A method and system for simulating gas pressure pulsation fluctuations of a gas generator

Abstract

The application provides a gas generator gas pressure pulse fluctuation simulation method and system, relates to the technical field of numerical simulation, and comprises the following steps: data acquisition, model establishment, grid division, simulation solution and verification of simulation results; the system comprises a data acquisition module, a model establishment module, a grid division module, a simulation solution module and a simulation result verification module; and the gas pressure pulse fluctuation model can accurately simulate the gas pressure pulse fluctuation in the gas generator based on the influence of coupling of combustion chemical reaction and acoustic fluctuation and other physical fields.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of numerical simulation, in particular to a simulation method and system for gas pressure pulse fluctuation of a gas generator. BACKGROUND

[0002] Gas combustion is an important means of energy conversion and utilization, and is widely used in industrial, transportation, civil and other fields. In the gas combustion system, the combustion process of the gas in the combustion chamber is a complex physical and chemical reaction process, accompanied by energy release and material transformation. Due to the uneven mixing of gas and air, the design differences of the combustor structure, and factors such as turbulence and vortex in the combustion process, a series of pressure fluctuations and pulse phenomena will occur in this process. These pressure pulses not only cause vibration and noise of the combustion system, but also may cause erosion and wear of the wall surface, nozzle and other components of the combustion chamber, thereby affecting the stability and service life of the system. In addition, pressure pulses can also affect combustion efficiency, leading to incomplete combustion and increased pollutant emissions.

[0003] In order to effectively control and optimize the gas combustion process and reduce the influence of pressure pulse fluctuation on the system, it is necessary to deeply study the generation mechanism, propagation characteristics and influencing factors of gas combustion pressure pulse. Through simulation technology, the pressure pulse phenomenon in the gas combustion process can be simulated, and the influence of different parameters on the pressure pulse can be analyzed, thereby providing theoretical basis and technical support for the design and optimization of the combustion system. The Chinese invention patent with publication date of August 15, 2023 and publication number CN116738894A provides a method for numerical simulation of gas flow of a rocket engine. The patent simplifies the gas as high-temperature and high-pressure air, and only needs to simulate one flow medium without simulating the combustion and other chemical reaction processes of the propellant. By obtaining the gas flow characteristics, gas interference effect and aerodynamic characteristics of the engine, gas vane and spoiler, the gas flow parameters at several characteristic positions such as the combustion chamber, throat and nozzle exit are given for numerical simulation. For the above technical solution, since the gas is simplified as high-temperature and high-pressure air, the influence of combustion chemical reaction and acoustic fluctuation and other physical field coupling is ignored, which cannot accurately simulate the pressure fluctuation process, resulting in inaccurate description of the pressure fluctuation characteristics and affecting the accuracy of the simulation of the gas pressure pulse fluctuation of the gas generator. SUMMARY

[0004] In order to improve the accuracy of the simulation of the gas pressure pulse fluctuation of the gas generator, the application provides a simulation method and system for gas pressure pulse fluctuation of a gas generator.

[0005] In a first aspect, the application provides a simulation method for gas pressure pulse fluctuation of a gas generator, which adopts the following technical solution:

[0006] A method for simulating gas pressure pulsation fluctuation of a gas generator, comprising the following steps:

[0007] Data collection: collecting experimental data of a combustion chamber in the gas generator;

[0008] Model establishment: establishing a gas pressure pulsation model based on combustion reaction, gas fluid process or acoustic wave propagation fluctuation in the gas generator, wherein the gas pressure pulsation model comprises a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model;

[0009] Mesh division: dividing the combustion chamber into a plurality of network units, and setting physical properties, boundary conditions and initial conditions for each network unit;

[0010] Simulation solving: inputting the boundary conditions and the initial conditions into the gas pressure pulsation model, solving the gas pressure pulsation model, and outputting simulation results;

[0011] Verification of simulation results: comparing and verifying the simulation results with the experimental data, and determining whether the simulation results are consistent with the experimental data;

[0012] If yes, a verified gas pressure pulsation model is obtained;

[0013] If no, optimizing the gas pressure pulsation model according to the verification results, and performing the step of simulation solving.

[0014] By adopting the above technical solution, the gas pressure pulsation model is established, and the reaction processes of combustion reaction, fluid fluctuation and acoustic fluctuation are coupled, thereby comprehensively reflecting the interaction of different physical processes and the influence on the gas pressure pulsation, and the accuracy of simulating the gas pressure pulsation in the combustion chamber is improved.

[0015] Optionally, after performing the step of mesh division and before performing the step of simulation solving, the method further comprises:

[0016] Mesh gradient calculation: collecting pressure, temperature and gas flow velocity in each network unit, and calculating the gradient of the pressure, the temperature and the gas flow velocity in each network unit in space by difference method;

[0017] First determination: determining whether the pressure gradient, the temperature gradient and the gas flow velocity gradient in the network unit all exceed corresponding preset threshold values;

[0018] If yes, the size of the current network unit is refined and the time step is shortened;

[0019] If no, the size of the current network unit and the time step are kept unchanged.

[0020] By adopting the technical scheme, according to the change of the physical quantity in different grid units, the grid is automatically refined in the area where the physical quantity changes greatly, so that the gas pressure pulsation model can accurately simulate the details of these areas, improve the calculation accuracy, and at the same time, maintain a relatively coarse grid in the stable area, thereby saving the calculation resources and improving the simulation efficiency. In addition, by dynamically adjusting the time step, it can better track the physical change process of the combustion chamber in the refined area, and avoid the instability of the numerical solution.

[0021] Optionally, after the step of performing grid division, before the step of performing simulation solving, further comprising:

[0022] The first processing: taking the experimental data as the input of the combustion reaction sub-model to obtain first data;

[0023] The first coupling: inputting the first data into the fluid fluctuation sub-model to obtain second data;

[0024] The first optimization: calculating the error between the second data and the experimental data as a first error, and optimizing the model parameters by using the first error to obtain an optimized gas pressure pulsation model, denoted as a first optimized model, and taking the first optimized model as a new gas pressure pulsation model.

[0025] By adopting the technical scheme, the combustion reaction sub-model and the fluid fluctuation sub-model are coupled, the simulation capability of the gas pressure pulsation model for the fluid fluctuation caused by the heat source change is enhanced, and the accuracy of the gas pressure pulsation simulation is improved.

[0026] Optionally, after the step of performing grid division, before the step of performing simulation solving, further comprising:

[0027] The second coupling: inputting the first data into the acoustic fluctuation sub-model to obtain third data;

[0028] The second optimization: calculating the error between the third data and the experimental data as a second error, and optimizing the model parameters by using the second error to obtain an optimized gas pressure pulsation model, denoted as a second optimized model, and taking the second optimized model as a new gas pressure pulsation model.

[0029] By adopting the technical scheme, the combustion reaction sub-model and the acoustic fluctuation sub-model are coupled, the simulation capability of the gas pressure pulsation model for the pressure pulse fluctuation on the acoustic level caused by the combustion process is enhanced, and the accuracy of the simulation of the gas pressure pulse fluctuation under the combustion acoustic effect is improved.

[0030] Optionally, after the step of performing grid division, before the step of performing simulation solving, further comprising:

[0031] Third coupling: inputting the second data into the acoustic wave sub-model to obtain fourth data;

[0032] Third optimization: calculating an error between the fourth data and the experimental data, denoted as a third error, and optimizing the model parameters using the third error to obtain an optimized gas pressure pulsation model, denoted as a third optimized model, and taking the third optimized model as the new gas pressure pulsation model.

[0033] By coupling the combustion reaction sub-model, the fluid wave sub-model, and the acoustic wave model, the influence of combustion on fluid, the pressure fluctuation generated in the flow, and the feedback influence of the acoustic wave on the combustion reaction can be captured simultaneously, the simulation capability of the gas pressure pulsation model for the combustion process and the fluctuation effect under complex conditions such as high temperature and high pressure is improved, and the accuracy of the simulation of the gas pressure pulse fluctuation under the complex combustion process of the combustion generator is improved.

[0034] Optionally, after the step of performing grid division, before the step of performing simulation solving, further comprising:

[0035] Adding disturbance: adding disturbance on the set boundary condition or initial condition, taking the boundary condition after adding disturbance as a new boundary condition, and taking the initial condition after adding disturbance as a new initial condition.

[0036] By adopting the above technical solution, the disturbance is added on the boundary condition and the initial condition, which can test the instantaneous response of the gas pressure pulsation model under non-ideal conditions, so that the gas pressure pulsation model can simulate complex dynamic response, and at the same time, it is helpful for the gas pressure pulsation model to better simulate small-scale changes in the flow field or temperature field, further improving the accuracy of the simulation results output by the gas pressure pulsation model.

[0037] Optionally, after the step of performing grid division, before the step of performing simulation solving, further comprising:

[0038] Probability modeling: selecting uncertain data in the experimental data, and performing probability modeling on the uncertain data to obtain a probability distribution of the uncertain data;

[0039] Uncertainty propagation: using an uncertainty quantification analysis method to sample in the probability distribution of the uncertain data, and taking the sampling value as the input of the gas pressure pulsation model.

[0040] By adopting the above technical solution, the uncertain data in the experimental data is modeled by probability, which can quantify the influence of these uncertainties on the model prediction results, thereby improving the robustness of the model. In addition, through uncertainty propagation analysis, it can effectively identify and quantify how the uncertainty of different input variables affects the final output, improving the credibility of the simulation results.

[0041] Optionally, after the step of performing simulation solving, before the step of verifying simulation result, further comprising:

[0042] Setting confidence interval: calculating the uncertainty range of simulation result to obtain the confidence interval of simulation result;

[0043] Coverage discrimination: judging whether all experimental data fall within the confidence interval of simulation result:

[0044] If yes, then performing the step of verifying simulation result;

[0045] If no, then analyzing the deviation mode of simulation result and experimental data, optimizing the gas pressure pulsation model according to the analysis result, and then performing the step of uncertainty propagation.

[0046] By adopting the above technical solution, the uncertainty range of simulation result is quantified by setting the confidence interval, which helps to improve the reliability and accuracy of simulation result, and enhances the adaptability of the gas pressure pulsation model to uncertainty. In addition, by analyzing the deviation of simulation result and experimental data, the gas pressure pulsation model can be optimized according to the deviation mode, which further improves the accuracy of the simulation result output by the gas pressure pulsation model.

[0047] In a second aspect, the present application provides a gas generator gas pressure pulsation simulation system, which adopts the following technical solution:

[0048] A gas generator gas pressure pulsation simulation system, comprising:

[0049] A data acquisition module for acquiring experimental data of a combustion chamber in a combustion generator;

[0050] A model establishment module for establishing a gas pressure pulsation model based on combustion reaction, gas fluid process or acoustic wave propagation fluctuation in the combustion generator, wherein the gas pressure pulsation model comprises a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model;

[0051] A grid division module for dividing the combustion chamber into a plurality of network units and setting physical properties and boundary conditions for each network unit;

[0052] A simulation solving module for solving the gas pressure pulsation model and outputting simulation result;

[0053] A simulation result verification module for comparing and verifying the simulation result with the experimental data to determine whether the simulation result is consistent with the experimental data;

[0054] If the simulation result is consistent with the experimental data, then a verified gas pressure pulsation model is obtained;

[0055] If the simulation result is inconsistent with the experimental data, the gas pressure pulsation model is optimized by using the verification result, and then a simulation solving module is triggered.

[0056] By adopting the technical scheme, the model module establishes the gas pressure pulsation model based on the combustion reaction, gas fluid process or acoustic wave fluctuation in the combustion generator, and based on the coupling of the combustion chemical reaction and the acoustic fluctuation and pressure wave fluctuation, the interaction of different physical processes and the influence on the gas pressure pulsation are comprehensively reflected, and the accuracy of simulating the gas pressure pulsation in the combustion chamber is improved.

[0057] In summary, the present application has at least one of the following beneficial technical effects:

[0058] 1. The combustion reaction sub-model, the fluid fluctuation sub-model and the acoustic fluctuation model are coupled, the influence of combustion on fluid, the pressure fluctuation generated in flow and the feedback influence of acoustic fluctuation on combustion reaction can be captured at the same time, the simulation capability of the gas pressure pulsation model for the combustion process and fluctuation effect under complex working conditions such as high temperature and high pressure is improved, and the accuracy of simulating the gas pressure pulsation under the complex combustion process of the combustion generator is improved.

[0059] 2. According to the change of the physical quantity in different grid units, the grid is automatically refined in the area where the physical quantity changes greatly, so that the gas pressure pulsation model can accurately simulate the details of these areas, the calculation accuracy is improved, and at the same time, the grid is kept relatively coarse in the stable area, so as to save the calculation resources and improve the simulation efficiency. In addition, by dynamically adjusting the time step, it can better track the physical change process of the combustion chamber in the refined area, and avoid the instability of numerical solution.

[0060] 3. Adding disturbance to the boundary condition and initial condition can test the instantaneous response of the gas pressure pulsation model under non-ideal conditions, so that the gas pressure pulsation model can simulate complex dynamic response, and at the same time, it is helpful for the gas pressure pulsation model to better simulate small-scale changes in the flow field or temperature field, and further improve the accuracy of the simulation results output by the gas pressure pulsation model. BRIEF DESCRIPTION OF DRAWINGS

[0061] Fig. 1 is a flow chart of embodiment 1 of the present application;

[0062] Fig. 2 is a flow chart of S3 grid division of embodiment 1 of the present application;

[0063] Fig. 3 is a flow chart of S5 adjusting grid resolution of embodiment 1 of the present application. DETAILED DESCRIPTION

[0064] The following will be combined with Figs. 1 to 3Further details of the application are described below.

[0065] Embodiment 1: The embodiment discloses a simulation method for gas pressure pulse fluctuation of a gas generator, as shown in the figure, the simulation method comprises the following steps: Fig. 1 The simulation method comprises the following steps: collecting experimental data of a combustion chamber in a combustion generator, then constructing a gas pressure pulse fluctuation model, dividing the combustion chamber into a plurality of network units, setting physical properties, boundary conditions and initial conditions for each network unit, inputting the boundary conditions and initial conditions into the gas pressure pulse fluctuation model for solving, outputting simulation results, comparing and verifying the simulation results with the experimental data, and determining whether the simulation results are consistent with the experimental data.

[0066] S1: Data collection: collecting experimental data of a combustion chamber in a combustion generator.

[0067] The experimental data of the combustion chamber of the combustion generator comprises temperature distribution at different positions in the combustion chamber, instantaneous temperature fluctuation in the combustion chamber, wall surface temperature in the combustion chamber, gas concentration of combustion products, concentration distribution of oxygen in the combustion chamber, static pressure distribution at different positions in the combustion chamber, dynamic pressure distribution at different positions in the combustion chamber, instantaneous fluctuation characteristics of internal pressure in the combustion chamber (including frequency, amplitude, periodicity of pressure fluctuation, etc.), pressure pulse signals in different frequency ranges in the combustion chamber (including frequency spectrum analysis, pulse intensity, phase, etc.), pressure wave propagation time in the combustion chamber, pressure wave propagation path in the combustion chamber, gas velocity distribution at different positions in the combustion chamber (i.e. airflow velocity field in the combustion chamber), fuel gas inlet flow rate, fuel gas outlet flow rate, turbulence intensity of fluid in the combustion chamber, turbulence energy spectrum, turbulence length scale, wall surface heat flow rate in the combustion chamber, combustion reaction temperature distribution, flow rate ratio of fuel and oxidant, vibration signal of the combustion chamber structure, sound pressure data inside and outside the combustion chamber, resonance frequency of the combustion chamber, etc. It also includes the external environment of the combustion chamber (such as atmospheric temperature, humidity, etc.).

[0068] In this embodiment, the temperature distribution at different positions in the combustion chamber, the instantaneous temperature fluctuation in the combustion chamber, and the combustion reaction temperature distribution can be collected by an infrared temperature measuring instrument or an infrared scanning temperature sensor. The wall surface temperature in the combustion chamber is collected by a temperature sensor installed on the wall surface of the combustion chamber, such as a surface thermocouple or an infrared temperature measuring instrument. The gas concentration of combustion products such as CO2, CO, NOx, SOx, etc. is measured by a gas analyzer such as a non-dispersive infrared (NDIR) analyzer, a chemical sensor, etc. The oxygen concentration distribution is measured at different positions in the combustion chamber by an oxygen sensor such as an oxygen electrochemical sensor or an oxygen laser analyzer. The static pressure distribution at different positions in the combustion chamber is collected and recorded by a piezoelectric pressure sensor or a strain gauge pressure sensor. The dynamic pressure distribution at different positions in the combustion chamber is collected by a piezoelectric pressure sensor or a dynamic pressure probe. The instantaneous fluctuation characteristics of the internal pressure of the combustion chamber are collected by a piezoelectric pressure sensor, an accelerometer, and a dynamic pressure recorder. The pressure pulsation signals in different frequency ranges in the combustion chamber are collected by a frequency spectrum analyzer, a pressure sensor, and a data acquisition system such as an NI data acquisition system. By setting multiple pressure sensors, the time for the pressure wave to propagate from one sensor to another is measured, and the pressure wave propagation time in the combustion chamber is calculated. The pressure wave propagation path in the combustion chamber is monitored by arranging pressure sensors at different positions in the combustion chamber. The gas velocity distribution at different positions in the combustion chamber is collected by a multi-point gas flow velocity sensor such as a hot wire anemometer or an ultrasonic flowmeter. The gas inlet flow rate and the gas exhaust flow rate are measured by a mass flowmeter or a turbine flowmeter. The turbulent intensity of the fluid in the combustion chamber is measured by a turbulent intensity sensor, a laser Doppler velocimeter (LDV), or a hot wire anemometer. The energy spectrum data of the turbulent flow of the gas stream are obtained by a laser Doppler velocimeter (LDV) and a turbulent energy analyzer. The heat flux of the wall surface is measured using a heat flow meter such as a thermocouple or a heat flow sheet. The heat flux of the wall surface is measured using a heat flow meter such as a thermocouple or a heat flow sheet. The flow rates of the fuel and the oxidant are measured by a flowmeter such as a mass flowmeter or a turbine flowmeter, and the ratio is calculated. The vibration signal of the combustion chamber wall is detected by a vibration sensor such as an accelerometer. The sound pressure data inside and outside the combustion chamber are recorded by a microphone or a sound pressure sensor, and frequency spectrum analysis is performed. The resonance frequency is obtained by analyzing the vibration and sound wave signals inside and outside the combustion chamber using a frequency spectrum analyzer or modal analysis.

[0069] In this embodiment, after collecting the experimental data of the combustion chamber, the data needs to be preprocessed. The steps of data preprocessing include data cleaning, time alignment, data normalization, and abnormal detection and correction.

[0070] Data cleaning: denoising, handling missing values, and removing outliers of the collected experimental data to ensure data quality.

[0071] Time Alignment: Time align the data from different sensors in the experimental data to ensure that all data have consistent timestamps for synchronized analysis.

[0072] Anomaly Detection and Correction: Identify and handle outliers or unreasonable fluctuations in the experimental data to avoid the impact of abnormal data on subsequent modeling.

[0073] S2 Model Building: Build a gas pressure pulsation model based on the combustion reaction, gas flow process, or acoustic wave propagation fluctuations in the combustion generator. The gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model, and an acoustic fluctuation sub-model.

[0074] In this embodiment, the combustion reaction sub-model is used to describe the combustion reaction rate, temperature field, gas concentration change, etc. The fluid fluctuation sub-model can simulate the air flow distribution, turbulence intensity, gas velocity field, etc. in the combustion chamber using computational fluid dynamics (CFD). The acoustic fluctuation sub-model is used to describe the propagation, reflection, resonance, etc. of acoustic waves inside and outside the combustion chamber, and to predict the mode and frequency of pressure fluctuations.

[0075] In this embodiment, the combustion reaction sub-model, fluid mechanics model, and acoustic wave propagation model can be selected as the combustion reaction sub-model, fluid fluctuation sub-model, and acoustic fluctuation sub-model in this embodiment according to the needs.

[0076] S3 Grid Division: Divide the combustion chamber into multiple network units, set physical properties, boundary conditions, and initial conditions for each network unit. Including S31 Geometric Modeling, S32 Selecting Grid Generation Tool, S33 Defining Grid Type and Refinement Area, S34 Setting Boundary Conditions, S35 Boundary Conditions, and S36 Grid Division, as shown in Fig. 2 .

[0077] S31 Geometric Modeling: Obtain the size and structure of the combustion chamber in the combustion generator to create a three-dimensional geometric model of the combustion chamber. The combustion chamber structure includes the internal shape of the combustion chamber, the arrangement of the burner, the location of the air inlet, the location of the exhaust port, and the wall surface inside the combustion chamber.

[0078] S32 Selecting Grid Generation Tool: Select a simulation software with grid generation tools or plug-ins as the grid generation tool according to the needs.

[0079] S33 Defining Grid Type and Refinement Area: Define the grid type, grid size, and hierarchical division of the grid according to the change of air flow. For example, the combustion area, boundary layer (area close to the wall of the combustion chamber), burner area, nozzle area, and turbulent flow field area are used as grid refinement areas, using higher grid resolution, and the area where the air flow is relatively smooth is used as low-resolution grid, using lower grid resolution.

[0080] In the embodiment, the grid type is defined as an unstructured network.

[0081] S34 sets boundary conditions: according to the physical process in the combustion chamber, the boundary conditions are set, including inlet boundary conditions, outlet boundary conditions and wall boundary conditions. The inlet boundary conditions include setting the gas inlet flow rate, temperature, velocity, etc. The outlet boundary conditions include setting the exhaust flow rate, pressure or outflow velocity. The wall boundary conditions include setting the wall temperature, heat flow, friction, etc.

[0082] S35 sets initial conditions: defines the initial gas temperature, pressure, flow velocity, concentration distribution, etc. in the combustion chamber, and sets the initial conditions of the combustion chamber according to the working condition of the combustion generator in the combustion chamber.

[0083] S36 grid division: using the selected grid generation tool, according to the three-dimensional geometric model of the combustion chamber, the set boundary conditions and initial conditions, and the defined grid type and refinement area, the combustion chamber is divided into a plurality of network units, and a plurality of network units are obtained.

[0084] S4 adds disturbance: adds disturbance on the set boundary conditions or initial conditions, and takes the disturbed boundary conditions as new boundary conditions and the disturbed initial conditions as new initial conditions.

[0085] The disturbance includes Gaussian noise, periodic disturbance and random disturbance.

[0086] S5 adjusts the grid resolution: including S51 grid gradient calculation, S52 pressure gradient judgment, S53 temperature gradient judgment, S54 flow velocity gradient judgment and S55 network unit verification, as shown in Fig. 3

[0087] S51 grid gradient calculation: the values of pressure, temperature and flow velocity in each network unit at the current grid node are collected, the finite difference method is used to calculate the difference values of pressure, temperature and flow velocity between adjacent grid nodes, and the gradients of pressure, temperature and flow velocity in space of the current network unit are obtained.

[0088] In the embodiment, the finite difference method is used to calculate the gradients of pressure, temperature and flow velocity in space of the current network unit, and high-order difference method or weighted difference method can be used to calculate the gradients of pressure, temperature and flow velocity in space of the current network unit according to the needs.

[0089] S52 pressure gradient judgment: whether the pressure gradient in the network unit exceeds the preset pressure gradient threshold value is judged.

[0090] If yes, the size of the current network unit is refined and the time step is shortened to improve the resolution of the current network unit. ​

[0091] If no, execute S53 temperature gradient judgment.

[0092] S53 temperature gradient judgment: judge whether the temperature gradient in the network unit exceeds a preset temperature gradient threshold.

[0093] If yes, refine the size of the current network unit and shorten the time step to improve the resolution of the current network unit.

[0094] If no, execute S54 airflow velocity gradient judgment.

[0095] S54 airflow velocity gradient judgment: judge whether the airflow velocity gradient in the network unit exceeds a preset airflow velocity gradient threshold.

[0096] If yes, refine the size of the current network unit and shorten the time step to improve the resolution of the current network unit.

[0097] If no, keep the size of the current network unit and the time step unchanged to maintain the resolution of the current network unit.

[0098] S55 network unit verification: calculate the difference of pressure, temperature and airflow velocity between two adjacent network units, and judge whether the calculated difference is less than a preset difference threshold.

[0099] If yes, do nothing.

[0100] If no, use an interpolation method to smooth the transition area between the current network unit and the adjacent network unit.

[0101] In this embodiment, in each time step, S51 grid gradient calculation to S54 airflow velocity gradient are executed to adaptively update the grid resolution. As the calculation proceeds, the resolution of the key area in the flow field will automatically increase, while in the smooth area, a relatively coarse grid can be maintained, thereby reducing unnecessary computational overhead.

[0102] S6 model coupling: including S61 first processing, S62 first coupling and S63 first optimization.

[0103] S61 first processing: input experimental data into a combustion reaction sub-model, and the combustion reaction sub-model simulates the combustion reaction process according to the input experimental data to obtain first data.

[0104] S62 first coupling: input the first data into a fluid wave sub-model, and the fluid wave sub-model simulates the gas flow and pressure wave propagation in the combustion chamber according to the first data to obtain second data.

[0105] S63 First optimization: calculate the error between the pressure fluctuation in the second data and the pressure fluctuation in the experimental data, denoted as the first error. Adjust the model parameters using the least squares method to minimize the first error, and obtain the optimized gas pressure fluctuation model, denoted as the first optimized model.

[0106] In this embodiment, the combustion reaction sub-model is coupled with the fluid fluctuation sub-model, enhancing the gas pressure fluctuation model's ability to simulate fluid fluctuations caused by changes in heat sources, and improving the accuracy of gas pressure fluctuation simulation.

[0107] Embodiment 2: The difference between this embodiment and embodiment 1 is that the model coupling S6 includes S61 first processing, S64 second coupling and S65 second optimization.

[0108] S61 First processing: input the experimental data into the combustion reaction sub-model, and the combustion reaction sub-model simulates the combustion reaction process according to the input experimental data to obtain first data.

[0109] S64 Second coupling: input the first data into the acoustic fluctuation sub-model, and the acoustic fluctuation sub-model simulates the pressure fluctuation, sound wave propagation and vibration phenomenon in the combustion reaction according to the first data to obtain third data.

[0110] S65 Second optimization: calculate the error between the second data and the experimental data, denoted as the second error, and adjust the model parameters using the least squares method to minimize the second error, and obtain the optimized gas pressure fluctuation model, denoted as the second optimized model.

[0111] In this embodiment, the combustion reaction sub-model is coupled with the acoustic fluctuation sub-model, enhancing the gas pressure fluctuation model's ability to simulate the pressure pulse fluctuation on the acoustic level caused by the combustion process, and improving the accuracy of the simulation of the gas pressure pulse fluctuation under the combustion acoustic effect.

[0112] Embodiment 3: The difference between this embodiment and embodiment 1 is that the model coupling S6 includes S61 first processing, S62 first coupling, S66 third coupling S67 third optimization.

[0113] S61 First processing: input the experimental data into the combustion reaction sub-model, and the combustion reaction sub-model simulates the combustion reaction process according to the input experimental data to obtain first data.

[0114] S62 First coupling: input the first data into the fluid fluctuation sub-model, and the fluid fluctuation sub-model simulates the gas flow and pressure wave propagation in the combustion chamber according to the first data to obtain second data.

[0115] S63 Third coupling: input the second data into the acoustic wave sub-model, the acoustic wave sub-model simulates the propagation behavior of acoustic waves, thermoacoustic waves, pressure waves, etc. in the fluid according to the second data, and obtains fourth data.

[0116] S64 Third optimization: calculate the error between the fourth data and the experimental data, denoted as the third error, adjust the model parameters using the least squares method to minimize the third error, and obtain the optimized gas pressure pulsation model, denoted as the third optimized model.

[0117] In this embodiment, the coupling of the combustion reaction sub-model, the fluid wave sub-model and the acoustic wave model can simultaneously capture the influence of combustion on fluid, the pressure fluctuation generated in the flow and the feedback effect of acoustic wave on combustion reaction, improve the modeling capability of the gas pressure pulsation model for the combustion process and wave effect under complex conditions such as high temperature and high pressure, and improve the accuracy of the simulation of gas pressure pulsation under the complex combustion process of the combustion generator.

[0118] S7 Uncertainty setting: including S71 probability modeling and S72 uncertainty propagation.

[0119] S71 Probability modeling: selecting uncertain data from the experimental data, the uncertain data including data with standard deviation greater than a preset standard deviation threshold, data with data difference greater than a preset fluctuation difference within a preset time period, and data with abnormal values. Statistical analysis is performed on the selected uncertain data to determine the probability distribution type of the uncertain data, and a probability model of the uncertain data is constructed using the data characteristics of the uncertain data and the corresponding probability distribution type, to obtain the probability distribution of the uncertain data.

[0120] S72 Uncertainty propagation: random sampling is performed in the probability distribution of the uncertain data using the Monte Carlo method, and the obtained sampling value is used as the input of the gas pressure pulsation model. Each input corresponds to an uncertain combination of a set of experimental data.

[0121] S8 Simulation solution: input the boundary conditions and initial conditions into the gas pressure pulsation model for calculation, solve the gas pressure pulsation model, and output the simulation results.

[0122] S9 Uncertainty verification: including S91 setting confidence interval and S92 coverage discrimination.

[0123] S91 Setting confidence interval: calculating the mean and standard deviation of the simulation results, and setting the confidence interval of the simulation results according to the probability distribution of the simulation results and the requirements and error range of the experimental data. In this embodiment, 95% confidence interval is used, which means that 95% of the simulation results should fall within this interval.

[0124] S92 coverage discrimination: determine whether the experimental data is all within the confidence interval of the simulation result:

[0125] If yes, execute S10 simulation result verification;

[0126] If no, analyze the deviation mode of the simulation result and the experimental data, the deviation mode including time deviation and space deviation. Based on the deviation mode analysis result, optimize the gas pressure pulsation model, and then execute S72 uncertainty propagation.

[0127] When the deviation mode of the simulation result and the experimental data is time deviation, it indicates that the model has delay or abnormal signal sampling frequency. The solution to the model delay is to add a time lag parameter in the gas pressure pulsation model or to increase the sampling frequency; the solution to the abnormal signal sampling frequency is to use a time synchronization algorithm to time-align the experimental data and the simulation result, and find the best time offset.

[0128] When the deviation mode of the simulation result and the experimental data is space deviation, it indicates that the resolution of the grid is low or the boundary condition is not accurate. The solution to the low resolution of the grid is to refine the size of each network unit in the grid refinement area and shorten the time step. The solution to the inaccurate boundary condition is to adjust the boundary condition.

[0129] S10 simulation result verification: compare and verify the simulation result with the experimental data, and determine whether the simulation result is below the consistency threshold value.

[0130] If yes, the verified gas pressure pulsation model is obtained.

[0131] If no, optimize the gas pressure pulsation model according to the verification result, and execute S8 simulation solving.

[0132] In other embodiments, after executing S2 model establishment, before executing S3 grid division, it further includes:

[0133] S11 working condition data setting: taking the boundary condition and the initial condition under the same working condition as a group of working condition conditions, constructing multiple groups of working condition conditions according to different working condition situations.

[0134] S12 multi-working condition training: based on the multiple groups of working condition conditions, executing S3 grid division to S8 simulation solving for each group of working condition conditions respectively, obtaining the corresponding simulation result under each group of working condition conditions, denoted as working condition simulation result, that is, obtaining the simulation result under different working conditions.

[0135] S13 first multi-working condition verification: determine whether each working condition simulation result exceeds the respective preset working condition simulation result threshold range.

[0136] If yes, the boundary condition and initial condition in the current working condition are reset, and then the S12 multi-working condition training is performed.

[0137] If no, the S13 second multi-working condition verification is performed.

[0138] The S14 second multi-working condition verification: the S10 verification simulation result is performed on each working condition simulation result, and it is judged whether the working condition simulation result is consistent with the experimental data in the corresponding working condition.

[0139] If yes, the gas pressure pulsation model after the second multi-working condition verification is taken as a new verified gas pressure pulsation model.

[0140] If no, the difference between the working condition simulation result and the corresponding experimental data is calculated, which is recorded as a first difference, the gas pressure pulsation model is optimized by using the first difference, and then the S12 multi-working condition training is performed.

[0141] Embodiment 4: The embodiment discloses a gas generator gas pressure pulsation simulation system, the simulation system comprises:

[0142] A data acquisition module is configured to acquire experimental data of a combustion chamber in a combustion generator.

[0143] A model establishment module is configured to construct a gas pressure pulsation model based on a combustion reaction, a gas fluid process or a sound wave propagation fluctuation in the combustion generator, the gas pressure pulsation model comprising a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model.

[0144] A grid division module is configured to divide the combustion chamber into a plurality of network units and set physical properties and boundary conditions for each network unit.

[0145] An added disturbance module is configured to add a disturbance to the set boundary condition or initial condition, take the added disturbance boundary condition as a new boundary condition and take the added disturbance initial condition as a new initial condition.

[0146] A probability modeling module is configured to select uncertain data in the experimental data, perform probability modeling on the uncertain data and obtain a probability distribution of the uncertain data.

[0147] An uncertainty propagation module is configured to perform sampling in the probability distribution of the uncertain data by using an uncertainty quantification analysis method and take a sampling value as an input of the gas pressure pulsation model.

[0148] A simulation solving module is configured to solve the gas pressure pulsation model and output a simulation result.

[0149] A confidence interval setting module is configured to calculate an uncertainty range of the simulation result and obtain a confidence interval of the simulation result.

[0150] A coverage discrimination module is configured to determine whether the experimental data is all within the confidence interval of the simulation result.

[0151] If yes, a simulation result verification module is triggered.

[0152] If no, a deviation mode between the simulation result and the experimental data is analyzed, the gas pressure pulsation model is optimized according to the analysis result, and then an uncertainty propagation module is triggered.

[0153] The simulation result verification module is configured to compare and verify the simulation result and the experimental data, and determine whether the simulation result is consistent with the experimental data.

[0154] If the simulation result is consistent with the experimental data, the verified gas pressure pulsation model is obtained.

[0155] If the simulation result is not consistent with the experimental data, the gas pressure pulsation model is optimized according to the verification result, and then a simulation solving module is triggered.

[0156] In the embodiment, the model establishing module establishes the gas pressure pulsation model based on the combustion reaction, the gas fluid process or the acoustic wave fluctuation in the combustion generator. Based on the coupling of the combustion chemical reaction and the acoustic fluctuation and the pressure wave fluctuation, the interaction of different physical processes and the influence on the gas pressure pulsation can be comprehensively reflected, and the accuracy of simulating the gas pressure pulsation in the combustion chamber is improved.

[0157] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, any equivalent changes made on the structure, shape and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method for simulating gas pressure pulse fluctuations in a gas generator, characterized in that, include: Data acquisition: Collect experimental data from the combustion chamber inside the combustion generator; Model establishment: A gas pressure pulsation model is constructed based on the combustion reaction, gas flow process, or acoustic wave propagation fluctuations within the combustion generator. The gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model, and an acoustic fluctuation sub-model. The combustion reaction sub-model, the fluid fluctuation sub-model, and the acoustic fluctuation model are coupled together to simultaneously capture the influence of combustion on the fluid, the pressure fluctuations generated during the flow, and the feedback influence of acoustic fluctuations on the combustion reaction. Mesh generation: The combustion chamber is divided into multiple network cells, and physical properties, boundary conditions, and initial conditions are set for each network cell; Simulation solution: Input the boundary conditions and initial conditions into the gas pressure pulsation model, solve the gas pressure pulsation model, and output the simulation results; Verify simulation results: Compare and verify the simulation results with the experimental data to determine whether the simulation results are consistent with the experimental data; If so, then the verified gas pressure pulsation model is obtained; If not, optimize the gas pressure pulsation model based on the verification results and perform the simulation solution steps; The process, which occurs after mesh generation and before the simulation solution, also includes: Grid gradient calculation: Collect pressure, temperature and airflow velocity in each network cell, and calculate the spatial gradient of pressure, temperature and airflow velocity in each network cell using the finite difference method; First judgment: Determine whether the pressure gradient, temperature gradient, and airflow velocity gradient within the network unit all exceed the corresponding preset thresholds; If so, refine the size of the current network unit and shorten the time step; If not, then keep the current network cell size and time step unchanged; The process, which occurs after mesh generation and before the simulation solution, also includes: Add perturbation: Add a perturbation to the set boundary conditions or initial conditions, and use the boundary conditions with added perturbation as the new boundary conditions and the initial conditions with added perturbation as the new initial conditions. The process, which occurs after mesh generation and before the simulation solution, also includes: Probabilistic modeling: Select uncertain data from the experimental data, perform probabilistic modeling on the uncertain data, and obtain the probability distribution of the uncertain data; Uncertainty propagation: An uncertainty quantification analysis method is used to sample the probability distribution of uncertain data, and the sampled values ​​are used as input to the gas pressure pulsation model.

2. The method for simulating gas pressure pulse fluctuations in a gas generator according to claim 1, characterized in that, The process, which occurs after mesh generation and before the simulation solution, also includes: First processing: The experimental data is used as input to the combustion reaction sub-model to obtain the first data; First coupling: Input the first data into the fluid wave sub-model to obtain the second data; First optimization: Calculate the error between the second data and the experimental data, denoted as the first error, and use the first error to optimize the model parameters to obtain the optimized gas pressure pulsation model, denoted as the first optimized model. Use the first optimized model as the new gas pressure pulse model.

3. The method for simulating gas pressure pulse fluctuations in a gas generator according to claim 2, characterized in that, The process, which occurs after mesh generation and before the simulation solution, also includes: Second coupling: Input the first data into the acoustic wave sub-model to obtain the third data; Second optimization: Calculate the error between the third data and the experimental data, denoted as the second error, and use the second error to optimize the model parameters to obtain the optimized gas pressure pulsation model, denoted as the second optimized model. Use the second optimized model as the new gas pressure pulse model.

4. The method for simulating gas pressure pulse fluctuations in a gas generator according to claim 3, characterized in that, The process, which occurs after mesh generation and before the simulation solution, also includes: Third coupling: Input the second data into the acoustic wave sub-model to obtain the fourth data; Third optimization: Calculate the error between the fourth data and the experimental data, denoted as the third error, and use the third error to optimize the model parameters to obtain the optimized gas pressure pulsation model, denoted as the third optimized model. Use the third optimized model as the new gas pressure pulse model.

5. The method for simulating gas pressure pulse fluctuations in a gas generator according to claim 1, characterized in that, After performing the simulation solution step and before performing the step of verifying the simulation results, the following steps are also included: Setting confidence intervals: Calculate the uncertainty range of the simulation results to obtain the confidence intervals of the simulation results; Coverage test: Determines whether all experimental data fall within the confidence interval of the simulation results. If so, then proceed with the steps to verify the simulation results; If not, analyze the deviation patterns between the simulation results and experimental data, optimize the gas pressure pulsation model based on the analysis results, and then perform the uncertainty propagation steps.

6. A gas pressure pulse fluctuation simulation system for a gas generator, said system being applicable to the method described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to collect experimental data from the combustion chamber inside the combustion generator; A model module is established to construct a gas pressure pulsation model based on the combustion reaction, gas fluid process, or acoustic wave propagation fluctuation within the combustion generator. The gas pressure pulsation model includes a combustion reaction sub-model, a fluid wave sub-model, and an acoustic wave sub-model. The mesh generation module is used to divide the combustion chamber into multiple network cells and set physical properties and boundary conditions for each network cell; The simulation solution module is used to solve the gas pressure pulsation model and output the simulation results; The simulation results verification module is used to compare and verify the simulation results with the experimental data to determine whether the simulation results are consistent with the experimental data. If the simulation results are consistent with the experimental data, then the validated gas pressure pulsation model is obtained; if the simulation results are inconsistent with the experimental data, then the gas pressure pulsation model is optimized using the validation results, and then the simulation solution module is triggered.

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