A method and system for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation

Abstract

The present invention discloses a method and system for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation. The method comprises: constructing a molecular structure model of a single explosive, and processing the molecular structure model of the single explosive to obtain α-RDX supercell models of different crystal forms and compression densities; applying a thermal bath load stimulus of different action durations to the α-RDX supercell models of different crystal forms and compression densities; setting corresponding boundary conditions, compiling a chemical reaction product visualization program, and analyzing the effects of different thermal bath load stimuli on the ignition and self-sustaining reaction of the α-RDX supercell model. The present invention can study the self-sustaining ignition and reaction growth process of the adiabatic process induced by the critical temperature of the hotspot, and simulate the hotspot self-sustaining reaction problem of RDX with different crystal forms and different compression densities under local thermal stimulation.

Description

Technical Field

[0001] The present invention relates to the technical field of explosive self-sustaining reaction simulation, in particular to a method and system for simulating the self-sustaining reaction of RDX explosive induced by local thermal stimulation. Background Art

[0002] Energetic materials are mainly divided into explosives (including detonators and main explosives), propellants, and pyrotechnics. They are widely used in defense and military equipment manufacturing, civil construction projects, and other fields, and have made significant contributions to the development of modern industry and the national economy. The safety characteristics of energetic materials are characterized and defined by their response or sensitivity to external stimuli. Under specific external thermal stimuli, their internal heterogeneity can easily induce significant energy localization, which may gradually form macroscopic and microscopic localized high-temperature regions (hot spots), accompanied by the rapid release of chemical energy, thereby triggering the ignition and detonation of energetic materials over a larger range. Therefore, exploring the micro-nanostructural response and reaction kinetic behavior of energetic materials under thermal loads and examining the energy localization phenomenon of energetic materials are conducive to understanding the physical mechanism and microscopic physical and chemical origin of hot spots in energetic materials, and have potential value for energetic material manufacturing and military engineering applications.

[0003] On the other hand, to accurately predict the safety of energetic materials under external thermal stimulation, especially focusing on the ability to predict combustion and explosion reactions based on microstructure, researchers need to focus on exploring the correlation between the localized energy deposition process, the sensitivity / reactivity of energetic materials, and the molecular structure of energetic materials. Therefore, studying the energy localization phenomenon closely related to micro-nanostructures will help researchers understand the microscopic mechanisms of energetic material hot spot physics from an intrinsic perspective and provide more realistic new physical insights for macro- and micro-scale / continuum models of energetic materials.

[0004] The chemical events that lead to the ignition of energetic materials, such as solid propellants and high explosives, have long garnered significant attention from both scientists and industry. Numerous theoretical analyses have been conducted on the self-sustaining reactions of energetic materials induced by localized thermal stimulation.

[0005] To understand the chemical reactions involved in the thermal ignition-detonation process, Joshi et al. used reaction molecular dynamics based on the ReaxFF force field to simulate the process in which approximately 5% of RDX molecules were subjected to constant-temperature thermal stimulation at different time periods. They then monitored the chemical evolution of RDX high-pressure phase thermal initiation and spontaneous combustion under adiabatic conditions after the thermal stimulation was removed.

[0006] Lee et al. combined multi-component thermodynamics to obtain the system-averaged temperature, pressure, and average molecular weight of each reactant component describing the ignition reaction process from the reaction molecular dynamics simulation, thereby constructing a mirror atom and continuous medium framework, expanding the simulation method for studying the ignition reaction of energetic materials.

[0007] Zhang et al. used the ReaxFF reaction molecular dynamics method to study the ignition, deflagration and detonation behaviors of PETN under local thermal stimulation. They found that during the hot spot ignition, the decomposition products of PETN were mainly NO2 and HONO, while the amount of N2 increased sharply during the thermal instability stage, a self-sustaining reaction occurred in the system, and the temperature could rise rapidly by about 2500K within 20ps.

[0008] Joshi et al. also used a reaction molecular dynamics method to identify the thermochemical reaction events when local thermal stimulation triggers the formation and propagation of a deflagration wave in condensed phase RDX. At the same time, they mapped the reaction molecular dynamics trajectory onto the Eulerian Control Volume in the traditional continuum scaling method to calculate the mass, energy, and chemical fluxes across the deflagration front. They found that RDX molecules in the hotspot region undergo exothermic chemical reactions due to local energy deposition, which further increases the temperature of the area surrounding the hotspot. When the mass transfer exceeds the heat transfer during the propagation process, a transition from ignition to a self-sustaining deflagration front occurs.

[0009] How to understand the self-sustaining ignition and reaction growth process of the adiabatic process induced by the critical temperature of the hotspot is an urgent problem that needs to be solved. Summary of the Invention

[0010] The present invention provides a method and system for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation, which can study the self-sustaining ignition and reaction growth process of the adiabatic process induced by the critical temperature of the hotspot, and simulate the hotspot self-sustaining reaction problem of RDX with different crystal forms and different compression densities under local thermal stimulation.

[0011] To achieve the above object, the present invention provides the following solutions:

[0012] A method for simulating a self-sustaining reaction of RDX explosive induced by local thermal stimulation includes:

[0013] Building a single-element explosive molecular structure model and processing the single-element explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities;

[0014] applying heat bath load stimulation of different action durations to the α-RDX supercell models of different crystal forms and compression densities;

[0015] The corresponding boundary conditions were set, a chemical reaction product visualization program was compiled, and the effects of different thermal bath loads on the ignition and self-sustaining reactions of the α-RDX supercell model were analyzed.

[0016] Optionally, the building of a single-substance explosive molecular structure model and processing the single-substance explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities specifically includes:

[0017] Obtain the orthorhombic α-RDX unit cell model from the Cambridge Crystallographic Database;

[0018] The orthorhombic α-RDX unit cell model is expanded 5×6×6 times along the a, b, and c axes respectively to obtain a three-dimensional size of α-RDX supercell model;

[0019] The left scale of the system fraction is kept unchanged, and the α-RDX supercell model is subjected to triaxial proportional compression to obtain five α-RDX supercell models with different compression densities.

[0020] Optionally, applying a heat bath load stimulus of different action durations to the α-RDX supercell models of different crystal forms and compression densities specifically includes:

[0021] Determining the method of applying thermal stimulation;

[0022] performing geometric relaxation on the α-RDX supercell model according to the thermal stimulation method;

[0023] Based on the Maxwell-Boltzmann distribution characteristics, the initial velocities of the atoms in the system were assigned by a random number seed generator, and the atomic coordinates were optimized by dynamic relaxation of 10 ps in the NVT ensemble using a Berendsen heat bath.

[0024] The α-RDX supercell model was placed in the NVE ensemble, and the three-dimensional space of 78 RDX molecules around the supercell centroid was selected as the simulation hotspot area. A thermal pulse load of 2000K was applied to the hotspot area using the Berendsen thermal bath method.

[0025] In setting t pulse The local thermal stimulation is terminated at this moment, and the α-RDX supercell model is still placed in the NVE ensemble, maintaining the adiabatic process to investigate the subsequent hot spot ignition and self-sustaining reaction, so that the total running time is greater than 200 ps.

[0026] Optionally, setting corresponding boundary conditions, compiling a chemical reaction product visualization program, and analyzing the effects of different thermal bath load stimulations on the ignition and self-sustaining reactions of the α-RDX supercell model specifically include:

[0027] Strachan's molecular recognition theory is used to identify relevant product information and bond-order cutoff parameters of energetic material systems;

[0028] Based on the relevant product information and the bond-level cutoff parameters, the self-compiled program BondFrag method is used to analyze the chemical reactions and product fragments in the molecular dynamics simulation of energetic material reactions. At the same time, the self-compiled Python script readFraAuto.py is used to quickly search and integrate the evolution of the series of products to obtain the energetic material simulation results;

[0029] The OVITO method is used to visualize and render the energetic material simulation results.

[0030] To achieve the above object, the present invention also provides the following solution:

[0031] A local thermal stimulation-induced RDX explosive self-sustaining reaction simulation system includes:

[0032] An α-RDX supercell model determination module is used to build a single-element explosive molecular structure model and process the single-element explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities;

[0033] A heat bath load stimulation application module is used to apply heat bath load stimulation of different action durations to the α-RDX supercell models of different crystal forms and compression densities;

[0034] The ignition and self-sustaining reaction influence determination module is used to set corresponding boundary conditions, compile a chemical reaction product visualization program, and analyze the influence of different heat bath load stimulations on the ignition and self-sustaining reactions of the α-RDX supercell model.

[0035] Optionally, the α-RDX supercell model determination module specifically includes:

[0036] α-RDX single unit cell model acquisition unit, used to obtain the orthorhombic α-RDX single unit cell model from the Cambridge Crystal Database;

[0037] The α-RDX supercell model determination unit is used to expand the orthorhombic α-RDX single unit cell model along the a, b, and c axes by 5×6×6 times to obtain a three-dimensional size of α-RDX supercell model;

[0038] The α-RDX supercell model compression unit is used to keep the left scale of the system fraction unchanged and perform triaxial proportional compression on the α-RDX supercell model to obtain five α-RDX supercell models with different compression densities.

[0039] Optionally, the thermal bath load stimulation application module specifically includes:

[0040] a thermal stimulation method determination unit, configured to determine a method for applying thermal stimulation;

[0041] a geometric relaxation determination unit, configured to perform geometric relaxation on the α-RDX supercell model according to the thermal stimulation method;

[0042] Atomic coordinate optimization unit, which is used to assign initial velocities to atoms in the system through a random number seed generator based on Maxwell-Boltzmann distribution characteristics, and optimize atomic coordinates by dynamic relaxation of 10 ps in the NVT ensemble using a Berendsen heat bath;

[0043] a thermal pulse load application unit, for placing the entire α-RDX supercell model in the NVE ensemble, selecting the three-dimensional space where 78 RDX molecules are located around the supercell mass center as a simulation hotspot area, and applying a thermal pulse load of 2000K to the hotspot area using the Berendsen thermal bath method;

[0044] The local thermal stimulation end unit is used to set t pulse The local thermal stimulation is terminated at this moment, and the α-RDX supercell model is still placed in the NVE ensemble, maintaining the adiabatic process to investigate the subsequent hot spot ignition and self-sustaining reaction, so that the total running time is greater than 200 ps.

[0045] Optionally, the ignition and self-sustaining reaction impact determination module specifically includes:

[0046] A unit for obtaining relevant product information and bond-order cutoff parameters, used to identify relevant product information and bond-order cutoff parameters of energetic material systems using Strachan's molecular recognition theory;

[0047] An energetic material simulation result determination unit is used to analyze chemical reactions and product fragments in the energetic material reaction molecular dynamics simulation based on the relevant product information and the bond-level cutoff parameters using the self-compiled program BondFrag method, and simultaneously combine the self-compiled Python script readFraAuto.py to quickly search and integrate the evolution of a series of products to obtain energetic material simulation results;

[0048] The visualization operation and rendering unit is used to perform visualization operation and rendering on the energetic material simulation results using the OVITO method.

[0049] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0050] The present invention provides a method for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation. The method comprises: constructing a molecular structure model of a single-element explosive and processing the molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities; applying a thermal bath load stimulus of different durations to the α-RDX supercell models of different crystal forms and compression densities; setting corresponding boundary conditions, compiling a chemical reaction product visualization program, and analyzing the effects of different thermal bath load stimuli on the ignition and self-sustaining reaction of the α-RDX supercell models. The present invention can study the self-sustaining ignition and reaction growth process of an adiabatic process induced by a hotspot critical temperature, and simulate the self-sustaining reaction of RDX of different crystal forms and compression densities under local thermal stimulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 This is a flow chart of the method for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation according to the present invention;

[0053] Figure 2 Calculation diagram for the three-dimensional model of energetic materials RDX and HMX and the simulation of local thermal stimulation;

[0054] Figure 3 Schematic diagram of the temperature evolution of α-RDX100V under different thermal stimulation times;

[0055] Figure 4 For t pulse = Schematic diagram of the system temperature evolution history of RDX models with different compression densities and crystal phase types under 20 ps;

[0056] Figure 5 This is a structural diagram of the simulation system for the local thermal stimulation-induced self-sustaining reaction of RDX explosives according to the present invention. DETAILED DESCRIPTION

[0057] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0058] The present invention provides a method and system for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation, which can study the self-sustaining ignition and reaction growth process of the adiabatic process induced by the critical temperature of the hotspot, and simulate the hotspot self-sustaining reaction problem of RDX with different crystal forms and different compression densities under local thermal stimulation.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] Example 1:

[0061] Figure 1 The flowchart of the method for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation of the present invention is shown in FIG. Figure 1 As shown, a method for simulating a self-sustaining reaction of RDX explosive induced by local thermal stimulation includes:

[0062] Step 101: constructing a molecular structure model of a single-element explosive, and processing the molecular structure model of the single-element explosive to obtain α-RDX supercell models of different crystal forms and compression densities.

[0063] This step specifically includes:

[0064] Step 1011, using the orthorhombic α-RDX unit cell from the Cambridge Crystal Database, the unit cell parameters are β=90°。

[0065] Step 1012: Expand the α-RDX unit cell model obtained in step 1012 by 5×6×6 times along the a, b, and c axes respectively, to obtain a three-dimensional size of α-RDX supercell model (total number of atoms N atom =30240);

[0066] Step 1013: Keep the left scale of the system fraction unchanged and perform triaxial uniform compression on the supercell model. The volume compression ratio V C / V0(V0 and V C are the volumes before and after compression) are 1, 0.95, 0.9, 0.85 and 0.8, respectively, thus obtaining five α-RDX supercell models with different compression densities.

[0067] An ensemble refers to the set of all possible states under given macroscopic conditions. It is a probability distribution at a phase point in statistical mechanics and is used to better describe the statistical behavior of microscopic particles in a system. The ensembles used in this model include the microcanonical ensemble (NVE), the canonical ensemble (NVT), and the isothermal and isobaric ensemble (NPT). Different ensembles yield different thermodynamic characteristic functions, such as entropy S, Helmholtz free energy H, and Gibbs free energy G. In the NVE ensemble, the number of atoms N, the system volume V, and the total energy E remain constant, while the system temperature T and pressure P are allowed to fluctuate around their average values. Under these conditions, the system is isolated from its environment, with neither energy nor particle exchange occurring between the two, thus maintaining statistical equilibrium. In the NVT ensemble, the number of atoms N, the system volume V, and the temperature T remain constant, while the total energy E and pressure P fluctuate around their respective average values. The NVT ensemble can be used to describe a closed system that is allowed to exchange thermal energy with its surroundings. However, to achieve statistical equilibrium in the NVT ensemble, particle exchange between the system and its environment is not permitted. In the NPT ensemble, the number of atoms N, the system pressure P, and the temperature T are kept constant. The simulation system is coupled to an external thermostat and pressure regulator, but no particle exchange with the environment is allowed.

[0068] This model employs both the Berendsen and Nosé-Hoover temperature control methods. The Berendsen method controls the simulation temperature by re-adjusting particle velocities, while the system is weakly coupled to an external heat bath at a specific temperature. The external thermostat dampens fluctuations in the system's kinetic energy, correcting the system temperature and ensuring an exponential relaxation of the instantaneous temperature.

[0069] The Nosé-Hoover temperature control method, also known as the extended system method, is based on the idea of ​​replacing the "heat bath" in the original thermostat with a virtual degree of freedom s and adding this degree of freedom to the Hamiltonian energy function of the system. Therefore, the Nosé-Hoover temperature control method has distinct real physical significance. The degree of freedom s can be regarded as the friction term in the Langevin equation. Its function is to act as a "heat bath" to slow down or accelerate particles, that is, the kinetic energy of the particles changes due to heat exchange until the system temperature reaches the target value. The potential energy associated with s can be expressed as (3N+1)k B Tlns, where 3N+1 is the total degree of freedom. It can be seen that there is one more degree of freedom than the usual 3N degrees of freedom, because it includes the virtual degree of freedom s. The kinetic energy related to s is Q(ds / dt) 2 / 2 represents, Q represents the virtual mass of s. Therefore, the equation of motion of the particles in the molecular dynamics system under the coupling of the Nosé-Hoover thermostat can be written as:

[0070]

[0071]

[0072] Where, γ i It is defined as the coefficient of particle friction that dissipates energy from the system.

[0073] A force field is a mathematical expression that describes the empirical equation of the potential energy surface. This model uses a molecular dynamics method based on the ReaxFF-lg force field to simulate the physical and chemical properties of energetic materials. The ReaxFF-lg force field is an upgraded version of the original ReaxFF force field. It primarily introduces the London dispersion term and rationally corrects long-range forces using a low-gradient model. The ReaxFF-lg force field optimizes its ability to describe non-bonded interactions. Table 1 shows the physical properties of RDX, such as the heat of sublimation and unit cell parameters, simulated using the ReaxFF-lg force field, and their comparison with experimentally measured values.

[0074] Table 1 Verification of RDX sublimation heat and unit cell parameters using ReaxFF-lg force field

[0075]

[0076] Energy E calculated by ReaxFF-lg force field Reax-lg It can be written as:

[0077]

[0078] Where, E Reax is the energy calculated by the original ReaxFF force field; E lg is the energy of the low-gradient correction term for the London dispersion force; r ij is the distance between atoms i and j; C lg,ij is the dispersion energy correction parameter; d is the scaling factor; R eij is the van der Waals equilibrium distance.

[0079] This model primarily utilizes an experimental single-cell initial structure verified by X-ray diffraction and included in the Cambridge Crystallographic Database. This selected crystal structure is thermodynamically stable under ambient conditions, and its topology and molecular energy levels are undistorted, ensuring the reliability of subsequent molecular dynamics calculations. This single-cell structure is then periodically expanded to obtain a supercell structure that meets the requirements of the computational model. The boundary conditions used in the model for simulating the thermal decomposition of energetic materials are three-dimensional periodic boundaries.

[0080] A high-temperature heat bath is set up in an NVT or NPT ensemble to heat the energetic material until decomposition begins. Maintaining a constant bath temperature is called isothermal heating, while programmable temperature ramping is called temperature programming. Specifically, for adiabatic heating, after removing the external heat bath, the system is placed in the NVE ensemble, where the temperature exchange rate between the system and the outside world is zero. Note that using both a Berendsen bath and the NVE ensemble results in a thermal decomposition process equivalent to that performed in the NVT ensemble.

[0081] This model uses the self-compiled program BondFrag method to analyze chemical reactions and product fragments in molecular dynamics simulations of energetic material reactions. At the same time, it combines the self-compiled Python script readFraAuto.py to achieve rapid search and integrated output of the evolution of a series of products. The bond-level cutoff parameters are shown in Table 2.

[0082] Table 2 Bond order cutoff values ​​of CHON elements

[0083]

[0084] OVITO is used in the model to visualize and render the energetic material simulation results. This data pipeline technology, with its highly flexible dynamic pipeline application and non-destructive data manipulation, makes OVITO suitable for a wider range of visualization and analysis processes.

[0085] The orthorhombic α-RDX unit cell (CCDC No. 1131953) from the Cambridge Crystal Database is used, which belongs to the Pbca space group (the number of occupied molecules per unit cell is Z = 8), and the unit cell parameters are β=90°. α-RDX belongs to the AAE conformation, with two N-N bonds in the quasi-vertical bond position (i.e., quasi-axial), and the third N-N bond in the quasi-equatorial bond position (i.e., quasi-equatorial). First, the α-RDX single crystal unit cell model is expanded 5×6×6 times along the a, b, and c axes respectively, and the three-dimensional size is obtained. α-RDX supercell model (the total number of atoms Natom is 30240), and then keeping the fractional coordinates of the system unchanged, the above supercell model is subjected to triaxial proportional compression, and the volume compression ratios VC / V0 (V0 and VC are the volumes before and after compression) are 1, 0.95, 0.9, 0.85 and 0.8, respectively, to obtain five α-RDX supercell models with different compression densities. The model parameters are shown in the table below.

[0086] Table 3 Parameters of the RDX supercell model for energetic materials

[0087]

[0088]

[0089] Five α-RDX supercell models with different compression densities were obtained through triaxial proportional compression operation. The results showed that the compression densities of α-RDX100V, α-RDX95V, α-RDX90V, α-RDX85V and α-RDX80V models were 1.8060 g·cm-3, 1.9010 g·cm-3, 2.0066 g·cm-3, 2.1247 g·cm-3 and 2.2575 g·cm-3 respectively (see Table 3). The system temperature responses of the above α-RDX models with different compression densities and different crystal RDX models under local thermal stimulation at 2000K are shown in Figure 3. Figure 4 shown.

[0090] Table 4 summarizes the ignition response results at 200 ps for the γ-RDX and ε-RDX models, as well as for α-RDX models with varying compression densities. Using 2000 K as the criterion for thermal instability or global ignition of the reaction system, a "+" in Table 4 indicates thermal instability at 200 ps. The intensity of the ignition response is defined as the degree of transition from thermal equilibrium to ignition. A greater number of "+"s indicates a stronger ignition response and a faster transition from thermal equilibrium to ignition. When tpulse = 20 ps, ​​after the local thermal stimulus ends, although none of the α-RDX reaction systems exhibit significant thermal instability at 200 ps and remain in thermal equilibrium within the early ignition delay period, the system temperature response of the α-RDX model at 200 ps increases with increasing compression density. Under the condition of tpulse = 20ps, the system temperature response of the α-RDX80V model (density 2.2575g·cm-3) is the largest at 200ps, reaching 1099.44K. Therefore, the reaction time of the model is extended to 400ps. The results show that the system temperature only rises by 161.3K in the subsequent 200ps reaction time. At this time, the α-RDX80V model has not yet reached the critical condition of thermal instability.

[0091] Table 4 Ignition response of RDX model systems with different compression densities and crystal phase types at 200ps

[0092]

[0093] On the other hand, for the uncompressed RDX system models with different crystal forms, the comparison of their ignition response results further confirms that the density factor shows a positive correlation with the RDX hotspot ignition temperature rise. When comparing γ-RDX and ε-RDX with similar density, it is found that the crystal form also has a significant effect on the RDX ignition behavior. Under the condition of tpulse = 20ps, γ-RDX (density 2.2673g·cm-3) and ε-RDX (density 2.2663g·cm-3) with similar density did not experience thermal instability at 200ps. Their temperature rise is similar to that of the highly compressed α-RDX80V model, and the global temperature of the system exceeds 1000K at 200ps. However, there are differences in the temperature response of γ-RDX and ε-RDX. Dreger and Gupta reported that γ-RDX is a high-pressure phase, while ε-RDX is a high-temperature and high-pressure phase

[220] , so ε-RDX has better heat resistance than γ-RDX. Therefore, under the condition of tpulse = 20 ps, ​​the system temperature response of ε-RDX at 200 ps is about 98.83 K lower than that of γ-RDX.

[0094] When tpulse = 50 ps, ​​the results in Table 4 show that thermal instability occurs within 200 ps for the γ-RDX, ε-RDX, and most α-RDX models. At the end of the 50 ps local thermal stimulation at 2000 K, the system temperatures of the α-RDX100V, α-RDX95V, α-RDX90V, α-RDX85V, α-RDX80V, γ-RDX, and ε-RDX models rise to 1136.12 K, 1172.89 K, 1295.39 K, 1379.93 K, 1482.21 K, 1498.61 K, and 1444.6 K, respectively. The ignition delay times (Δτign) before they reach critical thermal instability are approximately 300 ps, ​​250 ps, ​​144.3 ps, 101.6 ps, 69.2 ps, 63.5 ps, and 117.3 ps, respectively. For all α-RDX models, Δτign decreases by 76.93% when the volume compression ratio VC / V0 reaches 0.8. These results indicate that during the adiabatic phase of the ignition response induced by local thermal stimulation, α-RDX samples with higher compression densities exhibit shorter ignition delays Δτign. High compression densities favor thermal instability and promote the early onset of self-sustaining reactions in the α-RDX system. Regarding different crystal forms, the ignition responses of α-RDX80V and γ-RDX are similar at tpulse = 50 ps, ​​primarily due to their similar high density characteristics. Therefore, density is the dominant factor influencing the ignition response under this condition. However, compared to γ-RDX, ε-RDX, due to its high-temperature and high-pressure phase, requires a longer Δτign to reach thermal instability. This indicates that both density and crystal form factors influence the occurrence of self-sustaining RDX reactions in hotspot ignition simulations.

[0095] Step 102: applying a heat bath load stimulus of different action durations to the α-RDX supercell models of different crystal forms and compression densities.

[0096] This step specifically includes:

[0097] Step 1021, determine the method of applying thermal stimulation. The basic idea is to use a Berendsen or Nosé-Hoover thermostat to set up a high-temperature heat bath in the NVT or NPT ensemble to heat the energetic material until it begins to decompose.

[0098] Step 1022, based on the method determined in step 1021, specifically, firstly perform geometric relaxation on the RDX supercell model in step 1013 to minimize the system energy and obtain a stable structure under 0K conditions. The force convergence threshold is The charge balance (QEq) method convergence threshold is 1×10 -6 .

[0099] In step 1023, based on the Maxwell-Boltzmann distribution characteristics, the initial velocities of the atoms in the system are assigned by a random number seed generator, and a Berendsen heat bath is used to perform dynamic relaxation for 10 ps in the NVT ensemble (300 K) to further optimize the atomic coordinates.

[0100] In step 1024, after the above-mentioned dynamic relaxation is completed, the entire model is placed in the NVE ensemble, and the three-dimensional space where 78 RDX molecules (the number of molecules accounts for 5.4167%) are located around the supercell center of mass is selected as the simulation hotspot area. A thermal pulse load of 2000K is applied to the hotspot area using the Berendsen thermal bath method.

[0101] Step 1025, finally set t pulse The local thermal stimulation is terminated (i.e., the heat bath is removed) and the model is still placed in the NVE ensemble. The adiabatic process is maintained to investigate the subsequent hot spot ignition and self-sustaining reaction, making the total running time greater than 200 ps.

[0102] The ReaxFF-lg force field is suitable for energetic materials such as RDX and HMX, and is particularly capable of accurately predicting the isotherms of different RDX phases. It is planned to use the LAMMPS code embedded with the ReaxFF-lg force field to perform all molecular dynamics simulations. Taking RDX as an example, the RDX supercell model is first geometrically relaxed to minimize the system energy and obtain a stable structure at 0K. The force convergence threshold is The charge balance (QEq) method convergence threshold was 1×10-6; then, based on the Maxwell-Boltzmann distribution characteristics, the initial velocities of the atoms in the system were assigned by a random number seed generator, and a Berendsen heat bath was used to perform dynamic relaxation for 10 ps in the NVT ensemble (300K) to further optimize the atomic coordinates. After the dynamic relaxation was completed, the entire model was placed in the NVE ensemble, and the three-dimensional space around the supercell center of mass of 78 RDX molecules (5.4167% of the total number of molecules) was selected as the simulation hotspot region (see Figure 2A 2000K heat pulse load was applied to the hotspot using the Berendsen heat bath method. For the HMX model, this required heating 58 HMX molecules around its center of mass, representing 5.3704% of the total number of molecules in the hotspot. Finally, at a set time tpulse, the local thermal stimulation was terminated (i.e., the heat bath was removed), and the model was maintained in the NVE ensemble, maintaining this adiabatic process to investigate subsequent hotspot ignition and self-sustaining reactions. The total run time was greater than 200 ps. All simulations employed three-dimensional periodic boundary conditions and a time step of 0.1 fs. The contribution of the atomic center of mass velocity was deducted from the temperature calculation. Thermodynamic quantities were calculated every 0.1 fs, and the data were averaged and output every 100 fs.

[0103] The ignition process of energetic materials under localized thermal stimulation can be divided into three stages: (I) the localized thermal stimulation stage: external stimulation generates heat, nucleating hot spots in the condensed phase energetic material; (II) the ignition delay stage: after the external stimulation is removed, the hot spots continue to evolve during the incubation period until the chemical reaction in the system reaches a critical state; and (III) the thermal instability stage: after reaching the critical state, the energetic material undergoes spontaneous ignition or deflagration, triggering a self-sustaining reaction propagation. Localized thermal stimulation heats a small number of RDX molecules in the form of a heat pulse, gradually raising the system temperature. Ultimately, after sufficient time, the RDX model tends to ignite or develop a self-sustaining reaction state. Whether the energetic material can self-sustain ignition or thermal instability occurs quickly is directly related to the duration of the thermal stimulation, tpulse.

[0104] like Figure 3 The figure shows the temperature evolution of α-RDX100V under different thermal stimulation durations. The red line (Hotspot) represents the temperature evolution of the simulated hotspot region, the green line (Rest of RDX) represents the temperature change of the remaining bulk phase of the RDX system excluding the simulated hotspot region, and the blue line (Overall RDX) represents the global average temperature evolution of the entire RDX system. For the α-RDX100V model, increasing the thermal stimulation duration (20 ps to 50 ps) significantly increases the thermal equilibrium temperature threshold at the 200 ps ignition delay stage, favoring spontaneous ignition or thermal instability of the RDX.

[0105] Step 103: Set corresponding boundary conditions, compile a chemical reaction product visualization program, and analyze the effects of different thermal bath loads on the ignition and self-sustaining reactions of the α-RDX supercell model.

[0106] This step specifically includes:

[0107] Step 1031, using Strachan's molecular recognition theory to identify relevant product information of energetic material systems, defines that two atoms are considered to be bonded when they are close enough in phase space (atomic spatial position and momentum state), and requires the relative energy E of the bonded atomic pair to be ij Less than zero:

[0108]

[0109] Where, int (ij) (r ij ) is the distance r ij The effective bonding potential well depth of atoms i and j; κ cm (ij) is the kinetic energy of its center of mass.

[0110] Step 1032, combining the above molecular recognition theory and bond-level cutoff parameters, uses the self-compiled program BondFrag method to analyze the chemical reactions and product fragments in the molecular dynamics simulation of energetic material reactions, and combines it with the self-compiled Python script readFraAuto.py to achieve rapid search and integrated output of the evolution of a series of products.

[0111] Step 1033 , based on the molecular recognition theory obtained in step 1031 , the OVITO method is used to complete the visualization operation and rendering of the energetic material simulation results.

[0112] Based on the above steps, molecular dynamics simulations were carried out and the following conclusions were obtained by comparing different thermal stimulation times:

[0113] (1) The ignition process of energetic materials under local thermal stimulation can be divided into three stages: (I) Local thermal stimulation stage: external stimulation generates heat, and hot spots nucleate in the condensed phase energetic materials; (II) Ignition delay stage: when the external stimulation is removed, the hot spots continue to evolve during the incubation period until the chemical reaction of the system reaches a critical state; (III) Thermal instability stage: after reaching the critical state, the energetic material spontaneously ignites or deflagrates, triggering the propagation of a self-sustaining reaction.

[0114] (2) For the α-RDX100V model, increasing the thermal stimulation time (20ps→50ps) will be conducive to the spontaneous ignition or thermal instability of RDX.

[0115] The temperature responses of the five α-RDX supercell models with different compression densities obtained in step 101 under local thermal stimulation at 2000K are observed.

[0116] The results show that α-RDX with higher compression density has a higher initial pressure state, and therefore has a shorter ignition delay time Δτign and time to semi-high temperature state tRHS, exhibiting a higher level of maximum local temperature Tmax response, and ultimately can quickly undergo thermal instability process and release a large amount of energy.

[0117] The beneficial effects of the present invention are:

[0118] (1) The self-sustaining reaction process of RDX induced by local thermal stimulation was simulated by the reaction molecular dynamics simulation method based on the ReaxFF-lg force field, and the self-sustaining ignition and reaction growth law of the adiabatic process induced by the hotspot critical temperature were obtained. The results show that the ignition process of RDX under local thermal stimulation will go through the complete process of local thermal stimulation-ignition delay-thermal instability. When reaching the critical state, RDX will spontaneously ignite or deflagrate in the thermal instability stage, triggering the propagation of the self-sustaining reaction.

[0119] (2) A compression confinement-localized thermal ignition loading simulation was conducted on the α-RDX model. Combining Δτign and the tRHS (tRHS) proposed for the first time in this paper, the ability of the energetic material to sustain its reaction from thermal stimulation to global ignition and thermal instability can be effectively quantified. The shorter the Δτign and tRHS, the more susceptible the energetic material system is to thermal instability. These physical parameters help to quantitatively evaluate the evolution of the energetic material from localized thermal stimulation-energy deposition to energy dissipation-globalization.

[0120] Example 2:

[0121] Figure 5 This is a structural diagram of the simulation system for the local thermal stimulation-induced self-sustaining reaction of RDX explosives according to the present invention. Figure 5 As shown, a local thermal stimulation-induced RDX explosive self-sustaining reaction simulation system includes:

[0122] α-RDX supercell model determination module 201 is used to build a single-element explosive molecular structure model and process the single-element explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities;

[0123] A heat bath load stimulation applying module 202 is used to apply heat bath load stimulation of different action durations to the α-RDX supercell models of different crystal forms and compression densities;

[0124] The ignition and self-sustaining reaction influence determination module 203 is used to set corresponding boundary conditions, compile a chemical reaction product visualization program, and analyze the influence of different heat bath loads on the ignition and self-sustaining reactions of the α-RDX supercell model.

[0125] The α-RDX supercell model determination module 201 specifically includes:

[0126] α-RDX single unit cell model acquisition unit, used to obtain the orthorhombic α-RDX single unit cell model from the Cambridge Crystal Database;

[0127] The α-RDX supercell model determination unit is used to expand the orthorhombic α-RDX single unit cell model along the a, b, and c axes by 5×6×6 times to obtain a three-dimensional size of α-RDX supercell model;

[0128] The α-RDX supercell model compression unit is used to keep the left scale of the system fraction unchanged and perform triaxial proportional compression on the α-RDX supercell model to obtain five α-RDX supercell models with different compression densities.

[0129] The thermal bath load stimulation applying module 202 specifically includes:

[0130] a thermal stimulation method determination unit, configured to determine a method for applying thermal stimulation;

[0131] a geometric relaxation determination unit, configured to perform geometric relaxation on the α-RDX supercell model according to the thermal stimulation method;

[0132] Atomic coordinate optimization unit, which is used to assign initial velocities to atoms in the system through a random number seed generator based on Maxwell-Boltzmann distribution characteristics, and optimize atomic coordinates by dynamic relaxation of 10 ps in the NVT ensemble using a Berendsen heat bath;

[0133] a thermal pulse load application unit, for placing the entire α-RDX supercell model in the NVE ensemble, selecting the three-dimensional space where 78 RDX molecules are located around the supercell mass center as a simulation hotspot area, and applying a thermal pulse load of 2000K to the hotspot area using the Berendsen thermal bath method;

[0134] The local thermal stimulation end unit is used to set t pulse The local thermal stimulation is terminated at this moment, and the α-RDX supercell model is still placed in the NVE ensemble, maintaining the adiabatic process to investigate the subsequent hot spot ignition and self-sustaining reaction, so that the total running time is greater than 200 ps.

[0135] The ignition and self-sustaining reaction impact determination module 203 specifically includes:

[0136] A unit for obtaining relevant product information and bond-order cutoff parameters, used to identify relevant product information and bond-order cutoff parameters of energetic material systems using Strachan's molecular recognition theory;

[0137] An energetic material simulation result determination unit is used to analyze chemical reactions and product fragments in the energetic material reaction molecular dynamics simulation based on the relevant product information and the bond-level cutoff parameters using the self-compiled program BondFrag method, and simultaneously combine the self-compiled Python script readFraAuto.py to quickly search and integrate the evolution of a series of products to obtain energetic material simulation results;

[0138] The visualization operation and rendering unit is used to perform visualization operation and rendering on the energetic material simulation results using the OVITO method.

[0139] Example 3:

[0140] This embodiment provides an electronic device including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the method for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation according to embodiment 1. The electronic device may be a server.

[0141] In addition, an embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for simulating the self-sustaining reaction of RDX explosive induced by local thermal stimulation according to the first embodiment is implemented.

[0142] Embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0143] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0146] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0147] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation, characterized in that: The method comprises: Building a single-element explosive molecular structure model and processing the single-element explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities; applying heat bath load stimulation of different action durations to the α-RDX supercell models of different crystal forms and compression densities; Set the corresponding boundary conditions, compile a chemical reaction product visualization program, and analyze the effects of different thermal bath loads on the ignition and self-sustaining reactions of the α-RDX supercell model; The method of building a single-substance explosive molecular structure model and processing the single-substance explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities specifically includes: Obtain the orthorhombic α-RDX unit cell model from the Cambridge Crystallographic Database; The orthorhombic α-RDX unit cell model is expanded 5×6×6 times along the a, b, and c axes respectively to obtain a three-dimensional size of α-RDX supercell model; Keeping the left scale of the system fraction unchanged, the α-RDX supercell model is subjected to triaxial proportional compression to obtain five α-RDX supercell models with different compression densities; The step of applying heat bath load stimulation of different durations to the α-RDX supercell models of different crystal forms and compression densities specifically includes: Determining the method of applying thermal stimulation; performing geometric relaxation on the α-RDX supercell model according to the thermal stimulation method; Based on the Maxwell-Boltzmann distribution characteristics, the initial velocities of the atoms in the system were assigned by a random number seed generator, and the atomic coordinates were optimized by dynamic relaxation of 10 ps in the NVT ensemble using a Berendsen heat bath. The α-RDX supercell model was placed in the NVE ensemble, and the three-dimensional space of 78 RDX molecules around the supercell centroid was selected as the simulation hotspot area. A thermal pulse load of 2000K was applied to the hotspot area using the Berendsen thermal bath method. In setting t pulse The local thermal stimulation is terminated at this moment, and the α-RDX supercell model is still placed in the NVE ensemble, maintaining the adiabatic process to investigate the subsequent hot spot ignition and self-sustaining reaction, so that the total running time is greater than 200 ps.

2. The method for simulating the self-sustaining reaction of RDX explosives induced by local thermal stimulation according to claim 1, characterized in that: The setting of corresponding boundary conditions, compiling a chemical reaction product visualization program, and analyzing the effects of different thermal bath load stimulations on the ignition and self-sustaining reactions of the α-RDX supercell model specifically include: Strachan's molecular recognition theory is used to identify relevant product information and bond-order cutoff parameters of energetic material systems; Based on the relevant product information and the bond-level cutoff parameters, the self-compiled program BondFrag method is used to analyze the chemical reactions and product fragments in the molecular dynamics simulation of energetic material reactions. At the same time, the self-compiled Python script readFraAuto.py is used to quickly search and integrate the evolution of the series of products to obtain the energetic material simulation results; The OVITO method is used to visualize and render the energetic material simulation results.

3. A local thermal stimulation induced RDX explosive self-sustaining reaction simulation system, characterized in that: The system comprises: An α-RDX supercell model determination module is used to build a single-element explosive molecular structure model and process the single-element explosive molecular structure model to obtain α-RDX supercell models of different crystal forms and compression densities; A heat bath load stimulation application module is used to apply heat bath load stimulation of different action durations to the α-RDX supercell models of different crystal forms and compression densities; An ignition and self-sustaining reaction influence determination module is used to set corresponding boundary conditions, compile a chemical reaction product visualization program, and analyze the influence of different thermal bath loads on the ignition and self-sustaining reactions of the α-RDX supercell model; The α-RDX supercell model determination module specifically includes: α-RDX single unit cell model acquisition unit, used to obtain the orthorhombic α-RDX single unit cell model from the Cambridge Crystal Database; The α-RDX supercell model determination unit is used to expand the orthorhombic α-RDX single unit cell model along the a, b, and c axes by 5×6×6 times to obtain a three-dimensional size of α-RDX supercell model; An α-RDX supercell model compression unit is used to keep the left scale of the system fraction unchanged and perform triaxial proportional compression on the α-RDX supercell model to obtain five α-RDX supercell models with different compression densities; The thermal bath load stimulation application module specifically includes: a thermal stimulation method determination unit, configured to determine a method for applying thermal stimulation; a geometric relaxation determination unit, configured to perform geometric relaxation on the α-RDX supercell model according to the thermal stimulation method; Atomic coordinate optimization unit, which is used to assign initial velocities to atoms in the system through a random number seed generator based on Maxwell-Boltzmann distribution characteristics, and optimize atomic coordinates by dynamic relaxation of 10 ps in the NVT ensemble using a Berendsen heat bath; a thermal pulse load application unit, for placing the entire α-RDX supercell model in the NVE ensemble, selecting the three-dimensional space where 78 RDX molecules are located around the supercell mass center as a simulation hotspot area, and applying a thermal pulse load of 2000K to the hotspot area using the Berendsen thermal bath method; The local thermal stimulation end unit is used to set t pulse The local thermal stimulation is terminated at this moment, and the α-RDX supercell model is still placed in the NVE ensemble, maintaining the adiabatic process to investigate the subsequent hot spot ignition and self-sustaining reaction, so that the total running time is greater than 200 ps.

4. The local thermal stimulation induced RDX explosive self-sustaining reaction simulation system according to claim 3, characterized in that: The ignition and self-sustaining reaction impact determination module specifically includes: A unit for obtaining relevant product information and bond-order cutoff parameters, used to identify relevant product information and bond-order cutoff parameters of energetic material systems using Strachan's molecular recognition theory; An energetic material simulation result determination unit is used to analyze chemical reactions and product fragments in the energetic material reaction molecular dynamics simulation based on the relevant product information and the bond-level cutoff parameters using the self-compiled program BondFrag method, and simultaneously combine the self-compiled Python script readFraAuto.py to quickly search and integrate the evolution of a series of products to obtain energetic material simulation results; The visualization operation and rendering unit is used to perform visualization operation and rendering on the energetic material simulation results using the OVITO method.

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