flight height;

[0133] Step 4.2.3), input P, G and Q into the evaluation function nondim_multiGA to obtain the performance index of each population and the fitness of each individual of the population in accordance with the main external flow path flow distribution principle;

[0134] Step 4.2.4), determine whether the value of the fitness of the optimal individual of the population is greater than a preset fitness threshold and whether the fitness of the optimal individual and the population fitness are increasing;

[0135] Step 4.2.4.1), if the calculated fitness of the optimal individual of the population is greater than the preset fitness threshold or the fitness of the optimal individual and the population fitness are not increasing, the performance index of each population and the corresponding engine design parameters at this time are taken as the Pareto optimal solution;

[0136] Step 4.2.4.2), if the calculated fitness is less than or equal to the preset fitness threshold and the fitness of the optimal individual and the population fitness are increasing, call the function stepgamultiobj to evolve the population for one generation, update the design parameters P, and call the function gamultiobjConvergrd to determine whether the iteration number is greater than a preset maximum evolution threshold;

[0137] Step 4.2.4.2.1), if the iteration number is greater than the preset maximum evolution threshold, call the functions gaplotpareto and gaplotparetodistance to draw the Pareto optimal solution graph and the Pareto optimal distance graph;

[0138] Step 4.2.4.2.2), if the iteration number is less than or equal to the preset maximum evolution threshold, jump to Step 4.2.2).

[0139] In this embodiment, the genetic algorithm optimization is set to have a population size of 200, a maximum evolution number of 1000 generations, a multi-point crossover, a crossover probability of 0.6, a mutation probability of 0.1, and the worst 30 individuals are eliminated each time. The above program is used for double-objective optimization. For each flight condition and each fitness function, 100 Pareto optimal solutions are obtained respectively, and a cluster of Pareto optimal solutions is obtained respectively, and the optimization results are as shown in Figure 3 、 Figure 4 .

[0140] The parallel combustion aero-engine optimization design method based on NSGA-II provided in the application starts with the increasing design parameters of the parallel combustion aero-engine and the mutual coupling phenomenon, optimizes by using the improved NSGA-II algorithm based on the established thermodynamic mathematical model, considers the flight conditions such as flight altitude and Mach number, and obtains the Pareto optimal solution set conforming to the main bypass flow path flow distribution principle under the corresponding flight conditions by solving the gamultiobj function. The performance indexes such as thermal efficiency, power, thrust and specific fuel consumption are comprehensively considered, the most suitable engine design parameters can be quickly screened out, the design cost is reduced, and reference is provided for the trial production of the prototype.

[0141] Those skilled in the art can understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0142] The above specific embodiments further specifically explain the purposes, technical solutions and beneficial effects of the application, and the above description is only a specific embodiment of the application and is not used to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A method for optimal design of a parallel combustion aero-engine based on NSGA-II, characterized in that, The method comprises the following steps: Step 1), based on aerodynamic thermodynamic calculation, performance indexes of the parallel combustion aero-engine are derived; Step 1.1), according to the main combustion flow path and the bypass flow path, a typical section of the engine is divided; Step 1.2), based on aerodynamic thermodynamics, parameters of the engine involved in the inlet, compressor, combustion chamber, turbine and nozzle are considered to obtain the pressure and temperature of the typical section; Step 1.3), from the pressure and temperature of the typical section and the engine component parameters, the engine performance indexes are derived; Step 2), design parameters affecting the engine performance indexes and their ranges are determined, and a multi-objective mathematical model of the engine design parameter optimization is constructed; The design parameters affecting the engine performance indicators include: main combustion chamber cycle temperature ratio τ main , outer combustion chamber cycle temperature ratio τ outer , compressor pressure ratio π C , turbine pressure ratio π T , compressor adiabatic efficiency η C , turbine internal efficiency η T , main combustion chamber loss σ mc , and outer combustion chamber loss σ oc ; Step 2.1), through orthogonal test trial, values of the pressure and temperature of the typical section, component parameters and performance indexes of the engine are obtained; Step 2.2), variance analysis is performed to analyze the influence of each factor on the engine performance indexes, and the design parameters affecting the engine performance indexes and their ranges are determined; Step 2.3), a multi-objective optimization model of the engine performance indexes is established, and the mathematical expression is: Find:min F1(x), F2(x) F1(x) = [-η(x), -W(x)] T , F2(x) = [-F(x), sfc(x)] T X = [x1, x2, x3, x4, x5, x6, x7, x8] T = [τ main , τ outer , π C , π T , η C , η T , σ mc , σ oc ] T subject to: x i ∈M, i = 1, 2, …, 8 Wherein, F1(x), F2(x) represent the objective function vector, min represents the vector minimization; X is a design variable vector with a dimension of 8, x1, x2, x3, x4, x5, x6, x7, x8 are eight vectors of X, respectively corresponding to τ main , τ outer , π C , π T , η C , η T , ς mc , ς oc ; M represents the design variable value space; Step 3), an evaluation function nondim_multiGA is constructed, which is used to calculate and output the performance indexes of each population and the fitness of each population individual according to the input Mach number, flight height and population design parameter set in accordance with the main bypass flow path flow distribution principle; Step 3.1), the input Mach number, flight altitude and each individual design parameter in the population design parameter set are sequentially substituted into the thermodynamic calculation program to obtain the engine performance index and the value of the main combustion flow path to total airflow mass ratio, wherein the calculation formula of the main combustion flow path to total airflow mass ratio is as follows: ； wherein Tt is the total temperature at the turbine inlet section, T0 is the total temperature at the compressor inlet section, Tt is the total temperature at the turbine inlet section, Tt is the total temperature at the turbine inlet section, Step 3.2), determining whether the value of is greater than or equal to 0 and less than or equal to 1; Step 3.2.1), if greater than or equal to 0 and less than or equal to 1, the primary exergy flow path flow distribution principle is met, and the value of the individual design parameter and performance index is stored; Step 3.2.2), if If the value is less than 0 or greater than 1, it does not meet the primary external flow path flow distribution principle, and the design parameters and performance indicators of the individual are eliminated. Step 3.3), in combination with the multi-objective optimization model, the performance indexes of each population and the fitness of each population individual are calculated according to the design parameters and the corresponding performance indexes left after screening according to the main bypass flow path flow distribution principle; Step 4), gamultiobj function is used for solving, and a Pareto frontier solution set of the multi-objective optimization of the engine performance indexes is obtained; Step 4.1), the function gacommon is called to determine the constraint type of the optimization problem; Step 4.2), the function gamultiobjsolve is called to solve the multi-objective optimization problem; Step 4.2.1), the function gamultiobjMakeState is called to generate the initial population, i.e., the initial design parameters of the engine population; Step 4.2.2), P is the initial design parameter of the population, G is the preset Mach number, and Q is the preset flight height; Step 4.2.3), P, G and Q are input into the evaluation function nondim_multiGA to obtain the performance indexes of each population and the fitness of each population individual in accordance with the main bypass flow path flow distribution principle; Step 4.2.4), it is judged whether the value of the fitness of the optimal individual of the population is greater than the preset fitness threshold, and whether the fitness of the optimal individual and the population fitness are rising; Step 4.2.4.1), if the calculated fitness of the optimal individual of the population is greater than the preset fitness threshold or the fitness of the optimal individual and the population fitness are not rising, the performance indexes of each population and the corresponding engine design parameters at this time are taken as the Pareto optimal solution; Step 4.2.4.2, if the calculated fitness is less than or equal to the preset fitness threshold and the fitness of the optimal individual and the population fitness increase, the function stepgamultiobj is called to evolve the population for a generation, the design parameter P is updated, and the function gamultiobjConvergrd is called to determine whether the iteration number is greater than the preset maximum evolution threshold value; Step 4.2.4.2.1, if the iteration number is greater than the preset maximum evolution threshold value, the functions gaplotpareto and gaplotparetodistance are called to draw a Pareto optimal solution graph and a Pareto optimal distance graph; Step 4.2.4.2.2, if the iteration number is less than or equal to the preset maximum evolution threshold value, the step 4.2.2 is executed.

2. The NSGA-II based parallel combustion aero-engine optimization design method according to claim 1, characterized in that, The engine performance indicators in the step 1) include: cycle thermal efficiency η, power W, thrust F and specific fuel consumption sfc.

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