Numerical simulation method and system for working process of adn-based space engine

Abstract

The application relates to the technical field of computer simulation, and provides a numerical simulation method and system for the working process of an ADN-based space engine, which comprises the following steps: collecting historical physical and chemical attribute data of ADN-based propellants under different combustion conditions, and constructing a combustion process simulation model containing chemical reaction dynamics action relationship and fluid mechanics action relationship; inputting propellant initial state data into the model, simulating the energy release process in the combustion chamber after ignition triggering, and obtaining simulation analysis results containing flow state and temperature gradient influence factors; generating an energy release state atlas of the propellant in the ignition stage according to the simulation analysis results to represent the energy release intensity distribution at different moments; mining the space-time evolution law of the energy release of the propellant based on the energy release state atlas, and outputting an energy release characteristic analysis report to guide the optimization of the combustion efficiency of the engine.

Description

Technical Field

[0001] This application belongs to the field of computer simulation technology, specifically relating to a numerical simulation method and system for the working process of an ADN-based space engine. Background Technology

[0002] In the aerospace field, the performance of ADN-based space engines is crucial for the normal and safe operation of spacecraft, and accurately simulating the combustion process of ADN-based propellants is one of the important means to optimize engine performance. Existing technologies have made some progress in simulating the combustion process of ADN-based propellants. Early research mainly focused on simulating single factors, such as considering a specific aspect of chemical reaction kinetics or fluid dynamics.

[0003] With technological advancements, some methods have begun to attempt to combine the two, but shortcomings remain in the combination method and accuracy. However, existing technologies have significant problems: on the one hand, current simulation methods struggle to comprehensively and accurately reflect the actual combustion process of ADN-based propellants under different combustion conditions, resulting in low reliability of simulation results; on the other hand, the lack of in-depth analysis of the spatiotemporal evolution of propellant energy release prevents the provision of targeted suggestions for optimizing engine combustion efficiency. Summary of the Invention

[0004] This application provides a numerical simulation method and system for the working process of an ADN-based space engine.

[0005] In a first aspect, embodiments of this application provide a numerical simulation method for the working process of an ADN-based space engine, applied to a numerical simulation system for the working process of an ADN-based space engine, the method comprising: Historical physicochemical property data of ADN-based propellants under different combustion conditions are collected. Based on the historical physicochemical property data, a combustion process simulation model of ADN-based propellants is constructed. The combustion process simulation model includes chemical reaction kinetics and hydrodynamics relationships. The initial state data of the ADN-based propellant is input into the combustion process simulation model to simulate the energy release process of the ADN-based propellant in the combustion chamber after ignition, and the simulation analysis results including the influencing factors of flow state and temperature gradient are obtained. Based on the flow state influencing factors and temperature gradient influencing factors in the simulation analysis results, an energy release state spectrum of the ADN-based propellant during the ignition stage is generated. The energy release state spectrum is used to characterize the energy release intensity distribution at different times. Based on the energy release state map, the spatiotemporal evolution of propellant energy release is explored, and an energy release characteristic analysis report is output. This energy release characteristic analysis report is used to guide the optimization of engine combustion efficiency.

[0006] Secondly, embodiments of this application provide a numerical simulation system for the working process of an ADN-based space engine, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the above-described method.

[0007] Thirdly, embodiments of this application provide a computer-readable storage medium including a computer program. When the computer program is run on an ADN-based space engine operating process numerical simulation system, the computer program is used to cause the ADN-based space engine operating process numerical simulation system to perform the steps of the above-described method.

[0008] This application embodiment collects historical physicochemical property data of ADN-based propellants under different combustion conditions and constructs a combustion process simulation model that includes chemical reaction kinetics and hydrodynamics. This achieves a comprehensive and accurate simulation of the propellant combustion process. This model, which comprehensively considers both types of interactions, can more realistically reflect the actual situation of the propellant in the combustion chamber and avoids the limitations of single-factor simulation in the prior art.

[0009] Furthermore, the initial state data of ADN-based propellant is input into the model to simulate the energy release process, and the simulation analysis results include the influencing factors of flow state and temperature gradient. Then, the energy release state map generated based on the influencing factors of flow state and temperature gradient can intuitively show the distribution of energy release intensity of propellant at different times during the ignition stage, thereby comprehensively recording the spatiotemporal changes of energy release.

[0010] Furthermore, based on the energy release state map, the spatiotemporal evolution of propellant energy release is mined, and an energy release characteristic analysis report is output. By deeply analyzing the periodicity of energy release, spatial expansion patterns, and energy distribution deviations, the propellant formulation or combustion chamber structural characteristics can be adjusted in a targeted manner, thereby improving the engine's combustion efficiency and performance. This solves the problem that existing technologies cannot provide specific suggestions for the refined design of engines.

[0011] In summary, the embodiments of this application improve the accuracy of numerical simulation of the working process of ADN-based space engines, and can provide a reliable reference for the optimized design of aerospace engines. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating a numerical simulation method for the working process of an ADN-based space engine provided in an embodiment of this application.

[0013] Figure 2This is a schematic diagram of the structure of a numerical simulation system for the working process of an ADN-based space engine provided in an embodiment of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this application. Obviously, the described embodiments are only some embodiments of the technical solutions of this application, and not all embodiments. Based on the embodiments recorded in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the technical solutions of this application.

[0015] See Figure 1 This is a numerical simulation method for the working process of an ADN-based space engine provided in the embodiments of this application. This method can be applied to the numerical simulation system for the working process of an ADN-based space engine. The specific process is as follows: steps 110-140.

[0016] Step 110: Collect historical physicochemical property data of ADN-based propellants under different combustion conditions, and construct a combustion process simulation model of ADN-based propellants based on the historical physicochemical property data. The combustion process simulation model includes chemical reaction kinetics and hydrodynamics relationships.

[0017] In the example application scenario of this application, in order to accurately construct a simulation model of the combustion process of ADN-based propellant, it is necessary to comprehensively collect historical physicochemical property data of the ADN-based propellant under different combustion conditions. Combustion conditions cover a variety of factors, such as different pressures, temperatures, oxygen contents, and combustion times. The sources of historical physicochemical property data are very extensive, including previous engine test experimental records, laboratory simulated combustion experimental data, theoretical calculation results, and relevant literature.

[0018] Specifically, the collected data may include physicochemical parameters of ADN-based propellants, such as density, specific heat capacity, thermal conductivity, chemical reaction rate constant, and diffusion coefficient. These parameters change under different combustion conditions, significantly impacting the combustion process of ADN-based propellants. For example, under high temperature and high pressure environments, the chemical reaction rate of ADN-based propellants will accelerate, and the thermal conductivity may also change.

[0019] Furthermore, a simulation model of the combustion process of ADN-based propellants is constructed based on collected historical physicochemical property data. This model needs to consider both chemical reaction kinetics and hydrodynamics. Chemical reaction kinetics describes the chemical reaction process of the propellant during combustion, including the consumption of reactants, the formation of intermediate products, and the formation of final products. This involves multiple chemical reaction steps, each with its defined reaction rate and mechanism. Hydrodynamics focuses on the flow characteristics of the fluid within the combustion chamber, such as velocity, pressure distribution, and turbulence intensity. Fluid flow affects the mixing of reactants, heat transfer, and the emission of combustion products, thus significantly influencing the combustion process.

[0020] When constructing the model, historical data is first organized and analyzed to determine the relationships between various physicochemical parameters. Then, based on the fundamental principles of chemical reaction kinetics and fluid mechanics, a corresponding mathematical model is established. This mathematical model describes the propellant combustion process through corresponding equations, including but not limited to mass conservation equations, energy conservation equations, momentum conservation equations, and chemical reaction kinetic equations. Finally, these equations are coupled to form a complete combustion process simulation model.

[0021] In this embodiment of the application, step 110 further includes: Step 111: Obtain chemical reaction rate data and molecular diffusion characteristic data from the historical physicochemical property data, and establish a chemical reaction kinetics database. The chemical reaction kinetics database contains the ADN decomposition reaction pathway and the generation and consumption patterns of each intermediate product.

[0022] Chemical reaction rate data and molecular diffusion characteristic data were extracted from the collected historical physicochemical property data. The chemical reaction rate data reflects the speed at which ADN-based propellants undergo chemical reactions under different conditions, and is influenced by various factors such as temperature, pressure, and reactant concentration. The molecular diffusion characteristic data describes the ability of molecules to diffuse within the medium.

[0023] A chemical reaction kinetics database was established using the above data. This database details the ADN decomposition reaction pathway, illustrating how ADN is progressively decomposed into various intermediate products. The ADN decomposition reaction pathway involves multiple decomposition steps, each with its defined reaction rate and conditions. Furthermore, the database clarifies the formation and consumption patterns of each intermediate product, showing how some intermediate products are formed and consumed at different time points.

[0024] To ensure the accuracy and completeness of the database, data validation and calibration are necessary. This can be achieved by comparing the data with experimental data and adjusting the parameters in the database to ensure that the model's predictions align with actual conditions. Furthermore, machine learning algorithms can be used to optimize the database and improve the model's predictive accuracy.

[0025] Step 112: Collect fluid flow characteristic data in the combustion chamber, and construct a fluid dynamics interaction description based on the fluid flow characteristic data. The fluid dynamics interaction description includes the flow field distribution law and pressure propagation characteristics.

[0026] Inside the combustion chamber, various sensors, including flow velocity sensors, pressure sensors, and temperature sensors, collect data on fluid flow characteristics. These sensors are installed at different locations within the combustion chamber to obtain comprehensive fluid information. The data collected by the sensors includes parameters such as fluid velocity, flow direction, pressure, and temperature, which vary at different times and locations.

[0027] Based on the collected fluid flow characteristic data, a fluid dynamics interaction description is constructed, which includes the flow field distribution law, that is, how the fluid flow state is distributed at different locations within the combustion chamber. The flow field distribution law is affected by factors such as the geometry of the combustion chamber, the design of the injectors, and the propellant injection method. For example, near the injectors, the fluid velocity is higher, forming a high-speed jet; while near the combustion chamber walls, the fluid velocity is lower, forming a boundary layer.

[0028] Pressure propagation characteristics are also an important aspect of describing fluid dynamics interactions. The propagation of pressure within the combustion chamber affects the stability and efficiency of combustion. By analyzing pressure propagation characteristics, we can understand the propagation speed, reflection, and refraction of pressure waves, as well as the impact of pressure fluctuations on the combustion process.

[0029] When constructing a description of fluid dynamic interactions, the collected data is first preprocessed to remove noise and outliers. Then, using the fundamental principles of fluid mechanics, corresponding mathematical models are established. These models describe the flow characteristics of the fluid through equations, such as the Navier-Stokes equations and the continuity equation. Finally, the equations are solved to obtain numerical solutions for the flow field distribution and pressure propagation characteristics.

[0030] Step 113: Couple the chemical reaction kinetics database with the fluid dynamics interaction description, and realize bidirectional feedback between chemical reaction and fluid flow through interface interaction to generate a preliminary simulation model.

[0031] The established chemical reaction kinetics database is coupled with the constructed fluid dynamics interaction description, that is, the two processes of chemical reaction and fluid flow are linked together, and bidirectional feedback between chemical reaction and fluid flow is realized through interface interaction.

[0032] During combustion, chemical reactions generate heat and undergo changes in matter, which affect the flow state of fluids. For example, the heat released by a chemical reaction causes the fluid temperature to rise, leading to fluid expansion and changes in flow velocity; the formation of reaction products alters the fluid's composition and density, thus affecting its flow characteristics. Conversely, fluid flow also influences chemical reactions. Fluid flow can promote the mixing of reactants, increasing the rate of chemical reactions; simultaneously, fluid flow can remove heat and products generated during combustion, affecting the equilibrium and extent of the reaction.

[0033] To achieve bidirectional feedback between chemical reactions and fluid flow, it is necessary to establish corresponding interfacial interactions in the model. This can be achieved by introducing mutually coupled terms into the chemical reaction kinetics equations and the fluid dynamics equations. For example, the influence of fluid flow on reactant concentration can be considered in the chemical reaction kinetics equations; while the influence of heat generated by the chemical reaction and changes in matter on the fluid state can be considered in the fluid dynamics equations.

[0034] Through a two-way feedback mechanism, a preliminary simulation model is generated. This model can comprehensively consider the interaction between chemical reactions and fluid flow, and more accurately simulate the combustion process of ADN-based propellants in the combustion chamber.

[0035] Step 114: Perform boundary condition calibration on the preliminary simulation model, adjust the thermal conductivity of the combustion chamber wall and the initial propellant filling density, and obtain the combustion process simulation model of the ADN-based propellant.

[0036] The initially generated simulation model may contain some errors and requires boundary condition calibration. Boundary conditions refer to the physical state and parameters of the model at the boundaries, which have a significant impact on the calculation results. In this embodiment, the main adjustments are made to the thermal conductivity characteristics of the combustion chamber walls and the initial propellant packing density.

[0037] The thermal conductivity of the combustion chamber walls affects heat transfer and temperature distribution within the combustion chamber. Inaccurate thermal conductivity settings can lead to discrepancies between model-predicted temperatures and actual temperatures. Therefore, the thermal conductivity coefficient of the inner walls needs to be adjusted according to actual conditions to allow the model to more accurately simulate the heat transfer process.

[0038] Initial propellant packing density is also an important parameter. Different packing densities can lead to changes in reactant concentration and pressure distribution during combustion, thus affecting combustion stability and efficiency. By adjusting the initial propellant packing density, the combustion process can be optimized, improving engine performance.

[0039] During boundary condition calibration, the preliminary simulation model was first calculated multiple times, and the calculation results were observed under different boundary conditions. Then, based on actual experimental data or experience, the thermal conductivity characteristics of the combustion chamber walls and the initial propellant filling density were gradually adjusted to make the model's calculation results as close as possible to the actual situation. After multiple iterations and adjustments, an accurate simulation model of the combustion process of ADN-based propellant was obtained.

[0040] Step 120: Input the initial state data of the ADN-based propellant into the combustion process simulation model to simulate the energy release process of the ADN-based propellant in the combustion chamber after ignition, and obtain the simulation analysis results including the influencing factors of flow state and temperature gradient.

[0041] The initial state data of the ADN-based propellant is input into the constructed combustion process simulation model. The initial state data includes information such as the initial temperature, initial pressure, initial density, and initial composition of the propellant.

[0042] The model was launched to simulate the energy release process of ADN-based propellant in the combustion chamber after ignition. Ignition is the starting point of the combustion process, providing sufficient energy to initiate the chemical reaction of the propellant. After ignition, the propellant begins to undergo combustion, releasing a large amount of energy.

[0043] During combustion, various factors influence the process, with flow state and temperature gradient being key areas of focus. Flow state factors include fluid velocity, flow direction, and turbulence intensity. Fluid flow affects reactant mixing, heat transfer, and combustion product emissions, thus significantly impacting the combustion process. Temperature gradient factors reflect temperature variations at different locations within the combustion chamber. The presence of a temperature gradient leads to heat transfer and mass diffusion, affecting combustion stability and efficiency.

[0044] By analyzing the model's calculation results, simulation analysis results incorporating factors affecting flow state and temperature gradient are obtained. The analysis process begins with organizing and visualizing the model's output data, such as plotting velocity and temperature distribution maps. Then, data analysis methods are used to extract key information about the influencing factors of flow state and temperature gradient, such as the maximum velocity and the average temperature gradient. Finally, the information is summarized and analyzed to obtain detailed simulation analysis results regarding the propellant combustion process.

[0045] In a preferred embodiment, step 120 includes: Step 121: Analyze the initial temperature distribution and initial pressure characteristics of the propellant in the initial state data, and set the initial temperature distribution and initial pressure characteristics as the initial boundary conditions of the combustion process simulation model.

[0046] The initial state data of the input ADN-based propellant were analyzed, with a focus on the initial temperature distribution and initial pressure characteristics. The initial temperature distribution indicates the initial temperature of the propellant at different locations within the combustion chamber, and is influenced by various factors, such as propellant storage conditions and combustion chamber preheating. The initial pressure characteristics reflect the magnitude and distribution of the initial pressure, which significantly impacts the propellant's combustion process.

[0047] The initial temperature distribution and pressure characteristics of the propellant obtained from the analysis were set as the initial boundary conditions for the combustion process simulation model. The initial boundary conditions are the starting point for the model calculations and have a significant impact on subsequent calculation results. In the model, the initial temperature distribution and initial pressure characteristics were input as boundary conditions into the corresponding equations to ensure that the model starts the calculations from the correct initial state.

[0048] When setting initial boundary conditions, it is necessary to consider their accuracy and rationality. Inaccurate initial temperature distribution and pressure characteristics may lead to discrepancies between the model's calculations and actual conditions. Therefore, careful verification and calibration of the initial state data are required to ensure their accuracy and reliability.

[0049] Step 122: Start the ignition trigger sub-model of the combustion process simulation model, set the ignition energy input position and energy input mode, and simulate the ignition energy excitation process of the local propellant.

[0050] The ignition trigger sub-model in the combustion process simulation model is activated. This sub-model is used to simulate the ignition process. By setting the ignition energy input position and energy input method, it simulates the ignition energy's excitation process on the local propellant.

[0051] The location of the ignition energy input determines where combustion begins within the combustion chamber, and this choice affects the starting point and propagation process of combustion. For example, ignition near the injector allows the propellant to mix and burn more quickly, while ignition at the center of the combustion chamber may result in a more uniform combustion process.

[0052] Energy input methods include whether a large amount of energy is input instantaneously or a certain amount of energy is input continuously. Different energy input methods will have different effects on the ignition process. Instantaneous input of a large amount of energy can rapidly increase the temperature of the local propellant, triggering a violent chemical reaction; while continuous input of a certain amount of energy can maintain the stability of combustion.

[0053] When activating the ignition trigger sub-model, the ignition energy input location and method are set according to the actual situation. Then, the model begins to simulate the activation process of the ignition energy on the local propellant. In this process, the propellant absorbs the ignition energy, its temperature rises, and a chemical reaction begins. As the reaction proceeds, more energy is released, further promoting the propagation of combustion.

[0054] Step 123: Calculate the decomposition reaction rate of the ADN-based propellant in the ignition region using the chemical reaction kinetics relationship of the combustion process simulation model, and analyze the flow and diffusion process of the reaction products in combination with the fluid dynamics relationship to obtain the influencing factors of the flow state, which include velocity distribution and turbulence intensity.

[0055] The decomposition rate of ADN-based propellant in the ignition region was calculated using chemical reaction kinetics from a combustion simulation model. In the ignition region, the propellant absorbs ignition energy and begins the decomposition reaction. Based on data and correlations from a chemical reaction kinetics database, combined with current temperature, pressure, and reactant concentrations, the decomposition rate was calculated.

[0056] The calculation of the decomposition reaction rate involves multiple chemical reaction steps, each with its own defined rate constant and reaction mechanism. By comprehensively considering the reaction steps, the overall decomposition reaction rate of ADN-based propellant in the ignition region is obtained.

[0057] Simultaneously, the flow and diffusion process of the reaction products is analyzed in conjunction with fluid dynamics. The reaction products diffuse along with the fluid flow, and this diffusion process is influenced by factors such as fluid velocity, flow direction, and turbulence intensity. By analyzing this process, the influencing factors of the flow state are obtained, including velocity distribution and turbulence intensity.

[0058] Velocity distribution describes the flow rate of fluid at different locations within the combustion chamber. Near the ignition zone, the fluid velocity is higher due to the vigorous reaction; while further away from the ignition zone, the fluid velocity is lower. Turbulence intensity reflects the degree of fluid disturbance. In a turbulent environment, the fluid mixes more thoroughly, and the diffusion rate of reaction products is accelerated.

[0059] When analyzing the flow and diffusion process of reaction products, the formation rate and concentration distribution of the reaction products are first calculated based on chemical reaction kinetics. Then, this information is input into a fluid dynamics model, and the flow and diffusion of the reaction products are calculated in conjunction with the fluid's flow characteristics. Finally, factors influencing the flow state, such as velocity distribution and turbulence intensity, are extracted from the calculation results.

[0060] In an alternative embodiment, step 123 includes: Step 1231: Initialize the reaction progress variable of the chemical reaction kinetic relationship, and set the initial weight distribution of the reaction path. The reaction progress variable is used to characterize the degree of progress of each decomposition reaction.

[0061] The reaction progress variable in the chemical reaction kinetics is initialized. The reaction progress variable characterizes the extent of each decomposition reaction and is a time-varying quantity. Initially, the value of the reaction progress variable is zero, indicating that the reaction has not yet started.

[0062] Simultaneously, an initial weight distribution for the reaction pathways is set. Different reaction pathways may have different importance in the reaction process, and this difference is reflected by setting the initial weight distribution. For example, some reaction pathways are more likely to occur in the initial stage, so these pathways are given higher weights; while some reaction pathways that are less likely to occur are given lower weights.

[0063] When initializing the reaction progress variable and setting the initial weight distribution of the reaction path, various factors need to be considered, such as the concentration of reactants, temperature, and pressure. These factors will affect the reaction rate and direction, and thus affect the weight distribution of the reaction progress variable and the reaction path.

[0064] Step 1232: Within each calculation time step, calculate the decomposition reaction rate of the ADN-based propellant based on the current reaction progress variable, and generate a reaction rate time series.

[0065] Within each computational time step, the decomposition reaction rate of the ADN-based propellant is calculated based on the current reaction progress variable. As time progresses, the reaction progress variable changes continuously, and therefore the decomposition reaction rate also changes accordingly.

[0066] When calculating the decomposition reaction rate, data and correlations from a chemical reaction kinetics database are used, along with current conditions such as temperature, pressure, reactant concentration, and the value of the reaction progress variable, to calculate the decomposition reaction rate. Since the reaction progress variable changes over time, the calculated decomposition reaction rate will differ at each time step.

[0067] The decomposition reaction rate calculated at each time step is recorded to generate a reaction rate time series, which reflects how the reaction rate changes over time.

[0068] Step 1233: Input the reaction rate time series into the fluid dynamics interaction relationship, calculate the mass flux and momentum flux of the reaction products, and obtain preliminary flow state parameters.

[0069] The generated reaction rate time series is input into the fluid dynamics interaction model. The reaction rate time series reflects the change in the formation rate of reaction products over time. By inputting it into the fluid dynamics model, the mass flux and momentum flux of the reaction products can be calculated.

[0070] Mass flux represents the mass passing through a unit area per unit time, and is related to the rate of product formation and fluid velocity. Momentum flux, on the other hand, reflects the transfer of momentum and is related to the mass flux of products and fluid velocity.

[0071] The calculation of the mass and momentum fluxes of the reaction products is based on the fundamental principles and relevant equations of fluid mechanics, combined with the reaction rate time series and the flow characteristics of the fluid. The calculations yield the mass and momentum fluxes of the reaction products, and subsequently, preliminary flow state parameters such as velocity and pressure are obtained.

[0072] Step 1234: Determine whether the preliminary flow state parameters meet the preset convergence conditions. If not, adjust the reaction path weight distribution of the chemical reaction kinetics relationship and recalculate the decomposition reaction rate.

[0073] The preset convergence conditions are used to determine whether the initial flow state parameters have reached a reasonable stable state. The convergence conditions can be set according to the actual situation, such as the rate of change of flow velocity being less than a certain threshold, or the pressure fluctuation range being within a certain range.

[0074] If the initial flow state parameters do not meet the convergence condition, it indicates that the current reaction path weight distribution may be unreasonable and needs to be adjusted. Adjusting the reaction path weight distribution can change the direction and rate of the reaction, thereby affecting the formation and flow of reaction products.

[0075] When adjusting the weight distribution of reaction paths, the weight distribution is modified appropriately based on the differences between the initial flow state parameters and the convergence conditions. For example, if the weight of a certain reaction path is found to be too high, resulting in an overly vigorous reaction, the weight of that path can be reduced; conversely, if the weight of a certain reaction path is too low, resulting in a slow reaction progress, the weight of that path can be increased.

[0076] After adjusting the reaction path weight distribution, recalculate the decomposition reaction rate and repeat the above steps until the initial flow state parameters meet the convergence condition.

[0077] Step 1235: If the convergence condition is met, the preliminary flow state parameters are determined as the influencing factors of the flow state, and the reaction progress variable of the current time step is stored for iterative calculation of the next time step.

[0078] When the initial flow state parameters meet the preset convergence conditions, these initial flow state parameters are determined as flow state influencing factors. Flow state influencing factors include velocity distribution, turbulence intensity, etc., which reflect the flow characteristics of the fluid in the combustion chamber.

[0079] Simultaneously, the reaction progress variable at the current time step is stored. Since the reaction is a time-varying process, the reaction progress variable at the current time step will affect the reaction at the next time step. Therefore, the reaction progress variable at the current time step is stored for iterative calculations at the next time step to continue simulating the subsequent combustion process.

[0080] Step 124: Use the temperature field calculation sub-model of the combustion process simulation model to monitor the temperature changes in different areas of the combustion chamber and generate temperature gradient influencing factors, which include the temperature change rate and temperature gradient distribution.

[0081] The temperature field calculation sub-model in the combustion process simulation model is used to monitor temperature changes in different regions of the combustion chamber. The temperature field calculation sub-model describes the heat transfer process, including heat conduction, convection, and radiation, through corresponding equations.

[0082] Within each calculation time step, the temperature field calculation sub-model calculates the temperature changes in different regions of the combustion chamber based on the current temperature distribution, the heat generated by the chemical reaction, and the fluid flow. Over time, the temperature distribution continuously changes, reflecting the heat transfer and accumulation during combustion.

[0083] From the calculated temperature distribution, factors influencing the temperature gradient, such as the rate of temperature change and the temperature gradient distribution, are extracted. The rate of temperature change represents the speed at which temperature changes over time, reflecting the intensity of the combustion process. The temperature gradient distribution describes the spatial variation of temperature, affecting the direction and speed of heat transfer.

[0084] When generating the factors influencing the temperature gradient, the calculated temperature distribution is first processed to calculate the temperature difference and rate of temperature change between adjacent regions. Then, a temperature gradient distribution map is plotted based on this data to visually display the distribution of the temperature gradient.

[0085] Step 1241: Initialize the spatial grid division method of the temperature field calculation sub-model, and set the initial temperature value of the grid cell. The spatial grid division method corresponds to the geometry of the combustion chamber.

[0086] The spatial meshing method for the temperature field calculation sub-model is initialized. The spatial meshing method determines how the combustion chamber space is divided into multiple small mesh cells, each mesh cell corresponding to a set spatial location.

[0087] The spatial mesh generation method needs to correspond to the geometry of the combustion chamber to ensure accurate simulation of the temperature distribution within the combustion chamber. For example, for combustion chambers with complex shapes, a finer mesh generation method is required to capture detailed temperature changes; while for combustion chambers with simple shapes, a coarser mesh generation method can be used to improve computational efficiency.

[0088] Simultaneously, the initial temperature value of the grid cells is set. The initial temperature value is the starting point for temperature field calculations and will affect the subsequent temperature calculation results. When setting the initial temperature value, factors such as the initial temperature distribution of the propellant and the preheating conditions of the combustion chamber need to be considered.

[0089] Step 1242: Within each calculation time step, based on the amount of heat generated from the reaction output by the chemical reaction kinetics relationship, update the temperature value of each grid cell to obtain the preliminary temperature field distribution.

[0090] Within each computation time step, the temperature value of each grid cell is updated based on the heat of reaction generated according to the chemical reaction kinetics. The heat of reaction is the heat released during a chemical reaction, which causes the temperature to rise.

[0091] When updating the temperature value of a grid cell, the heat generated by the reaction in each grid cell is first calculated based on chemical reaction kinetics. Then, this heat generated is input into the temperature field calculation sub-model, and the temperature change of each grid cell is calculated by combining heat transfer methods such as conduction, convection, and radiation. Finally, the temperature change is added to the current temperature value of the grid cell to obtain the updated temperature value.

[0092] After multiple time-step calculations and updates, a preliminary temperature field distribution was obtained. This preliminary temperature field distribution reflects the temperature changes within the combustion chamber over a certain period of time, but it may still contain some errors and requires further processing.

[0093] Step 1243: Calculate the temperature gradient values ​​between adjacent grid cells to generate a temperature gradient field, wherein the direction of the temperature gradient field points in the direction of increasing temperature.

[0094] Based on the initial temperature field distribution, the temperature gradient values ​​between adjacent grid cells are calculated. The temperature gradient value represents the rate of temperature change in space; it can be a vector value pointing in the direction of increasing temperature. To calculate the temperature gradient, the positional relationship between adjacent grid cells is first determined, and then the temperature difference between them is calculated. Based on the temperature difference and the distance between grid cells, the magnitude and direction of the temperature gradient are calculated. The temperature gradient values ​​between all adjacent grid cells are then aggregated to generate a temperature gradient field. This temperature gradient field visually illustrates the spatial variation of temperature.

[0095] Step 1244: Determine whether the maximum value of the temperature gradient field exceeds the preset gradient threshold. If it does, adjust the spatial grid division method and perform grid densification processing on the target temperature gradient region.

[0096] A preset gradient threshold is set to determine whether the maximum value of the temperature gradient field is within a reasonable range. If the maximum value of the temperature gradient field exceeds the preset gradient threshold, it indicates that the temperature change is too drastic in some areas, and a finer mesh may be needed to accurately simulate the temperature changes.

[0097] When the maximum value of the temperature gradient field exceeds the gradient threshold, the spatial mesh generation method is adjusted to refine the mesh in the target temperature gradient region. The target temperature gradient region refers to the area with a large temperature gradient value; these areas are typically regions of intense combustion reactions.

[0098] During mesh refinement, the target temperature gradient region is divided into smaller mesh cells to improve the accuracy of temperature calculations. Simultaneously, the initial temperature values ​​of these mesh cells are reset to ensure the continuity of the temperature field.

[0099] Step 1245: Recalculate the temperature field distribution and temperature gradient field of the encrypted area until the maximum value of the temperature gradient field is lower than the gradient threshold, and determine the final temperature gradient field as the temperature gradient influencing factor.

[0100] The temperature field distribution and temperature gradient field of the encrypted region need to be recalculated. Because the mesh generation method has changed, temperature and gradient calculations need to be performed again.

[0101] During the recalculation, the temperature value of each grid cell in the refined region is updated based on the heat of reaction generated by the chemical reaction kinetics. Then, the temperature gradient values ​​between adjacent grid cells are calculated to generate a new temperature gradient field.

[0102] Repeat the above steps, continuously adjusting the mesh generation method and recalculating, until the maximum value of the temperature gradient field is lower than the preset gradient threshold. At this point, the final temperature gradient field is determined as the influencing factor of the temperature gradient, accurately reflecting the spatial variation of temperature within the combustion chamber.

[0103] Step 125: Based on the influencing factors of the flow state and the influencing factors of the temperature gradient, the simulation analysis results are generated, and the time sampling interval of the simulation analysis results is consistent with the calculation step size of the combustion process simulation model.

[0104] Based on the obtained factors influencing the flow state and temperature gradient, simulation analysis results were generated. These results provide a comprehensive description of the combustion process of ADN-based propellant within the combustion chamber, including crucial information such as flow state and temperature changes.

[0105] The time sampling interval of the simulation analysis results is consistent with the calculation step size of the combustion process simulation model, ensuring that the simulation analysis results can accurately reflect the dynamic changes of the combustion process. Within each calculation step, the values ​​of the influencing factors of flow state and temperature gradient are recorded to form a time series.

[0106] These time series data are organized and analyzed to obtain simulation analysis results. The simulation analysis results can be displayed in the form of charts, graphs, etc., to intuitively present the flow state and temperature distribution in the combustion chamber.

[0107] Step 130: Based on the flow state influencing factors and temperature gradient influencing factors in the simulation analysis results, generate an energy release state spectrum of the ADN-based propellant during the ignition stage. The energy release state spectrum is used to characterize the energy release intensity distribution at different times.

[0108] Based on the flow state and temperature gradient influencing factors in the simulation analysis results, an energy release state map of ADN-based propellant during the ignition stage is generated. The energy release state map is an intuitive visualization tool used to characterize the energy release intensity distribution at different times.

[0109] The factors influencing flow state and temperature gradient are closely related to the intensity of energy release. Fluid flow affects the mixing of reactants and the transfer of heat, thus affecting energy release; the temperature gradient reflects the distribution of heat and is also related to the intensity of energy release.

[0110] When generating the energy release state map, the relationship between energy release intensity and these factors is first established based on the influencing factors of flow state and temperature gradient. Then, based on this relationship, the energy release intensity values ​​at different times and locations are calculated. Finally, these energy release intensity values ​​are displayed in the form of a map, using color or grayscale to represent the magnitude of the energy release intensity.

[0111] Optionally, step 130 includes: Step 131: Construct a flow characteristic matrix based on the velocity distribution and turbulence intensity among the factors influencing the flow state. The row dimension of the flow characteristic matrix corresponds to different spatial locations, and the column dimension corresponds to different time points.

[0112] A flow characteristic matrix is ​​constructed based on velocity distribution and turbulence intensity, which are among the factors influencing flow state. Velocity distribution and turbulence intensity are important characteristics of flow state, reflecting the motion and turbulence of the fluid.

[0113] The flow characteristic matrix has rows corresponding to different spatial locations and columns corresponding to different time points. At each spatial location and time point, the values ​​of velocity distribution and turbulence intensity are recorded, forming a two-dimensional matrix. This matrix can visually display the spatial and temporal changes in the flow state.

[0114] When constructing the flow characteristic matrix, the spatial location and time point division method is first determined. The spatial location can be divided according to the geometry of the combustion chamber, and the time points can be determined according to the time sampling interval of the simulation analysis results. Then, the values ​​of velocity distribution and turbulence intensity are filled into the matrix in the order of spatial location and time points.

[0115] Step 132: Construct a temperature characteristic matrix based on the temperature change rate and temperature gradient distribution among the factors influencing the temperature gradient. The dimension of the temperature characteristic matrix is ​​consistent with that of the flow characteristic matrix.

[0116] A temperature characteristic matrix is ​​constructed based on the rate of temperature change and the temperature gradient distribution, which are among the factors influencing the temperature gradient. The rate of temperature change and the temperature gradient distribution are important components of the factors influencing the temperature gradient, reflecting the changes and spatial distribution of temperature.

[0117] The temperature characteristic matrix maintains the same dimensions as the flow characteristic matrix, with rows corresponding to different spatial locations and columns corresponding to different time points. At each spatial location and time point, the values ​​of the rate of temperature change and the temperature gradient distribution are recorded, forming a two-dimensional matrix. This matrix provides a visual representation of how temperature changes spatially and temporally.

[0118] When constructing the temperature characteristic matrix, it is also necessary to determine the spatial location and time point division method. Then, the values ​​of temperature change rate and temperature gradient distribution are filled into the matrix according to the spatial location and time point order.

[0119] Step 133: Input the flow characteristic matrix and the temperature characteristic matrix into the energy release intensity calculation relationship, calculate the energy release intensity value at each spatial location and time point, and generate the spatiotemporal distribution matrix of energy release intensity.

[0120] The constructed flow characteristic matrix and temperature characteristic matrix are input into the energy release intensity calculation relation to calculate the energy release intensity value at each spatial location and time point. The energy release intensity calculation relation is a pre-established mathematical model that describes the relationship between flow state and temperature changes and energy release intensity.

[0121] When calculating the energy release intensity value, the calculation is performed based on the energy release intensity calculation relationship, combined with the values ​​in the flow characteristic matrix and temperature characteristic matrix. For each spatial location and time point, a corresponding energy release intensity value is obtained.

[0122] The energy release intensity values ​​at all spatial locations and time points are summarized to generate a spatiotemporal distribution matrix of energy release intensity. The row dimension of this matrix corresponds to different spatial locations, and the column dimension corresponds to different time points. The elements in the matrix represent the energy release intensity value at each spatial location and time point.

[0123] Step 134: Determine whether there are abnormally high value regions in the spatiotemporal distribution matrix of the energy release intensity. If so, perform spatial smoothing on the abnormally high value regions and retain the intensity gradient information of the region boundaries.

[0124] Determine if there are abnormally high value regions in the spatiotemporal distribution matrix of energy release intensity. Abnormally high value regions may be caused by calculation errors, abnormal local combustion, etc., and the energy release intensity values ​​in these regions are significantly higher than those in the surrounding areas.

[0125] When identifying abnormally high value regions, a threshold is set, and regions where the energy release intensity value exceeds the threshold are marked as abnormally high value regions. Then, the marked abnormally high value regions are further analyzed and processed.

[0126] If regions with abnormally high values ​​are found, spatial smoothing is applied. The purpose of spatial smoothing is to eliminate abnormally high values ​​and make the distribution of energy release intensity smoother. During the process, the intensity gradient information at the region boundaries is preserved to ensure that the changing trend of energy release intensity is accurately reflected.

[0127] In one implementation, step 134 includes: Step 1341: Set a threshold range for energy release intensity, the threshold range being determined based on statistical values ​​of energy release intensity from historical simulation data.

[0128] A threshold range for energy release intensity is set to determine whether the energy release intensity value is abnormal. The threshold range is determined based on the statistical values ​​of energy release intensity from historical simulation data. By analyzing a large amount of historical simulation data, statistical parameters such as the average value and standard deviation of energy release intensity are calculated.

[0129] Based on statistical parameters, determine the upper and lower limits of the threshold range. Generally, the upper limit of the threshold range can be set as the average value plus a certain multiple of the standard deviation, and the lower limit of the threshold range can be set as the average value minus a certain multiple of the standard deviation. This ensures that most normal energy release intensity values ​​are within the threshold range, while abnormally high values ​​will exceed the threshold range.

[0130] Step 1342: Traverse each element of the spatiotemporal distribution matrix of energy release intensity and mark elements whose values ​​exceed the upper limit of the threshold range as outliers.

[0131] The process iterates through each element of the spatiotemporal distribution matrix of energy release intensity, marking elements with values ​​exceeding the upper limit of a threshold range as outliers. During this process, each element in the matrix is ​​checked, and those meeting the criteria are marked. Marking outliers is crucial for subsequent processing and analysis. By marking outliers, regions of abnormally high values ​​can be quickly located, providing a foundation for further spatial smoothing.

[0132] Step 1343: Perform regional connectivity analysis on the marked outliers, merge spatially adjacent outliers into high-value outlier regions, and record the coordinates of the minimum bounding rectangle of each high-value outlier region.

[0133] Perform region connectivity analysis on the marked outliers. Region connectivity analysis involves determining which outliers are spatially adjacent and merging adjacent outliers into a single high-value region. During region connectivity analysis, a neighborhood search algorithm can be used to check if each outlier's neighbors are also outliers. If so, the outliers are merged into a region. This process is repeated until all adjacent outliers have been merged. Record the coordinates of the minimum bounding rectangle for each high-value region. The minimum bounding rectangle is the smallest rectangle containing the high-value region; its coordinates represent the location and extent of the high-value region.

[0134] Step 1344: Calculate the intensity gradient of each point in the abnormally high value region, retain the boundary points where the gradient direction points to the outside of the region, and perform Gaussian smoothing on the points inside the region.

[0135] Calculate the intensity gradient at each point within the region of abnormally high energy release. The intensity gradient represents the rate of change of energy release intensity in space. It is also a vector, pointing in the direction of the fastest increase in energy release intensity. Numerical methods such as the finite difference method can be used to calculate the magnitude and direction of the gradient based on the energy release intensity values ​​of adjacent points. Boundary points whose gradient direction points outwards are retained. These boundary points reflect the boundary characteristics of the abnormally high energy release region, ensuring that the boundary information of the region is preserved after spatial smoothing.

[0136] Gaussian smoothing is applied to points within the region. Gaussian smoothing is a commonly used smoothing method that eliminates outliers by weighting the values ​​of surrounding points. During the process, the energy release intensity values ​​of points within the region are adjusted according to the weights of the Gaussian function, resulting in a smoother distribution of energy release intensity within the region.

[0137] Step 1345: Recombine the smoothed internal point intensity values ​​with the retained boundary point intensity values ​​to update the corresponding region's values ​​in the spatiotemporal distribution matrix of energy release intensity.

[0138] The smoothed intensity values ​​of points within the region are recombine with the retained intensity values ​​of boundary points. During this recombination process, the smoothed intensity values ​​of points within the region are filled into the corresponding positions, while the intensity values ​​of the boundary points remain unchanged. The values ​​of the corresponding regions in the spatiotemporal distribution matrix of energy release intensity are then updated. Finally, the recombined intensity values ​​replace the original values ​​in the abnormally high-value regions of the matrix, resulting in the updated spatiotemporal distribution matrix of energy release intensity.

[0139] Step 135: Convert the processed spatiotemporal distribution matrix of energy release intensity into a visualization map format to generate an energy release state map containing time axis and spatial coordinates. The color coding of the visualization map format is used to characterize the differences in energy release intensity.

[0140] The processed spatiotemporal distribution matrix of energy release intensity is converted into a visualization map format. This visualization map format can be in the form of images, graphs, etc., and can intuitively display the distribution of energy release intensity. An energy release state map containing a time axis and spatial coordinates is generated. The time axis represents the change over time, and the spatial coordinates represent the spatial location within the combustion chamber. In the map, color coding is used to characterize differences in energy release intensity; for example, a darker color indicates a higher energy release intensity.

[0141] The conversion process begins by determining the size and resolution of the graph, based on the size and precision of the spatiotemporal distribution matrix of energy release intensity. Then, elements from the matrix are mapped to pixels in the graph, and the corresponding colors are selected for filling based on the element values. Finally, time axis and spatial coordinate scales and labels are added to make the graph clearer and easier to understand. Simultaneously, a color legend is generated to explain the correspondence between colors and energy release intensity.

[0142] As an exemplary embodiment, step 135 includes: Step 1351: Determine the time axis range and spatial coordinate range of the spatiotemporal distribution matrix of the energy release intensity, and set the time resolution and spatial resolution of the spectrum. The time resolution and spatial resolution are determined based on the sampling frequency of the simulation analysis results.

[0143] In this diagram, the time axis range represents the time interval of the simulation analysis, and the spatial coordinate range represents the spatial range within the combustion chamber. The time resolution represents the minimum interval between the graphs on the time axis, and the spatial resolution represents the minimum interval between the graphs on the spatial coordinates. Both time and spatial resolutions are determined based on the sampling frequency of the simulation analysis results to ensure that the graphs accurately reflect the dynamic changes in the combustion process. When setting the time and spatial resolutions, the display quality and computational efficiency of the graphs must be considered. If the resolution is too high, the graphs may become overly complex and the display quality may be poor; if the resolution is too low, some important information may be lost.

[0144] Step 1352: Perform dimensionality compression on the processed spatiotemporal distribution matrix of energy release intensity, projecting the three-dimensional spatiotemporal data onto a two-dimensional plane, where the time axis is expanded horizontally and the spatial coordinates are arranged vertically.

[0145] Since the matrix is ​​a three-dimensional spatiotemporal data set containing time, space, and energy release intensity, it needs to be projected onto a two-dimensional plane for easy visualization. During the projection process, the time axis is expanded horizontally, and the spatial coordinates are arranged vertically. In this way, each element in the matrix corresponds to a point on the two-dimensional plane, with its horizontal coordinate representing time, its vertical coordinate representing spatial location, and the color of the point representing the energy release intensity.

[0146] By using dimensionality compression, three-dimensional spatiotemporal data is converted into two-dimensional images, making the distribution of energy release intensity more intuitive.

[0147] Step 1353: Assign corresponding color coding schemes to different ranges of energy release intensity values, and ensure that the hue variation of the color coding scheme is consistent with the increasing trend of energy release intensity.

[0148] In this embodiment, a color coding scheme is used to map energy release intensity values ​​to different colors, thereby enabling a direct visual assessment of the energy release intensity through color. The hue variation of the color coding scheme aligns with the increasing trend of energy release intensity. For example, a hue variation from blue to red can be used, where blue represents lower energy release intensity and red represents higher energy release intensity. When assigning the color coding scheme, the energy release intensity values ​​are first divided into different ranges, with each range corresponding to a set color. Then, based on the magnitude of the energy release intensity value in the matrix, the appropriate color is selected for filling.

[0149] Step 1354: Determine whether the visual distinguishability of the color coding scheme in the spectrum meets the preset standard. If not, adjust the brightness interval of the color coding scheme to enhance the contrast between the first intensity region and the second intensity region.

[0150] The preset standards can be set according to actual needs. For example, it is required that the color difference between adjacent intensity areas be obvious and easily distinguishable.

[0151] If the visual distinguishability of the color coding scheme does not meet the preset standard, adjust the brightness interval of the color coding scheme. The brightness interval represents the brightness difference between adjacent colors. By adjusting the brightness interval, the contrast between the first intensity area and the second intensity area can be enhanced.

[0152] When adjusting the brightness interval, methods such as linear interpolation can be used to adjust the brightness of colors. For example, increasing the brightness of colors in low-intensity areas and decreasing the brightness of colors in high-intensity areas can make the color difference between adjacent intensity areas more obvious.

[0153] Step 1355: Combine the compressed two-dimensional data with the color coding scheme to generate an energy release state map that includes a time scale, a spatial scale, and color legends. The color legends are used to explain the correspondence between color and energy release intensity.

[0154] The compressed two-dimensional data is combined with a color coding scheme to generate an energy release state map. During the combination process, the corresponding color from the color coding scheme is selected and filled in according to the energy release intensity value in the two-dimensional data, resulting in a visualized map. Time scales, spatial scales, and color legends are added to the map. The time scale represents the change over time, the spatial scale represents the spatial location within the combustion chamber, and the color legend explains the correspondence between color and energy release intensity. Adding time scales, spatial scales, and color legends makes the energy release state map more complete and clear, facilitating its interpretation and analysis.

[0155] Step 140: Based on the energy release state map, explore the spatiotemporal evolution law of propellant energy release and output an energy release characteristic analysis report. The energy release characteristic analysis report is used to guide the optimization of engine combustion efficiency.

[0156] Based on the generated energy release state map, the spatiotemporal evolution of propellant energy release is explored. This spatiotemporal evolution describes the changing trends of energy release intensity over time and space. In exploring these patterns, the energy release state map is first analyzed to extract key features and information. For example, the peak times and locations of peak energy release intensity are identified, and the variation patterns of these peaks are analyzed. The spatial expansion pattern of energy release intensity is observed to determine whether it is radial, directional, or irregular. Then, based on the extracted features and information, the spatiotemporal evolution of propellant energy release is summarized. These patterns can be expressed using mathematical models, charts, and other forms.

[0157] Furthermore, the energy release characteristic analysis report details the features and patterns of propellant energy release, and proposes corresponding improvement suggestions to guide engine combustion efficiency optimization. The report may include the periodicity of energy release, spatial expansion patterns, energy distribution deviations, and recommendations for adjusting the propellant formulation or combustion chamber structural characteristics to address these issues.

[0158] In one alternative design approach, step 140 includes: Step 141: Extract spatiotemporal features from the energy release state spectrum, identify the peak occurrence time and peak region location of the energy release intensity, and generate a peak feature set.

[0159] Spatiotemporal feature extraction is performed on the energy release state spectrum. Spatiotemporal feature extraction refers to extracting key features related to the energy release intensity from the spectrum, such as the time of peak occurrence and the location of peak regions. In the extraction process, the spectrum is first scanned row by row and column by column to find the maximum energy release intensity. For each maximum value, its corresponding time and spatial location are recorded as a peak feature. All peak features are then summarized to generate a peak feature set. The peak feature set contains all peak information of energy release intensity and is crucial for analyzing the spatiotemporal evolution of energy release.

[0160] Step 142: Determine the periodicity and spatial expansion pattern of energy release based on the time interval between peak occurrences and the movement trajectory of the peak region in the peak feature set.

[0161] It is understandable that the time intervals between peak occurrences reflect the periodicity of energy release. By calculating the time difference between adjacent peak occurrences, a time interval sequence is obtained. Analyzing this time interval sequence determines whether periodic changes exist. If periodic changes exist, the period value of energy release can be determined. The trajectory of the peak region reflects the spatial expansion pattern of energy release. By observing the changes in the peak region's position over time, it can be determined whether the energy release is expanding radially, directionally, or irregularly. For example, if the peak region gradually moves outward radially, it indicates a radial expansion pattern; if the peak region moves along a predetermined direction, it indicates a directional expansion pattern.

[0162] As one implementation, step 142 includes: Step 1421: Extract all peak occurrence times from the peak feature set and calculate the time interval between adjacent peak times to generate a time interval sequence.

[0163] Extract all peak occurrence times from the peak feature set. Peak occurrence times refer to the points in time when the energy release intensity reaches its maximum value; these times are recorded in the peak feature set. Calculate the time interval between adjacent peak occurrence times. The time interval is the time difference between two adjacent peak occurrence times. Summarize the time intervals of all adjacent peak occurrence times to generate a time interval sequence. The time interval sequence reflects the periodic changes in energy release; by analyzing the time interval sequence, the periodic value of energy release can be identified.

[0164] Step 1422: Perform Fourier transform processing on the time interval sequence to obtain frequency domain features, identify the main frequency component and the period value corresponding to the main frequency component based on the frequency domain features, and determine the periodicity of energy release.

[0165] Fourier transform can convert a time interval sequence into frequency domain features. Frequency domain features represent the components and amplitudes of the signal at different frequencies. Within these features, the dominant frequency component, i.e., the frequency component with the largest amplitude, is identified. The period value corresponding to the dominant frequency component is the period value of energy release. Based on the dominant frequency component and its corresponding period value, the periodicity of energy release is determined; this periodicity describes the repetitive variation of energy release intensity over time.

[0166] Step 1423: Initialize the period verification window and set the window length to a preset multiple of the initially determined period value. Slide the period verification window in the energy release state spectrum and calculate the number of times the peak value appears within the period verification window.

[0167] The period verification window is a time window used to verify the periodicity of energy release. The window length is set to a preset multiple of the initially determined period value. The preset multiple can be set according to actual conditions, generally 2-3 times. The period verification window is slid across the energy release state graph, and the number of peak occurrences within the window is calculated. The number of peak occurrences reflects the number of times the energy release intensity reaches its maximum value within a period. By sliding the period verification window and calculating the number of peak occurrences, the accuracy of the initially determined period value can be verified. If the number of peak occurrences meets expectations, the period value is reasonable; if it does not meet expectations, the period value needs to be adjusted and the verification repeated.

[0168] Step 1424: Determine whether the number of peak occurrences within the periodic verification window matches the expected number of occurrences of the periodic pattern. If not, adjust the period value and perform the Fourier transform again.

[0169] The expected frequency is the number of peak occurrences within a period, calculated based on a periodic pattern. If the number of peak occurrences within the period verification window does not match the expected frequency, it indicates that the initially determined period value may be inaccurate, and the period value needs to be adjusted. When adjusting the period value, it can be appropriately increased or decreased, and then the Fourier transform process can be performed again to obtain new frequency domain characteristics and period values. Repeat the above steps, continuously adjusting the period value and performing verification, until the number of peak occurrences within the period verification window matches the expected frequency of the periodic pattern.

[0170] Step 1425: If the conditions are met, calculate the rate of change of the centroid coordinates based on the movement trajectory of the peak region location, and determine the spatial expansion mode, which includes radial expansion, directional expansion, or irregular expansion.

[0171] If the number of peak occurrences within the periodic verification window matches the expected number of occurrences according to the periodic pattern, the periodic value is accurate. At this point, the rate of change of the centroid coordinates is calculated based on the movement trajectory of the peak region.

[0172] The centroid coordinates refer to the geometric center of the peak region. By calculating the rate of change of the centroid coordinates, the movement of the peak region can be determined. If the centroid coordinates change radially, it indicates that the energy release is in a radial expansion mode; if the centroid coordinates change along a predetermined direction, it indicates that the energy release is in a directional expansion mode; if the change of the centroid coordinates has no obvious pattern, it indicates that the energy release is in an irregular expansion mode. It is understandable that different spatial expansion modes may require different optimization strategies.

[0173] Step 143: Calculate the total energy release in different combustion stages. The combustion stages are divided according to the time after ignition, and each combustion stage corresponds to a continuous time segment of the energy release state spectrum.

[0174] The combustion process is divided into different stages based on the time elapsed after ignition, with each stage corresponding to a continuous time segment in the energy release state spectrum. The division of combustion stages can be customized based on specific circumstances, such as ignition, stable combustion, and flameout. The total energy release for each combustion stage is calculated. Total energy release refers to the sum of energy release intensities within a single combustion stage. This is achieved by summing the energy release intensities of the corresponding time segments in the energy release state spectrum. By calculating the total energy release for each combustion stage, the energy release characteristics of the propellant at different stages can be understood.

[0175] Step 144: Compare the total energy release of each combustion stage with the preset standard energy distribution curve to generate an energy distribution deviation value. The standard energy distribution curve is constructed based on historical best combustion data.

[0176] The standard energy distribution curve is constructed based on historical best combustion data, representing the total energy release of the propellant at different combustion stages under ideal conditions. The difference between the total energy release at each combustion stage and the corresponding value on the standard energy distribution curve is calculated to generate the energy distribution deviation value. The energy distribution deviation value reflects the difference between the actual energy release and the ideal situation. By comparing and generating the energy distribution deviation value, problems in the propellant combustion process can be identified, such as excessive or insufficient energy release at certain combustion stages. These problems can then serve as directions for optimization.

[0177] Step 145: Combining the periodicity, the spatial expansion pattern, and the energy distribution deviation value, generate an energy release characteristic analysis report containing improvement suggestions, which are used to adjust the propellant formulation or combustion chamber structural characteristics.

[0178] By combining the obtained periodic patterns, spatial expansion patterns, and energy distribution deviations, an energy release characteristic analysis report containing improvement suggestions is generated. The report details the characteristics and patterns of propellant energy release, as well as existing problems. Based on the analysis results, improvement suggestions are proposed. These suggestions primarily aim to adjust the propellant formulation or combustion chamber structural characteristics to optimize engine combustion efficiency. For example, if periodic instability in energy release is found, the propellant component ratio can be adjusted to change the chemical reaction rate; if the spatial expansion pattern of energy release is found to be unreasonable, the combustion chamber structural design can be optimized to improve fluid flow characteristics. The energy release characteristic analysis report can serve as a basis for engine design and optimization. By implementing the improvement suggestions in the report, engine performance and reliability can be improved.

[0179] As a non-limiting embodiment, it also includes: Step 210: Analyze the energy distribution deviation value in the energy release characteristic analysis report to determine the key propellant component sensitive factors affecting energy release efficiency. The key propellant component sensitive factors include the ratio of oxidant to fuel and the characteristics of catalyst activity.

[0180] Energy distribution deviation reflects the difference between actual and ideal energy release. Analyzing this deviation can identify key factors affecting energy release efficiency. Key propellant component-sensitive factors include the oxidizer-to-fuel ratio and the influence of catalyst activity. The oxidizer-to-fuel ratio affects the rate and extent of the chemical reaction, thus impacting energy release efficiency; catalyst activity characteristics affect the activation energy of the chemical reaction, accelerating or inhibiting its progress.

[0181] When identifying key propellant component sensitivity factors, the relationship between energy distribution deviation and propellant composition is first analyzed. By comparing the energy release under different component ratios, the components with the greatest impact on energy release efficiency are identified. Then, the mechanisms of action of these components are further investigated to determine key factors such as the oxidizer-fuel ratio and the characteristics affecting catalyst activity.

[0182] Step 220: Construct a formulation adjustment parameter space based on the key propellant component sensitivity factors, wherein the dimension of the formulation adjustment parameter space corresponds to the number of types of propellant components.

[0183] A formulation adjustment parameter space is constructed based on identified key propellant component sensitivity factors. This parameter space is a multi-dimensional space, with its dimensions corresponding to the number of propellant component types. When constructing the parameter space, each key propellant component is treated as a dimension, and the value range for each dimension is set according to actual conditions. For example, the oxidizer-fuel ratio can be set to a reasonable proportion range; the influence characteristics on catalyst activity can be set to different activity levels. The formulation adjustment parameter space provides a search space for subsequent formulation optimization; by searching within this space, the optimal propellant formulation can be found.

[0184] Step 230: Perform multi-objective optimization search within the formula adjustment parameter space to generate a set of candidate formula adjustment schemes that satisfy the constraints of energy release uniformity and peak intensity; perform energy release simulation verification on each scheme in the set of candidate formula adjustment schemes, calculate the energy distribution deviation improvement rate corresponding to the scheme, and determine the scheme with the highest improvement rate as the optimal formula adjustment scheme; generate a propellant formula adjustment report based on the optimal formula adjustment scheme, the propellant formula adjustment report including the adjustment ratio of each component and the expected improvement effect of energy release characteristics.

[0185] The goal of the multi-objective optimization search is to find candidate formulation adjustments that satisfy the constraints of energy release uniformity and peak intensity. Energy release uniformity requires stable energy release during propellant combustion, avoiding excessive fluctuations; peak intensity constraint requires the peak intensity of energy release to be within a reasonable range.

[0186] During the search process, optimization algorithms, such as genetic algorithms and particle swarm optimization, are employed to search within the propellant adjustment parameter space. For each found propellant adjustment scheme, energy release simulations are performed for verification. The simulations utilize a previously constructed combustion process simulation model, taking the propellant adjustment scheme as input to simulate the propellant combustion process and obtain an energy release state spectrum.

[0187] The energy distribution deviation improvement rate refers to the degree of improvement in the energy distribution deviation value after the implementation of the solution relative to the original situation. By comparing the energy distribution deviation improvement rates of each solution, the solution with the highest improvement rate is determined as the optimal formula adjustment solution.

[0188] A propellant formulation adjustment report is generated based on the optimal formulation adjustment scheme. The report includes the adjustment ratios of each component and the expected improvements in energy release characteristics. The propellant formulation adjustment report provides guidance for propellant production and use; by implementing the adjustments outlined in the report, the energy release efficiency of the propellant and the performance of the engine can be improved.

[0189] As a non-limiting embodiment, it also includes: Step 310: Extract the spatial expansion pattern description information from the energy release characteristic analysis report, identify the limiting region of the energy release distribution by the combustion chamber geometry, and the limiting region is a set of spatial locations where the energy release intensity changes abruptly.

[0190] The spatial expansion pattern description information reflects the spatial distribution of propellant energy release. Analyzing this information reveals the influence of combustion chamber geometry on the energy release distribution. Restricted regions are sets of spatial locations exhibiting anomalous jumps in energy release intensity. These regions are typically caused by factors such as combustion chamber geometry and wall roughness, preventing normal energy release expansion. To identify restricted regions, the energy release state spectrum is analyzed to pinpoint areas with anomalous jumps in energy release intensity. These regions show significant differences in energy release intensity compared to their surrounding areas. By marking the spatial locations of these regions, the set of restricted regions is obtained.

[0191] Step 320: Perform geometric feature parameterization on the restricted area to generate a set of structural feature parameters including the region's contour curvature, surface area, and volume ratio; input the set of structural feature parameters into the combustion chamber structure optimization model, and perform topological sensitivity analysis in conjunction with the energy release uniformity objective function to determine the key structural adjustment dimensions affecting energy distribution; generate a combustion chamber structural feature adjustment scheme based on the key structural adjustment dimensions, the adjustment scheme including injector layout optimization suggestions, combustion chamber convergence half-angle correction values, and throat diameter adjustment coefficients.

[0192] Geometric feature parameterization refers to representing the geometric shape and size of the restricted area using specific parameters. This generates a set of structural feature parameters, including the region's contour curvature, surface area to volume ratio, etc. The combustion chamber structure optimization model is a machine learning or numerical simulation-based model used to optimize the combustion chamber's structural design. Topology sensitivity analysis is performed in conjunction with the energy release uniformity objective function. Topology sensitivity analysis analyzes the degree to which changes in the combustion chamber's topology affect energy distribution. Key structural adjustment dimensions refer to structural parameters that significantly impact energy distribution, such as injector layout, combustion chamber convergence angle, and throat diameter.

[0193] Based on key structural adjustment dimensions, combustion chamber structural feature adjustment schemes are generated. These schemes include injector layout optimization suggestions, combustion chamber convergence half-angle correction values, and throat diameter adjustment coefficients. These adjustments aim to improve the combustion chamber's structural design and enhance the uniformity and efficiency of energy release.

[0194] Step 330: Verify the energy release improvement effect of the combustion chamber structural feature adjustment scheme through the combustion process simulation model, and output a combustion chamber optimization design report containing the parameters of the three-dimensional structural model.

[0195] The energy release improvement effect of the combustion chamber structural feature adjustment scheme was verified using a combustion process simulation model. The adjustment scheme was used as input to run the combustion process simulation model, obtaining the adjusted energy release state spectrum. The energy release state spectra before and after adjustment were compared to analyze whether the uniformity and efficiency of energy release were improved. If the improvement was significant, the adjustment scheme was effective; if the improvement was not significant, further adjustments were needed.

[0196] Finally, a combustion chamber optimization design report containing the parameters of the 3D structural model is output. The report details the parameters of the combustion chamber's 3D structural model, including injector layout, combustion chamber convergence angle, throat diameter, etc. It also includes the implementation effects and expected benefits of the adjustment scheme. The combustion chamber optimization design report provides detailed guidance for the manufacture and improvement of the combustion chamber.

[0197] As a non-limiting embodiment, it also includes: Step 410: Analyze the temperature gradient influencing factors in the energy release characteristic analysis report, and determine the critical time window and spatial hot spot area of ​​thermal shock during the ignition process. The critical time window of thermal shock is a continuous period of time in which the temperature change rate exceeds a preset safety threshold.

[0198] This analysis examines the temperature gradient influencing factors in the energy release characteristic analysis report. These factors reflect the temperature changes within the combustion chamber. In-depth analysis of these factors allows for the identification of critical time windows and hotspot regions for thermal shock during ignition. A critical time window for thermal shock refers to the continuous period during which the rate of temperature change exceeds a preset safety threshold. This preset safety threshold is set based on the material's thermal resistance and the engine's safe operation requirements. To determine the critical time window for thermal shock, the temperature change rate data for each time point is first extracted from the temperature gradient influencing factors. Then, this data is compared one by one with the preset safety threshold, marking the time points where the rate of temperature change exceeds the threshold. Next, a continuity analysis is performed on these marked time points to identify the continuous periods exceeding the threshold; these periods constitute the critical time window for thermal shock.

[0199] Spatial hotspots refer to areas within the combustion chamber with abnormally high temperatures, which may damage the engine's structure and performance. To identify spatial hotspots, temperature distribution data is first obtained from factors influencing the temperature gradient. Then, a criterion for determining abnormally high temperatures is established, which can be determined based on the engine's design requirements and material properties. The temperature distribution data is compared with the criterion to mark spatial locations where temperatures exceed the criterion. Finally, cluster analysis is performed on these marked spatial locations, merging adjacent high-temperature locations into a single spatial hotspot.

[0200] Step 420: Construct a thermal management parameter matrix based on the critical time window of thermal shock and the spatial hotspot area. The row dimension of the thermal management parameter matrix corresponds to different types of thermal management measures, and the column dimension corresponds to the time node of the critical time window of thermal shock.

[0201] A thermal management parameter matrix is ​​constructed based on the identified critical time window and spatial hotspot regions of thermal shock. The rows of this matrix correspond to different types of thermal management measures, including active cooling, insulation, and ignition energy adjustment. Active cooling removes heat through coolant flow, lowering the temperature; insulation reduces heat transfer by using insulating materials; and ignition energy adjustment controls combustion intensity, thus affecting temperature rise. The columns correspond to the time nodes within the critical time window of thermal shock. Different time nodes within this window may require different intensities of thermal management measures. For each combination of thermal management measure type and time node, corresponding parameter values ​​need to be determined. For example, for active cooling, the parameter value could be the coolant flow rate; for insulation, the parameter value could be the insulation material thickness; and for ignition energy adjustment, the parameter value could be the ignition energy level.

[0202] When determining parameter values, it is necessary to comprehensively consider the temperature changes within the critical time window of thermal shock, as well as the location and temperature of the space hotspot region. Optimal parameter values ​​under different conditions can be determined through simulation experiments or historical data. For example, at time points with large temperature change rates, it may be necessary to increase the coolant flow rate or adjust the ignition energy; near the space hotspot region, it may be necessary to increase the thickness of the insulation material.

[0203] Step 430: Perform a dynamic response strategy search in the thermal management parameter matrix to generate a multi-dimensional thermal management strategy set that includes active cooling flow regulation, insulation layer thickness distribution, and ignition energy input control; input the multi-dimensional thermal management strategy set into the heat conduction simulation sub-model to simulate the temperature field evolution process under different strategies, and calculate the thermal stress relief coefficient and thermal management energy efficiency ratio; determine the optimal thermal management strategy based on the thermal stress relief coefficient and thermal management energy efficiency ratio, and generate an ignition process thermal management update strategy, which includes thermal management control parameters at each time point.

[0204] Dynamic response strategy search refers to adjusting the parameter values ​​of thermal management measures in real time within the critical time window of thermal shock, based on different temperature changes and dynamic changes in spatial hotspot areas. The search process needs to consider various factors, such as the response speed, cost, and effectiveness of the thermal management measures.

[0205] Through dynamic response strategy search, a multi-dimensional set of thermal management strategies is generated, including active cooling flow regulation, insulation layer thickness distribution, and ignition energy input control. Each thermal management strategy corresponds to a set of preset parameter value combinations, which determine the intensity of thermal management measures at different time points within the critical time window of thermal shock.

[0206] A set of multi-dimensional thermal management strategies is input into the heat conduction simulation sub-model, which is a physics-based model used to simulate the temperature field evolution within the combustion chamber under different thermal management strategies. During the simulation, the heat conduction simulation sub-model calculates the temperature changes at each time point and spatial location based on the input thermal management strategy parameter values.

[0207] Based on the simulation results, the thermal stress relief coefficient and thermal management efficiency ratio were calculated. The thermal stress relief coefficient refers to the degree of reduction in thermal stress after the implementation of the thermal management strategy. Thermal stress is the internal stress of a material caused by temperature changes; excessive thermal stress may lead to material damage. The thermal management efficiency ratio is the ratio between the energy consumed by the thermal management strategy and the reduction in thermal stress, reflecting the efficiency of the thermal management strategy.

[0208] To calculate the thermal stress mitigation coefficient, the thermal stress distribution before and after the implementation of the thermal management strategy is first calculated based on the temperature field data obtained from the simulation. Then, the reduction in thermal stress is calculated by comparing the thermal stress distribution. The reduction in thermal stress is then compared with the initial thermal stress to obtain the thermal stress mitigation coefficient.

[0209] Calculating the thermal management efficiency ratio (ERR) requires determining the energy consumed by the thermal management strategy. For active cooling measures, the energy consumed is primarily the pumping energy of the coolant; for measures that adjust ignition energy, the energy consumed is mainly the additional ignition energy. The EERR is obtained by comparing the energy consumed by the thermal management strategy with the thermal stress relief coefficient.

[0210] The optimal thermal management strategy is determined based on the thermal stress relief coefficient and the thermal management efficiency ratio. The optimal thermal management strategy is the one that achieves the highest thermal management efficiency ratio while meeting the thermal stress relief requirements. The optimal strategy can be selected by comprehensively evaluating the thermal stress relief coefficient and thermal management efficiency ratio of each strategy.

[0211] Based on the optimal thermal management strategy, an updated thermal management strategy for the ignition process is generated. This strategy includes thermal management control parameters at each time point, whose values ​​are determined according to the optimal thermal management strategy. During ignition, adjusting thermal management measures according to the parameter values ​​of the updated strategy can effectively reduce thermal stress and improve engine reliability and performance. Furthermore, the updated thermal management strategy can be adjusted in real time according to actual operating conditions to adapt to different changes in operating conditions.

[0212] This application embodiment collects historical physicochemical property data of ADN-based propellants under different combustion conditions and constructs a combustion process simulation model that includes chemical reaction kinetics and hydrodynamics. This achieves a comprehensive and accurate simulation of the propellant combustion process. This model, which comprehensively considers both types of interactions, can more realistically reflect the actual situation of the propellant in the combustion chamber and avoids the limitations of single-factor simulation in the prior art.

[0213] Furthermore, the initial state data of ADN-based propellant is input into the model to simulate the energy release process, and the simulation analysis results include the influencing factors of flow state and temperature gradient. Then, the energy release state map generated based on the influencing factors of flow state and temperature gradient can intuitively show the distribution of energy release intensity of propellant at different times during the ignition stage, thereby comprehensively recording the spatiotemporal changes of energy release.

[0214] Furthermore, based on the energy release state map, the spatiotemporal evolution of propellant energy release is mined, and an energy release characteristic analysis report is output. By deeply analyzing the periodicity of energy release, spatial expansion patterns, and energy distribution deviations, the propellant formulation or combustion chamber structural characteristics can be adjusted in a targeted manner, thereby improving the engine's combustion efficiency and performance. This solves the problem that existing technologies cannot provide specific suggestions for the refined design of engines.

[0215] In summary, the embodiments of this application improve the accuracy of numerical simulation of the working process of ADN-based space engines, and can provide a reliable reference for the optimized design of aerospace engines.

[0216] Based on the same inventive concept, this application also provides a numerical simulation system for the operation of an ADN-based space engine. (See also...) Figure 2 As shown, it is a schematic diagram of the structure of a possible numerical simulation system for the working process of an ADN-based space engine provided in an embodiment of this application. Figure 2 The ADN-based space engine operating process numerical simulation system 200 includes a processor 210 and a memory 220. The memory 220 stores computer programs executable by the processor 210. By executing the instructions stored in the memory 220, the processor 210 can perform the steps of the aforementioned ADN-based space engine operating process numerical simulation method.

[0217] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium including a computer program. When the computer program is run on an ADN-based space engine operating process numerical simulation system, the computer program is used to cause the ADN-based space engine operating process numerical simulation system to perform the steps of the aforementioned ADN-based space engine operating process numerical simulation method. In some possible embodiments, various aspects of the ADN-based space engine operating process numerical simulation method provided in this application can also be implemented in the form of a program product, including a computer program. When the program product is run on an ADN-based space engine operating process numerical simulation system, the computer program is used to cause the ADN-based space engine operating process numerical simulation system to perform the steps in the aforementioned ADN-based space engine operating process numerical simulation method. For example, the ADN-based space engine operating process numerical simulation system can perform, for example, the following steps: Figure 1 The steps are shown in the figure.

[0218] The above content is only a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application.

Claims

1. A numerical simulation method for the working process of an ADN-based space engine, characterized in that, The method includes: Historical physicochemical property data of ADN-based propellants under different combustion conditions are collected. Based on the historical physicochemical property data, a combustion process simulation model of ADN-based propellants is constructed. The combustion process simulation model includes chemical reaction kinetics and hydrodynamics relationships. The initial state data of the ADN-based propellant is input into the combustion process simulation model to simulate the energy release process of the ADN-based propellant in the combustion chamber after ignition. Simulation analysis results, including flow state influencing factors and temperature gradient influencing factors, are obtained. The initial temperature distribution and initial pressure characteristics of the propellant in the initial state data are analyzed and set as the initial boundary conditions of the combustion process simulation model. The ignition triggering sub-model of the combustion process simulation model is started, and the ignition energy input position and energy input method are set to simulate the excitation process of the ignition energy on the local propellant. The chemical reaction kinetics of the combustion process simulation model are then analyzed. The decomposition reaction rate of the ADN-based propellant in the ignition region is calculated using the relationship. The flow and diffusion process of the reaction products is analyzed in conjunction with the hydrodynamic interaction relationship to obtain the influencing factors of the flow state, which include velocity distribution and turbulence intensity. The temperature field calculation sub-model of the combustion process simulation model is used to monitor temperature changes in different regions of the combustion chamber, generating temperature gradient influencing factors, which include temperature change rate and temperature gradient distribution. Based on the influencing factors of the flow state and the temperature gradient influencing factors, the simulation analysis results are formed, and the time sampling interval of the simulation analysis results is consistent with the calculation step size of the combustion process simulation model. Based on the flow state influencing factors and temperature gradient influencing factors in the simulation analysis results, an energy release state map of the ADN-based propellant during the ignition stage is generated: a flow characteristic matrix is ​​constructed based on the velocity distribution and turbulence intensity in the flow state influencing factors, where the row dimension of the flow characteristic matrix corresponds to different spatial locations and the column dimension corresponds to different time points; a temperature characteristic matrix is ​​constructed based on the temperature change rate and temperature gradient distribution in the temperature gradient influencing factors, where the dimensions of the temperature characteristic matrix are consistent with those of the flow characteristic matrix; the flow characteristic matrix and the temperature characteristic matrix are input into the energy release intensity calculation relationship to calculate the energy release intensity value at each spatial location and time point, generating a spatiotemporal distribution matrix of energy release intensity; it is determined whether there are abnormally high value regions in the spatiotemporal distribution matrix of energy release intensity. If so, spatial smoothing is performed on the abnormally high value regions to retain the intensity gradient information of the region boundaries; the processed spatiotemporal distribution matrix of energy release intensity is converted into a visualization map format to generate an energy release state map containing a time axis and spatial coordinates. The color coding of the visualization map format is used to characterize the differences in energy release intensity, and the energy release state map is used to characterize the energy release intensity distribution at different times. Based on the energy release state map, the spatiotemporal evolution of propellant energy release is explored, and an energy release characteristic analysis report is output. This energy release characteristic analysis report is used to guide the optimization of engine combustion efficiency.

2. The method of claim 1, wherein, The process of collecting historical physicochemical property data of ADN-based propellants under different combustion conditions and constructing a combustion process simulation model of ADN-based propellants based on the historical physicochemical property data includes: Chemical reaction rate data and molecular diffusion characteristic data are obtained from the historical physicochemical property data to establish a chemical reaction kinetics database, which includes the ADN decomposition reaction pathway and the generation and consumption patterns of each intermediate product. Collect fluid flow characteristic data in the combustion chamber, and construct a fluid dynamic interaction description based on the fluid flow characteristic data. The fluid dynamic interaction description includes the flow field distribution law and pressure propagation characteristics. The chemical reaction kinetics database is coupled with the fluid dynamics interaction description, and bidirectional feedback between chemical reaction and fluid flow is achieved through interface interaction to generate a preliminary simulation model. The preliminary simulation model was calibrated by boundary conditions, and the thermal conductivity of the combustion chamber wall and the initial propellant filling density were adjusted to obtain the combustion process simulation model of the ADN-based propellant.

3. The method of claim 1, wherein, The decomposition reaction rate of the ADN-based propellant in the ignition region is calculated using the chemical reaction kinetics relationship of the combustion process simulation model. Combined with the analysis of the flow and diffusion process of the reaction products using the hydrodynamic relationship, the influencing factors of the flow state are obtained, including: Initialize the reaction progress variable of the chemical reaction kinetics relationship, set the initial weight distribution of the reaction path, and the reaction progress variable is used to characterize the degree of each decomposition reaction; Within each computation time step, the decomposition reaction rate of the ADN-based propellant is calculated based on the current reaction progress variable, generating a reaction rate time series; The reaction rate time series is input into the fluid dynamics interaction relationship to calculate the mass flux and momentum flux of the reaction products, and to obtain preliminary flow state parameters. Determine whether the preliminary flow state parameters meet the preset convergence conditions. If not, adjust the reaction path weight distribution of the chemical reaction kinetics relationship and recalculate the decomposition reaction rate. If the convergence condition is met, the preliminary flow state parameters are determined as the influencing factors of the flow state, and the reaction progress variable at the current time step is stored for iterative calculation at the next time step.

4. The method of claim 1, wherein, The temperature field calculation sub-model of the combustion process simulation model is used to monitor temperature changes in different regions of the combustion chamber and generate factors affecting temperature gradients, including: The spatial grid division method of the temperature field calculation sub-model is initialized, and the initial temperature value of the grid cell is set. The spatial grid division method corresponds to the geometry of the combustion chamber. Within each computation time step, the temperature value of each grid cell is updated based on the reaction heat generated output by the chemical reaction kinetics relationship, thus obtaining the preliminary temperature field distribution. Calculate the temperature gradient between adjacent grid cells to generate a temperature gradient field, the direction of which points in the direction of increasing temperature; Determine whether the maximum value of the temperature gradient field exceeds a preset gradient threshold. If it does, adjust the spatial grid division method and perform grid refinement processing on the target temperature gradient region. The temperature field distribution and temperature gradient field of the encrypted area are recalculated until the maximum value of the temperature gradient field is lower than the gradient threshold. The final temperature gradient field is then determined as the temperature gradient influencing factor.

5. The method according to claim 1, characterized in that, The step of determining whether there are abnormally high value regions in the spatiotemporal distribution matrix of the energy release intensity, and if so, performing spatial smoothing on the abnormally high value regions while preserving the intensity gradient information of the region boundaries, includes: A threshold range for energy release intensity is set, and the threshold range is determined based on statistical values ​​of energy release intensity from historical simulation data; Traverse each element of the spatiotemporal distribution matrix of energy release intensity, and mark elements whose values ​​exceed the upper limit of the threshold range as outliers; Perform regional connectivity analysis on the marked outliers, merge spatially adjacent outliers into high-value outlier regions, and record the coordinates of the minimum bounding rectangle of each high-value outlier region; Calculate the intensity gradient of each point in the abnormally high value region, retain the boundary points where the gradient direction points to the outside of the region, and perform Gaussian smoothing on the points inside the region. The intensity values ​​of points inside the smoothed region are recombined with the intensity values ​​of the retained boundary points to update the values ​​of the corresponding region in the spatiotemporal distribution matrix of energy release intensity.

6. The method according to claim 1, characterized in that, The process of converting the processed spatiotemporal distribution matrix of energy release intensity into a visual graph format to generate an energy release state graph containing time axes and spatial coordinates includes: The time axis range and spatial coordinate range of the spatiotemporal distribution matrix of the energy release intensity are determined, and the time resolution and spatial resolution of the spectrum are set. The time resolution and the spatial resolution are determined based on the sampling frequency of the simulation analysis results. The spatiotemporal distribution matrix of the processed energy release intensity is subjected to dimensionality compression, and the three-dimensional spatiotemporal data is projected onto a two-dimensional plane, where the time axis is expanded along the horizontal direction and the spatial coordinates are arranged along the vertical direction. Different color coding schemes are assigned to different ranges of energy release intensity values, and the hue changes of the color coding schemes are consistent with the increasing trend of energy release intensity. Determine whether the visual distinguishability of the color coding scheme in the spectrum meets the preset standard. If it does not meet the standard, adjust the brightness interval of the color coding scheme to enhance the contrast between the first intensity region and the second intensity region. The compressed two-dimensional data is combined with a color coding scheme to generate an energy release state map that includes a time scale, a spatial scale, and color legends. The color legends are used to explain the correspondence between color and energy release intensity.

7. The method according to claim 1, characterized in that, The process involves mining the spatiotemporal evolution of propellant energy release based on the energy release state map and outputting an energy release characteristic analysis report, including: Spatiotemporal features are extracted from the energy release state spectrum to identify the peak occurrence time and peak region location of the energy release intensity, and a peak feature set is generated. Based on the time interval between peak occurrences and the movement trajectory of peak regions in the peak feature set, the periodicity of energy release and the spatial expansion pattern are determined. Calculate the total energy release in different combustion stages, wherein the combustion stages are divided according to the time after ignition, and each combustion stage corresponds to a continuous time segment of the energy release state spectrum; The total energy release at each combustion stage is compared with a preset standard energy distribution curve to generate an energy distribution deviation value. The standard energy distribution curve is constructed based on historical best combustion data. Combining the periodicity, spatial expansion pattern, and energy distribution deviation, an energy release characteristic analysis report containing improvement suggestions is generated. These improvement suggestions are used to adjust the propellant formulation or combustion chamber structural characteristics. The step of determining the periodicity and spatial expansion pattern of energy release based on the time interval between peak occurrences and the movement trajectory of peak regions in the peak feature set includes: Extract all peak occurrence times from the peak feature set and calculate the time interval between adjacent peak times to generate a time interval sequence; The time interval sequence is subjected to Fourier transform to obtain frequency domain features. Based on the frequency domain features, the main frequency components and the corresponding periodic values ​​of the main frequency components are identified, and the periodicity of energy release is determined. Initialize the periodic verification window and set the window length to a preset multiple of the initially determined period value. Slide the periodic verification window in the energy release state spectrum and calculate the number of peak occurrences within the periodic verification window. Determine whether the number of peak occurrences within the periodic verification window matches the expected number of occurrences of the periodic pattern. If not, adjust the period value and perform the Fourier transform again. If the conditions are met, the rate of change of the centroid coordinates is calculated based on the movement trajectory of the peak region location to determine the spatial expansion mode, which includes radial expansion, directional expansion, or irregular expansion.

8. A numerical simulation system for the working process of an ADN-based space engine, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of any one of the methods described in claims 1 to 7.

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