A method and device for calculating the posture of a non-spherical penetrating body moving at high speed in a liquid, an electronic device and a storage medium

Abstract

The application discloses a method and device for calculating the posture of a non-spherical penetrating body moving at high speed in a liquid, electronic equipment and a storage medium, and relates to the technical field of high-speed motion testing. The method comprises the following steps: obtaining a drag coefficient dataset of a non-spherical penetrating body under different working conditions through finite element simulation, and constructing a training dataset based on the drag coefficient dataset under different working conditions; constructing a drag coefficient model of the non-spherical penetrating body, training the drag coefficient model by using the training dataset, and obtaining a trained drag coefficient model; establishing a motion control equation of the non-spherical penetrating body when the non-spherical penetrating body hits a liquid-filled tank; inputting the working condition information of a target non-spherical penetrating body into the trained drag coefficient model to obtain the drag coefficient of the target non-spherical penetrating body, and calculating real-time motion posture data of the target non-spherical penetrating body in the liquid by using the drag coefficient of the target non-spherical penetrating body and the motion control equation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-speed motion testing, and in particular to a method and device for calculating the attitude of a non-spherical penetrating body in high-speed motion in a liquid, an electronic device, and a storage medium. BACKGROUND

[0002] High-speed projectiles in motion in a liquid, torpedoes in motion in water, and other engineering problems essentially involve the problem of attitude stability of high-speed penetrating bodies in fluid motion. The attitude stability of a penetrating body during its entry into a liquid is a complex physical phenomenon. A high-speed penetrating body that can maintain attitude stability in air motion often has difficulty in maintaining attitude angle stability in liquid motion, and thus easily rolls over. In addition to spherical penetrating bodies, flat penetrating bodies, and specially designed slender penetrating bodies, penetrating bodies with shapes such as cylinders, hemispheres, and conical heads have difficulty in maintaining good attitude stability in the presence of initial attitude angle disturbances. As is known, the resistance experienced by a penetrating body in fluid motion depends on the shape of the penetrating body, the size of the object, the speed of motion, and the attitude angle. Fluctuations in the attitude angle of a penetrating body during its entry into water will result in changes in the drag coefficient C d , and thus changes in the real-time resistance of the penetrating body. However, there is no mature calculation method for accurately predicting the motion trajectory of a non-spherical penetrating body. SUMMARY

[0003] The present application provides a method and device for calculating the attitude of a non-spherical penetrating body in high-speed motion in a liquid, an electronic device, and a storage medium, in order to solve the technical problem of how to accurately predict the motion trajectory of a non-spherical penetrating body in high-speed motion in a liquid.

[0004] The present application provides a method for calculating the attitude of a non-spherical penetrating body in high-speed motion in a liquid, comprising:

[0005] Obtaining a drag coefficient dataset of a non-spherical penetrating body under different working conditions through finite element simulation, and constructing a training dataset based on the drag coefficient dataset under different working conditions;

[0006] Constructing a drag coefficient model of the non-spherical penetrating body, and training the drag coefficient model using the training dataset to obtain a trained drag coefficient model;

[0007] Establishing a motion control equation of the non-spherical penetrating body when it hits a liquid-filled tank;

[0008] Obtaining the drag coefficient of the target non-spherical penetrating body by inputting the working condition information of the target non-spherical penetrating body into the trained drag coefficient model, and calculating the real-time motion attitude data of the target non-spherical penetrating body in the liquid using the drag coefficient of the target non-spherical penetrating body and the motion control equation.

[0009] Preferably, the resistance coefficient data set of the non-spherical penetrating body under different working conditions is obtained by finite element simulation, including:

[0010] extracting feature data under different working conditions from the finite element simulation results; wherein the feature data includes non-spherical penetrating body mass m, non-spherical penetrating body real-time speed v, time t, liquid density p, non-spherical penetrating body projection area S in the plane perpendicular to v;

[0011] According to the motion equation of the non-spherical penetrating body in the liquid and the feature data under different working conditions, the resistance coefficient data set of the non-spherical penetrating body under different working conditions is obtained;

[0012] Preferably, constructing a training data set based on the resistance coefficient data set under different working conditions includes:

[0013] According to the yaw angle ψ and the pitch angle θ in the working condition information of each working condition, the sideslip angle β and the angle of attack α of each working condition are obtained;

[0014] by one-to-one processing of the resistance coefficient C d , v, β, α and S of each working condition, a plurality of training data sets composed of C d , v, β, α and S are obtained.

[0015] Preferably, the resistance coefficient model is a BPNN (Back propagation neural network, Back propagation neural network) structure, and the BPNN structure includes an input layer, a hidden layer and an output layer in turn; wherein the input layer is v, β, α and S respectively; the output layer is C d ; the hidden layer is a single hidden layer, and the number of nodes of the hidden layer is n;

[0016] wherein the number of nodes of the hidden layer is:

[0017]

[0018] wherein n1 is the number of input layer nodes; n2 is the number of output layer nodes; b is a constant, the value range is 1-10; n is a positive integer.

[0019] Preferably, the motion control equation includes:

[0020]

[0021] wherein r is the radius of the non-spherical penetrating body; L is the length of the non-spherical penetrating body; t is the motion time of the non-spherical penetrating body in the liquid.

[0022] Preferably, calculating real-time motion posture data of the target non-spherical penetrator in the liquid by using the drag coefficient of the target non-spherical penetrator and the motion control equation comprises:

[0023] By inputting the drag coefficient of the target non-spherical penetrator and the working condition information of the target non-spherical penetrator into the motion control equation, real-time motion posture data of the target non-spherical penetrator in the liquid is obtained.

[0024] The embodiment of the present application provides a posture calculation device for high-speed motion of a non-spherical penetrator in a liquid, comprising:

[0025] The training data set construction module is configured to obtain a drag coefficient data set of the non-spherical penetrator under different working conditions through finite element simulation, and construct a training data set based on the drag coefficient data set under different working conditions.

[0026] The model construction and training module is configured to construct a drag coefficient model of the non-spherical penetrator, and train the drag coefficient model by using the training data set to obtain a trained drag coefficient model.

[0027] The motion control equation establishment module is configured to establish a motion control equation of the non-spherical penetrator when impacting a liquid-filled tank.

[0028] The posture data calculation module is configured to input working condition information of a target non-spherical penetrator into the trained drag coefficient model to obtain a drag coefficient of the target non-spherical penetrator, and calculate real-time motion posture data of the target non-spherical penetrator in the liquid by using the drag coefficient of the target non-spherical penetrator and the motion control equation.

[0029] Preferably, the drag coefficient model is a back propagation neural network (BPNN) structure, and the BPNN structure comprises an input layer, a hidden layer and an output layer in sequence; wherein the input layer is v, β, α and S respectively; the output layer is C d ; the hidden layer is a single hidden layer, and the number of nodes of the hidden layer is n;

[0030] The number of nodes of the hidden layer is:

[0031]

[0032] In the formula, n1 is the number of input layer nodes; n2 is the number of output layer nodes; b is a constant, and the value range is 1-10; n is a positive integer.

[0033] The embodiment of the present application provides an electronic device, comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to realize the steps of the attitude calculation method of the non-spherical penetrating body in high-speed motion in a liquid.

[0034] The embodiment of the present application provides a computer readable storage medium, which stores a computer program; the computer program is executed by a processor to realize the steps of the attitude calculation method of the non-spherical penetrating body in high-speed motion in a liquid.

[0035] The beneficial effects of the present application are that, in combination with finite element simulation and artificial intelligence algorithm and other methods, a construction method of a drag coefficient model of a non-spherical penetrating body is given, a motion control equation of the non-spherical penetrating body is derived, a real-time motion attitude of the non-spherical penetrating body in liquid motion is converted into a solution problem of a differential equation set, the rolling phenomenon of the non-spherical penetrating body in liquid motion is simulated efficiently, and a quantitative result can be obtained according to the differential equation set. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 It is a flow chart of the attitude calculation method of the non-spherical penetrating body in high-speed motion in a liquid provided by the present application;

[0037] Figure 2 (a)-(c) are respectively schematic diagrams of relative positions of EAS (Earth-fixed Axis System, ground-fixed coordinate system) and BAS (Body Axis System, body coordinate system), relative positions of AAS (Air-path Axis System, air path coordinate system) and BAS and three Euler angles of a projectile provided by the present application;

[0038] Figure 3 It is a fluid-structure coupling finite element model provided by the present application;

[0039] Figure 4 (a)-(c) are respectively schematic diagrams of load analysis of the projectile in the EAS, BAS-1 and BAS three-dimensional coordinate systems provided by the present application;

[0040] Figure 5 (a)-(b) are respectively schematic diagrams of relationships between and under conditions of different initial yaw angles and initial pitch angles provided by the present application for training a neural network and for verifying a prediction result of the neural network; d

[0041] Figure 6 It is a BPNN structure schematic diagram provided by the present application;

[0042] ​Figure 7 (a)-(f) are respectively the C d comparison schematic diagram obtained by finite element simulation and neural network prediction under different working conditions provided by the application;

[0043] Figure 8 (a)-(d) are respectively the probability density function diagram of the neural network model of the training group, the verification group, the test group and all data provided by the application. DETAILED DESCRIPTION

[0044] It should be understood that the specific embodiments described herein are merely intended to explain the application and are not intended to limit the application. In the subsequent description, the suffixes such as "module", "component", or "unit" used to represent elements are only for the convenience of the description of the application, and have no specific meaning in itself. Therefore, "module", "component", or "unit" can be used interchangeably.

[0045] The application needs to accurately predict the attitude angle change of the penetrating body when it rolls, and clarifies the relationship between the attitude angle of the non-spherical penetrating body and its resistance coefficient C d in the fluid, i.e. the resistance coefficient model; at the same time, the motion control equation of the non-spherical penetrating body is established, by establishing the resistance coefficient model and the motion control equation of the non-spherical penetrating body, the real-time motion attitude of the non-spherical penetrating body in the liquid is converted into the solution of the differential equation system, the rolling phenomenon of the non-spherical penetrating body in the liquid is simulated efficiently, which has important significance for promoting its military weaponization application.

[0046] Figure 1 is a flow chart of a method for calculating the attitude of a non-spherical penetrating body moving at high speed in a liquid provided by the application, as shown in Figure 1 Fig. 1, comprising: step S1: obtaining a resistance coefficient data set of a non-spherical penetrating body under different working conditions through finite element simulation, and constructing a training data set based on the resistance coefficient data set under different working conditions; step S2: constructing a resistance coefficient model of the non-spherical penetrating body, and training the resistance coefficient model using the training data set to obtain a trained resistance coefficient model; step S3: establishing a motion control equation of the non-spherical penetrating body when it hits a liquid-filled tank; step S4: inputting the working condition information of a target non-spherical penetrating body into the trained resistance coefficient model to obtain the resistance coefficient of the target non-spherical penetrating body, and using the resistance coefficient of the target non-spherical penetrating body and the motion control equation to calculate the real-time motion attitude data of the target non-spherical penetrating body in the liquid.

[0047] In the embodiment of the present application, the drag coefficient data set of the non-spherical penetrating body under different working conditions is obtained through finite element simulation, comprising: extracting feature data under different working conditions from the finite element simulation results; wherein the feature data comprises the mass m of the non-spherical penetrating body, the real-time speed v of the non-spherical penetrating body, the time t, the liquid density p, and the projection area S of the non-spherical penetrating body in the plane perpendicular to v; and obtaining the drag coefficient data set of the non-spherical penetrating body under different working conditions according to the motion equation of the non-spherical penetrating body in the liquid and the feature data under different working conditions.

[0048] In the embodiment of the present application, the training data set is constructed based on the drag coefficient data set under different working conditions, comprising: obtaining the sideslip angle β and the angle of attack a of each working condition according to the yaw angle ψ and the pitch angle θ in the working condition information of each working condition; and obtaining a plurality of training data sets composed of C d , v, β, a and S by one-to-one corresponding processing of C d , v, β, a and S.

[0049] In the embodiment of the present application, the drag coefficient model is a back propagation neural network (BPNN) structure, which comprises an input layer, a hidden layer and an output layer in sequence; wherein the input layer is v, β, a and S respectively; the output layer is C d ; the hidden layer is a single hidden layer, and the number of nodes of the hidden layer is n; wherein the number of nodes of the hidden layer is:

[0050]

[0051] wherein n1 is the number of input layer nodes; n2 is the number of output layer nodes; b is a constant, and the value range is 1-10; n is a positive integer.

[0052] In the embodiment of the present application, the motion control equation comprises:

[0053]

[0054] wherein r is the radius of the non-spherical penetrating body; L is the length of the non-spherical penetrating body; and t is the motion time of the non-spherical penetrating body in the liquid.

[0055] In the embodiment of the present application, the real-time motion posture data of the target non-spherical penetrating body in the liquid is calculated by using the drag coefficient of the target non-spherical penetrating body and the motion control equation, comprising: inputting the drag coefficient of the target non-spherical penetrating body and the working condition information of the target non-spherical penetrating body into the motion control equation to obtain the real-time motion posture data of the target non-spherical penetrating body in the liquid.

[0056] The embodiment of the application provides a device for calculating the posture of a non-spherical penetrating body moving at high speed in a liquid, comprising: a training data set construction module, configured to obtain a drag coefficient data set of the non-spherical penetrating body under different working conditions through finite element simulation, and construct a training data set based on the drag coefficient data set under the different working conditions; a model construction and training module, configured to construct a drag coefficient model of the non-spherical penetrating body, and train the drag coefficient model using the training data set to obtain a trained drag coefficient model; a motion control equation establishment module, configured to establish a motion control equation of the non-spherical penetrating body when impacting a liquid-filled tank; and a posture data calculation module, configured to input working condition information of a target non-spherical penetrating body into the trained drag coefficient model to obtain a drag coefficient of the target non-spherical penetrating body, and calculate real-time motion posture data of the target non-spherical penetrating body in the liquid using the drag coefficient of the target non-spherical penetrating body and the motion control equation.

[0057] In the embodiment of the application, the drag coefficient model is a back propagation neural network (BPNN) structure, the BPNN structure comprises an input layer, a hidden layer and an output layer in sequence; the input layer is v, β, α and S respectively; the output layer is C d ; the hidden layer is a single hidden layer, and the number of nodes of the hidden layer is n; the number of nodes of the hidden layer is:

[0058]

[0059] In the formula, n1 is the number of input layer nodes; n2 is the number of output layer nodes; b is a constant, and the value range is 1-10; n is a positive integer. For example, when n1=4; n2=1; b=1, n is about 3.23, and the value of n is a positive integer, that is, n=4.

[0060] The embodiment of the application provides an electronic device, comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the steps of the posture calculation method of the non-spherical penetrating body moving at high speed in a liquid.

[0061] The embodiment of the application provides a computer readable storage medium, which stores a computer program; the computer program is executed by a processor to implement the steps of the posture calculation method of the non-spherical penetrating body moving at high speed in a liquid.

[0062] The embodiment of the application provides an electronic device, comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the steps of the posture calculation method of the non-spherical penetrating body moving at high speed in a liquid. Figures 2-8 The embodiment of the application provides an electronic device, comprising: a memory; a processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the steps of the posture calculation method of the non-spherical penetrating body moving at high speed in a liquid.

[0063] S101, obtaining a drag coefficient data set of a non-spherical penetrating body through finite element simulation;

[0064] First, this invention provides the definition of relevant angles and the transformation relationship between coordinate systems.

[0065] The initial attitude angle of the projectile upon impact with the fuel tank is designed as a variable. Euler angles are used to describe the projectile's attitude angles, that is, the angular relationship between the projectile's ground-fixed coordinate system (EAS) and the aircraft's body coordinate system (BAS). Euler angles consist of three angles: roll, yaw, and pitch, denoted by _____. ψ and θ represent the coordinates. This represents the state of Euler angles.

[0066] Figure 2 The EAS (black), AAS (yellow), and BAS (green) designations for medium-range projectiles. Figure 2 In (a), blue represents the coordinate system after EAS rotates around the z-axis, orange represents the coordinate system after blue rotates around the y1-axis, and green represents the coordinate system after orange rotates around the x2-axis. Figure 2 (b) Orange represents AAS surrounding y a The coordinate system after rotation around the x3 axis, with green representing the coordinate system after rotation around the orange axis. Figure 2 (c) Designs two symmetrical initial attitude angle conditions, under which the projectile does not deflect during its flight. In EAS, the Euler angles for these two conditions are (0, 0, 0) and (0, 0, -90).

[0067] Figure 2 (a) Provides the relative positions of EAS and BAS. In EAS, the zero point coordinate is the geometric center of the projectile; the Z-axis is along the trajectory direction and points in the positive direction; the y-axis is perpendicular to the line upwards; the x-axis, z-axis, and y-axis form a Cartesian coordinate system. It is y b The angle between the y-axis and the z-axis; when y b When the positive half-axis is located on the left side of the plumb line, It is a positive value; ψ is z b The angle between the projection of the z-axis onto the horizontal plane and the z-axis; when z b When the positive semi-axis of the z-axis is projected onto the right side of the z-axis, ψ is positive; θ is the z-axis. b The angle between the axis and the horizontal plane; when z b θ is positive when the positive semi-axis of the axis lies above the horizontal plane passing through the origin. Since the projectile used is cylindrical and axisymmetric, attitude equivalence can be considered. Therefore, Negligible, ψ and θ range from -90° to 0°.

[0068] Figure 2 (b) Provides the relative positions of AAS and BAS. Because z a axis and z bThe two angles are side-slip angle β and angle of attack α. β is the angle between the velocity of the projectile v and the longitudinal symmetry plane of the projectile. When v is along x b The component of the axis is positive when it is positive. α is the angle between the projection of v on the longitudinal symmetry plane of the projectile and z b The component of the axis is positive when it is positive. α is the angle between the projection of v on the longitudinal symmetry plane of the projectile and z b The component of the axis is positive when it is positive. α is the angle between the projection of v on the longitudinal symmetry plane of the projectile and z

[0069]

[0070] In the formula, L z , L y , and L x are the transformation matrices of the coordinate system rotating around the three axes, and γ is the rotation angle. The transformation relationship between the coordinate systems is formula (4) and (5):

[0071]

[0072] In the formula, v xb , v yb , and v zb are the velocities of the projectile along the three axes of BAS. v x , v y , and v z are the velocities of the projectile along the three axes of EAS.

[0073]

[0074] In the formula, v xa , v ya , and v za are the velocities of the projectile along the three axes of AAS.

[0075] In the present application, the data set of the drag coefficient using the back propagation neural network BPNN structure is from the finite element simulation results. The finite element simulation uses a total of 75 groups of working conditions to obtain the drag coefficient data set of the non-spherical penetrating body.

[0076] The fluid-structure coupling phenomenon of the penetrating body impacting the oil tank is numerically simulated using the S-ALE (Structured Arbitrary Lagrange-Euler) algorithm in LS-DYNAmp R12.1, and the finite element model is as shown in Figure 3The finite element model mainly includes five parts: projectile, tank, rivet, kerosene, and air. The material of the projectile is 45# steel, the tank is riveted by six 2A12-T4 aluminum alloy plates, the rivets connecting the front and rear plates and the side plates are 5052 aluminum alloy, and the rivets connecting the side plates are 304L stainless steel.

[0077] In the finite element model, the keyword *CONSTRAINED_LAGRANGE_IN_SOLID is used to set the interaction between fluid and solid, and then the fluid-structure interaction calculation is realized. The Johnson-Cook (J-C) constitutive model, J-C failure model, and Gruneisen equation of state are used for each plate of the tank. The Plastic Kinematic constitutive model is used for the projectile and rivet. The Null model is used for air and oil, and the Gruneisen equation of state and Linear Polynomial equation of state are used respectively. The parameters of each component are shown in Tables 1-3.

[0078] Table 1: Constitutive model parameters used in finite element analysis

[0079]

[0080] Table 2: Failure model parameters used in finite element analysis

[0081]

[0082] Table 3: Equation of state parameters used in finite element analysis

[0083]

[0084] In the simulation, the influence of the projectile attitude angle on the hole shape of the tank rear plate is systematically explored by changing the initial pitch angle θ0 and the initial yaw angle ψ0 of the projectile. A total of 75 simulation conditions are designed, as shown in Table 4.

[0085] Table 4: Simulation conditions

[0086]

[0087] Firstly, from the simulation results, the known data of m, v, t, ρ, S corresponding to each working condition information can be extracted, and then the C d of each working condition information can be calculated through the projectile motion equation (8) in kerosene.

[0088] Secondly, ψ and θ can be calculated by extracting the data in the simulation, and then β and α can be calculated.

[0089] The application provides a method for predicting the drag coefficient of a non-spherical projectile using a BPNN d The attitude of the projectile at the time of ejection can be calculated according to the control equation, and the distribution of lambda in the space of ψ0-θ0 can be obtained. From the simulation results, known data such as m, v, t, ρ, S can be extracted, and C d of the projectile in each working condition can be calculated through equation (8). d The C d of the projectile in each working condition is calculated by this method and is plotted in the four-dimensional space of θ, v and C d The value of C d is color-mapped, as shown in the figure, and C d is obviously larger than that in other working conditions under the condition of ψ0=0°, θ0=0°. At the same time, C -1 gradually decreases with the decrease of v. Figure 5 (a) is the training set data, Figure 5 (b) is the validation set data. The training and validation of the model use a total of 75 working conditions and 11250 groups of data, and the speed range is 400m·s -1 -1600m·s d .

[0090] S102, a drag coefficient model of a non-spherical penetrating body is constructed by an artificial intelligence algorithm;

[0091] Figure 6 A BPNN structure is provided, mainly including an input layer, a hidden layer and an output layer. The input layer has 4 groups of data, namely v, β, α and S. The output layer has only one group of data, namely C d . For the hidden layer, a single hidden layer is adopted, and the number of nodes of the hidden layer is n. The selection of n will directly affect the prediction accuracy of the model. The application compares the difference between C d obtained by finite element simulation and C d predicted by the drag coefficient model (referred to as BPNN) of the BPNN structure using different n. It can be found by comparing the correlation coefficient R value that with the increase of the number of nodes, the accuracy of the prediction result of the BPNN is also improved. However, when n reaches 7, the R value does not improve obviously even if the number of nodes increases again, and even decreases. When n=7, the C d predicted by the BPNN is closer to the C d obtained by finite element simulation, the R value is closest to 1 and is greater than 0.94, the prediction effect is optimal, and it is indicated that the BPNN with n=7 can accurately predict C -1 , and therefore it is used as the drag coefficient model.

[0092] In the application, Figure 7 Figure 7 ​(a) ψ0= 0°, θ0= -90° and initial velocity of non-spherical penetrator v0= 1600 m·s -1 ; Figure 7 (b) ψ0= -30°, θ0= -45° and v0= 1600 m·s -1 ; Figure 7 (c) ψ0= -45°, θ0= 0° and v0= 1300 m·s -1 ; Figure 7 (d) ψ0= -45°, θ0= -60° and v0= 1300 m·s -1 ; Figure 7 (e) ψ0= -60°, θ0= -30° and v0= 1200 m·s -1 ; Figure 7 (f) ψ0= -90°, θ0= -60° and v0= 1200 m·s -1 .

[0093] The BPNN divides the training set data into three parts, training group, validation group and test group during the training process, Figure 8 The probability density diagram of the BPNN prediction results when n = 7 is given, the abscissa is calculated by using the finite element method, and the ordinate is predicted by the BPNN in the normalized space. The correlation coefficient R value of each group of data is higher than 0.94, indicating that there is a strong correlation between the output and the target, which verifies the accuracy of the BPNN prediction results. Figure 8 (a) is the probability density diagram of the training group; Figure 8 (b) is the probability density diagram of the validation group; Figure 8 (c) is the probability density diagram of the test group; Figure 8 (d) is the probability density diagram of all data.

[0094] S3, based on Newton's law, the motion control equation of the non-spherical penetrator is established;

[0095] The projectile decelerates and rotates in the fluid, and the tumbling of the projectile in kerosene is due to the uneven force, Figure 4 The three-dimensional load analysis of the projectile in kerosene is given. Figure 4 (a) is EAS; Figure 4 (b) is BAS-1 (rotation around the y-axis of EAS); Figure 4 (c) is BAS (rotation around the x1-axis of BAS-1). When the projectile moves in the oil, the curvature of the trajectory is very small, so it is ignored. AAS coincides with EAS. When the projectile rotates ψ around the y-axis of EAS, EAS becomes BAS-1; when the projectile continues to rotate θ around the x1-axis of BAS-1, BAS-1 becomes BAS.

[0096] In EAS, the projectile is in force balance in x and y axis direction. In Z axis direction, the motion equation is satisfied:

[0097]

[0098] where F xb ,F yb , and F zb are the forces of the projectile in the three axis directions in BAS; F x ,F y , and F z are the forces of the projectile in the three axis directions in EAS.

[0099] According to the momentum conservation law, the motion equation of the projectile in kerosene is:

[0100]

[0101] (F zb -F xb )l=IJ ψ , (9)

[0102] (F zb -F yb )l=IJ θ , (10)

[0103] where l is the radius of rotation, I is the polar moment of inertia, J ψ is the angular acceleration of ψ, and J θ is the angular acceleration of θ.

[0104] The formulas (6)-(10) are solved together and the result is simplified to:

[0105]

[0106] C d =C d (v,β,α,s), (12)

[0107] where r=D / 2. C d is a function of v, β, α, and S, which can be obtained by BPNN prediction. The formulas (11) and (12) together define the motion control equation of the projectile when it hits the liquid-filled tank.

[0108] S104, the real-time motion posture of the non-spherical penetrating body is solved by numerical calculation method;

[0109] Given any different working condition initial value (i.e. v0, ψ0, θ0), the formulas (11) and (12) are solved by numerical method using 4-5 order Runge-Kutta algorithm, and the real-time motion posture of the non-spherical penetrating body is solved.

[0110] Embodiment

[0111] Suppose that the target cylindrical penetrator moves at high speed in water, the material of the penetrator is 45# steel, the radius of the bottom surface of the penetrator is 1 cm, the length of the penetrator is 1 cm, the density of the penetrator is 8.75 g / cm 3 , so the mass of the penetrator is 6.165 g, the density of water is 0.775 g / cm 3 , the initial speed of the penetrator (v0) is 1300 m / s, the initial attitude angle (ψ0=-60°, θ0=-30°);

[0112] Because |β|=|ψ|, |α|=|θ|, so β0=-60°, α0=30°; S is related to β and α, so S can be further obtained;

[0113] S is related to the shape of the penetrator, for example, if the target non-spherical penetrator is a cylindrical body, the formula for calculating S is:

[0114]

[0115] that is, S=1.2415 cm 2

[0116] Because v0, β0, α0 and S are known, by inputting v0, β0, α0 and S into the trained drag coefficient model, the initial C d of the target penetrator is obtained, which is 0.5088.

[0117] The real-time attitude of the target penetrator can be obtained by using the 4-5 order Runge-Kutta algorithm to solve formula (20):

[0118]

[0119] v is v0, ψ is ψ0, and θ is θ0; suppose that the attitude of the penetrator at 170 μs in the liquid is to be known, the termination time is calculated to be 170, and one step is calculated every 1 μs, so a total of 170 steps are calculated. Knowing v, ψ, θ, S and C d at the first 1 μs, v, ψ, θ, S and C d at the next 1 μs can be obtained, so ψ=-80.17° and θ=-34.37° at the final time.

[0120] The preferred embodiments of the present application are described above with reference to the accompanying drawings, and the scope of the right of the present application is not limited thereby. Any modification, equivalent replacement and improvement made by those skilled in the art without departing from the scope and essence of the present application shall be within the scope of the right of the present application.

Claims

1. A method for calculating the attitude of a non-spherical penetrator moving at high speed in a liquid, characterized in that, The method comprises the following steps: A resistance coefficient dataset of the non-spherical penetrating body under different working conditions is obtained through finite element simulation, and a training dataset is constructed based on the resistance coefficient dataset under different working conditions, which comprises: extract feature data under different working conditions from the finite element simulation results; wherein the feature data includes non-spherical penetration body mass m, non-spherical penetration body real-time speed , time t, liquid density p, projection area S of the non-spherical penetration body on the plane perpendicular to v; according to the motion equation of the non-spherical penetration body in the liquid and the feature data under different working conditions, a drag coefficient data set of the non-spherical penetration body under different working conditions is obtained; According to the yaw angle ψ and the pitch angle θ in the working condition information of each working condition, a sideslip angle β and an angle of attack α of each working condition are obtained; by one-to-one corresponding processing of the drag coefficient C d 、 , the sideslip angle β, the angle of attack α and S of each working condition, a plurality of training data sets composed of C d 、 , β, α and S are obtained; A resistance coefficient model of the non-spherical penetrating body is constructed, and the resistance coefficient model is trained using the training dataset to obtain a trained resistance coefficient model; A motion control equation of the non-spherical penetrating body when impacting a liquid-filled tank is established; The resistance coefficient of the target non-spherical penetrating body is obtained by inputting the working condition information of the target non-spherical penetrating body into the trained resistance coefficient model, and the real-time motion posture data of the target non-spherical penetrating body in the liquid is calculated using the resistance coefficient of the target non-spherical penetrating body and the motion control equation; The drag coefficient model is a back propagation neural network (BPNN) structure, the BPNN structure sequentially comprises an input layer, a hidden layer and an output layer; the input layer is respectively , β, α and S; the output layer is C d ; the hidden layer is a single hidden layer, and the number of nodes of the hidden layer is n. The number of nodes of the hidden layer is: ； In the formula, is the number of input layer nodes; is the number of output layer nodes; b is a constant, and the value range is 1-10; is a positive integer; The motion control equation comprises: ； wherein, R is the radius of the non-spherical penetrator; L is the length of the non-spherical penetrator; t is the time of motion of the non-spherical penetrator in the liquid.

2. The method of claim 1, wherein, The real-time motion posture data of the target non-spherical penetrating body in the liquid is calculated using the resistance coefficient of the target non-spherical penetrating body and the motion control equation, which comprises: The real-time motion posture data of the target non-spherical penetrating body in the liquid is obtained by inputting the resistance coefficient of the target non-spherical penetrating body and the working condition information of the target non-spherical penetrating body into the motion control equation.

3. An apparatus for calculating the attitude of a non-spherical penetrator moving at high speed in a liquid, characterized in that, The device comprises a processor for implementing the posture calculation method of the non-spherical penetrating body in the liquid under high-speed motion according to claim 1, and the device comprises: A training dataset construction module is configured to obtain a resistance coefficient dataset of the non-spherical penetrating body under different working conditions through finite element simulation, and construct a training dataset based on the resistance coefficient dataset under different working conditions; A model construction and training module is configured to construct a resistance coefficient model of the non-spherical penetrating body, and train the resistance coefficient model using the training dataset to obtain a trained resistance coefficient model; A motion control equation establishment module is configured to establish a motion control equation of the non-spherical penetrating body when impacting a liquid-filled tank; A posture data calculation module is configured to obtain the resistance coefficient of the target non-spherical penetrating body by inputting the working condition information of the target non-spherical penetrating body into the trained resistance coefficient model, and calculate the real-time motion posture data of the target non-spherical penetrating body in the liquid using the resistance coefficient of the target non-spherical penetrating body and the motion control equation.

4. An electronic device, comprising: The method comprises the following steps: A memory; A processor; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to claim 1 or 2.

5. A computer readable storage medium, characterized in that, A computer program is stored thereon; the computer program is executed by a processor to implement the method according to claim 1 or 2.

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