CN105975651B - Guided missile Parameters design based on Genetic Particle Swarm algorithms for multidisciplinary design optimization - Google Patents

Abstract

Guided missile Parameters design based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, is related to guided missile Parameters design.The present invention is not suitable for the case where multidisciplinary lower design parameter is more and there are Coupled Variable phenomenons to solve the existing guided missile Parameters design based on intelligent optimization algorithm.The present invention chooses engine larynx diameter, engine combustion face area, motor charge meat thickness, guided missile outer diameter, engine/motor specific impulse, engine quality, fuel mass, missile wing root chord length, empennage root tip length, empennage tip chord length as design parameter：Then utilize genetic algorithm to three control parameters w, c in standard particle group's algorithm₁、c₂It carries out preferably, genetic idea being introduced population position updating process when optimizing for particle cluster algorithm during preferred with design parameter.The present invention is suitable for guided missile parameter designing field.

Description

Guided missile Parameters design based on Genetic Particle Swarm algorithms for multidisciplinary design optimization

Technical field

The present invention relates to guided missile Parameters designs.

Background technology

It is certainly existed between subsystems for the angle of total system to many subjects involved in Flight Vehicle Design A degree of interaction and coupling effect.Since subsystem has separate analysis and design tool, traditional order Type design can not often consider, to fully take into account the coupling factor between each system.It is excellent using multi-subject design The design problem for dissolving above-mentioned total system of determining, both improves design efficiency, further improves the performance of product.

Missile armament design needs to use the multi-disciplinary comprehensive knowledges such as pneumatic, trajectory, power.With the hair of design level Exhibition, the design process of such complication system is it is necessary to carry out multidisciplinary synthesis design, in the base for considering every subjects interaction The design scheme of global optimum is found on plinth.And some existing Missile Design methods are limited in some subject (such as trajectory subject) It is designed for the guided missile parameter under another subject under fixed condition, this design method has the overall performance for designing guided missile It is poor；It is that the multidisciplinary parameter combined is designed, but this design method is not suitable for there are also Missile Design method Design parameter is more and the case where there are Multivariable Coupling phenomenons under different subjects.

Nowadays, intelligent optimization algorithm has given many facilities that many fields are brought, and there are also scholars by intelligent optimization Algorithm introduces Missile Design field, but the general guided missile parameter designing based on intelligent optimization algorithm is less suitable for parameter Situation, faces the design method of multidisciplinary lower multi-parameter, and general intelligent optimization algorithm cannot restrain, cannot obtain and preferably set Count effect.

Invention content

The present invention in order to solve the existing guided missile Parameters design based on intelligent optimization algorithm be not suitable for it is multidisciplinary The case where lower design parameter is more and there are Coupled Variable phenomenons.

Guided missile Parameters design based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, includes the following steps：

Step 1 chooses following design variable as design parameter：

Engine larynx diameter x₁, engine combustion face area x₂, motor charge meat thickness x₃, guided missile outer diameter x₄, engine/motor specific impulse x₅, engine quality x₆, fuel mass x₇, missile wing root chord length x₈, the long x of the empennage root tip₉, empennage tip chord length x₁₀；

In conjunction with pneumatic subject, power subject and trajectory subject, according to weight of fuel x₇Limitation, in the horizontal plane to guided missile The equation of motion is integrated, and the total voyage of missile trajectory, as object function F (X are obtained_i)；

There are following design constraints：

(1) missile flight end speed Ma=1.4 is chosen as end condition, while reducing and overloading demand at this time, if available Overload is more than or equal to 8g；

(2) the ratio between nozzle hole back pressure and outside pressure meet：p_e/p_a＞ 0.3；

(3) combustion chamber operational pressure is more than fuel combustion critical pressure, and meets upper limit value constraint：2MP ＜ p_c＜ 30MP；

(4) motor charge volume packing factor meets：η_v＜ 0.95；

Step 2, using genetic algorithm to three control parameters w, c in standard particle group's algorithm₁、c₂With design parameter into Row is preferred：

Step 2.1, selected genetic groups scale n_p, crossing-over rate p_cWith aberration rate p_m, give problem maximum evolutionary generation T；

Step 2.2：The random initialization population for generating genetic algorithm；

Step 2.3：Genetic algorithm individual fitness is calculated, detailed process is as follows：

Step 2.3.1：Selected Particles group algorithm population scale m, determines maximum iteration；

Step 2.3.2：Random initializtion particle group velocity and position v₀And x₀；

Step 2.3.3：Its adaptive value F (X are calculated to each particle_i)；

Step 2.3.4：The adaptive value of more each particle records its own optimal value p_iWith group optimal particle label g；

Step 2.3.5：Speed using following formula more new particle and position；

Wherein each parameter meaning is：In the search space of n=10 dimensions, by the molecular population X=of m grain {x₁,...,x_i,...,x_m, wherein the position of i-th of particle is x_i={ x_i1,x_i2,...,x_in}^T, i.e. x_i={ x_i1,x_i2,..., x_i10}^T, speed v_i={ v_i1,v_i2,...,v_in}^T；The individual extreme value of i-th of particle is p_i={ p_i1,p_i2,...,p_i10}^T, The global extremum of population is p_g={ p_g1,p_g2,...,p_g10}^T；D=1,2 .., n, i=1,2 ..., m, t are when evolution generation Number, r₁And r₂For the random number being distributed between [0,1], c₁And c₂For acceleration constant；

Step 2.3.6：Genetic idea is introduced into population position updating process,

The position of any two particle in population is subjected to hereditary update, new position is calculated by following formula：

x₁' (t)=rand () * x₁(t)+(1-rand())*x₂(t)

x₂' (t)=rand () * x₂(t)+(1-rand())*x₁(t)

Wherein, x₁′(t)、x₂' (t) indicates updated position respectively；Rand () expressions randomly select a certain position and carry out Crossover operation；

Step 2.3.7：It checks whether and meets algorithm end condition, if it is not, then going to step 2.3.3；If so, finding out most The figure of merit；

Step 2.3.8：The optimal value that particle cluster algorithm is found is the adaptive value of genetic algorithm individual；

Step 2.4：The select probability that each individual is calculated according to genetic algorithm individual fitness, usually based on linear ranking It calculates；

Step 2.5：Selection, intersection and mutation operator are executed, population of new generation is generated；

Step 2.6：It checks whether and meets genetic algorithm end condition, if it is not, going to step 2.3；If so, finding out optimal Solution, this solution are the value of design parameter.

The present invention has the following effects that：

The present invention be directed to pneumatic subject, power subject and trajectory subject under engine larynx diameter, engine combustion face area, Motor charge meat thickness, guided missile outer diameter, engine/motor specific impulse, engine quality, fuel mass, missile wing root chord length, the empennage root tip be long, Empennage tip chord length carries out the Multidisciplinary Optimization based on intelligent algorithm, and not only design can restrain, and in particle cluster algorithm Genetic idea is introduced into population position updating process when optimizing link, avoids and is absorbed in local optimum in searching process The case where.Preferable design effect can be obtained by being designed using the present invention, compare MDF design methods, what the present invention designed Guided missile can improve 2.41 ‰ or more in voyage.

And the subsystems couple in traditional design method can be solved the problems, such as using the present invention；It is totally set from missile armament Meter reduces the link designed repeatedly in the process, and then improves design efficiency；The overall design time for reducing missile weapon system, into And reduce development cost.Meanwhile the present invention can also improve missile armament design accuracy, shorten total system development when Between.

Description of the drawings

Fig. 1 information interchange such as figure below between each subject；

Fig. 2 is the data flow schematic diagram after being divided in embodiment；

Fig. 3 is the object function fitness curve graph of the present invention.

Specific implementation mode

Specific implementation mode one：

Guided missile Parameters design based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, includes the following steps：

Step 1 chooses following design variable as design parameter：

Engine larynx diameter x₁, engine combustion face area x₂, motor charge meat thickness x₃, guided missile outer diameter x₄, engine/motor specific impulse x₅, engine quality x₆, fuel mass x₇, missile wing root chord length x₈, the long x of the empennage root tip₉, empennage tip chord length x₁₀；

In conjunction with pneumatic subject, power subject and trajectory subject, according to weight of fuel x₇Limitation, in the horizontal plane to guided missile The equation of motion is integrated, and the total voyage of missile trajectory, as object function F (X are obtained_i)；

There are following design constraints：

(1) missile flight end speed Ma=1.4 is chosen as end condition, while reducing and overloading demand at this time, if available Overload is more than or equal to 8g；

(2) the ratio between nozzle hole back pressure and outside pressure meet：p_e/p_a＞ 0.3；

(3) combustion chamber operational pressure is more than fuel combustion critical pressure, and meets upper limit value constraint：2MP ＜ p_c＜ 30MP；

(4) motor charge volume packing factor meets：η_v＜ 0.95；

Step 2, using genetic algorithm to three control parameters w, c in standard particle group's algorithm₁、c₂With design parameter into Row is preferred：

Step 2.1, selected genetic groups scale n_p, crossing-over rate p_cWith aberration rate p_m, give problem maximum evolutionary generation T；

Step 2.2：The random initialization population for generating genetic algorithm；

Step 2.3：Genetic algorithm individual fitness is calculated, detailed process is as follows：

Step 2.3.1：Selected Particles group algorithm population scale m, determines maximum iteration；

Step 2.3.2：Random initializtion particle group velocity and position v₀And x₀；

Step 2.3.3：Its adaptive value F (X are calculated to each particle_i)；

Step 2.3.4：The adaptive value of more each particle records its own optimal value p_iWith group optimal particle label g；

Step 2.3.5：Speed using following formula more new particle and position；

Wherein each parameter meaning is：In the search space of n=10 dimensions, by the molecular population X=of m grain {x₁,...,x_i,...,x_m, wherein the position of i-th of particle is x_i={ x_i1,x_i2,...,x_in}^T, i.e. x_i={ x_i1,x_i2,..., x_i10}^T, speed v_i={ v_i1,v_i2,...,v_in}^T；The individual extreme value of i-th of particle is p_i={ p_i1,p_i2,...,p_i10}^T, The global extremum of population is p_g={ p_g1,p_g2,...,p_g10}^T；D=1,2 .., n, i=1,2 ..., m, t are when evolution generation Number, r₁And r₂For the random number being distributed between [0,1], c₁And c₂For acceleration constant；

Step 2.3.6：Genetic idea is introduced into population position updating process,

The position of any two particle in population is subjected to hereditary update, new position is calculated by following formula：

x₁' (t)=rand () * x₁(t)+(1-rand())*x₂(t)

x₂' (t)=rand () * x₂(t)+(1-rand())*x₁(t)

Wherein, x₁′(t)、x₂' (t) indicates updated position respectively；Rand () expressions randomly select a certain position and carry out Crossover operation；

Step 2.3.7：It checks whether and meets algorithm end condition, if it is not, then going to step 2.3.3；If so, finding out most The figure of merit；

Step 2.3.8：The optimal value that particle cluster algorithm is found is the adaptive value of genetic algorithm individual；

Step 2.4：The select probability that each individual is calculated according to genetic algorithm individual fitness, usually based on linear ranking It calculates；

Step 2.5：Selection, intersection and mutation operator are executed, population of new generation is generated；

Step 2.6：It checks whether and meets genetic algorithm end condition, if it is not, going to step 2.3；If so, finding out optimal Solution, this solution are the value of design parameter.

Specific implementation mode two：

Described in present embodiment step 1 according to weight of fuel x₇It limits and the equation of motion of guided missile in the horizontal plane is carried out The detailed process that integral obtains the total voyage of missile trajectory is as follows：

Step 1.1, Pneumatic Calculation：

The total lift coefficient of body is：

C_y=C_yW+C_yT+C_yB

In formula, C_yWFor the lift coefficient of missile wing；C_yTThe lift coefficient on empennage is acted on for body；C_yBFor the liter of body Force coefficient；

Step 1.2, Cable Power Computation：

The solid propellant rocket zero dimension inner trajectory differential equation for interior ballistic calculation is：

Wherein, ρ_cFor combustion chamber combustion gas averag density；T ' expression the times,It indicates to time derivation；V_cCertainly for combustion chamber By volume；ρ_pFor propellant density；S is combustion front area, as x₂；R is propellant burning velocity；Γ is the function of specific impulse, i.e., For x₅；p_cFor combustion chamber operational pressure；A_tFor nozzle throat area, asT_cFor mean temperature；R is gas constant；

Powder charge meat thickness E and combustion front area can be obtained according to finite element method, powder charge meat thickness E is x₃；

The relationship of combustion front area is：

Wherein, V_eFor after-flame charge volume, Δ V_jFor charge volume in Finite Volume Element, lower footnote e, e- Δ e is respectively represented The solid fuel state at burning moment and last moment；N ' expression finite element numbers；

To in Cable Power Computation it is above-mentioned it is various make it is corresponding simplify and can be calculated engine smooth working section thrust P be：

In above formula,For the mass flow of jet pipe, p_eFor nozzle hole back pressure, p_aFor outside pressure, ε_AFor nozzle-divergence Than ε_pFor nozzle expansion ratio, k is combustion gas specific heat ratio；

Step 1.3, ballistic computation：

Main to consider guided missile in the movement of horizontal plane, the equation of motion of guided missile in the horizontal plane, which is given below, is：

Mg=P α+Y

In above formula, V is guided missile transient-flight speed, and x, z are respectively that body is axial in horizontal plane and body vertical direction is sat Mark component；G is acceleration of gravity, and m is guided missile quality, x₆For the major influence factors of m；P is motor power；Y, Z are respectively Lift and skid force；V is flying speed；α is the missile flight angle of attack；φ_vFor flight path drift angle；β is yaw angle；m_cIt is fired for engine Expect second consumption, m_cBy x₇Limitation；

According to weight of fuel x₇Limitation integrates the above-mentioned equation of motion, can obtain the total voyage of missile trajectory, as target Function F (X_i)。

Other steps and parameter are same as the specific embodiment one.

Specific implementation mode three：

C described in present embodiment step 1.1_yW、C_yT、C_yBCalculating process it is as follows：

Wherein, K_W,K_T,K_BThe respectively interference factor of missile wing, empennage and body can get correlation by empirical equation； S_W,S_T,S_BThe respectively area of reference of missile wing, empennage and body.

Other steps and parameter are identical with embodiment two.

Specific implementation mode four：

S described in present embodiment step 1.1_W,S_T,S_BCalculating process it is as follows：

S_W=x₈·l_W

S_T=(x₉+x₁₀)·l_T/2

S_B=S_W+S_T+π(x₄/2)²·l_B

Wherein, l_W、l_T、l_BThe length of missile wing, empennage and body is indicated respectively.

Other structures and parameter are the same as the specific implementation mode 3.

Embodiment

In the design process of the present invention, information interchange between each subject is as shown in Figure 1, according to the subject described as shown in Figure 1 Between information interchange figure the design variable of the Missile Design problem under multidisciplinary environment is carried out in conjunction with Genetic Particle Swarm Algorithm It divides, figure below is that the data flow schematic diagram after dividing is as shown in Figure 2；

Emulation experiment is carried out according to specific implementation mode four, optimum results of the invention are shown in Table 1 as follows；Meanwhile being verification The present invention is using multidisciplinary feasible method (MDF) method to comparing.Optimum results see the table below 1, object function of the invention Fitness curve graph is as shown in Figure 3.

Table 1

Claims (4)

1. the guided missile Parameters design based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, it is characterised in that including walking as follows Suddenly：

Step 1 chooses following design variable as design parameter：

Engine larynx diameter x₁, engine combustion face area x₂, motor charge meat thickness x₃, guided missile outer diameter x₄, engine/motor specific impulse x₅, hair Motivation quality x₆, fuel mass x₇, missile wing root chord length x₈, the long x of the empennage root tip₉, empennage tip chord length x₁₀；

According to weight of fuel x₇Limitation, integrates the equation of motion of guided missile in the horizontal plane, obtains the total voyage of missile trajectory, As object function F (X_i)；

There are following design constraints：

(1) missile flight end speed Ma=1.4 is chosen as end condition, if permissible load factor is more than or equal to 8g；

(2) the ratio between nozzle hole back pressure and outside pressure meet：p_e/p_a＞ 0.3；

(3) combustion chamber operational pressure is more than fuel combustion critical pressure, and meets upper limit value constraint：2MP ＜ p_c＜ 30MP；

(4) motor charge volume packing factor meets：η_v＜ 0.95；

Step 2, using genetic algorithm to three control parameters w, c in standard particle group's algorithm₁、c₂It is carried out with design parameter excellent Choosing：

Step 2.1, selected genetic groups scale n_p, crossing-over rate p_cWith aberration rate p_m, give problem maximum evolutionary generation T；

Step 2.2：The random initialization population for generating genetic algorithm；

Step 2.3：Genetic algorithm individual fitness is calculated, detailed process is as follows：

Step 2.3.1：Selected Particles group algorithm population scale m, determines maximum iteration；

Step 2.3.2：Random initializtion particle group velocity and position v₀And x₀；

Step 2.3.3：Its adaptive value F (X are calculated to each particle_i)；

Step 2.3.4：The adaptive value of more each particle records its own optimal value p_iWith group optimal particle label g；

Step 2.3.5：Speed using following formula more new particle and position；

Wherein each parameter meaning is：In the search space of n=10 dimensions, by the molecular population X={ x of m grain₁,..., x_i,...,x_m, wherein the position of i-th of particle is x_i={ x_i1,x_i2,...,x_in}^T, i.e. x_i={ x_i1,x_i2,...,x_i10}^T, Speed is v_i={ v_i1,v_i2,...,v_in}^T；The individual extreme value of i-th of particle is p_i={ p_i1,p_i2,...,p_i10}^T, population it is complete Office's extreme value is p_g={ p_g1,p_g2,...,p_g10}^T；D=1,2 .., n, i=1,2 ..., m, t are current evolutionary generation, r₁And r₂For The random number being distributed between [0,1], c₁And c₂For acceleration constant；

Step 2.3.6：The position of any two particle in population is subjected to hereditary update, new position is calculated by following formula：

x₁' (t)=rand () * x₁(t)+(1-rand())*x₂(t)

x₂' (t)=rand () * x₂(t)+(1-rand())*x₁(t)

Wherein, x₁′(t)、x₂' (t) indicates updated position respectively；Rand () expressions randomly select a certain position and are intersected Operation；

Step 2.3.7：It checks whether and meets algorithm end condition, if it is not, then going to step 2.3.3；If so, finding out optimal Value；

Step 2.3.8：The optimal value that particle cluster algorithm is found is the adaptive value of genetic algorithm individual；

Step 2.4：The select probability that each individual is calculated according to genetic algorithm individual fitness, is calculated by linear ranking；

Step 2.5：Selection, intersection and mutation operator are executed, population of new generation is generated；

Step 2.6：It checks whether and meets genetic algorithm end condition, if it is not, going to step 2.3；If so, optimal solution is found out, This solution is the value of design parameter.

2. the guided missile Parameters design according to claim 1 based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, It is characterized in that described in step 1 according to weight of fuel x₇Limitation is integrated to obtain to the equation of motion of guided missile in the horizontal plane The detailed process of the total voyage of missile trajectory is as follows：

Step 1.1, Pneumatic Calculation：

The total lift coefficient of body is：

C_y=C_yW+C_yT+C_yB

In formula, C_yWFor the lift coefficient of missile wing；C_yTThe lift coefficient on empennage is acted on for body；C_yBFor the lift system of body Number；

Step 1.2, Cable Power Computation：

The solid propellant rocket zero dimension inner trajectory differential equation for interior ballistic calculation is：

Wherein, ρ_cFor combustion chamber combustion gas averag density；T ' expression the times,It indicates to time derivation；V_cFreely hold for combustion chamber Product；ρ_pFor propellant density；S is combustion front area, as x₂；R is propellant burning velocity；Γ is the function of specific impulse, as x₅；p_cFor combustion chamber operational pressure；A_tFor nozzle throat area, asT_cFor mean temperature；R is gas constant；

Powder charge meat thickness E and combustion front area can be obtained according to finite element method, powder charge meat thickness E is x₃；

The relationship of combustion front area is：

Wherein, V_eFor after-flame charge volume, Δ V_jFor charge volume in Finite Volume Element, lower footnote e, e- Δ e respectively represents burning The solid fuel state at moment and last moment；N ' expression finite element numbers；

To in Cable Power Computation it is above-mentioned it is various make it is corresponding simplify and calculate engine smooth working section thrust P is：

In above formula,For the mass flow of jet pipe, p_eFor nozzle hole back pressure, p_aFor outside pressure, ε_AFor nozzle expansion ratio, ε_p For nozzle expansion ratio, k is combustion gas specific heat ratio；

Step 1.3, ballistic computation：

Guided missile is considered in the movement of horizontal plane, and the equation of motion of guided missile in the horizontal plane, which is given below, is：

Mg=P α+Y

In above formula, V is guided missile transient-flight speed, and x, z are respectively that body is axial in horizontal plane and body vertical direction coordinate divides Amount；G is acceleration of gravity, and m is guided missile quality, x₆For the major influence factors of m；P is motor power；Y, Z are respectively lift And skid force；V is flying speed；α is the missile flight angle of attack；φ_vFor flight path drift angle；β is yaw angle；m_cFor the engine fuel second Consumption, m_cBy x₇Limitation；

According to weight of fuel x₇Limitation integrates the above-mentioned equation of motion, can obtain the total voyage of missile trajectory, as object function F (X_i)。

3. the guided missile Parameters design according to claim 2 based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, It is characterized in that the C described in step 1.1_yW、C_yT、C_yBCalculating process it is as follows：

Wherein, K_W,K_T,K_BThe respectively interference factor of missile wing, empennage and body；S_W,S_T,S_BRespectively missile wing, empennage and body Area of reference.

4. the guided missile Parameters design according to claim 3 based on Genetic Particle Swarm algorithms for multidisciplinary design optimization, It is characterized in that the S described in step 1.1_W,S_T,S_BCalculating process it is as follows：

S_W=x₈·l_W

S_T=(x₉+x₁₀)·l_T/2

S_B=S_W+S_T+π(x₄/2)²·l_B

Wherein, l_W、l_T、l_BThe length of missile wing, empennage and body is indicated respectively.