Optimization design method for ballistic trajectory in ultra-high-speed fragment launching device

Abstract

The invention discloses an optimization design method for ballistic charging parameters in an ultra-high-speed fragment launching device. A multi-target simulated annealing algorithm is adopted, a ballistic calculation program in the ultra-high-speed fragment launching device and a Runge-Kutta normal equation resolving program are combined, and ballistic optimization calculation in the ultra-high-speed fragment launching device is carried out, so that the problem that in the ultra-high-speed fragment charging design, the ballistic load in the ultra-high-speed fragment launching device is influenced is solved. And the method is suitable for solving the optimization target requirements of various ultra-high-speed fragment tests. The method provided by the invention is suitable for solving the optimization target requirements of the ultra-high-speed fragment tests.

Description

Technical Field

[0001] The invention belongs to the field of interior ballistic optimization calculation design, and specifically, is an optimization design method for interior ballistic charge parameters of a hypervelocity fragment launcher. Background Art

[0002] The hypervelocity fragmentation launch system is mainly used to provide prefabricated fragments with regular shapes and high speed motion, simulating the high speed shrapnel produced by the explosion of ammunition. In the field of ammunition safety, it is used to study the possible response and damage mode of ammunition under the direct impact of high speed shrapnel in battlefield environment, and based on the test results, the safety of ammunition is evaluated and the safety protection measures to be taken in the actual storage and use of ammunition are determined; and the hypervelocity fragmentation launch system is a test platform urgently needed in the current ammunition safety test. At present, there are no relevant papers and materials on the detailed study of the fragmentation launch system at home and abroad. The internal ballistic simulation of conventional ammunition is to calculate the corresponding internal ballistic results for a specific caliber and corresponding propellant. In the safety test of ammunition fragment impact, the fragment velocity and mass are important assessment indicators. If the test requirements are to be met, it is necessary to continuously adjust the charge parameters and find an internal ballistic charge parameter that meets the current fragmentation test speed requirements.

[0003] Therefore, it is particularly important to accurately and quickly find the internal ballistic charge parameters of hypervelocity fragments that meet the test requirements to ensure the normal operation of the test. In traditional hypervelocity fragment test research, the internal ballistic parameters are still adjusted manually to achieve the expected speed of the expected fragments. This method consumes a lot of manpower and computing resources, and cannot consider multiple optimization goals at the same time. Therefore, how to quickly design the internal ballistic charge of the hypervelocity fragment launcher that meets the test requirements to reduce the manpower consumption of the test process is also the focus of current research. Summary of the invention

[0004] The present invention mainly aims to solve the problem that when the initial velocity of fragments reaches an ultra-high-speed level of 2400m / s under the condition that the maximum average chamber pressure does not exceed the maximum value, it is impossible to accurately and high-quality propellant charge design, and provides an internal ballistic charge optimization design calculation method for an ultra-high-speed fragment launch system.

[0005] To solve the above problems, the present invention adopts the following technical solutions:

[0006] A method for optimizing the interior ballistics of a hypervelocity fragment launcher comprises the following steps:

[0007] Step 1: For the hypervelocity fragment launcher to be optimized, determine the target parameters required for optimization design, the hypervelocity fragment velocity is greater than 2400m / s and the maximum average pressure in the bore does not exceed 410MPa; determine the variable range of propellant design parameters, the gunpowder force f range is 900kJ / kg~950kJ / kg, the 1 / 2 arc thickness e1 range is 0.2mm~0.4mm, the aperture d0 range is 0.2mm~0.4mm, the burning rate coefficient u1 range is 1.6e-8~1.8e-8, the burning number index n range is 0.82~0.85, and randomly select a value in these parameter ranges as the initial value of the hypervelocity fragment interior ballistic optimization calculation.

[0008] Step 2: Based on the initial design parameters of the hypervelocity fragmentation propellant charge selected in step 1, build an interior ballistic calculation model for the 30mm hypervelocity fragmentation launcher.

[0009] Step 3: For the internal ballistic calculation model of the 30mm hypervelocity fragment launcher established in step 2, the Runge-Kutta method is used to solve it, and the specific values ​​of the internal ballistic characteristic parameters of the 30mm hypervelocity fragment launcher are obtained. The results with fragment velocity above 1500m / s and maximum average pressure below 410MPa are taken as the initial results. If the results do not meet the requirements, repeat step 1.

[0010] Step 4: Adopt a new multi-objective acceptance criterion, that is, on the basis of the original acceptance criterion, add the probability of accepting the suboptimal solution under the condition of satisfying the fragment velocity and the maximum chamber pressure, and set the maximum number of Pareto solution sets to 10-20; when setting the global optimal solution set, take the first solution of the Pareto solution set as the global optimal solution and compare the solution in the Pareto solution set with the solution in the global optimal solution set after each 50-100 optimization calculations, and update the method of the global optimal solution to build an improved multi-objective simulated annealing algorithm. The computational solution model jointly constructed by steps 2 and 3 is used as the objective function of the improved multi-objective simulated annealing algorithm, embedded in the improved multi-objective simulated annealing algorithm model established in step 4, and the initial result calculated in step 3 is used as the initial random solution of the improved multi-objective simulated annealing algorithm model to obtain the internal ballistic optimization model of the hypervelocity fragment launcher.

[0011] Step 5: For the optimization of the internal ballistic calculation of hypervelocity fragments, the target fragment velocity is greater than 2400m / s and the maximum pressure in the chamber is less than 410MPa. The internal ballistic optimization model of the 30mm hypervelocity fragment launcher is used to perform optimization calculations to obtain the internal ballistic charge design parameters that meet the requirements of the hypervelocity fragment launch velocity and chamber pressure.

[0012] Preferably, the specific equation of the established interior ballistic calculation model is:

[0013] The burning rate equation of gunpowder is:

[0014]

[0015]

[0016] Among them, Z is the relative combustion thickness, Z k is the splitting point of gunpowder combustion, ψ is the percentage of gunpowder combustion, χ, λ, μ, χ s , s is the shape characteristic of the gunpowder, u1 is the burning rate coefficient, e1 is the 1 / 2 arc thickness, p is the chamber pressure, and n is the burning rate index

[0017] Projectile motion equation:

[0018]

[0019] Among them, S is the cross-sectional area of ​​the launch device, φ is the secondary power coefficient, v is the fragment movement speed, m is the fragment mass, and l is the fragment movement distance.

[0020] Calculate the energy balance equation:

[0021]

[0022] Among them, l0 is the length of the gun chamber, f is the gunpowder force, ω is the charge, Δ is the filling density, ρ p is the density of gunpowder gas, θ is the specific heat coefficient, and α is the residual capacity.

[0023] Compared with the traditional charge design technology, the present invention can achieve the following beneficial effects:

[0024] It solves the problem that the traditional internal ballistic optimization design cannot make the initial velocity of fragments reach the ultra-high speed level of 2400m / s without exceeding the maximum average chamber pressure, thus reducing the consumption of human resources and computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of the ballistic calculation operation in the hypervelocity launcher.

[0026] Figure 2 This is a schematic diagram of the Runge-Kutta method program operation.

[0027] Figure 3 This is the program running diagram of the multi-objective simulated annealing algorithm.

[0028] Figure 4 This is the relationship between the algorithm search times and the fragment velocity.

[0029] Figure 5To optimize the vt diagram of the interior ballistics of hypervelocity fragments of explosive charges.

[0030] Figure 6 To optimize the interior ballistics pt diagram of hypervelocity fragments. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] The present invention provides a method for optimizing the interior ballistics of a hypervelocity fragment launcher, which specifically comprises the following steps:

[0033] (1) Determine the target parameters required for the optimization design of the hypervelocity fragment launcher to be optimized.

[0034] Determine the structural parameters of the hypervelocity launcher involved in the calculation and the requirements such as fragment velocity and maximum average chamber pressure that need to be met. Combined with specific parameters, a 30mm hypervelocity fragment launcher is optimized and designed. On the basis of a charge of 300g, the gunpowder parameters are optimized, including gunpowder force f, gunpowder grain arc thickness e1, gunpowder grain inner diameter d0, burning rate coefficient u1, and burning rate index n. The optimization goal is: under the requirement that the average maximum pressure does not exceed 410MPa, a multi-objective simulated annealing algorithm is used to meet the muzzle velocity greater than 2400m / s, and the smaller the average maximum pressure, the better the solution.

[0035] The variation range of the variable charge design parameters is stipulated to determine the variable range of the propellant design parameters, the powder force f range is 900kJ / kg~950kJ / kg, the 1 / 2 arc thickness e1 range is 0.2mm~0.4mm, the aperture d0 range is 0.2mm~0.4mm, the burning rate coefficient u1 range is 1.6e-8~1.8e-8, and the burning number index n range is 0.82~0.85, so as to ensure the feasibility of the charge parameter optimization calculation results; and randomly select a value in this parameter range as the initial value of the hypervelocity fragment interior ballistic optimization calculation.

[0036] (2) Based on the initial design parameters of the hypervelocity fragmentation propellant charge selected in step 1, build an interior ballistic calculation model for the 30mm hypervelocity fragmentation launcher.

[0037] in accordance with Figure 1 The calculation flow chart is combined with the following internal ballistic calculation equation to build a related calculation model

[0038] The burning rate equation of gunpowder is:

[0039]

[0040] Among them, Z is the relative combustion thickness, Z kis the splitting point of gunpowder combustion, ψ is the percentage of gunpowder combustion, χ, λ, μ, χ s , s is the shape characteristic of the gunpowder, u1 is the burning rate coefficient, e1 is the 1 / 2 arc thickness, p is the chamber pressure, and n is the burning rate index

[0041] Projectile motion equation:

[0042]

[0043] Among them, S is the cross-sectional area of ​​the launch device, φ is the secondary power coefficient, v is the fragment movement speed, m is the fragment mass, and l is the fragment movement distance.

[0044] Calculate the energy balance equation:

[0045]

[0046] Among them, l0 is the length of the gun chamber, f is the gunpowder force, ω is the charge, Δ is the filling density, ρ p is the density of gunpowder gas, θ is the specific heat coefficient, and α is the residual capacity.

[0047] And bring the initial values ​​selected in step 1 into the trajectory calculation model of the hypervelocity launcher to prepare for the initial result calculation.

[0048] (3) The internal ballistic calculation model of the 30 mm ultra-high-speed fragmentation launcher established in step 2 is solved using the Runge-Kutta method to obtain the specific values ​​of its optimization objectives, namely, fragment velocity and maximum average pressure.

[0049] according to Figure 2 The calculation flow chart of Runge-Kutta method is shown in the figure. A program for solving the internal ballistic equations of the hypervelocity fragment launcher is built to solve the equation group and obtain the specific values ​​of the internal ballistic characteristic parameters of the 30mm hypervelocity fragment launcher. The results with fragment velocity above 1500m / s and maximum average pressure below 410MPa are taken as the initial results. If the results do not meet the requirements, repeat step 1.

[0050] (4) In order to record the initial results calculated in step 3 and search for a better charging parameter structure, an improved multi-objective simulated annealing algorithm calculation model is built. The internal ballistic calculation model of the 30mm hypervelocity fragmentation launcher and the related calculation model written by the Runge-Kutta method are used as the objective function in the improved multi-objective simulated annealing algorithm model, thereby obtaining the internal ballistic optimization model of the hypervelocity launcher.

[0051] like Figure 3As shown in the figure, the present invention adopts an improved SMOSA algorithm, and a new multi-objective acceptance criterion is used. That is, on the basis of the original acceptance criterion, a method of probabilistically accepting sub-optimal solutions under multi-objectives (fragment velocity and maximum chamber pressure) is added, and the maximum number of Pareto solution sets is set to 10 (all solutions that meet the multi-objective requirements can be fully accepted). When setting the global optimal solution set, the first solution in the Pareto solution set is used as the global optimal solution. After every 50 optimization calculations, the solutions in the Pareto solution set are compared with the solutions in the global optimal solution set, and the global optimal solution is updated. The specific steps are as follows:

[0052] Step 1: Randomly initialize the solution set x of parameters, calculate all its objective functions, and add their objective function values to the Pareto solution set (non-dominated solution set).

[0053] Step 2: Given a random perturbation, let it generate a neighborhood solution set y of the x solution set, and substitute the y solution set into the objective function to calculate all its objective function values.

[0054] Step 3: Compare the objective function values of the newly generated neighborhood solution with the objective function values in the Pareto solution set. At this time, there are three situations. First, if the newly generated neighborhood solution is better than any solution in the Pareto solution set, then update the y solution set into the Pareto solution set, replace the x solution set with the y solution set, and go to Step 4. Second, if the newly generated neighborhood solution does not enter the Pareto solution set, then accept the new solution according to the following probability:

[0055]

[0056] And generate a random number t in (0, 1). If p > t, then replace the x solution set with the y solution set and go to Step 4.

[0057] Third, if p < t, it means that the new solution is not accepted, then keep the current solution and enter Step 4.

[0058] Step 4: Every certain number of generations k, randomly select a solution from the Pareto set as the initial solution and search again.

[0059] Step 5: Cool down once every certain number of generations.

[0060] Step 6: Repeat Step 2 to Step 5 until the temperature drops to the lowest temperature.

[0061] Taking Step 2 and Step 3 as the objective functions, combined with the multi-objective simulated annealing algorithm, the interior ballistic optimization model of this hypervelocity launch device is obtained.

[0062] The random solution generated by the multi-objective simulated annealing algorithm is set as the input data of the internal ballistic calculation model of the hypervelocity launcher, and the fragment velocity and maximum average pressure calculated by the model are substituted into the multi-objective simulated annealing algorithm as the objective function values ​​for optimization calculation to obtain the internal ballistic optimization model of the hypervelocity launcher.

[0063] (5) The optimization target for the internal ballistic calculation of hypervelocity fragments is that the fragment velocity is greater than 2400 m / s and the maximum pressure in the chamber is less than 410 MPa. The internal ballistic optimization model of the 30 mm hypervelocity fragment launcher is used to perform optimization calculations and obtain the internal ballistic charge design parameters that meet the requirements of the hypervelocity fragment launch velocity and chamber pressure.

[0064] The basic parameters of the optimization model such as the number of cycles are set, and then the internal ballistic optimization design of the hypervelocity fragment launcher is performed to obtain the required optimal charge design. The search process is as follows Figure 4 As shown, the optimal solution and its charging scheme structure are shown in the following table

[0065]

[0066]

[0067] Figure 5 , Figure 6 The curve of the internal ballistic characteristic parameters of the hypervelocity internal ballistics optimization charge is obtained by calculation.

[0068] The above results show that based on the 300g charge, the optimal solution is to comprehensively consider the maximum initial velocity and the average maximum chamber pressure.

[0069] The present invention first solves the problem that the traditional internal ballistic optimization design cannot meet the condition that the maximum chamber pressure does not exceed the maximum barrel pressure of 410MPa, and the initial velocity of the fragments reaches the ultra-high speed level of 2400m / s. At the same time, the maximum average chamber pressure is reduced as much as possible under the condition of achieving ultra-high speed fragments. The internal ballistic optimization design is carried out from two aspects, which solves the drawback that the traditional optimization design can only optimize the initial velocity unilaterally.

[0070] The traditional internal ballistic optimization design obtains an internal ballistic design scheme that meets the requirements by selecting standard standard gunpowder. This is also one of the reasons why the traditional optimization design cannot make the initial velocity of the fragments reach ultra-high speed on the basis of ensuring the maximum average chamber pressure in the chamber. The ultra-high-speed fragment internal ballistic optimization design adopted by the present invention is more flexible in the selection of gunpowder parameters and does not have to be limited to standard standard gunpowder. Therefore, the charge design can be more flexible, so as to find a better design scheme, so that the fragment velocity reaches the ultra-high speed level.

[0071] In addition, most traditional charge designs rely on manpower to continuously adjust and calculate relevant parameters, which consumes a lot of human resources, and the parameters taken are too affected by subjective factors, and it is impossible to fully consider the results of all parameters; the present invention can perform a global search, while introducing a method of accepting deteriorated solutions with a certain probability, and introducing an optimal solution set system, and performing random re-search during the optimization calculation process, so that during the global search, it is avoided to fall into the local optimal solution and miss the optimal solution set. The present invention saves human computing resources, and obtains the best charge design scheme under the condition of fully considering the calculation results of various parameters.

Claims

1. A method for optimizing the interior ballistics of a hypervelocity fragment launcher, characterized in that: The following steps are involved: Step 1: For the hypervelocity fragment launcher to be optimized, determine the target required parameters for optimization design, determine the variable range of the initial parameters of the hypervelocity fragment launcher charge design, and randomly select a value in these parameter ranges as the initial value for the hypervelocity fragment interior ballistic optimization calculation; Step 2: Based on the initial design value of the hypervelocity fragmentation propellant charge selected in step 1, build an interior ballistic calculation model for the 30mm hypervelocity fragmentation launcher; Step 3: For the 30mm hypervelocity fragment launcher internal ballistic calculation model established in step 2, the Runge-Kutta method is used to solve it, and the specific values ​​of the internal ballistic characteristic parameters of the 30mm hypervelocity fragment launcher are obtained. The results where the fragment velocity is above 1500m / s and the maximum average pressure is below 410MPa are taken as the initial results, and the results that do not meet the requirements are returned to step 1; Step 4: adopt a new multi-objective new acceptance criterion, that is, on the basis of the original acceptance criterion, add the probability of accepting the suboptimal solution under the condition of satisfying the fragment velocity and the maximum chamber pressure, and set the maximum number of Pareto solution sets to 10-20; when setting the global optimal solution set, take the first solution of the Pareto solution set as the global optimal solution, and after each 50-100 optimization calculations, compare the solution in the Pareto solution set with the solution in the global optimal solution set, and update the method of the global optimal solution to build an improved multi-objective simulated annealing algorithm; use the calculation and solution model jointly built by steps 2 and 3 as the objective function of the improved multi-objective simulated annealing algorithm, and embed it into the improved multi-objective simulated annealing algorithm model established in step 4, and use the initial result calculated in step 3 as the initial random solution of the improved multi-objective simulated annealing algorithm model to obtain the internal ballistic optimization model of the hypervelocity fragment launcher; Step 5: Aiming at the optimization objectives of the fragment velocity and the maximum average pressure in the chamber of the hypervelocity fragment launcher, the constructed 30mm hypervelocity fragment launcher internal ballistic optimization model is used to perform optimization calculations to obtain the internal ballistic charge design parameters that meet the requirements of the hypervelocity fragment launch velocity and chamber pressure.

2. The method for optimizing the interior ballistics of a hypervelocity fragment launcher according to claim 1, characterized in that: The target required parameters in step 1 include: the velocity of the hypervelocity fragments is greater than 2400m / s, and the maximum average pressure in the bore of the hypervelocity fragment launcher does not exceed 410MPa; the variable range of the propellant design parameters is: the range of the powder force f is 900kJ / kg to 950kJ / kg, the range of the 1 / 2 arc thickness e1 is 0.2mm to 0.4mm, the range of the aperture d0 is 0.2mm to 0.4mm, the range of the burning rate coefficient u1 is 1.6e-8 to 1.8e-8, and the range of the burning number index n is 0.82 to 0.

85.

3. The method for optimizing the interior ballistics of a hypervelocity fragment launcher according to claim 1, characterized in that: The specific equation of the internal ballistic calculation model of the hypervelocity fragment launcher established in step 2 is: The burning rate equation of gunpowder is: Among them, Z is the relative combustion thickness, Z k is the splitting point of gunpowder combustion, ψ is the percentage of gunpowder combustion, χ, λ, μ, χ s , s is the characteristic quantity of the powder shape, u1 is the burning rate coefficient, e1 is the 1 / 2 arc thickness, p is the chamber pressure, and n is the burning rate index; Projectile motion equation: Among them, S is the cross-sectional area of ​​the launch device, φ is the secondary power coefficient, v is the fragment movement speed, m is the fragment mass, and l is the fragment movement distance; Calculate the energy balance equation: Among them, l0 is the length of the gun chamber, f is the gunpowder force, ω is the charge, Δ is the filling density, ρ p is the density of gunpowder gas, θ is the specific heat coefficient, and α is the residual capacity.

4. The method for optimizing the interior ballistics of a hypervelocity fragment launcher according to claim 1, characterized in that: The specific steps for improving the multi-objective simulated annealing algorithm in Step 4 are as follows: Step 1: Randomly initialize the solution set x of parameters, calculate all its objective functions, and add their objective function values to the Pareto solution set; Step 2: Given a random perturbation, let it generate a neighborhood solution set y of the x solution set, and substitute the y solution set into the objective function to calculate all its objective function values; Step 3: Compare the objective function values of the newly generated neighborhood solution with those in the Pareto solution set. At this time, there are three cases. First, if the newly generated neighborhood solution is better than any solution in the Pareto solution set, then update the y solution set into the Pareto solution set, replace the x solution set with the y solution set, and go to Step 4. Second, if the newly generated neighborhood solution does not enter the Pareto solution set, then accept the new solution according to the following probability: Generate a random number t in the range of (0, 1). If p > t, then replace the x solution set with the y solution set and go to Step 4; Third, if p < t, it means that the new solution is not accepted, then retain the current solution and enter Step 4; Step 4: Every certain number of generations k, randomly select a solution from the Pareto set as the initial solution and re-search; Step 5: Cool down once every certain number of generations; Step 6: Repeat Step 2 to Step 5 until the lowest temperature is reached.

Images