Thermodynamic simulation method and device for composite structure and electronic equipment

Abstract

The invention provides a thermodynamic simulation method and device for a composite structure and electronic equipment, and relates to the technical field of composite structure simulation, and the thermodynamic simulation method for the composite structure comprises the following steps: carrying out proton irradiation simulation on a mesoscale model according to a preset proton irradiation condition, primary recoil atomic data of indium tin oxide and primary recoil atomic data of polyimide are obtained; performing cascade collision simulation according to the indium tin oxide primary recoil atomic data and the polyimide primary recoil atomic data to obtain an indium tin oxide material defect rate and a polyimide material defect rate; combining and modeling defect models respectively constructed according to the indium tin oxide material defect rate and the polyimide material defect rate to obtain a composite structure defect model; and performing energy transmission simulation on the composite structure defect model to obtain a temperature gradient curve. The accuracy of thermodynamic simulation of the composite structure can be improved.

Description

A thermodynamic simulation method, device and electronic equipment for composite structures Technical Field

[0001] This invention relates to the field of composite structure simulation technology, and more specifically, to a composite structure thermodynamic simulation method, apparatus, and electronic equipment. Background Technology

[0002] Indium tin oxide (ITO)-polyimide (PI) composites combine the high conductivity and high light transmittance of ITO with the high temperature resistance, flexibility, and excellent insulation properties of PI, making them a core functional material in many fields. In flexible electronics, as a core substrate for flexible thin-film temperature sensors, it meets the high-sensitivity temperature testing requirements of wearable devices and lithium battery health monitoring. In the new energy vehicle sector, leveraging the extreme environmental adaptability of PI and the thermal conductivity regulation capabilities of ITO, this composite material is used to construct battery pack thermal protection systems, effectively preventing thermal runaway chain reactions. Simultaneously, it has irreplaceable applications in cutting-edge scenarios such as aerospace thermal control films, undertaking critical thermal management functions. Thermodynamic properties are the core indicators determining the reliability of this composite material in service. Its thermal conductivity, temperature stability, and other parameters directly affect the heat transfer efficiency, operating accuracy, and service life of devices. Accurately understanding thermodynamic properties is a crucial prerequisite for material structure optimization and application scenario adaptation.

[0003] In existing technologies, thermodynamic simulations of ITO-PI composites only construct a single system model of ITO or PI, ignoring the interface effect after the two are combined. This makes it difficult to accurately reproduce the heat transfer path of the "inorganic-organic" composite structure, resulting in deviations between the thermodynamic simulation results and the actual experimental results of the composite material, thus affecting the accuracy of the thermodynamic simulation of the composite material. Summary of the Invention

[0004] The problem addressed by this invention is how to improve the accuracy of thermodynamic simulations of composite structures.

[0005] To address the above problems, this invention provides a method, apparatus, and electronic device for simulating the thermodynamics of composite structures.

[0006] In a first aspect, the present invention provides a thermodynamic simulation method for composite structures, comprising: obtaining a mesoscale model of an indium tin oxide (ITO)-polyimide composite structure, as well as an initial crystal model of ITO and a polymer model of polyimide; performing proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data, wherein the primary recoil atom data includes ITO primary recoil atom data and polyimide primary recoil atom data; and performing cascade collision simulation based on the ITO primary recoil atom data and the initial crystal model of ITO to obtain the defect rate of the ITO material. The defect rate of the polyimide material is obtained by cascade collision simulation based on the primary recoil atom data of the polyimide and the polyimide polymer model. Based on the random defect introduction method, an indium tin oxide (ITO) defect model is constructed according to the ITO defect rate, and a polyimide defect model is constructed according to the polyimide defect rate, wherein the ITO defect model and the polyimide defect model are size-matched. The ITO defect model and the polyimide defect model are then merged to obtain a composite structure defect model. Energy transfer simulation is performed on the composite structure defect model to obtain the temperature gradient curve.

[0007] Optionally, the mesoscale model includes an indium tin oxide layer and a polyimide layer; the step of performing proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data includes: obtaining the incident position and incident energy of the proton according to the proton irradiation conditions; performing proton irradiation simulation on the mesoscale model according to the incident position and incident energy to obtain the trajectory data corresponding to the proton; obtaining the atomic kinetic energy of the target atom colliding with the proton according to the trajectory data and a preset kinetic energy relationship; identifying the target atom with the atomic kinetic energy greater than a preset kinetic energy threshold as the initial recoil atom; generating the indium tin oxide primary recoil atom data according to the atomic proportion, energy distribution, and spatial distribution of all the primary recoil atoms in the indium tin oxide layer; and generating the polyimide primary recoil atom data according to the atomic proportion, energy distribution, and spatial distribution of all the primary recoil atoms in the polyimide layer.

[0008] Optionally, the trajectory data includes the proton energy before collision, the proton mass, the target atom mass, and the proton scattering angle; the kinetic energy relationship satisfies: Among them, E t E is the kinetic energy of the atom. p Let m be the energy before the proton collision. t m is the mass of the target atom. p Let θ be the mass of the proton and θ be the scattering angle of the proton.

[0009] Optionally, the step of obtaining the defect rate of indium tin oxide material by performing cascade collision simulation based on the primary recoil atom data of indium tin oxide and the initial crystal model of indium tin oxide includes: expanding the cell of the initial crystal model of indium tin oxide to obtain an expanded cell model of indium tin oxide; and performing cascade collision simulation based on the primary recoil atom data of indium tin oxide to obtain the defect rate of indium tin oxide material.

[0010] Optionally, the indium tin oxide defect rate satisfies: Where η is the indium tin oxide defect rate, f is the comprehensive defect reservation factor, f0 is the effective triggering factor, k is the conversion factor, Y is the average defect yield per cascade, and N is the total defect yield. PKA N represents the total number of indium tin oxide primary recoil atoms determined using the indium tin oxide primary recoil atom data. total This represents the total number of atoms in the indium tin oxide expanded cell model.

[0011] Optionally, the step of obtaining the polyimide material defect rate by performing cascade collision simulation based on the polyimide primary recoil atom data and the polyimide polymer model includes: obtaining a constructed polyimide single-molecule model; polymerizing multiple polyimide single-molecule models to obtain a single polyimide polymer chain; constructing a polyimide polymer phase model based on multiple polyimide single polymer chains according to a preset density; performing the cascade collision simulation on the polyimide polymer model based on the polyimide primary recoil atom data to obtain the polyimide material defect rate; the polyimide material defect rate satisfies: Where μ is the defect rate of the polyimide material, μ max M is the saturation defect rate. PKA M0 is the total number of polyimide primary recoil atoms determined by the polyimide primary recoil atom data, where M0 is the number of atoms in the polyimide primary recoil atom data when the defect rate is equal to... The number of primary recoil atoms of polyimide corresponding to the time.

[0012] Optionally, the method for determining the termination of the cascade collision includes: after the cascade collision in the polyimide polymer model begins, obtaining the defect change rate of the number of defects over time; and determining that the cascade collision terminates when the defect change rate is less than a preset change rate.

[0013] Optionally, the composite structural defect model includes an indium tin oxide (ITO) defect layer and a polyimide defect layer. The ITO defect layer is vertically positioned above the polyimide defect layer. The two ends of the composite structural defect model along the horizontal direction are respectively designated as cold source ends, and the middle part of the composite structural defect model is designated as a heat source end. The step of simulating energy transfer to obtain a temperature gradient curve from the composite structural defect model includes: injecting a first preset energy into the middle part of the composite structural defect model at a preset rate through the heat source end, and extracting a second preset energy from both ends of the composite structural defect model at the preset rate through the cold source end; after the temperature of the composite structural defect model stabilizes, acquiring the temperature gradient data of the composite structural defect model, wherein the temperature gradient data includes a one-to-one correspondence between temperature and blocks, and the blocks are obtained by equally dividing the composite structural defect model along the horizontal direction according to a preset number; and generating a temperature gradient curve corresponding to the composite structural defect model based on the temperature gradient data.

[0014] Secondly, the present invention provides a composite structure thermodynamic simulation device, comprising: an acquisition module for acquiring a mesoscopic-scale model of an indium tin oxide (ITO)-polyimide composite structure, as well as an initial crystal model of ITO and a polyimide polymer model; an irradiation module for performing proton irradiation simulation on the mesoscopic-scale model according to preset proton irradiation conditions to obtain primary recoil atom data, wherein the primary recoil atom data includes ITO primary recoil atom data and polyimide primary recoil atom data; a first collision module for performing cascade collision simulation based on the ITO primary recoil atom data and the initial crystal model of ITO to obtain the defect rate of the ITO material; and a second collision module. The system comprises the following modules: a collision module for performing cascade collision simulations based on the primary recoil atom data of the polyimide and the polyimide polymer model to obtain the defect rate of the polyimide material; a construction module for constructing an indium tin oxide (ITO) defect model based on the defect rate of the ITO material using a random defect introduction method, and constructing a polyimide defect model based on the defect rate of the polyimide material, wherein the ITO defect model and the polyimide defect model are size-matched; a merging module for merging the ITO defect model and the polyimide defect model to obtain a composite structure defect model; and a generation module for performing energy transfer simulations on the composite structure defect model to obtain a temperature gradient curve.

[0015] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store a computer program; the processor is used to implement the composite structure thermodynamic simulation method as described in the first aspect when the computer program is executed.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the composite structure thermodynamic simulation method as described in the first aspect.

[0017] The beneficial effects of the composite structure thermodynamic simulation method of this invention are as follows: It obtains a mesoscale model of the indium tin oxide (ITO)-polyimide composite structure, an initial crystal model of ITO, and a polymer model of polyimide, providing a basic framework for the simulation of the true structure of the composite material and avoiding simulation deviations caused by distortion of the initial model; it performs proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions, differentially obtaining primary recoil atom data of ITO and PI, ensuring the consistency of irradiation input parameters with the real space environment, and providing accurate source data for subsequent defect simulation; based on the primary recoil atom data of the two materials, it conducts cascade collision simulations to obtain the defect rates of the indium tin oxide material and the polyimide material, accurately capturing the differences between inorganic crystals and organic polymers. The study of the formation and evolution patterns of defects in materials with different characteristics makes the defect rate calculation more consistent with the actual irradiation damage mechanism. A random defect introduction method is employed, and defect models for indium tin oxide (ITO) and polyimide are constructed with precisely matched dimensions based on their respective defect rates, avoiding potential deviations when merging the two defect models. When merging the two types of defect models, the fusion of the inorganic-organic interface is optimized to construct a stable and realistic composite structure defect model, providing a reliable structural basis for heat transfer simulation. Finally, energy transfer simulation is performed on the composite structure defect model, yielding a temperature gradient curve that closely approximates the actual experimental results. This curve accurately reflects the heat transfer characteristics under different defect concentrations, comprehensively improving the reliability and accuracy of the thermal simulation results. Attached Figure Description

[0018] Figure 1 is a flowchart illustrating a composite structure thermodynamic simulation method according to an embodiment of the present invention; Figure 2 is a schematic diagram illustrating the evolution of defects in indium tin oxide according to an embodiment of the present invention; Figure 3 is a schematic diagram illustrating the evolution of defects in polyimide according to an embodiment of the present invention; Figure 4 is a schematic diagram illustrating the structure of a composite structure defect model according to an embodiment of the present invention; Figure 5 is a temperature gradient curve of the composite structure under different defect concentrations according to an embodiment of the present invention; Figure 6 is a thermal conductivity variation curve under different defect concentrations according to an embodiment of the present invention; Figure 7 is a schematic diagram illustrating the structure of a composite structure thermodynamic simulation device according to an embodiment of the present invention; Figure 8 is a schematic diagram illustrating the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0020] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0021] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0022] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0023] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0024] In related technologies, thermodynamic simulations of ITO-PI composites only construct single-system models of ITO or PI, neglecting the interface effects after the two are combined. This makes it difficult to accurately reproduce the heat transfer path of the "inorganic-organic" composite structure (ITO is an inorganic material, and PI is an organic material). As an inorganic crystalline material, ITO's heat transfer depends on lattice vibrations, while PI, as an organic polymer, transfers heat through molecular chain vibrations and inter-chain interactions. Their heat conduction mechanisms are fundamentally different. The "inorganic-organic" interface formed after the composite is the core channel for heat transfer across layers. Its bonding state directly determines the overall thermal conductivity; even small changes in interface properties can cause the composite's thermal conductivity to jump by several orders of magnitude. Single-system models cannot simulate the thermodynamic properties of composite structures; they can only reflect the thermal properties of individual materials, not the overall heat transfer law of the composite. Ultimately, this leads to significant deviations between thermodynamic simulation results and actual experimental results, severely affecting the accuracy of the simulation and its engineering reference value.

[0025] To address the problems existing in the aforementioned related technologies, embodiments of the present invention provide a method, apparatus, and electronic device for thermodynamic simulation of composite structures.

[0026] As shown in Figure 1, the thermodynamic simulation method for composite structures provided in this embodiment of the invention includes: S1, obtaining a mesoscale model of the indium tin oxide-polyimide composite structure, as well as an initial crystal model of indium tin oxide and a polymer model of polyimide.

[0027] Specifically, the mesoscale model of the indium tin oxide (ITO)-polyimide (PI) composite structure is constructed using Monte Carlo software, such as the General Particle Transport Simulation Toolkit (GEANT4), the Monte Carlo N-Particle Transport Code (MCNP), or the Multifunctional Particle Transport and Interaction Simulation Program (FLUKA). The core of this model is used to extract primary recoil atom (PKA) data characterizing irradiation conditions. During modeling, not only the proton fluence gradient is input, but also a model based on the real space environment and the proton energy spectrum are systematically input. By simulating the trajectory of incident particles within the material, the model accurately obtains the differential information such as the average energy and quantity of PKA generated within ITO and PI. The initial crystal model of ITO, based on its intrinsic structural parameters (such as space group symmetry of Ia-3 and lattice constants a=b=c=10.12Å) and a Sn doping concentration of 10 at.%, can be constructed using VESTA software. For example, by using eight atoms located at Wyckoff II... Doping is achieved by replacing In atoms with Sn atoms at equivalent atomic sites. Then, structural relaxation and energy minimization are used to eliminate lattice distortion, providing an atomic-scale initial structure for irradiation damage evolution simulation. The polyimide single-molecule model can be constructed using Avogadro software (a molecular modeling and visualization tool). Four complete repeating imide structural units containing PI electron donor-acceptor characteristic units are polymerized to form a single-molecule polymer chain, ensuring that the chain segment has sufficient main chain conformational freedom. After construction, geometric optimization and energy minimization are performed to form the lowest energy stable conformational state, eliminating high-energy conformations and unreasonable atomic contacts, so as to achieve efficient matching with the ITO crystal model and provide an initial conformation for subsequent cascade process simulation. After constructing the single chains, the 100 single chains are then polymerized into a polymer model. It is recommended to "use Packmol software (molecular modeling and system filling software) to fill 100 PI single chains into a box of a set size at a density of 1.4 g / cm3 to form an overall PI model. That is, the overall model construction method is to first construct a single molecule, then construct a single molecular chain composed of 4 single molecules, and then construct a polymer model from 100 single molecular chains. Then, perform structural optimization and random defects."

[0028] S2, perform proton irradiation simulation on the mesoscopic scale model according to the preset proton irradiation conditions to obtain primary recoil atom data, wherein the primary recoil atom data includes indium tin oxide primary recoil atom data and polyimide primary recoil atom data.

[0029] Specifically, proton irradiation simulation is performed on the mesoscopic-scale model according to preset proton irradiation conditions. The core of this simulation is to input a proton energy spectrum covering the range of 0.1 MeV to 100 MeV, along with 5.296 × 10⁻⁶ ppm, into Monte Carlo software, based on a real space environment model (such as the Jupiter space environment model).15 Up to 2.648×10 16 p / cm 2 By setting up a complete proton irradiation physical reaction process, the gradient proton fluence is accurately simulated to depict the trajectory and interaction of incident protons in different regions of ITO and PI. During the simulation, the energy transfer effect generated after the collision of protons with material atoms is statistically analyzed to obtain PKA data within ITO and PI respectively. The primary recoil atom data for ITO includes the average energy, quantity, and energy spectrum distribution of all PKAs, while the PKA data for PI also covers the corresponding average energy, quantity, and energy spectrum characteristics. The two types of data will reflect the differences in PKA information caused by the essential differences between ITO (inorganic crystal) and PI (organic polymer), providing a precise set of input parameters for subsequent atomic-scale cascade collision simulations.

[0030] S3. Based on the primary recoil atom data of indium tin oxide and the initial crystal model of indium tin oxide, the defect rate of indium tin oxide material is obtained by cascade collision simulation.

[0031] Specifically, based on the primary recoil atom (PKA) data of indium tin oxide (ITO) obtained from Monte Carlo simulations (such as the kinetic energy, quantity, and spectral characteristics of the actual irradiation energy spectrum of Jupiter), and combined with the constructed initial ITO crystal model, cascade collision simulations were carried out. In the simulation, the initial ITO crystal model was first subjected to cell expansion processing. Based on the PKA data, PKA atoms were selectively chosen and assigned a characteristic kinetic energy of 4.23 keV. Their motion direction was controlled by differentiated incident velocities in the x, y, and z directions. The simulation captured the defect formation and evolution process. After the defect number stabilized, the ITO defect number was simulated using a double exponential function model: N1 = A1 + B1(1 / 2). e -Ct )×e -Dt The total number of defects is calculated, where N1 is the total number of defects in the ITO material at irradiation time t, A1 is the correction constant for defect evolution, B1 is the maximum potential defect capacity coefficient of the ITO material, C is the characteristic rate constant for the rapid defect formation stage, D is the characteristic rate constant for the stable defect stage, e is the natural constant, and t is the simulation time of proton irradiation. Combined with the total number of atoms in the model, the defect rate of ITO material under different proton irradiation conditions is calculated, providing accurate parameter support for the subsequent construction of a quantitative defect ITO model.

[0032] S4. Based on the primary recoil atom data of the polyimide and the single-chain model of the polyimide polymer, the cascade collision simulation is performed to obtain the defect rate of the polyimide material.

[0033] Specifically, based on primary recoil atom (PKA) data of polyimide (PI) obtained from Monte Carlo simulations, a PI polymer single-chain model was constructed (e.g., formed by the polymerization of four complete repeating imide structural units containing electron donor-acceptor characteristic units, and subjected to geometric optimization and energy minimization to the lowest energy stable conformation). For example, 100 PI single chains were first processed using Packmol software (molecular modeling and system filling software) at a density of 1.4 g / cm³. 3 The density of the material was filled into a box of a set size to form the overall PI model. Then, cascade collision simulation was carried out using LAMMPS software. LAMMPS (Large-scale Atomic / Molecular Massively Parallel Simulator) is an open-source large-scale atomic / molecular parallel simulation software. In the simulation, PKA atoms were selected according to the characteristic energy spectrum of PI and given a characteristic kinetic energy of 2.7 keV. The motion direction was controlled by the differential incident velocity in the x, y, and z directions. The Nose-Hoover hot bath method was used to control the irradiation temperature. The ReaxFF-lg potential function was used to accurately calculate the interatomic interaction. The simulation time was set to 10 ps to fully capture the defect formation and evolution process. After the number of defects stabilized, the total number of defects was counted based on the logarithmic function model of PI defect number N2=A2+B2×ln(t+E), where N2 is the total number of defects in PI material at irradiation time t, A2 is the initial defect baseline value of PI material, B2 is the defect multiplication coefficient of PI material, E is the time correction term of the corresponding defect evolution, and t is the simulation time. This allows us to clearly identify the types and distribution of irradiation-induced defects in PI materials, such as molecular chain breakage and bond disruption, providing accurate structural and parameter support for the subsequent construction of quantitative defect PI models.

[0034] S5. Based on the random defect introduction method, an indium tin oxide defect model is constructed according to the defect rate of the indium tin oxide material, and a polyimide defect model is constructed according to the defect rate of the polyimide material, wherein the indium tin oxide defect model and the polyimide defect model are matched in size.

[0035] Specifically, based on the random defect introduction method, and combining the obtained defect rates of ITO and PI materials, size-accurately matched ITO and PI defect models are constructed respectively: For ITO, a crystal model (e.g., size 38a×9a×3a, a=10.18Å, containing 82080 atoms, space group Ia-3, 10 at.% Sn doping) can be constructed using VESTA software as a basis, and the atom random selection command (delete_atoms) of LAMMPS software is used as a basis. The random defect introduction function removes a corresponding number of atoms based on the calculated defect rate, precisely introducing irradiation defects such as vacancies and interstitial atoms to ensure that the defect concentration corresponds to the service life. For PI, an initial model (containing 90,090 atoms in 391.24 Å × 98.89 Å × 103.88 Å, composed of 6 PI structures with 100 molecular chains each) can be constructed using Avogadro software as a blueprint. Similarly, the random defect introduction function of LAMMPS software is used to remove a specific proportion of atoms based on the PI defect rate to simulate defects such as molecular chain breakage and bond destruction, while strictly maintaining the precise fit of its length, width, and height dimensions with the size parameters of the ITO defect model. During the construction process, both types of models use statistical methods to ensure that the defect distribution is consistent with the multi-scale simulation prediction in a statistical sense, forming a basic model that includes quantitative defects and meets the required size for interface fit. This provides a structurally compatible prerequisite for the subsequent merging modeling of ITO-PI composite structures and the optimization of inorganic-organic interfaces.

[0036] S6, the indium tin oxide defect model and the polyimide defect model are combined to obtain a composite structure defect model.

[0037] Specifically, based on the previously constructed ITO and PI defect models with precise size matching, LAMMPS software was used to perform merged modeling to obtain a composite structural defect model. First, the two models were positioned according to the stacking method of "ITO on the bottom layer and PI on the top layer". Some atoms in the overlapping area between the lower layer of the PI model and the ITO interface were deleted to avoid atomic spatial conflicts. Then, the two types of defect models were fused using software commands. Their inorganic-organic interface was combined through van der Waals interactions. At the same time, an iterative energy minimization method and a dynamic relaxation process were initiated to gradually optimize the spatial arrangement and interaction state of atoms at the interface and eliminate interface stress and unreasonable contact. Finally, a composite structural defect model containing the corresponding defect concentration was constructed. This model completely preserves the vacancies and interstitial atomic defects of ITO and the molecular chain breakage and bonding destruction defects of PI. The interface structure is stable, providing an accurate structural basis for subsequent simulation of non-equilibrium molecular dynamics and thermal performance.

[0038] S7, perform energy transfer simulation on the composite structure defect model to obtain the temperature gradient curve.

[0039] Specifically, LAMMPS software was used to perform non-equilibrium molecular dynamics energy transfer simulations on the defect model of the ITO-PI composite structure containing quantitative defects to obtain temperature gradient curves. By plotting the temperature gradient curves that reflect the heat transfer characteristics of the composite structure, the evolution of the thermal performance of the composite structure under extreme proton irradiation can be accurately evaluated, providing quantitative data support for spacecraft thermal control system design, material selection, and mission risk assessment.

[0040] It should be noted that the construction of mesoscale models, indium tin oxide (ITO) initial crystal models, polyimide polymer single-chain models, ITO defect models, polyimide defect models, and composite structure defect models are based on the logical requirements of cross-scale simulation. The core function of the mesoscale model is to accurately extract key parameters such as the energy and quantity of primary recoil atoms by simulating the proton irradiation process using the Monte Carlo method. However, it lacks atomic or molecular-level microstructural details and cannot characterize the formation and evolution of defects such as vacancies and interstitial atoms. The influence of defects on the thermal properties of materials is essentially the result of atomic-level hot carrier scattering, and therefore requires atomic-level fundamental models such as ITO initial crystals and polyimide single molecules. Defect generation is achieved through cascaded collision simulation, which then constructs two defect models containing quantitative defects. The subsequent composite structure defect model needs to integrate these two defect models, construct and optimize the interface through van der Waals interactions, and restore the real structure of inorganic and organic composites. However, directly using the mesoscopic model will lose key information such as atomic-level defect distribution and interface interactions, resulting in thermodynamic tests (such as thermal conductivity calculations) that cannot accurately reflect the real physical mechanism and cannot accurately reflect the correlation between defect concentration and thermal performance under proton irradiation. Only by constructing a complete logical chain of "irradiation parameter acquisition - atomic-level modeling - defect generation - composite structure integration - performance testing" in stages can the accuracy and reliability of the simulation results be guaranteed.

[0041] In this embodiment, a mesoscale model of the indium tin oxide (ITO)-polyimide composite structure, an initial crystal model of ITO, and a polymer model of polyimide were obtained, providing a basic framework for the simulation of the true structure of the composite material and avoiding simulation deviations caused by distortion of the initial model. Proton irradiation simulation was performed on the mesoscale model according to preset proton irradiation conditions, and primary recoil atom data of ITO and PI were obtained differentially, ensuring the consistency of irradiation input parameters with the real space environment and providing accurate source data for subsequent defect simulation. Based on the primary recoil atom data of the two materials, cascade collision simulations were conducted to obtain the defect rates of the ITO and polyimide materials, accurately capturing the differentiated defect shapes of inorganic crystals and organic polymer materials. The study investigated the formation and evolution of defects, making the defect rate calculation more closely resemble the actual irradiation damage mechanism. A random defect introduction method was employed, constructing precisely sized defect models for indium tin oxide (ITO) and polyimide based on their respective defect rates, avoiding potential deviations when merging the two defect models. When merging the two defect models, optimization of the inorganic-organic interface resulted in a stable and realistic composite structure defect model, providing a reliable structural basis for heat transfer simulation. Finally, energy transfer simulation of the composite structure defect model yielded a temperature gradient curve closely resembling real experimental results. This curve accurately reflects the heat transfer characteristics under different defect concentrations, comprehensively improving the reliability and accuracy of the thermal simulation results.

[0042] Optionally, the mesoscale model includes an indium tin oxide layer and a polyimide layer; the step of performing proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data includes: obtaining the incident position and incident energy of the proton according to the proton irradiation conditions; performing proton irradiation simulation on the mesoscale model according to the incident position and incident energy to obtain the trajectory data corresponding to the proton; obtaining the atomic kinetic energy of the target atom colliding with the proton according to the trajectory data and a preset kinetic energy relationship; identifying the target atom with the atomic kinetic energy greater than a preset kinetic energy threshold as the initial recoil atom; generating the indium tin oxide primary recoil atom data according to the atomic proportion, energy distribution, and spatial distribution of all the primary recoil atoms in the indium tin oxide layer; and generating the polyimide primary recoil atom data according to the atomic proportion, energy distribution, and spatial distribution of all the primary recoil atoms in the polyimide layer.

[0043] Optionally, the trajectory data includes the proton energy before collision, the proton mass, the target atom mass, and the proton scattering angle; the kinetic energy relationship satisfies: Among them, E t E is the kinetic energy of the atom. p Let m be the energy before the proton collision. t m is the mass of the target atom.p Let θ be the mass of the proton and θ be the scattering angle of the proton.

[0044] In this optional embodiment, the preset proton irradiation conditions are first determined based on a real space environment model (such as Jupiter's orbit). These conditions cover an energy range of 0.1 MeV to 100 MeV and a maximum energy of 5.296 × 10⁻⁶ MeV. 15 Up to 2.648×10 16 p / cm 2 The gradient flux was used to determine the incident position (uniformly distributed on the model surface) and incident energy (allocated according to the true energy spectrum gradient) of protons in the mesoscopic model of indium tin oxide-polyimide. Subsequently, a proton irradiation simulation was initiated in Monte Carlo software. By setting up a complete proton irradiation physical reaction process, the motion path of each proton in the ITO and PI layers was tracked, accurately recording the trajectory of the proton before collision with material atoms, the collision site, and the energy loss after the collision, forming corresponding trajectory data. Based on the preset kinetic energy transfer relationship (combining proton incident energy, target atom mass, and collision angle to calculate energy transfer efficiency), the target atoms that collided with the protons (In, Sn, and O atoms in ITO, and C, H, and N atoms in PI) were deduced from the trajectory data. The atomic kinetic energy of O atoms is obtained; a preset kinetic energy threshold adapted to the material properties is set, and target atoms whose atomic kinetic energy exceeds the threshold and can trigger subsequent cascade collisions are identified as primary recoil atoms (PKA); finally, the atomic proportion (the proportion of different element atoms becoming PKAs), energy distribution (the distribution characteristics of PKA kinetic energy with the energy spectrum), and spatial distribution (the distribution of collision sites of PKAs in the ITO layer) of all primary recoil atoms in the ITO layer are statistically analyzed to generate ITO primary recoil atom data. At the same time, using the same statistical logic, based on the atomic proportion, energy distribution, and spatial distribution of all primary recoil atoms in the PI layer, polyimide primary recoil atom data is generated to provide accurate and realistic irradiation scenario input parameters for subsequent atomic-scale cascade collision simulations.

[0045] Optionally, the step of obtaining the defect rate of indium tin oxide material by performing cascade collision simulation based on the primary recoil atom data of indium tin oxide and the initial crystal model of indium tin oxide includes: expanding the cell of the initial crystal model of indium tin oxide to obtain an expanded cell model of indium tin oxide; and performing cascade collision simulation based on the primary recoil atom data of indium tin oxide to obtain the defect rate of indium tin oxide material.

[0046] Optionally, the indium tin oxide defect rate satisfies: Where η is the indium tin oxide defect rate, f is the comprehensive defect reservation factor, f0 is the effective triggering factor, k is the conversion factor, Y is the average defect yield per cascade, and N is the total defect yield. PKAN represents the total number of indium tin oxide primary recoil atoms determined using the indium tin oxide primary recoil atom data. total This represents the total number of atoms in the indium tin oxide expanded cell model.

[0047] In this optional embodiment, based on the constructed initial indium tin oxide crystal model (space group symmetry Ia-3, lattice constant a=b=c=10.12Å, 10 at.% doping achieved by replacing four In atoms at the second Wyckoff position with Sn atoms, and eliminating lattice distortion through structural relaxation and energy minimization), a cell expansion operation is performed on it using LAMMPS software to obtain an ITO expanded cell model with a size adapted to the requirements of cascade collision simulation, ensuring that the number of atoms and spatial scale of the model can completely capture the formation and evolution process of defects induced by proton irradiation; subsequently, based on the ITO primary recoil atom data obtained from previous Monte Carlo simulations (including kinetic energy, atomic ratio, and spatial distribution characteristics that conform to the real proton energy spectrum distribution), the difference is performed in the expanded cell model. PKA atoms were selectively induced and given a characteristic kinetic energy of 4.23 keV. Their motion direction was controlled by differential incident velocities in the x, y, and z directions. The irradiation temperature was controlled using the Nose-Hoover thermotherapy method. The interatomic interactions were accurately calculated using a hybrid potential function of ZBL (short-range potential function) and Buckingham (long-range potential function). The simulation duration was set to 10 ps to cover the complete cycle of defect formation from rapid formation to slow stabilization. After the number of defects stabilized at the end of the simulation, the total number of defects was counted based on the ITO defect number double exponential function model. Combined with the total number of atoms in the ITO expanded cell model, the defect rate of ITO material under the corresponding proton irradiation conditions was calculated, providing accurate concentration parameters to support the subsequent construction of a quantitative defect ITO model. Figure 2 shows the formation and evolution of defects in the indium tin oxide expanded cell model over time during the cascade collision process. A, b, c, and d represent the changes in defects in the indium tin oxide expanded cell model at four time points: 0 ps, ​​0.1 ps, 1 ps, and 2 ps, respectively.

[0048] Optionally, the step of obtaining the polyimide material defect rate by performing cascade collision simulation based on the polyimide primary recoil atom data and the polyimide polymer model includes: obtaining a constructed polyimide single-molecule model; polymerizing multiple polyimide single-molecule models to obtain a single polyimide polymer chain; constructing a polyimide polymer phase model based on multiple polyimide single polymer chains according to a preset density; performing the cascade collision simulation on the polyimide polymer model based on the polyimide primary recoil atom data to obtain the polyimide material defect rate; the polyimide material defect rate satisfies: Where μ is the defect rate of the polyimide material, μ max M is the saturation defect rate. PKAM0 is the total number of polyimide primary recoil atoms determined by the polyimide primary recoil atom data, where M0 is the number of atoms in the polyimide primary recoil atom data when the defect rate is equal to... The number of primary recoil atoms of polyimide corresponding to the time.

[0049] In this optional embodiment, firstly, a constructed polyimide monomolecular model is obtained. Specifically, four repeating imide structural units are selected and polymerized to form a polyimide monomolecular chain. Then, the monomolecular chain is subjected to geometric optimization and energy minimization processes in sequence to eliminate high-energy conformations and unreasonable atomic contacts of the monomolecular chain, so that the chain segment structure of the polyimide monomolecular chain is in a low-energy stable state. The selection of the four repeating imide structural units can ensure the conformational freedom of the main chain of the monomolecular chain and make the simulation system exhibit representative intra-chain torsion and inter-chain stacking behavior. At the same time, molecular simulation software (such as Materials) is used to simulate the process. Studio, LAMMPS, and other methods are used to combine the chemical structure characteristics of polyimide to ensure that the single-molecule model is consistent with the actual chemical structure of polyimide, laying the foundation for the subsequent polymerization process. Next, multiple polyimide single-molecule models are polymerized to obtain single polyimide polymer chains. Specifically, based on the polymerization reaction mechanism of polyimide, the single-molecule models are sequentially connected end-to-end through chemical bonding to form a long-chain polyimide polymerization model with a continuous main chain structure and complete conformational features. The polymerization process precisely preserves sufficient conformational freedom of the chain segments, enabling the resulting long chain to possess representative intra-chain torsion structure characteristics. Simultaneously, the degree of polymerization is controlled to match the molecular chain length of the actual material, ensuring the structural integrity and rationality of the single polymer chain. Then, multiple polyimide single polymer chains are used to construct a polyimide polymer phase model according to a preset density. The key feature is that the preset density is set with reference to the density parameters of the actual polyimide bulk material. The polyimide polymer phase model is subjected to isothermal and isobaric testing using an NPT ensemble on the initial polyimide polymer phase model. The process involves relaxation, during which polymer segments within the bulk model rearrange and entangle, dynamically adjusting the segment conformation, rearranging van der Waals interactions, and partially forming interchain hydrogen bonds until the bulk model reaches a stable thermodynamic equilibrium. This process yields a polymer bulk model that accurately reflects the glassy stacking structure of polyimide segments, simulating the internal spatial distribution of real polyimide materials. Finally, based on the primary recoil atom data of polyimide, a cascade collision simulation is performed on the polyimide polymer model to obtain the defect rate of the polyimide material. Specifically, relevant data on primary recoil atoms of polyimide (including key parameters such as the type, energy, and direction of motion of recoil atoms) are collected and used as input conditions to perform a cascade collision simulation on the constructed polyimide polymer model. By simulating the collision process, energy transfer, and atomic displacement between primary recoil atoms and other atoms in the model, the number and distribution of defects such as chemical bond breakage, atomic vacancies, and lattice distortion in the model are statistically analyzed, and the defect rate of the polyimide material is calculated.This process, through the step-by-step construction from single-molecule to bulk model, achieves accurate simulation of the microstructure of polyimide materials. Combined with cascade collision simulation using primary recoil atom data, it can efficiently and accurately obtain the defect rate of polyimide materials without the need for complex and time-consuming experimental testing. This reduces experimental costs, shortens the research cycle, and intuitively presents the defect formation mechanism, providing reliable theoretical support and data reference for the performance optimization, defect control, and application expansion of polyimide materials.

[0050] Optionally, the method for determining the termination of the cascade collision includes: after the cascade collision in the polyimide polymer model begins, obtaining the defect change rate of the number of defects over time; and determining that the cascade collision terminates when the defect change rate is less than a preset change rate.

[0051] In this optional embodiment, after the cascade collisions in the polyimide basic model are officially started, the real-time number of various defects such as bond breakage, vacancies, and interstitial atoms caused by atomic collisions and energy transfer within the model is continuously tracked and counted. The total number of defects corresponding to different time nodes is recorded synchronously. Based on the fluctuation of the number of defects over time, the defect change rate is calculated to accurately reflect the generation and evolution rate of defects during the cascade collision process. A preset change rate threshold is set in advance to determine the termination of the collision. This threshold is determined in combination with the characteristics of polyimide materials and the simulation accuracy requirements, which can effectively define the effective action stage of the cascade collision. During the collision process, the real-time calculated defect change rate is continuously compared with the preset change rate. When the real-time defect change rate is less than the preset change rate, it indicates that the energy transfer between atoms in the model has tended to be exhausted, the generation rate of new defects has decreased significantly and there are no obvious new defects, the polyimide molecule and atomic arrangement gradually tend to stabilize, and the core action process of the cascade collision has been completed. Based on this, the cascade collision of the polyimide basic model is determined to be terminated.

[0052] Optionally, the composite structural defect model includes an indium tin oxide (ITO) defect layer and a polyimide defect layer. The ITO defect layer is vertically positioned above the polyimide defect layer. The two ends of the composite structural defect model along the horizontal direction are respectively designated as cold source ends, and the middle part of the composite structural defect model is designated as a heat source end. The step of simulating energy transfer to obtain a temperature gradient curve from the composite structural defect model includes: injecting a first preset energy into the middle part of the composite structural defect model at a preset rate through the heat source end, and extracting a second preset energy from both ends of the composite structural defect model at the preset rate through the cold source end; after the temperature of the composite structural defect model stabilizes, acquiring the temperature gradient data of the composite structural defect model, wherein the temperature gradient data includes a one-to-one correspondence between temperature and blocks, and the blocks are obtained by equally dividing the composite structural defect model along the horizontal direction according to a preset number; and generating a temperature gradient curve corresponding to the composite structural defect model based on the temperature gradient data.

[0053] The temperature gradient data is determined based on the temperature data of the evenly divided blocks recorded under a preset number of steps and the average temperature data of each block recorded under a preset number of steps.

[0054] In this optional embodiment, as shown in FIG4, the composite structure defect model includes an indium tin oxide defect layer 1 and a polyimide defect layer 2, wherein the indium tin oxide defect layer 1 is attached to the polyimide defect layer 2 in the vertical direction, and the two form a composite structure stacked on top of each other. The two ends of the composite structure defect model in the horizontal direction are cold source ends, and the middle part of the model is a heat source end. The specific process for simulating energy transfer to obtain the temperature gradient curve of the composite structural defect model is as follows: First, an action is applied to the central region of the composite structural defect model from the heat source end, continuously injecting a first preset energy at a preset energy transfer rate. Simultaneously, a second preset energy is extracted from both horizontal ends of the composite structural defect model from the cold source end at the same preset rate, ensuring that the energy injection and extraction rates are consistent and guaranteeing the stability and controllability of energy transfer during the simulation. After the overall temperature fluctuation of the composite structural defect model is less than a preset threshold and reaches a thermodynamically stable state, the temperature gradient data of the composite structural defect model is collected. The core of this temperature gradient data is a one-to-one correspondence between temperature and blocks. The blocks are several continuous and uniformly sized unit regions formed by equally dividing the composite structural defect model horizontally according to a preset number. The divided blocks can accurately correspond to different positions in the horizontal direction of the model. Subsequently, based on this temperature gradient data, data mapping and fitting are performed with the blocks as the horizontal position dimension and the temperature of the corresponding blocks as the numerical dimension, finally generating the temperature gradient curve corresponding to the composite structural defect model.

[0055] For example, firstly, aerodynamic simulation is performed in the NVE ensemble (the core microcanonical ensemble in statistical mechanics and molecular dynamics simulations). A first preset energy (total injected energy 1 eV / ps) is continuously injected into the central heat source region of the composite structure defect model at a preset rate (1 eV / ps). Simultaneously, a second preset energy is extracted from both ends of the model through cold sources at the same preset rate (0.5 eV / ps extracted from each end), ensuring energy input and output balance and conforming to the heat transfer logic of non-equilibrium molecular dynamics simulations. During the simulation, the model first undergoes 300K thermal bath relaxation in the NVE ensemble (running 800,000 steps, time step 0.0005 ps) to eliminate high-energy conformations and unreasonable atomic contact before continuous... A 4,000,000-step energy transfer simulation (time step 0.0005 ps) was performed until the temperature of the composite structure defect model stabilized. Then, the model was uniformly divided into blocks along the horizontal direction according to a preset number (100 blocks). Based on the instantaneous temperature of each block recorded every 1,000 steps and the average temperature of each block output every 100,000 steps, temperature gradient data containing a one-to-one correspondence between temperature and corresponding blocks was obtained. For models with different defect concentrations, three sets of independent random numbers were used for repeated simulations to ensure data reliability. Finally, the temperature gradient data was processed and analyzed to plot temperature gradient curves that intuitively reflect the temperature distribution along the horizontal direction of the composite structure defect model. Figure 5 shows the temperature gradient curves for no defects, defect rates of 0.538%, 1.076%, 1.614%, 2.15%, and 2.69%, respectively. Further, from the temperature gradient curves corresponding to different defect concentrations, the temperature difference ΔT and heat flow path length Δx in the heat flow direction are extracted. Combined with the simulated preset heat power P and the cross-sectional area S of the composite structure, the thermal conductivity under each defect rate is calculated after unifying the units (e.g., Å to m, eV / ps to W) according to Fourier's law k = P × Δx / (S × ΔT). The error is reduced by averaging multiple independent simulations. Then, the defect rate is plotted on the x-axis and the thermal conductivity on the y-axis. Data is fitted using data visualization and scientific analysis software to generate the defect thermal conductivity curve shown in Figure 6, which intuitively reflects the relationship between the two and clearly presents the influence of the increase in defect rate on the thermal conductivity of the material.

[0056] As shown in Figure 7, an embodiment of the present invention provides a composite structure thermodynamic simulation device 700, comprising: an acquisition module 710, used to acquire a mesoscale model of an indium tin oxide (ITO)-polyimide composite structure, as well as an initial crystal model of ITO and a polyimide polymer model; an irradiation module 720, used to perform proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data, wherein the primary recoil atom data includes ITO primary recoil atom data and polyimide primary recoil atom data; a first collision module 730, used to perform cascade collision simulation based on the ITO primary recoil atom data and the initial crystal model of ITO to obtain the defect rate of the ITO material; and a second collision module 730. The two-collision module 740 is used to perform cascade collision simulations based on the primary recoil atom data of the polyimide and the polyimide polymer model to obtain the defect rate of the polyimide material; the construction module 750 is used to construct an indium tin oxide defect model based on the defect rate of the indium tin oxide material and a polyimide defect model based on the defect rate of the polyimide material, wherein the indium tin oxide defect model and the polyimide defect model are size-matched; the merging module 760 is used to merge the indium tin oxide defect model and the polyimide defect model to obtain a composite structure defect model; the generation module 770 is used to perform energy transfer simulations on the composite structure defect model to obtain a temperature gradient curve.

[0057] The composite structure thermodynamic simulation device of this embodiment is used to implement the composite structure thermodynamic simulation method as described above. Its advantages over the prior art are the same as the advantages of the composite structure thermodynamic simulation method over the prior art, and will not be repeated here.

[0058] As shown in Figure 8, an electronic device 800 provided in this embodiment of the invention includes a memory 810 and a processor 820; the memory 810 is used to store a computer program; the processor 820 is used to implement the composite structure thermodynamic simulation method as described above when the computer program is executed.

[0059] Alternatively, an electronic device 800 includes a memory 810 and a processor 820 coupled to the memory 810; the memory 810 is configured to store a computer program; the processor 820 is configured to, when executing the computer program, perform the following operations: acquire a mesoscale model of an indium tin oxide-polyimide composite structure, as well as an initial crystal model of indium tin oxide and a polyimide polymer model; perform proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data, wherein the primary recoil atom data includes indium tin oxide primary recoil atom data and polyimide primary recoil atom data; and perform proton irradiation simulation on the mesoscale model according to the indium tin oxide primary recoil atom data and... The defect rate of the indium tin oxide (ITO) material is obtained by cascade collision simulation using the initial crystal model of ITO; the defect rate of the polyimide material is obtained by cascade collision simulation using the primary recoil atom data of the polyimide and the polyimide polymer model; based on the random defect introduction method, an ITO defect model is constructed according to the defect rate of the ITO material, and a polyimide defect model is constructed according to the defect rate of the polyimide material, wherein the ITO defect model and the polyimide defect model are size-matched; the ITO defect model and the polyimide defect model are merged to obtain a composite structure defect model; and the temperature gradient curve is obtained by energy transfer simulation of the composite structure defect model.

[0060] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the composite structure thermodynamic simulation method described above.

[0061] Alternatively, a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following operations: acquire a mesoscale model of the indium tin oxide-polyimide composite structure, as well as an initial crystal model of indium tin oxide and a polymer model of polyimide; perform proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data, wherein the primary recoil atom data includes primary recoil atom data of indium tin oxide and primary recoil atom data of polyimide; and perform proton irradiation simulation based on the primary recoil atom data of indium tin oxide and the initial crystal model of indium tin oxide. The defect rate of indium tin oxide (ITO) material is obtained through cascade collision simulation. The defect rate of polyimide material is also obtained through cascade collision simulation based on the primary recoil atom data of the polyimide and the polyimide polymer model. An ITO defect model is constructed based on the ITO defect rate using a random defect introduction method, and a polyimide defect model is constructed based on the polyimide defect rate, wherein the ITO defect model and the polyimide defect model are size-matched. A composite structure defect model is obtained by merging the ITO defect model and the polyimide defect model. Energy transfer simulation is performed on the composite structure defect model to obtain a temperature gradient curve.

[0062] The present invention will now be described an electronic device 800 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic device 800 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 800 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0063] Electronic device 800 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0064] 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 program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0065] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A thermodynamic simulation method for composite structures, characterized in that, include: A mesoscale model of the indium tin oxide-polyimide composite structure, as well as an initial crystal model of indium tin oxide and a polymer model of polyimide, were obtained. Proton irradiation simulation is performed on the mesoscopic-scale model according to preset proton irradiation conditions to obtain primary recoil atom data, which includes primary recoil atom data of indium tin oxide (ITO) and polyimide. Cascade collision simulation is performed based on the ITO primary recoil atom data and the initial ITO crystal model to obtain the ITO material defect rate. Cascade collision simulation is performed based on the polyimide primary recoil atom data and the polyimide polymer model to obtain the polyimide material defect rate. Based on the random defect introduction method, an ITO defect model is constructed according to the ITO material defect rate, and a polyimide defect model is constructed according to the polyimide material defect rate, wherein the ITO defect model and the polyimide defect model are size-matched. The ITO defect model and the polyimide defect model are merged to obtain a composite structure defect model. Energy transfer simulation is performed on the composite structure defect model to obtain a temperature gradient curve.

2. The composite structure thermodynamic simulation method according to claim 1, characterized in that, The mesoscale model includes an indium tin oxide layer and a polyimide layer. The step of performing proton irradiation simulation on the mesoscale model according to preset proton irradiation conditions to obtain primary recoil atom data includes: obtaining the incident position and incident energy of the proton according to the proton irradiation conditions; performing proton irradiation simulation on the mesoscale model according to the incident position and incident energy to obtain trajectory data corresponding to the proton; obtaining the atomic kinetic energy of the target atom colliding with the proton according to the trajectory data and a preset kinetic energy relationship; identifying the target atom with an atomic kinetic energy greater than a preset kinetic energy threshold as the initial recoil atom; generating the indium tin oxide primary recoil atom data based on the atomic percentage, energy distribution, and spatial distribution of all the primary recoil atoms in the indium tin oxide layer; and generating the polyimide primary recoil atom data based on the atomic percentage, energy distribution, and spatial distribution of all the primary recoil atoms in the polyimide layer.

3. The composite structure thermodynamic simulation method according to claim 2, characterized in that, The trajectory data includes the proton's energy before collision, proton mass, target atom mass, and proton scattering angle; the kinetic energy relationship satisfies: Among them, E t E is the kinetic energy of the atom. p Let m be the energy before the proton collision. t m is the mass of the target atom. p Let θ be the mass of the proton and θ be the scattering angle of the proton.

4. The composite structure thermodynamic simulation method according to claim 1, characterized in that, The step of obtaining the defect rate of indium tin oxide material by performing cascade collision simulation based on the primary recoil atom data of indium tin oxide and the initial crystal model of indium tin oxide includes: expanding the cell of the initial crystal model of indium tin oxide to obtain an expanded cell model of indium tin oxide; and performing cascade collision simulation based on the primary recoil atom data of indium tin oxide and the expanded cell model of indium tin oxide to obtain the defect rate of indium tin oxide material.

5. The composite structure thermodynamic simulation method according to claim 4, characterized in that, The indium tin oxide defect rate satisfies: Where η is the indium tin oxide defect rate, f is the comprehensive defect reservation factor, f0 is the effective triggering factor, k is the conversion factor, Y is the average defect yield per cascade, and N is the total defect yield. PKA N represents the total number of indium tin oxide primary recoil atoms determined using the indium tin oxide primary recoil atom data. total This represents the total number of atoms in the indium tin oxide expanded cell model.

6. The composite structure thermodynamic simulation method according to claim 1, characterized in that, The step of obtaining the defect rate of polyimide material by performing cascade collision simulation based on the primary recoil atom data of polyimide and the polyimide polymer model includes: obtaining a constructed polyimide single-molecule model; polymerizing multiple polyimide single-molecule models to obtain a single polyimide polymer chain; constructing a polyimide polymer phase model based on multiple polyimide single polymer chains according to a preset density; performing the cascade collision simulation on the polyimide polymer model based on the primary recoil atom data of polyimide to obtain the defect rate of the polyimide material; the defect rate of the polyimide material satisfies: Where μ is the defect rate of the polyimide material, μ max M represents the saturation defect rate. PKA M0 is the total number of polyimide primary recoil atoms determined by the polyimide primary recoil atom data, where M0 is the number of primary recoil atoms when the defect rate is equal to... The number of primary recoil atoms of polyimide corresponding to the time.

7. The composite structure thermodynamic simulation method according to claim 6, characterized in that, The method for determining the termination of cascade collisions includes: after the cascade collisions in the polyimide polymer model begin, obtaining the defect change rate of the number of defects over time; when the defect change rate is less than a preset change rate, determining that the cascade collisions have terminated.

8. The composite structure thermodynamic simulation method according to claim 1, characterized in that, The composite structural defect model includes an indium tin oxide (ITO) defect layer and a polyimide (PI) defect layer. The ITO defect layer is vertically positioned above the PI defect layer. The two ends of the composite structural defect model along the horizontal direction are respectively designated as cold source ends, and the middle part of the composite structural defect model is designated as a heat source end. The process of simulating energy transfer to obtain a temperature gradient curve from the composite structural defect model includes: injecting a first preset energy into the middle part of the composite structural defect model at a preset rate through the heat source ends, and extracting a second preset energy from both ends of the composite structural defect model at a preset rate through the cold source ends; after the temperature of the composite structural defect model stabilizes, acquiring the temperature gradient data of the composite structural defect model, wherein the temperature gradient data includes a one-to-one correspondence between temperature and blocks, and the blocks are obtained by equally dividing the composite structural defect model along the horizontal direction according to a preset number; and generating a temperature gradient curve corresponding to the composite structural defect model based on the temperature gradient data.

9. A composite structure thermodynamic simulation device, characterized in that, include: The acquisition module is used to acquire the mesoscale model of the indium tin oxide-polyimide composite structure, as well as the initial crystal model of indium tin oxide and the polymer model of polyimide. The system comprises the following modules: an irradiation module for simulating proton irradiation on the mesoscopic model under preset proton irradiation conditions to obtain primary recoil atom data, which includes primary recoil atom data of indium tin oxide (ITO) and polyimide; a first collision module for performing cascade collision simulations based on the ITO primary recoil atom data and the initial ITO crystal model to obtain the ITO material defect rate; a second collision module for performing cascade collision simulations based on the polyimide primary recoil atom data and the polyimide polymer model to obtain the polyimide material defect rate; a construction module for constructing an ITO defect model based on the ITO material defect rate using a random defect introduction method, and constructing a polyimide defect model based on the polyimide material defect rate, wherein the ITO defect model and the polyimide defect model are size-matched; a merging module for merging the ITO defect model and the polyimide defect model to obtain a composite structure defect model; and a generation module for performing energy transfer simulations on the composite structure defect model to obtain a temperature gradient curve.

10. An electronic device, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the composite structure thermodynamic simulation method as described in any one of claims 1 to 8 when the computer program is executed.

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