Intelligent mixed numerical simulation method, device and equipment for explosive shock waves

Abstract

The invention discloses an explosive shock wave intelligent mixed numerical simulation method, device and equipment, and relates to the field of numerical simulation, and the method comprises the steps: determining a computational domain range, grid node information and simulation termination time of an explosive shock wave simulation problem, and constructing a tetrahedral grid; initializing a physical flow field based on a computational domain range, and constructing a flow field analysis model based on the tetrahedral mesh; using a mean total variation bounded discontinuous detection method to identify a discontinuous region and a smooth region in a computational domain range; for the discontinuous region, reconstructing physical parameters in the flow field analytical model by adopting a non-structural multi-resolution weighted essential oscillation-free reconstruction method, and for the smooth region, reconstructing physical parameters in the flow field analytical model by adopting a linear windward reconstruction method; and performing spatial discretization on the physical flow field based on the flow field analysis model, then performing time discretization, and obtaining a numerical simulation result until the calculation time exceeds the simulation termination time. According to the invention, optimal balance of precision and efficiency is realized.

Description

Technical Field

[0001] This application relates to the field of numerical simulation, and in particular to a method, apparatus and equipment for intelligent hybrid numerical simulation of explosion shock waves. Background Technology

[0002] High-precision numerical simulation technology for explosion shock waves is of great significance for key applications such as gas deflagration safety protection in the petrochemical industry and supersonic impact load analysis in the aerospace field. This type of simulation faces three core challenges: the inherent strong discontinuity of explosion waves (shock wave characteristics), high gradient changes in extreme physical quantities (pressure / temperature abrupt changes), and the complex geometric characteristics of the explosion field (irregular boundary morphology). Traditional numerical methods generally suffer from inherent defects in handling these problems, such as excessive numerical dissipation, low computational efficiency, and insufficient adaptability to complex geometries.

[0003] High-precision numerical simulation of explosion shock waves has always been a key technical challenge in fields such as petrochemicals and aerospace. These problems involve highly discontinuous shock waves, abrupt pressure-temperature changes with high gradients, and wave-system interactions under complex geometric boundaries, placing extremely high demands on the accuracy, stability, and geometric adaptability of numerical methods. While traditional weighted essential non-oscillatory (WENO) schemes based on structured grids can handle shock wave capture problems well, they suffer from inherent drawbacks in practical explosion simulations, including poor geometric adaptability, low computational efficiency, and numerical oscillations under strong impacts. Especially when dealing with complex scenarios involving multiple obstacles and curved reflective boundaries, generating structured grids often requires significant preprocessing time and struggles to accurately describe the true geometric features.

[0004] While the unstructured mesh finite volume method commonly used in industry improves geometric adaptability, it often suffers from significant numerical dissipation and low computational efficiency when dealing with strong shock wave problems. This contradiction is particularly prominent in practical engineering applications such as warhead damage assessment and explosion-proof design of chemical equipment. For example, in near-field detonation simulation of trinitrotoluene (TNT), traditional methods not only struggle to accurately capture the fine structure of the detonation wave front, but also frequently cause computational interruptions due to numerical oscillations, severely impacting the reliability and efficiency of engineering designs. Summary of the Invention

[0005] The purpose of this application is to provide a smart hybrid numerical simulation method, apparatus and equipment for explosive shock waves, which can achieve an optimized balance between accuracy and efficiency.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a smart hybrid numerical simulation method for explosion shock waves, including: The computational domain, grid node information, and simulation termination time of the explosion shock wave simulation problem are determined, and a tetrahedral mesh is constructed based on the grid node information. The physical flow field is initialized based on the computational domain, and an analytical flow field model is constructed based on the tetrahedral mesh. Based on the tetrahedral mesh, the bounded discontinuity detection method of mean total variation is used to identify discontinuous and smooth regions within the computational domain. For discontinuous regions, the physical parameters in the analytical flow field model are reconstructed using the unstructured multi-resolution weighted essentially non-oscillatory reconstruction method to determine the numerical reconstruction solution at each quadrature node. For smooth regions, the physical parameters in the analytical flow field model are reconstructed using the linear upwind reconstruction method to determine the numerical reconstruction solution at each quadrature node. Based on the numerical reconstruction solutions at each quadrature node, the physical flow field is spatially discretized according to the analytical flow field model. Then, the spatially discretized physical flow field is temporally discretized until the calculation time exceeds the simulation termination time, at which point the numerical simulation results are obtained.

[0007] Secondly, this application provides an intelligent hybrid numerical simulation device for explosion shock waves, comprising: The mesh generation module is used to determine the computational domain, mesh node information, and simulation termination time of the explosion shock wave simulation problem, and to construct a tetrahedral mesh based on the mesh node information. The flow field solution module is used to initialize the physical flow field based on the computational domain and to construct an analytical flow field model based on the tetrahedral mesh. The discontinuity identification module is used to identify discontinuous regions and smooth regions within the computational domain based on the tetrahedral mesh and using the mean total variation bounded discontinuity detection method. The hybrid reconstruction module is used to reconstruct the physical parameters in the analytical flow field model for discontinuous regions using an unstructured multi-resolution weighted essentially non-oscillatory reconstruction method, and to determine the numerical reconstruction solution at each quadrature node; for smooth regions, it uses a linear upwind reconstruction method to reconstruct the physical parameters in the analytical flow field model, and to determine the numerical reconstruction solution at each quadrature node. The numerical simulation module is used to spatially discretize the physical flow field based on the analytical flow field model according to the numerical reconstruction solution at each quadrature node, and then time-discretize the spatially discretized physical flow field until the calculation time exceeds the simulation termination time to obtain the numerical simulation result.

[0008] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described intelligent hybrid numerical simulation method for explosion shock waves.

[0009] According to the specific embodiments provided in this application, this application achieves the following technical effects: It automatically identifies and accurately calibrates discontinuous regions within the computational domain using a bounded discontinuity detection method based on the total mean variation. In discontinuous regions, an unstructured multi-resolution weighted essentially oscillatory reconstruction method is employed to ensure numerical robustness, while in smooth regions, it intelligently switches to a linear upwind reconstruction method with higher computational efficiency and lower numerical dissipation. This achieves an optimized balance between accuracy and efficiency. This hybrid strategy combines the flexibility of tetrahedral meshes in handling complex geometries with the accuracy advantages of the WENO method in shock wave capture, providing an effective technical approach to solving the long-standing problem of shock wave simulation robustness in industry. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is an application environment diagram of an intelligent hybrid numerical simulation method for explosion shock waves according to an embodiment of this application.

[0012] Figure 2 This is a flowchart illustrating an intelligent hybrid numerical simulation method for explosion shock waves provided in an embodiment of this application.

[0013] Figure 3 This is a schematic diagram of the functional modules of an intelligent hybrid numerical simulation device for explosion shock waves provided in an embodiment of this application.

[0014] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] This application employs a bounded discontinuity detection technique based on the mean total variation to automatically identify and accurately calibrate discontinuous regions within the computational domain. In discontinuous regions, the WENO reconstruction method, with its multi-resolution characteristics, is used to ensure numerical robustness. In smooth regions, it intelligently switches to a linear upwind reconstruction scheme, which offers higher computational efficiency and lower numerical dissipation, achieving an optimized balance between accuracy and efficiency. This hybrid strategy combines the flexibility of tetrahedral meshes in handling complex geometries with the precision of the WENO method in shock wave capture, providing an effective technical approach to address the long-standing issue of shock wave simulation robustness in industry. This, in turn, provides a more reliable numerical simulation tool for key areas such as weapon effectiveness assessment, protective engineering design, and industrial safety analysis.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] The intelligent hybrid numerical simulation method for explosion shock waves provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send the computational domain, grid node information, and simulation termination time of the explosion shock wave simulation problem to server 102. Server 102 performs numerical simulation based on the received computational domain, grid node information, and simulation termination time. Server 102 can feed back the numerical simulation results to terminal 101. Furthermore, in some embodiments, the intelligent hybrid numerical simulation method for explosion shock waves can also be implemented independently by server 102 or terminal 101.

[0019] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 102 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0020] In one exemplary embodiment, such as Figure 2 As shown, a smart hybrid numerical simulation method for explosion shock waves is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1Taking server 102 as an example, the explanation includes the following steps 201 to 205.

[0021] Step 201: Determine the computational domain, grid node information, and simulation termination time for the explosion shock wave simulation problem, and construct a tetrahedral mesh based on the grid node information. The grid node information includes key parameters such as the total number of elements, the total number of nodes, node coordinates, element node index array, and adjacent element index array.

[0022] Specifically, the computational domain is determined based on the physical requirements of the explosion shock wave simulation problem. Mesh node information is defined and read and stored as a data structure suitable for processing by a computer programming language. Simultaneously, the simulation termination time is determined based on the target results required for the explosion shock wave simulation.

[0023] Step 202: Initialize the physical flow field based on the computational domain range, and construct an analytical flow field model based on the tetrahedral mesh.

[0024] In a specific application example, step 202 includes steps 21 to 23.

[0025] Step 21: Initialize the calculation parameters and, based on these parameters, determine the initial physical flow field within the computational domain. The calculation parameters include the inflow Mach number, angle of attack, gas medium parameters, density, and gas internal energy.

[0026] Specifically, based on the initial shock wave characteristics (including key physical parameters such as shock wave location and propagation velocity) of the explosion shock wave simulation problem, the initial conditions and boundary conditions of the computational domain are set, specifically covering the initial distribution of density field, velocity field, pressure field, energy field and medium physical property parameters.

[0027] The initial physical flow field is: .

[0028] in, The velocity is in the x-direction. The velocity in the y-direction, The velocity in the z-direction is... For total energy, For pressure, For the Mach number of the flow, For gaseous medium parameters, , For density, For the angle of attack, e This refers to the internal energy of the gas.

[0029] Step 22: Based on the initial physical flow field, the flow field is analyzed using the three-dimensional compressible Euler equations in the conservation form to obtain the analytical equations of the flow field.

[0030] .

[0031] .

[0032] .

[0033] .

[0034] in, U t Indicates to U Find the partial derivative with respect to time. U For physical quantities, , For target unit Physical quantity at location, Initial time The physical quantity of the element at the location. f Let x be the inviscid flux in the x-direction. g Let be the inviscid flux in the y-direction. h Let z be the inviscid flux in the z-direction. Indicates to f Find the partial derivative with respect to x. This indicates taking the partial derivative of g with respect to y. Indicates to h Find the partial derivative with respect to z.

[0035] Step 23: Spatial discretize the analytical equations of the flow field to obtain the analytical model of the flow field.

[0036] Specifically in the target unit Integrating the analytical equations of the flow field yields the following semi-discrete form as the analytical model of the flow field.

[0037] .

[0038] in, The target unit mean, , The volume of the target cell. The boundary of the target unit, It is the flux vector. , Let be the unit outward normal vector of the target cell. This is a representation of physical quantities after spatial discretization, which facilitates their substitution into the subsequent three-dimensional Runge-Kutta equations for time progression.

[0039] The intermediate terms of the above semi-discrete scheme can be calculated using numerical integration.

[0040] .

[0041] in, For the target unit L The area of ​​each face. For the first The quadrature coefficients of each quadrature node. For the target unit L The first on the surface Coordinates of the integrand node t To calculate time, For the target unit L The normal unit vector of each face.

[0042] The following Lax-Friedrichs numerical flux can be given.

[0043] ; in, for The largest eigenvalue of the Jacobian matrix in the direction. For the first The negative solution is reconstructed from the values ​​at each integrand node. For the first The correct solution is obtained by reconstructing the values ​​at each quadrature node through subsequent steps 203 and 204.

[0044] Step 203: Based on the tetrahedral mesh, the bounded discontinuity detection method of mean total variation is used to identify discontinuous regions and smooth regions within the computational domain.

[0045] In a specific application example, step 203 includes steps 31 to 36.

[0046] Step 31, target unit First layer adjacent units Adjacent units of the second layer Combined into a refactoring template and using the reconstruction template Construct a ternary quadratic polynomial based on the density mean of the contained units. .

[0047] .

[0048] in, There are 9 basis functions. basis functions The corresponding coefficient, The expression is as follows.

[0049] .

[0050] .

[0051] .

[0052] .

[0053] .

[0054] .

[0055] .

[0056] .

[0057] .

[0058] .

[0059] in, For target unit The center of gravity.

[0060] The above polynomials satisfy the following least squares condition.

[0061] .

[0062] Where A represents the set of indices of the target unit and its adjacent units in each layer. , Representation unit volume, For target unit The average density on the surface, For unit The average density on the surface.

[0063] The target cell is any cell in the tetrahedral mesh. The first layer of adjacent cells are the four adjacent cells of the target cell in the first layer of the tetrahedral mesh, and the second layer of adjacent cells are the twelve adjacent cells of the target cell in the second layer of the tetrahedral mesh.

[0064] Step 32: Calculate the second-order partial derivatives of the ternary quadratic polynomial to obtain the following 6 formulas.

[0065] .

[0066] .

[0067] .

[0068] .

[0069] .

[0070] .

[0071] in, Indicates to Find the second partial derivative with respect to x. Indicates to Find the first partial derivatives with respect to x and y. express Find the first-order partial derivatives with respect to x and z. Indicates to Find the second partial derivative with respect to y. express Find the first partial derivatives with respect to y and with respect to z. Indicates to Find the second-order partial derivative with respect to z.

[0072] Calculate the maximum absolute value of the second-order partial derivative on the target cell. : .

[0073] Step 33, based on the centroid of the target element and the centroids of the four faces of the target element. and the centroids of the four adjacent tetrahedrons Construct a system of equations about the centroid of the unit.

[0074] .

[0075] .

[0076] .

[0077] .

[0078] in, For the target unit L The centroid coordinates of each face Let L be the coordinates of the barycenter of the Lth adjacent tetrahedron. These are the coefficients of the system of equations, i.e., the variables to be solved.

[0079] Calculate the non-negative solutions to the above system of equations. .

[0080] Step 34: Define the density mean correction amount according to the non-negative solution of the equation system, and determine the density term of the target element according to the density mean of the target element and the centroid of the four faces of the target element.

[0081] Specifically, assuming the system of equations Corresponding solution Non-negative, definition , .in, This is the density mean correction amount. This is the density term.

[0082] Step 35: Based on the maximum absolute value of the second-order partial derivative on the target cell and the density mean correction amount, the density term of the target cell is corrected using the bounded function of total mean variation to obtain the density correction term of the target cell.

[0083] .

[0084] in, This is a density correction term. It is a bounded function of the total variation of the mean.

[0085] The expression for the bounded function of the total variation of the mean is as follows.

[0086] .

[0087] in, As the independent variable, when the variable When the symbols are the same Take 1, otherwise Set to 0, K is the mean total variation parameter, which is based on the maximum absolute value of the second-order partial derivative on the target cell. The calculation yields the following formula.

[0088] .

[0089] in, As an intermediate quantity, .

[0090] Step 36: Determine the category of the target element based on its density term and density correction term. The category is either a problematic element or a regular element. In a tetrahedral mesh, the region containing problematic elements is a discontinuous region, while the region containing regular elements is a smooth region.

[0091] Specifically, when When the target element is selected, it is marked as a problem element; otherwise, it is marked as a regular element. Label=1 indicates a problem cell, and Label=0 indicates a regular cell.

[0092] Step 204: For discontinuous regions, the physical parameters in the analytical flow field model are reconstructed using the unstructured multi-resolution weighted essentially non-oscillatory reconstruction method to determine the numerical reconstruction solution at each quadrature node. For smooth regions, the physical parameters in the analytical flow field model are reconstructed using the linear upwind reconstruction method to determine the numerical reconstruction solution at each quadrature node.

[0093] In a specific application example, different numerical reconstruction methods are used to reconstruct step 23 based on the cell type. Step 204 includes steps 41 to 45.

[0094] Step 41: For the problem cell, a robust unstructured multi-resolution WENO reconstruction method is adopted.

[0095] Select the first template Second template and the third template Construct zero-degree polynomials on them respectively A linear polynomial and quadratic polynomial .

[0096] Among them, the first-order polynomial and quadratic polynomial The exact expression for is given by the following least squares condition.

[0097] .

[0098] .

[0099] here, Representing the target unit A vector composed of the mean values ​​of the physical quantities. Representation unit The mean vector of physical quantities on the surface and They are respectively and The set of indices of the included units. , .

[0100] The equivalent expression for the above polynomial is: .

[0101] in, Representing a polynomial The linear weights, whose values ​​are any positive numbers that sum to one, for The equivalent expression.

[0102] Step 42, calculate the smoothing factor It reflects the polynomial In the target unit The smoothness of the surface. The calculation formula is: .

[0103] in, , , They represent the polynomials respectively. The degree of partial derivatives with respect to x, y, and z. To find the partial derivative whose degree is equal to the magnitude of the vector, Find the answer for x. Second partial derivative, For y, find Second partial derivative, For z Secondary partial derivatives.

[0104] To avoid smoothing factor If the result is zero, the following method is used for calculation: First, construct four polynomials. It meets the following conditions.

[0105] .

[0106] .

[0107] .

[0108] .

[0109] in, Representation unit The center of gravity Representation unit The average value of physical quantities on the surface.

[0110] according to Obtain relevant smoothing indicators Then, set the relevant parameters using the following formula.

[0111] .

[0112] .

[0113] .

[0114] .

[0115] .

[0116] .

[0117] .

[0118] in, Indicates a given initial value, Indicates intermediate variables. For polynomials Smoothing index, For overall smoothing index, It is a small positive number, which avoids the denominator becoming zero. Therefore, .

[0119] Step 43, calculate the nonlinear weights .

[0120] .

[0121] in, Representing a polynomial The corresponding number Linear weights, Representing a polynomial The corresponding number A non-linear weight, Pick To prevent the denominator from being zero, a global smoothing index .

[0122] Step 44, the reconstruction polynomial of the unstructured multi-resolution weighted essential non-oscillatory reconstruction method is as follows.

[0123] .

[0124] in, for The reconstruction result of the cell at the location, This is a polynomial constructed based on a tetrahedral mesh.

[0125] The above expression is used to calculate the integrator nodes on the target element. The higher-order reconstruction values ​​approximated on the inner and outer sides of the point .For example, .for It can be obtained in a similar way.

[0126] Step 45: For the problem cell, a simple linear reconstruction method is used to reconstruct the polynomial as follows: Substituting the coordinates of the quadrature nodes into the equation yields the higher-order reconstructed values ​​on the target element. ,for It can be obtained in a similar way.

[0127] Step 205: Based on the numerical reconstruction solution at each quadrature node, the physical flow field is spatially discretized according to the analytical flow field model. Then, the spatially discretized physical flow field is temporally discretized until the calculation time exceeds the simulation termination time to obtain the numerical simulation result.

[0128] In a specific application example, the solutions obtained in steps 44 and 45 are... and Substitute the analytical flow field model from step 23 to complete the spatial discretization. Use the third-order Runge-Kutta method to perform time discretization on the spatially discretized physical flow field; the specific formula is as follows.

[0129] .

[0130] in, Indicates the current time level. Indicates the next time level, time step. , and These represent the physical parameter vectors for the current time layer and the next time layer, respectively. and This represents the physical parameter vector of the intermediate time layer.

[0131] Furthermore, this application can also extract flow field data such as density distribution, pressure distribution, and energy distribution corresponding to the explosion shock wave simulation problem based on the numerical simulation results, and provide theoretical guidance and engineering reference for complex flow problems through post-processing analysis.

[0132] The present application will now be compared with other traditional methods.

[0133] (1) Based on the traditional single-format method, this scheme does not adopt an intelligent hybrid strategy, but uses a high-precision format uniformly throughout the computational domain. For example, it uses a pure high-order WENO format in all cells, or a high-order central difference / finite volume format. If a pure WENO format is used, the computational cost of the WENO format in smooth regions is much greater than that of the linear format, and its global use will significantly reduce computational efficiency, failing to achieve the high efficiency objective of this application. If a pure central format is used, near high gradients and strong discontinuities, the central format is prone to numerical oscillations, and may even lead to computational collapse, failing to meet the core requirements of strong robustness and accurate shock wave capture. Therefore, although this method has the advantages of simple implementation and no need to develop complex discontinuity identifiers and format switching logic, it cannot resolve the contradiction between computational efficiency and numerical robustness.

[0134] (2) Based on a hybrid format of other types of limiters / detectors, this scheme retains the idea of ​​a hybrid format but uses different techniques to identify discontinuities and trigger format switching. For example, a multi-resolution analysis sensor or an entropy field detector is used to replace the bounded total mean variation discontinuity identifyr in this application. However, these traditional detectors (such as resolution analysis sensors) contain threshold parameters that need to be manually adjusted according to the problem, which contradicts the advantage of this application that it does not require parameter adjustment. In addition, in different types of complex flows (such as strong eddies and shear layers), the accuracy and robustness of these detectors may not be as good as the identifyr based on the bounded total mean variation theory. Therefore, this scheme can achieve the basic purpose of the hybrid format, but it may not reach the level of the bounded total mean variation discontinuity identifyr proposed in this application in terms of adaptability, versatility and automation.

[0135] (3) Based on the strategy of other higher-order methods, this scheme completely abandons the WENO framework and adopts other types of higher-order numerical methods, such as the discontinuous Galerkin method (DG) and the spectral difference method (SD). DG, SD and other methods can achieve very high accuracy within the element, especially suitable for smooth flow problems. However, these methods are usually more complex than the finite volume method, with greater computational and memory requirements, especially on three-dimensional unstructured meshes. In addition, when the DG method deals with strong shock waves, it still needs to introduce complex limiters or a hybrid strategy similar to WENO, which does not avoid the problem of scheme selection, but rather increases the difficulty of implementation. In the field of complex explosion shock wave simulation, the finite volume method based on unstructured meshes and its hybrid schemes are more mature and widely verified than DG and other methods. Therefore, this scheme needs to solve a series of new technical problems (such as efficient implicit computation of DG, limiter design, etc.).

[0136] It is evident that the intelligent hybrid numerical simulation method for explosion shock waves proposed in this application achieves a relatively optimal balance between accuracy, efficiency, robustness, and automation for the specific requirement of explosion shock wave simulation.

[0137] Therefore, the intelligent hybrid numerical simulation method for explosion shock waves proposed in this application has the following significant advantages.

[0138] (1) A novel discontinuity indicator is proposed based on the bounded total variation theory of mean. This indicator has excellent adaptability to complex geometries, does not require parameter adjustment for specific problems, and can be effectively applied to tetrahedral meshes with complex geometric features to accurately identify and mark mesh cells containing complex flow features such as shock waves.

[0139] (2) An intelligent hybrid WENO reconstruction strategy was designed, which can dynamically adjust the numerical scheme according to flow field characteristics such as density gradient, effectively avoiding the accuracy loss or numerical oscillation problems that occur in traditional single schemes in complex flow fields. This strategy uses a robust WENO reconstruction algorithm for elements with complex flow structures, and automatically switches to an efficient linear reconstruction algorithm for smooth regions. Thus, while ensuring high accuracy and strong robustness, it significantly improves the computational efficiency of practical engineering problems, and also has the ability to capture shock waves and suppress numerical oscillations.

[0140] (3) It has good physical model compatibility and supports a variety of mathematical models such as Euler equation, Navier-Stokes equation and JWL detonation model. It can be widely used in numerical simulation of different explosion scenarios such as air, underground and underwater.

[0141] In summary, this application achieves a harmonious balance of accuracy, efficiency, and applicability in the simulation of explosion shock waves, providing a reliable and efficient solution for the numerical simulation of complex flow problems.

[0142] Based on the same inventive concept, this application also provides an intelligent hybrid numerical simulation device for implementing the aforementioned intelligent hybrid numerical simulation method for explosion shock waves. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the intelligent hybrid numerical simulation device for explosion shock waves provided below can be found in the limitations of the intelligent hybrid numerical simulation method for explosion shock waves described above, and will not be repeated here.

[0143] In one exemplary embodiment, such as Figure 3 As shown, an intelligent hybrid numerical simulation device for explosion shock waves is provided, comprising: a mesh generation module 301, a flow field solving module 302, a discontinuity identification module 303, a hybrid reconstruction module 304, and a numerical simulation module 305.

[0144] The mesh generation module 301 is used to determine the computational domain, mesh node information, and simulation termination time of the explosion shock wave simulation problem, and to construct a tetrahedral mesh based on the mesh node information.

[0145] The flow field solution module 302 is used to initialize the physical flow field based on the computational domain range and construct an analytical flow field model based on the tetrahedral mesh.

[0146] The discontinuity identification module 303 is used to identify discontinuous regions and smooth regions within the computational domain based on the tetrahedral mesh and using the mean total variation bounded discontinuity detection method.

[0147] The hybrid reconstruction module 304 is used to reconstruct the physical parameters in the analytical flow field model for discontinuous regions using an unstructured multi-resolution weighted essentially non-oscillatory reconstruction method, and to determine the numerical reconstruction solution at each quadrature node. For smooth regions, it uses a linear upwind reconstruction method to reconstruct the physical parameters in the analytical flow field model, and to determine the numerical reconstruction solution at each quadrature node.

[0148] The numerical simulation module 305 is used to spatially discretize the physical flow field based on the analytical flow field model according to the numerical reconstruction solution at each quadrature node, and then time discretize the spatially discretized physical flow field until the calculation time exceeds the simulation termination time to obtain the numerical simulation result.

[0149] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs in the non-volatile storage media to run. The database stores the computational domain, grid node information, and simulation termination time for the explosion shock wave simulation problem. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a smart hybrid numerical simulation method for explosion shock waves.

[0150] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0151] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0152] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0153] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0154] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0155] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0156] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0158] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A smart hybrid numerical simulation method for explosion shock waves, characterized in that, The method includes: The computational domain, grid node information, and simulation termination time of the explosion shock wave simulation problem are determined, and a tetrahedral mesh is constructed based on the grid node information. The physical flow field is initialized based on the computational domain, and an analytical flow field model is constructed based on the tetrahedral mesh. Based on the tetrahedral mesh, the bounded discontinuity detection method of mean total variation is used to identify discontinuous and smooth regions within the computational domain. For discontinuous regions, the physical parameters in the analytical flow field model are reconstructed using the unstructured multi-resolution weighted essentially non-oscillatory reconstruction method to determine the numerical reconstruction solution at each quadrature node. For smooth regions, the physical parameters in the analytical flow field model are reconstructed using the linear upwind reconstruction method to determine the numerical reconstruction solution at each quadrature node. Based on the numerical reconstruction solutions at each quadrature node, the physical flow field is spatially discretized according to the analytical flow field model. Then, the spatially discretized physical flow field is temporally discretized until the calculation time exceeds the simulation termination time, at which point the numerical simulation results are obtained.

2. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 1, characterized in that, The physical flow field is initialized based on the computational domain, and an analytical flow field model is constructed based on the tetrahedral mesh, specifically including: Initialize the calculation parameters, and based on the calculation parameters, determine the initial physical flow field within the calculation domain; Based on the initial physical flow field, the flow field is analyzed using the three-dimensional compressible Euler equations in the conservation form, resulting in the analytical equations of the flow field. The analytical equations of the flow field are spatially discretized to obtain the analytical model of the flow field.

3. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 2, characterized in that, The calculation parameters include the inflow Mach number, angle of attack, gas medium parameters, density, and gas internal energy; The initial physical flow field is: ； in, The velocity is in the x-direction. The velocity in the y-direction, The velocity in the z-direction is... For total energy, For pressure, For the Mach number of the flow, For gaseous medium parameters, For density, For the angle of attack, e This refers to the internal energy of the gas.

4. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 3, characterized in that, The analytical equation for the flow field is: ； ； ； ； in, U t Indicates to U Find the partial derivative with respect to time. U For physical quantities, For target unit Physical quantity at location, Initial time The physical quantity of the element at the location. , f Let x be the inviscid flux in the x-direction. g Let be the inviscid flux in the y-direction. h Let z be the inviscid flux in the z-direction. Indicates to f Find the partial derivative with respect to x. This indicates taking the partial derivative of g with respect to y. Indicates to h Find the partial derivative with respect to z.

5. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 2, characterized in that, Spatially discretize the analytical equations of the flow field to obtain an analytical model of the flow field, including: Integrating the analytical equations of the flow field on the target cell yields the analytical model of the flow field: ； ； ； in, The target unit mean, For the target unit, The volume of the target cell. The boundary of the target unit, It is the flux vector. Let be the unit outward normal vector of the target cell. This is the representation of physical quantities after spatial discretization. U For physical quantities, For the target unit L The area of ​​each face. For the first The quadrature coefficients of each quadrature node. For the target unit L The first on the surface Coordinates of the integrand node t To calculate time, For the target unit L The normal unit vector of each face. for The largest eigenvalue of the Jacobian matrix in the direction. For the first The negative solution is reconstructed from the values ​​at each product node. For the first The correct solution is to reconstruct the values ​​at each quadrature node.

6. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 1, characterized in that, Based on the tetrahedral mesh, the bounded discontinuity detection method using the mean total variation is employed to identify discontinuous and smooth regions within the computational domain, including: The target cell, the first layer of adjacent cells, and the second layer of adjacent cells are combined into a reconstruction template, and a ternary quadratic polynomial is constructed using the average density of the cells contained in the reconstruction template; the target cell is any cell in the tetrahedral mesh. Find the second-order partial derivative of the ternary quadratic polynomial and calculate the maximum absolute value of the second-order partial derivative on the target unit; Based on the centroid of the target element, the centroids of the four faces of the target element, and the centroids of the four adjacent tetrahedrons, a system of equations about the centroid of the element is constructed, and the non-negative solutions of the system of equations are calculated. The density mean correction amount is defined based on the non-negative solution of the equation system, and the density term of the target element is determined based on the density mean of the target element and the centroid of the four faces of the target element. Based on the maximum absolute value of the second-order partial derivative on the target cell and the density mean correction amount, the density term of the target cell is corrected using the bounded function of total mean variation to obtain the density correction term of the target cell. The category of the target cell is determined based on the density term and the density correction term of the target cell; the category is a problem cell or a regular cell; the region where the problem cell is located in the tetrahedral mesh is a discontinuous region, and the region where the regular cell is located is a smooth region.

7. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 1, characterized in that, The reconstruction polynomial of the unstructured multi-resolution weighted essentially non-oscillatory reconstruction method is: ； in, for The reconstruction result of the cell at the location, For polynomials constructed based on tetrahedral meshes, For the first k The corresponding polynomial of the _th ... One non-linear weight.

8. The intelligent hybrid numerical simulation method for explosion shock waves according to claim 1, characterized in that, The third-order Runge-Kutta method is used to discretize the physical flow field after spatial discretization in time.

9. A smart hybrid numerical simulation device for explosion shock waves, characterized in that, The device is applied to the intelligent hybrid numerical simulation method for explosion shock waves according to any one of claims 1-7, and the device comprises: The mesh generation module is used to determine the computational domain, mesh node information, and simulation termination time of the explosion shock wave simulation problem, and to construct a tetrahedral mesh based on the mesh node information. The flow field solution module is used to initialize the physical flow field based on the computational domain and to construct an analytical flow field model based on the tetrahedral mesh. The discontinuity identification module is used to identify discontinuous regions and smooth regions within the computational domain based on the tetrahedral mesh and using the mean total variation bounded discontinuity detection method. The hybrid reconstruction module is used to reconstruct the physical parameters in the analytical flow field model for discontinuous regions using an unstructured multi-resolution weighted essentially non-oscillatory reconstruction method, and to determine the numerical reconstruction solution at each quadrature node; for smooth regions, it uses a linear upwind reconstruction method to reconstruct the physical parameters in the analytical flow field model, and to determine the numerical reconstruction solution at each quadrature node. The numerical simulation module is used to spatially discretize the physical flow field based on the analytical flow field model according to the numerical reconstruction solution at each quadrature node, and then time-discretize the spatially discretized physical flow field until the calculation time exceeds the simulation termination time to obtain the numerical simulation result.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the intelligent hybrid numerical simulation method for explosion shock waves according to any one of claims 1-8.

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