A fragment force field simulation method based on SPH algorithm of LS-DYNA

Abstract

The present invention discloses a fragment force field simulation method based on the SPH algorithm of LS-DYNA, and obtains the full process information of the shell SPH particles based on the SCL language of LS-PREPOST. Based on the spatial coordinates and influence radius information of the SPH particles, the spatial correlation between the SPH particles is determined by constructing a KD tree. The SPH particles in each natural fragment are identified by a depth-first search algorithm, and all the SPH particles contained in each natural fragment are searched and counted in turn, and then the mass, volume, component velocity in the three directions of X, Y, and Z, total velocity and the center of mass coordinates of the fragment are calculated. Based on the SPH particle information of each identified natural fragment, each fragment is morphologically and dynamically reconstructed to form a fragment model. Through this reconstructed model, the distribution of natural fragments in different time and space can be simulated and predicted, and finally the full-time and space-time fragment force field is generated. The present invention simplifies the modeling process and provides a feasible solution for large-scale fragment simulation in engineering applications.

Description

Technical Field

[0001] The present invention belongs to the technical field of numerical simulation, and in particular relates to a natural fragment spatial distribution simulation method based on an SPH algorithm of LS-DYNA. Background Art

[0002] Axisymmetrical shell structures are common in natural fragmentation warheads. Driven by the detonation of the internal explosive, these shells expand and fracture, generating a random, high-speed dispersion of fragments. This process has long been a key research focus and a challenge in weapon damage effectiveness assessment. Currently, both domestic and international approaches to assessing the power field of natural fragmentation still rely primarily on traditional methods combining experiments with empirical formulas. An efficient and reliable method for simulating the power field of natural fragmentation warheads has yet to be developed.

[0003] In recent years, with the rapid development of numerical simulation technology, methods for simulating the expansion and rupture of natural fragmentation warheads under internal explosions have become increasingly diverse, primarily including the Lagrange, Euler, Arbitrary Lagrange Euler (ALE), and Smoothed Particle Hydrodynamics (SPH) algorithms. However, traditional Lagrange, Euler, and ALE algorithms exhibit significant limitations when dealing with interfacial contact and large deformations of materials under high-speed, instantaneous, and high-intensity loading. In particular, these algorithms are prone to mesh distortion during the expansion and rupture of fragments, failing to effectively capture the dramatic deformation and fragmentation of materials. In contrast, the SPH algorithm, a meshless numerical method that uses discrete particles to describe a macroscopic continuous medium, exhibits significant advantages in addressing large, instantaneous material deformations. This algorithm avoids the element deletion problem caused by mesh distortion, ensures the conservation of mass, momentum, and energy, and thus more realistically simulates the dynamic fragmentation process of materials. Therefore, the SPH algorithm offers significant advantages in numerically simulating the rupture and dispersion of natural fragmentation warheads, providing a more realistic fit.

[0004] The existing LSDYNA solver not only has a mature SPH algorithm, but also has a rich material library and high computational efficiency. However, its numerical model establishment and SPH particle conversion still need to be done through manual click operations, which cannot be automated and batch processed. Publication No. CN118246256A proposes a fragment spatial distribution simulation and verification method, which uses the LSDYNA solver to perform numerical simulation on traditional anti-explosive warheads, and combines the Shapiro formula and the uniform distribution assumption to obtain a fragment spatial distribution. However, this method requires skilled LSDYNA pre-processing operation experience when establishing the warhead numerical model, including complex geometric modeling, material parameter definition, and SPH particle conversion operations, which makes it difficult to model and set parameters for large-scale models.

[0005] At the same time, LSDYNA's post-processing program has significant limitations and is unable to effectively identify and count the key information of natural fragments generated by the SPH algorithm (such as spatial position, velocity, mass, etc.). This limitation has led to restrictions on the application analysis of the calculation results, especially the inability to quickly identify and output detailed information about natural fragments, which greatly limits the application analysis of the calculation results. Publication No. CN103455669B proposes a quantitative automatic statistics method for fragments based on LSDYNA calculation results. This method reconstructs the fragment field through ANSYS based on the calculation results of LSDYNA, retrieves all unit information and outputs information such as the mass, velocity and volume of each fragment. However, this method relies on secondary input of ANSYS software and lacks statistics on the spatial coordinates of the fragments. Publication No. CN115099120A proposes a fragment identification method for the SPH algorithm of LSDYNA. This method constructs an SPH particle information matrix, screens and identifies all fragments, and finally outputs key information such as the mass and coordinates of each fragment. However, when the number of SPH particles reaches tens of thousands or even higher, the running memory required to construct the non-independent matrix representing the interaction relationship between particles can reach hundreds of GB, which greatly increases the demand for computing resources and limits the practical application of this method. Summary of the Invention

[0006] This paper addresses the complexity and uncertainty of the spatiotemporal distribution of random fragments during the explosion of a natural fragmentation warhead and proposes an innovative method that can efficiently and reliably simulate their spatiotemporal distribution. This is achieved through the following specific technical solutions:

[0007] (i) Using the LS-PREPOST SCL (Scripting Command Language), a parameterized natural fragmentation warhead model was constructed. The SPH algorithm in LSDYNA was then used to simulate the fragmentation process of the natural fragmentation warhead. This provided comprehensive information on the spatial coordinates, mass, velocity, and impact radius of the shell SPH particles.

[0008] (ii) Based on the spatial coordinates and influence radius information of the SPH particles obtained in the previous step, the spatial correlation between SPH particles can be quickly determined by constructing a KD tree (K-Dimensional Tree), that is, which particles belong to the same natural fragment through neighborhood search.

[0009] (iii) The SPH particles in each natural fragment are identified by the depth-first search algorithm. All the SPH particles contained in each natural fragment are searched and counted in turn, and then the mass, volume, component velocities in the X, Y, and Z directions, total velocity, and center of mass coordinates of the fragment are calculated.

[0010] (iv) Based on the SPH particle information of each natural fragment identified in the previous step, each fragment is morphologically and dynamically reconstructed to form a complete fragment model. This reconstructed model can simulate and predict the distribution of natural fragments in different time and space, ultimately generating a full-space fragment power field. This power field can intuitively demonstrate the destructive effect of fragments on surrounding targets. Combined with target vulnerability analysis, it provides a scientific basis for evaluating the damage effectiveness of the warhead.

[0011] Compared with the prior art, the present invention has the following significant advantages:

[0012] 1. This paper integrates geometry definition, meshing, model conversion, and parameter assignment using TUREGRID parametric modeling, the LS-PREPOST SCL language, and the SPH algorithm within LSDYNA, achieving efficient modeling and high-precision simulation of the natural fragmentation warhead fragmentation process. The spatiotemporal distribution of natural fragments generated by this method not only achieves high computational accuracy but also significantly simplifies the modeling process, providing a viable solution for large-scale fragmentation simulation in engineering applications.

[0013] 2. By establishing a KD tree and depth-first search algorithm, this paper proposes a fast and accurate method for identifying natural fragments from SPH particle swarms, overcoming the limitations of traditional LSDYNA post-processing software in counting and identifying SPH fragment information. This method greatly simplifies the processing of SPH particle fragment information, ensuring accurate processing of large amounts of SPH particle data in large-scale natural fragment simulations. This method overcomes the computational bottleneck of traditional identification methods when processing high-dimensional data, and ensures accurate and efficient identification.

[0014] 3. The SPH algorithm-based natural fragment reconstruction model proposed in this paper comprehensively displays the morphological and dynamic characteristics of natural fragments. By generating a full-time and spacetime fragment force field, it accurately simulates the damage effects of fragments on targets, providing reliable technical support for warhead damage effectiveness and target vulnerability analysis. This model has broad application prospects in the military industry and can provide an important reference for warhead design and evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the parametric modeling of natural fragment warhead and the SPH conversion display image.

[0016] Figure 2 It is a simulation of the fragmentation of a warhead caused by natural fragments.

[0017] Figure 3 It is an overall flow chart of the method of the present invention.

[0018] Figure 4 This is the image showing the SPH particle recognition results.

[0019] Figure 5 It is the SPH fragmentation force field. DETAILED DESCRIPTION

[0020] The present invention is described in detail below with reference to the accompanying drawings.

[0021] Example 1

[0022] Step 1: Define the geometric parameters of the natural fragmentation warhead, including the thickness r of the shell d , diameter d and length h, the cylinder command of TRUEGRID is called through the PYTHON script to define the two parts (part) of the shell and the explosive respectively to generate the TRUEGRID input file (.tgf) to describe the geometry and mesh division of the finite element model. Then, the subprocess module of PYTHON is used to call TRUEGRID to execute the generated .tgf file to generate the finite element model. Subsequently, the SCL language is used in LSPREPOST to write an automation script to further process the generated finite element model. The SCL script will complete the following operations: First, the finite element mesh model is converted into an SPH model by converting the mesh units into SPH particles; second, the K file containing the material model definition and calculation parameters is imported into the SPH model through the keyword *INCLUDE. Finally, the generated command file (cfile) is called by the PYTHON script to realize the conversion of the SPH model and the assignment of calculation parameters, and finally a complete numerical calculation model is established, such as Figure 1 The natural fragmentation warhead fragmentation simulation results are shown in Figure 2 shown.

[0023] Step 2: Use a PYTHON program to read the spatial coordinates, mass, velocity, volume, and influence radius information of SPH particles at each time step of the natural fragment warhead. Based on the spatial coordinate information of the SPH particles, a KD tree is established. By looping through each particle, neighborhood search is performed one by one. For the i-th particle, obtain the influence radius R i of this particle as the maximum search range, and then use the query_ball_point method of the KD tree to find all adjacent particles within the influence radius of this particle. For each found neighbor particle j, if the condition i < j is satisfied (to avoid duplicate records), then calculate the Euclidean distance D between the i-th particle and the j-th particle, and compare it with the larger value R = max(R i ,R j ) of Ri and Rj. If D is less than R, it means that these two particles are correlated, and add the corresponding SPH numbers to the linked_particles list. Finally, the program loops through all SPH particles and outputs all found correlated SPH particle pairs one by one.

[0024] Step 3: Use the collections module in PYTHON to create a graph data structure, where the keys are the IDs of the SPH particles and the values are the sets of other SPH particles connected to them. Then, according to the given linked_particles list, loop through each pair of connected SPH particle numbers and add the relationship between each pair of SPH particles to the undirected graph. Then, use the depth-first search algorithm (DFS) to find all connected components, that is, all SPH particles contained within each natural fragment. The DFS function takes a starting node, a set of visited nodes, and a set of current connected components as parameters and uses a stack to perform iterative search. The initial state of the stack is the passed-in node. Each time a node is popped from the stack, if the node has not been visited, mark it as visited and add it to the set of current connected components. Next, add all unvisited neighbor nodes directly connected to the current node to the stack, and repeat this process until the stack is empty. At this time, the traversal of one connected component is completed. Traverse each node in the undirected graph. If a node has not been visited, use it as the starting point to perform a DFS operation once, and add the found connected components corresponding to each natural fragment to the fragments list. For each isolated SPH particle, add it as an independent connected component (i.e., a single natural fragment) to the fragments list to ensure that each SPH particle belongs to a certain natural fragment. Finally, traverse all nodes to group all SPH particles and output the SPH particle information corresponding to each natural fragment. The recognition results are as Figure 4 shown.

[0025] Step 4: Based on the fragments list obtained in the previous step, renumber the natural fragments. Natural fragment i is all the SPH particles contained in the i-th row in the fragments list. The mass of the i-th natural fragment is This is the sum of the masses of all SPH particles in the fragment, N represents the number of SPH particles in the fragment, and j represents the jth SPH particle. The mass of all fragments in the fragment force field is calculated.

[0026] The velocity of each natural fragment is determined by the following method: The velocity of the i-th natural fragment in the X direction is It is the weighted velocity sum of all SPH particles in the natural fragment, N represents the number of SPH particles in the fragment, j represents the jth SPH particle, M i represents the mass of the i-th natural fragment, vx j represents the velocity of the jth SPH particle in the X direction. Similarly, the velocity of the i-th natural fragment in the Y direction is and the velocity in the Z direction is The combined velocity of the final i-th natural fragment is The speed of all fragments in the fragment force field is calculated.

[0027] The coordinates of the center of mass of each natural fragment are determined by the following method: the X coordinate of the i-th natural fragment is That is, the weighted X coordinate sum of all SPH particles in the natural fragment, N represents the number of SPH particles contained in the fragment, j represents the jth SPH particle, M i represents the mass of the i-th natural fragment, x j represents the X-coordinate of the j-th SPH particle. Similarly, the Y-coordinate of the i-th natural fragment is and the Z coordinate is The center of mass coordinates of all fragments in the fragment force field are counted.

[0028] Step 5: Based on the SPH particle information of each natural fragment obtained in step 3, the spatial coordinate information of all SPH particles containing the natural fragment i is extracted, and the minimum outer convex polyhedron of the particles is generated using the ConvexHull algorithm to reconstruct the geometric shape of the natural fragment. The constructed convex hull is then visualized using the matplotlib module, so that the spatial distribution and morphology of the natural fragments can be intuitively displayed, as shown in the figure below. Figure 5 As shown in the figure, the accurate simulation of the natural fragment force field is achieved, providing a reliable basis for effectiveness evaluation and target vulnerability analysis.

Claims

1. A fragment force field simulation method based on the SPH algorithm of LS-DYNA, characterized by: The steps include: (i) Based on the SCL language of LS-PREPOST, a parameterized natural fragmentation warhead model was established, and the SPH algorithm in LSDYNA was used to simulate the fragmentation process of the natural fragmentation warhead. The spatial coordinates, mass, velocity, and impact radius of the shell SPH particles were obtained. (ii) Based on the spatial coordinates and influence radius information of the SPH particles obtained in the previous step, the spatial correlation between the SPH particles is quickly determined by constructing a KD tree, that is, which particles belong to the same natural fragment through neighborhood search; (iii) Using a depth-first search algorithm, the SPH particles in each natural fragment are identified. All SPH particles contained in each natural fragment are searched and counted in sequence, and then the mass, volume, velocity components in the X, Y, and Z directions, total velocity, and the center of mass coordinates of the fragment are calculated. (iv) Based on the SPH particle information of each identified natural fragment, each fragment is morphologically and dynamically reconstructed to form a complete fragment model. This fragment model can simulate and predict the distribution of natural fragments in different time and space, and ultimately generate a full-time and space-time fragment power field. This full-time and space-time fragment power field can intuitively demonstrate the destructive effect of fragments on surrounding targets and, combined with target vulnerability analysis, provide a basis for evaluating the damage effectiveness of the warhead. The PYTHON program is used to read the spatial coordinates, mass, velocity, volume and impact radius information of the SPH particles at each time step of the natural fragment warhead; a KD tree is established based on the spatial coordinate information of the SPH particles, and each particle is traversed in a loop to perform a neighborhood search one by one; i Particles, get the influence radius of the particle R i As the maximum search range, the query_ball_point method of the KD tree is then used to find all neighboring particles within the influence radius of the particle; for each neighbor particle found j , if satisfied i < j , then calculate the i Particles and j The Euclidean distance between particles D , and with R i and R j The larger value of For comparison, if D Less than R , indicating that the two particles are related to each other, and the corresponding SPH numbers are put into the linked_particles list; finally, the program traverses all SPH particles and outputs all the found related SPH particle pairs one by one; Based on the SPH particle information of each natural fragment, the natural fragment i By extracting the spatial coordinate information of all SPH particles containing the natural fragments, the ConvexHull algorithm is used to generate the minimum outer convex polyhedron of the particles, thereby reconstructing the geometric shape of the natural fragments. The matplotlib module is then used to visualize the constructed convex hull, so that the spatial distribution and morphology of the natural fragments can be intuitively displayed, achieving accurate simulation of the natural fragment force field, and providing a reliable basis for effectiveness evaluation and target vulnerability analysis.

2. The fragment force field simulation method based on the SPH algorithm of LS-DYNA according to claim 1 is characterized by: Define the geometric parameters of the natural fragmentation warhead, including the thickness of the shell r d ,diameter d and length h , the cylinder command of TRUEGRID is called through the PYTHON script to define the two components of the shell and the explosive respectively to generate the TRUEGRID input file .tgf to describe the geometry and mesh division of the finite element model; then, the subprocess module of PYTHON is used to call TRUEGRID to execute the generated .tgf file to generate the finite element model; subsequently, the SCL language is used in LSPREPOST to write an automation script to further process the generated finite element model. The SCL script will complete the following operations: first, the finite element mesh model is converted into an SPH model by converting the mesh units into SPH particles; second, the K file containing the material model definition and calculation parameters is imported into the SPH model through the keyword *INCLUDE; finally, the command file cfile is called by the PYTHON script to realize the conversion of the SPH model and the assignment of calculation parameters, and finally a complete numerical calculation model is established.

3. The fragment force field simulation method based on the SPH algorithm of LS-DYNA according to claim 1 is characterized by: The collections module of Python is used to create a graph data structure, in which the key is the ID of the SPH particle and the value is the set of other SPH particles connected to it. Then, according to the given linked_particles list, each pair of interconnected SPH particle numbers is traversed and the relationship between each pair of SPH particles is added to the undirected graph. Then, the depth-first search algorithm DFS is used to find all connected components, that is, all SPH particles contained in each natural fragment. The DFS function receives a starting node, a set of visited nodes, and a set of current connected components as parameters, and uses a stack to perform iterative search. The initial state of the stack is the passed node. Each time a node is popped from the stack, if the node has not been visited, it is marked as has been visited and added to the current connected component set; next, all neighboring nodes that are directly connected to the current node and have not been visited are added to the stack, and this process is repeated until the stack is empty, at which time the traversal of a connected component is completed; traverse each node in the undirected graph, and if the node has not been visited, use it as the starting point to perform a DFS operation, and add the connected component corresponding to each natural fragment found to the fragments list; for each isolated SPH particle, add it as an independent connected component, that is, a single natural fragment, to the fragments list to ensure that each SPH particle belongs to a natural fragment; finally, traverse all nodes, group all SPH particles, and output the SPH particle information corresponding to each natural fragment.

4. The fragment force field simulation method based on the SPH algorithm of LS-DYNA according to claim 3 is characterized by: Based on the fragments list, renumber the natural fragments. i For the first i All SPH particles contained in the row; i Natural fragment mass , which is the sum of the masses of all SPH particles in the fragment, N Represents the number of SPH particles contained in the fragment, j Representing the j SPH particles are used to count the masses of all fragments in the fragment force field; The velocity of each natural fragment is determined by the following method: i The velocity of a natural fragment in the X direction is , which is the weighted velocity sum of all SPH particles in the natural fragment, N Represents the number of SPH particles contained in the fragment, j Representing the j SPH particles, M i Representative i The quality of natural fragments, vx j Representative j The velocity of the SPH particle in the X direction; similarly, we can get the i The velocity of a natural fragment in the Y direction is and the velocity in the Z direction is , the last i The combined velocity of the natural fragments is , the speed of all fragments in the fragment power field is calculated; The coordinates of the center of mass of each natural fragment are determined by the following method: i The X coordinate of a natural fragment is , which is the weighted X coordinate sum of all SPH particles in the natural fragment, N Represents the number of SPH particles contained in the fragment, j Representing the j SPH particles, M i Representative i The quality of natural fragments, x j Representative j The coordinate of the SPH particle in the X direction; similarly, we can get the i The Y coordinate of a natural fragment is and the Z coordinate is , the centroid coordinates of all fragments in the fragment force field are counted.

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