Fuel gas generator fuel gas pressure pulse fluctuation simulation method and system

Abstract

The invention provides a fuel gas generator fuel gas pressure pulse fluctuation simulation method and system, and relates to the technical field of numerical simulation, and the method comprises the steps of data acquisition, model establishment, grid division, simulation solution, simulation result verification and the like. The system comprises a data acquisition module, a model building module, a mesh generation module, a simulation solving module and a simulation result verification module. Through a built gas pressure pulsation model, the gas pressure pulsation can be simulated on the basis of the influence of coupling of a combustion chemical reaction and other physical fields such as acoustic fluctuation; and the gas pressure pulse fluctuation in the gas generator can be accurately simulated.

Description

Technical Field

[0001] The present application relates to the field of numerical simulation technology, and in particular to a method and system for simulating gas pressure pulse fluctuations of a gas generator. Background Art

[0002] As an important means of energy conversion and utilization, gas combustion is widely used in many fields such as industry, transportation, and civil use. 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 the release of energy and the transformation of substances. In this process, due to the uneven mixing of gas and air, the design differences of the burner structure, and the turbulence and vortex in the combustion process, a series of pressure fluctuations and pulse phenomena will be generated. These pressure pulses will not only cause vibration and noise in the combustion system, but may also cause scouring and wear on the walls, nozzles and other components of the combustion chamber, thereby affecting the stability and life of the system. In addition, pressure pulses may also affect the 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 impact of pressure pulse fluctuations on the system, it is necessary to conduct in-depth research on the generation mechanism, propagation characteristics and influencing factors of gas combustion pressure pulses. 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 to provide theoretical basis and technical support for the design and optimization of the combustion system. The Chinese invention patent with the application publication date of August 15, 2023 and publication number CN116738894A provides a method for numerical simulation of rocket engine gas flow. The patent treats the gas as air under high temperature and high pressure. The numerical simulation only needs to simulate a flow medium, and does not need to simulate chemical reaction processes such as the combustion of the propellant. By obtaining the gas flow characteristics, gas interference effects and aerodynamic characteristics such as the engine, gas rudder, and spoiler, the gas flow parameters of several characteristic positions such as the combustion chamber, throat, and nozzle outlet are given for numerical simulation. Regarding the above technical solution, since the gas is simplified into high-temperature and high-pressure air, the influence of the coupling between the combustion chemical reaction and other physical fields such as acoustic fluctuations is ignored, and the pressure fluctuation process cannot be accurately simulated, resulting in an inaccurate description of the pressure fluctuation characteristics, affecting the accuracy of the simulation of the gas pressure pulse fluctuation of the gas generator. Summary of the invention

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

[0005] In the first aspect, the present application provides a method for simulating gas pressure pulse fluctuations of a gas generator, which adopts the following technical solution: A method for simulating gas pressure pulse fluctuations of a gas generator comprises the following steps: Data collection: Collect experimental data of the combustion chamber in the combustion generator; Establishing a model: constructing a gas pressure pulsation model based on the combustion reaction, gas fluid process or acoustic wave propagation fluctuation in the combustion generator, wherein the gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model; Meshing: Divide the combustion chamber into multiple network units, and set physical properties, boundary conditions and initial conditions for each network unit; Simulation solution: Input boundary conditions and initial conditions into the gas pressure pulsation model, solve the gas pressure pulsation model, and output simulation results; Verify simulation results: Compare and verify the simulation results with experimental data to determine whether the simulation results are consistent with the experimental data; If so, the verified gas pressure pulsation model is obtained; If not, the gas pressure pulsation model is optimized according to the verification results and the simulation solution steps are performed.

[0006] By adopting the above technical solution, a gas pressure pulsation model is established, and the reaction processes of combustion reaction, fluid fluctuation and acoustic fluctuation are coupled, which comprehensively reflects the interaction of different physical processes and their influence on gas pressure pulsation, thereby improving the accuracy of simulating gas pressure pulsation in the combustion chamber.

[0007] Optionally, after performing the meshing step and before performing the simulation solution step, the method further includes: Grid gradient calculation: collect the pressure, temperature and airflow velocity in each network unit, and calculate the pressure, temperature and airflow velocity gradient in space in each network unit by differential method; First judgment: judging whether the pressure gradient, temperature gradient and air flow velocity gradient in the network unit all exceed the corresponding preset thresholds; If yes, refine the size of the current network unit and shorten the time step; If not, keep the current network unit size and time step unchanged.

[0008] By adopting the above technical solution, the grid is automatically refined in the area where the physical quantity changes greatly according to the changes in the physical quantity in different grid units, so that the gas pressure pulsation model can accurately simulate the details of these areas, improve the accuracy of the calculation, and keep a coarse grid in the stable area, thereby saving computing resources and improving simulation efficiency. In addition, by dynamically adjusting the time step, it is ensured that the physical change process of the combustion chamber can be better tracked in the refined area to avoid the instability of the numerical solution.

[0009] Optionally, after performing the meshing step and before performing the simulation solution step, the method further includes: First processing: using the experimental data as the input of the combustion reaction sub-model to obtain first data; First coupling: inputting 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, record it as the first error, use the first error to optimize the model parameters, obtain the optimized gas pressure pulsation model, record it as the first optimization model, and use the first optimization model as the new gas pressure pulsation model.

[0010] By adopting the above technical solution, the combustion reaction sub-model and the fluid fluctuation sub-model are coupled, which enhances the simulation capability of the gas pressure pulsation model for fluid fluctuations caused by heat source changes and improves the accuracy of the gas pressure pulsation simulation.

[0011] Optionally, after performing the meshing step and before performing the simulation solution step, the method further includes: Second coupling: inputting the first data into the acoustic wave sub-model to obtain third data; Second optimization: calculate the error between the third data and the experimental data, record it as the second error, use the second error to optimize the model parameters, and obtain the optimized gas pressure pulsation model, record it as the second optimization model, and use the second optimization model as the new gas pressure pulsation model.

[0012] By adopting the above technical solution, the combustion reaction sub-model is coupled with the acoustic fluctuation sub-model, which enhances the gas pressure pulsation model for the pressure pulse fluctuations on the acoustic level caused by the combustion process, and improves the accuracy of the simulation of the gas pressure pulse fluctuations under the combustion acoustic effect.

[0013] Optionally, after performing the meshing step and before performing the simulation solution step, the method further includes: Third coupling: inputting the second data into the acoustic wave sub-model to obtain fourth data; Third optimization: calculate the error between the fourth data and the experimental data, record it as the third error, use the third error to optimize the model parameters, and obtain the optimized gas pressure pulsation model, record it as the third optimization model, and use the third optimization model as the new gas pressure pulsation model.

[0014] By adopting the above technical scheme, the combustion reaction sub-model and the fluid fluctuation sub-model are coupled with the acoustic fluctuation model, which can simultaneously capture the impact of combustion on the fluid, the pressure fluctuations generated in the flow, and the feedback effect of acoustic fluctuations on the combustion reaction, thereby improving the simulation capability of the gas pressure pulsation model for the combustion process and fluctuation effects under complex working conditions such as high temperature and high pressure, and improving the accuracy of the simulation of the gas pressure pulse fluctuation under the complex combustion process of the combustion generator.

[0015] Optionally, after performing the meshing step and before performing the simulation solution step, the method further includes: Add perturbation: Add perturbation to the set boundary conditions or initial conditions, and use the boundary conditions after adding the perturbation as the new boundary conditions, and use the initial conditions after adding the perturbation as the new initial conditions.

[0016] By adopting the above technical solution and adding disturbances to the boundary conditions and initial conditions, the instantaneous response of the gas pressure pulsation model under non-ideal conditions can be tested, so that the gas pressure pulsation model can simulate complex dynamic responses. At the same time, it helps 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.

[0017] Optionally, after performing the meshing step and before performing the simulation solution step, the method further includes: Probabilistic modeling: Select uncertain data from experimental data, perform probabilistic modeling on the uncertain data, and obtain the probability distribution of the uncertain data; Uncertainty propagation: The uncertainty quantification analysis method is used to sample the probability distribution of uncertain data, and the sampled values ​​are used as the input of the gas pressure pulsation model.

[0018] By adopting the above technical solutions and probabilistically modeling the uncertain data in the experimental data, the impact of these uncertainties on the model prediction results can be quantified, thereby improving the robustness of the model. In addition, through uncertainty propagation analysis, it is possible to effectively identify and quantify how the uncertainty of different input variables affects the final output, thereby improving the credibility of the simulation results.

[0019] Optionally, after executing the step of simulating and solving the problem and before executing the step of verifying the simulation result, the method further includes: Set confidence interval: Calculate the uncertainty range of the simulation results and obtain the confidence interval of the simulation results; Coverage judgment: Determine whether all experimental data fall within the confidence interval of the simulation results: If yes, then the steps of verifying the simulation results are performed; If not, the deviation pattern between the simulation results and the experimental data is analyzed, and the gas pressure pulsation model is optimized according to the analysis results, and then the uncertainty propagation step is performed.

[0020] By adopting the above technical solution, setting the confidence interval and quantifying the uncertainty range of the simulation results, it is helpful to improve the reliability and accuracy of the simulation results and enhance the adaptability of the gas pressure pulsation model to uncertainty. In addition, by analyzing the deviation between the simulation results and the experimental data, the gas pressure pulsation model can be optimized according to the deviation mode, further improving the accuracy of the simulation results output by the gas pressure pulsation model.

[0021] In a second aspect, the present invention provides a gas generator gas pressure pulse fluctuation simulation system, which adopts the following technical solution: A gas generator gas pressure pulse fluctuation simulation system, comprising: A data acquisition module, used to collect experimental data of the combustion chamber in the combustion generator; Establishing a model module for constructing a gas pressure pulsation model based on a combustion reaction, a gas fluid process or an acoustic wave propagation fluctuation in a combustion generator, wherein the gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model; A meshing module is used to divide the combustion chamber into multiple network units and set physical properties and boundary conditions for each network unit; The simulation solution module is used to solve the gas pressure pulsation model and output the simulation results; The simulation result 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, the verified gas pressure pulsation model is obtained; If the simulation results are inconsistent with the experimental data, the gas pressure pulsation model is optimized using the verification results, and then the simulation solution module is triggered.

[0022] By adopting the above technical scheme, a model module is established to build a gas pressure pulsation model based on the combustion reaction, gas fluid process or sound wave propagation fluctuation in the combustion generator. Based on the coupling of combustion chemical reaction with acoustic fluctuation and pressure wave propagation fluctuation, it can comprehensively reflect the interaction of different physical processes and the influence on gas pressure pulsation, thereby improving the accuracy of simulating gas pressure pulsation in the combustion chamber.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. The combustion reaction sub-model and fluid fluctuation sub-model are coupled with the acoustic fluctuation model, which can simultaneously capture the impact of combustion on the fluid, the pressure fluctuations generated in the flow, and the feedback effect of acoustic fluctuations on the combustion reaction. This improves the simulation capability of the gas pressure pulsation model for the combustion process and fluctuation effects under complex working conditions such as high temperature and high pressure, and improves the accuracy of the simulation of gas pressure pulse fluctuations under complex combustion processes in the combustion generator.

[0024] 2. According to the changes in physical quantities in different grid cells, the grid is automatically refined in areas with large changes in physical quantities, so that the gas pressure pulsation model can accurately simulate the details of these areas, improving the accuracy of the calculation, while maintaining a coarser grid in the stable area, thereby saving computing resources and improving simulation efficiency. In addition, by dynamically adjusting the time step, it is ensured that the physical changes of the combustion chamber can be better tracked in the refined area to avoid the instability of the numerical solution.

[0025] 3. Adding disturbances to the boundary conditions and initial conditions 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 responses. At the same time, it helps 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flow chart of Example 1 of the present application; Figure 2 is a flowchart of S3 grid division in Example 1 of the present application; Figure 3 This is a flowchart of S5 adjusting the grid resolution in Example 1 of the present application. DETAILED DESCRIPTION

[0027] The following combination Figures 1 to 3 This application is described in further detail.

[0028] Embodiment 1: This embodiment discloses a method for simulating gas pressure pulse fluctuations of a gas generator, such as Figure 1 As shown, the simulation method includes: collecting experimental data of the combustion chamber in the combustion generator, then constructing a gas pressure pulsation model, dividing the combustion chamber into multiple 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 pulsation model for solving, outputting simulation results, comparing and verifying the simulation results with the experimental data, and judging whether the simulation results are consistent with the experimental data. This embodiment includes the following steps: S1 Data Collection: Collect experimental data of the combustion chamber in the combustion generator.

[0029] The experimental data of the combustion chamber of the combustion generator include temperature distribution at different positions in the combustion chamber, instantaneous temperature fluctuation in the combustion chamber, temperature of the combustion chamber wall, gas concentration of combustion products, oxygen concentration distribution 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 pressure inside the combustion chamber (including frequency, amplitude, periodicity, etc. of pressure fluctuation), pressure pulsation signal in the combustion chamber within different frequency ranges (including spectrum analysis of pulsation signal, pulsation 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., gas flow velocity field in the combustion chamber), gas intake flow, gas exhaust flow, turbulence intensity of fluid in the combustion chamber, turbulence energy spectrum, turbulence length scale, heat flow of the combustion chamber wall, combustion reaction temperature distribution, fuel and oxidant flow ratio, 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.).

[0030] 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 thermometer or an infrared scanning temperature sensor. The temperature of the inner wall of the combustion chamber is collected by a temperature sensor (such as a surface thermocouple or infrared temperature measurement) installed on the wall of the combustion chamber. 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 signal in the combustion chamber within different frequency ranges is collected by a spectrum analyzer, a pressure sensor, and a data acquisition system (such as an NI data acquisition system). By setting up multiple pressure sensors, the time it takes for the pressure wave to propagate from one sensor to another is measured, and the propagation time of the pressure wave in the combustion chamber is calculated. By arranging pressure sensors at different positions in the combustion chamber, the propagation path of the pressure wave in the combustion chamber is monitored. The gas velocity distribution at different positions in the combustion chamber is collected by multi-point air flow velocity sensors (such as hot wire anemometers or ultrasonic flowmeters). The gas intake flow rate and gas exhaust flow rate are measured by mass flowmeters or turbine flowmeters. The turbulence intensity of the fluid in the combustion chamber is measured by turbulence intensity sensors, laser Doppler velocimeters (LDVs) or hot wire anemometers. The energy spectrum data of the air flow turbulence is obtained by laser Doppler velocimeters (LDVs) and turbulence energy analyzers. The heat flux of the wall is measured by heat flow meters (such as thermocouples and heat flow sheets). The heat flux of the wall is measured by heat flow meters (such as thermocouples and heat flow sheets). The flow rates of fuel and oxidant are measured by flowmeters (such as mass flowmeters and turbine flowmeters) and the ratio is calculated. The vibration signal of the combustion chamber wall is detected by vibration sensors (such as accelerometers). The sound pressure data inside and outside the combustion chamber is recorded by a microphone or sound pressure sensor, and the spectrum is analyzed. The resonance frequency is obtained by analyzing the vibration and sound wave signals inside and outside the combustion chamber using a spectrum analyzer or modal analysis.

[0031] 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 abnormality detection and correction.

[0032] Data cleaning: De-noise the collected experimental data, process missing values, and remove outliers to ensure data quality.

[0033] Time alignment: Time-align the data from different sensors in the experimental data to ensure that the timestamps of all data are consistent for synchronous analysis.

[0034] Anomaly detection and correction: Identify and handle sudden changes or unreasonable fluctuations in experimental data to avoid the impact of abnormal data on subsequent models.

[0035] S2: Establishing a model: Building a gas pressure pulsation model based on the combustion reaction, gas fluid process or acoustic wave propagation fluctuation 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.

[0036] In this embodiment, the combustion reaction submodel is used to describe the combustion reaction rate, temperature field, gas concentration change, etc. The fluid wave submodel can use computational fluid dynamics (CFD) to simulate the airflow distribution, turbulence intensity, gas velocity field, etc. in the combustion chamber. The acoustic wave submodel is used to describe the propagation, reflection, resonance and other characteristics of sound waves inside and outside the combustion chamber, and predict the mode and frequency of pressure fluctuations.

[0037] In this embodiment, the combustion reaction sub-model, the fluid mechanics model and the acoustic wave propagation model can be selected as needed as the combustion reaction sub-model, the fluid wave sub-model and the acoustic wave sub-model in this embodiment.

[0038] S3 Meshing: 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 selection of mesh generation tools, S33 definition of mesh type and refinement area, S34 setting boundary conditions, S35 boundary conditions and S36 meshing, such as Figure 2 shown.

[0039] 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 layout of the burner, the position of the air inlet, the position of the exhaust port, and the wall surface inside the combustion chamber.

[0040] S32 Select a mesh generation tool: Select a simulation software with a mesh generation tool or plug-in as the mesh generation tool as needed.

[0041] S33 defines mesh type and refinement area: defines mesh type, mesh size, and hierarchical differentiation of meshes according to air flow changes. For example, the combustion area, boundary layer (area close to the combustion chamber wall), burner area, nozzle area, and turbulent flow field area are used as mesh refinement areas with higher mesh resolution, and the area with relatively stable air flow is used as low-resolution mesh with lower mesh resolution.

[0042] In this embodiment, the grid type is defined as an unstructured network.

[0043] S34 sets boundary conditions: according to the physical process in the combustion chamber, boundary conditions are set, including inlet boundary conditions, outlet boundary conditions and wall boundary conditions. Inlet boundary conditions include setting the gas inlet flow rate, temperature, speed, etc. Outlet boundary conditions include setting the exhaust flow rate, pressure or outflow speed. Wall boundary conditions include setting the wall temperature, heat flow, friction, etc.

[0044] S35 sets initial conditions: defines the initial gas temperature, pressure, air flow velocity, concentration distribution, etc. in the combustion chamber, and sets the initial conditions of the combustion chamber according to the working conditions of the combustion chamber in the combustion generator.

[0045] S36 Meshing: Use the selected mesh generation tool to divide the combustion chamber into multiple network units according to the three-dimensional geometric model of the combustion chamber, the set boundary conditions and initial conditions, and the defined mesh type and refinement area to obtain several network units.

[0046] S4 Add Perturbation: Add perturbation to the set boundary conditions or initial conditions, use the boundary conditions after adding the perturbation as the new boundary conditions, and use the initial conditions after adding the perturbation as the new initial conditions.

[0047] The disturbance includes Gaussian noise, periodic disturbance and random disturbance.

[0048] S5 adjusts the grid resolution: including S51 grid gradient calculation, S52 pressure gradient judgment, S53 temperature gradient judgment, S54 air flow velocity gradient judgment and S55 network unit verification, such as Figure 3 shown.

[0049] S51 grid gradient calculation: Collect the values ​​of pressure, temperature and airflow velocity in each network unit at the current grid node, use the finite difference method to calculate the difference in pressure, temperature and airflow velocity between adjacent grid nodes, and obtain the gradient of pressure, temperature and airflow velocity in space of the current network unit.

[0050] In this embodiment, the finite difference method is used to calculate the gradient of the pressure, temperature and airflow velocity in the space of the current network unit. The high-order difference method or the weighted difference method can be used as needed to calculate the gradient of the pressure, temperature and airflow velocity in the space of the current network unit.

[0051] S52 Pressure gradient determination: Determine whether the pressure gradient in the network unit exceeds a preset pressure gradient threshold.

[0052] If so, the size of the current network unit is refined and the time step is shortened to improve the resolution of the current network unit.

[0053] If not, execute S53 temperature gradient determination.

[0054] S53 Temperature gradient determination: determining whether the temperature gradient in the network unit exceeds a preset temperature gradient threshold.

[0055] If so, the size of the current network unit is refined and the time step is shortened to improve the resolution of the current network unit.

[0056] If not, then S54 is executed to determine the airflow velocity gradient.

[0057] S54 air flow velocity gradient determination: determining whether the air flow velocity gradient in the network unit exceeds a preset air flow velocity gradient threshold.

[0058] If so, the size of the current network unit is refined and the time step is shortened to improve the resolution of the current network unit.

[0059] If not, the size and time step of the current network unit are kept unchanged to maintain the resolution of the current network unit.

[0060] S55 Network unit verification: Calculate the difference in pressure, temperature and airflow velocity between two adjacent network units, and determine whether the calculated differences are all less than a preset difference threshold.

[0061] If so, no processing is done.

[0062] If not, an interpolation method is used to smooth the transition area between the current network unit and the adjacent network unit.

[0063] In this embodiment, in each time step, the grid gradient calculation from S51 to S54 airflow velocity gradient is performed, and the grid resolution is adaptively updated. As the calculation proceeds, the resolution of the key area in the flow field is automatically increased, and a coarser grid can be maintained in the stable area, thereby reducing unnecessary calculation overhead.

[0064] S6 model coupling: includes S61 first processing, S62 first coupling and S63 first optimization.

[0065] 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 the first data.

[0066] S62 First coupling: input the first data into the 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 the second data.

[0067] S63 First Optimization: Calculate the error between the pressure fluctuation in the second data and the pressure fluctuation in the experimental data, record it as the first error, adjust the model parameters using the least square method to minimize the first error, and obtain an optimized gas pressure pulsation model, record it as the first optimization model.

[0068] In this embodiment, the combustion reaction sub-model is coupled with the fluid fluctuation sub-model, which enhances the simulation capability of the gas pressure pulsation model for fluid fluctuations caused by changes in the heat source and improves the accuracy of the gas pressure pulsation simulation.

[0069] Embodiment 2: This embodiment differs from Embodiment 1 in that S6 model coupling includes S61 first processing, S64 second coupling and S65 second optimization.

[0070] 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 the first data.

[0071] S64 second coupling: input the first data into the acoustic wave sub-model, and the acoustic wave sub-model simulates the pressure pulsation, sound wave propagation and vibration phenomena in the combustion reaction according to the first data to obtain the third data.

[0072] S65 Second Optimization: Calculate the error between the second data and the experimental data, record it as the second error, use the least square method to adjust the model parameters to minimize the second error, and obtain the optimized gas pressure pulsation model, record it as the second optimization model.

[0073] In this embodiment, the combustion reaction sub-model is coupled with the acoustic fluctuation sub-model, which enhances the gas pressure pulsation model for the pressure pulse fluctuations on the acoustic level caused by the combustion process, and improves the accuracy of the simulation of the gas pressure pulse fluctuations under the combustion acoustic effect.

[0074] Embodiment 3: This embodiment differs from Embodiment 1 in that S6 model coupling includes S61 first processing, S62 first coupling, S66 third coupling and S67 third optimization.

[0075] 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 the first data.

[0076] S62 First coupling: input the first data into the 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 the second data.

[0077] S63 third coupling: input the second data into the acoustic wave sub-model, and the acoustic wave sub-model simulates the propagation behavior of sound waves, thermoacoustic waves, pressure waves, etc. in the fluid according to the second data to obtain fourth data.

[0078] S64 third optimization: calculate the error between the fourth data and the experimental data, recorded as the third error, use the least squares method to adjust the model parameters to minimize the third error, and obtain the optimized gas pressure pulsation model, recorded as the third optimization model.

[0079] In this embodiment, the combustion reaction sub-model and the fluid fluctuation sub-model are coupled with the acoustic fluctuation model, which can simultaneously capture the impact of combustion on the fluid, the pressure fluctuations generated in the flow, and the feedback effect of acoustic fluctuations on the combustion reaction, thereby improving the simulation capability of the gas pressure pulsation model for the combustion process and fluctuation effects under complex working conditions such as high temperature and high pressure, and improving the accuracy of the simulation of the gas pressure pulse fluctuation under the complex combustion process of the combustion generator.

[0080] S7 Uncertainty Setting: includes S71 Probabilistic Modeling and S72 Uncertainty Propagation.

[0081] S71 Probabilistic modeling: Select uncertain data from the experimental data, including data with a standard deviation greater than a preset standard deviation threshold, data with a data difference greater than a preset fluctuation difference within a preset time period, and data with abnormal values. Perform statistical analysis on the selected uncertain data to determine the probability distribution type of the uncertain data, and construct a probability model of the uncertain data using the data characteristics of the uncertain data and the corresponding probability distribution type to obtain the probability distribution of the uncertain data.

[0082] S72 Uncertainty Propagation: Monte Carlo method is used to randomly sample in the probability distribution of uncertain data, and the obtained sampled values ​​are used as the input of the gas pressure pulsation model. Each input corresponds to an uncertain combination of a set of experimental data.

[0083] S8 simulation solution: input boundary conditions and initial conditions into the gas pressure pulsation model for calculation, solve the gas pressure pulsation model, and output simulation results.

[0084] S9 Uncertainty Verification: includes S91 setting confidence interval and S92 coverage judgment.

[0085] S91 sets the confidence interval: calculates the mean and standard deviation of the simulation results, and sets 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, a 95% confidence interval is used, which means that 95% of the simulation results should fall within this interval.

[0086] S92 Coverage Judgment: Determine whether all experimental data fall within the confidence interval of the simulation results: If yes, execute S10 to verify the simulation results; If not, then the deviation pattern between the simulation result and the experimental data is analyzed, and the deviation pattern includes time deviation and space deviation. Based on the deviation pattern analysis result, the gas pressure pulsation model is optimized, and then S72 uncertainty propagation is performed.

[0087] When the deviation pattern between the simulation results and the experimental data is time deviation, it means that the model is delayed or the signal sampling frequency is abnormal. The solution to the model delay is to add a time lag parameter or increase the sampling frequency in the gas pressure pulsation model; the solution to the abnormal signal sampling frequency is to use a time synchronization algorithm to align the experimental data and simulation results in time and find the best time offset.

[0088] When the deviation pattern between the simulation results and the experimental data is spatial, it means that the mesh resolution is low or the boundary conditions are inaccurate. The solution to low mesh resolution is to refine the size of each network element in the mesh refinement area and shorten the time step. The solution to inaccurate boundary conditions is to adjust the boundary conditions.

[0089] S10 Verify simulation results: Compare and verify the simulation results with the experimental data to determine whether the simulation results and the experimental data are below a consistency threshold.

[0090] If so, the verified gas pressure pulsation model is obtained.

[0091] If not, the gas pressure pulsation model is optimized according to the verification results and the S8 simulation solution is performed.

[0092] In other embodiments, after executing S2 to establish the model and before executing S3 to divide the mesh, the method further includes: S11 working condition data setting: Take the boundary conditions and initial conditions under the same working condition as a set of working conditions, and construct multiple sets of working conditions according to different working conditions.

[0093] S12 multi-operating condition training: Based on multiple groups of operating conditions, perform S3 grid differentiation to S8 simulation solution for each group of operating conditions, and obtain the corresponding simulation results under each group of operating conditions, which are recorded as the operating condition simulation results, that is, obtain the simulation results under different operating conditions.

[0094] S13 First multi-operating condition verification: determine whether each operating condition simulation result exceeds the corresponding preset operating condition simulation result threshold range.

[0095] If so, reset the boundary conditions and initial conditions under the current working condition, and then execute S12 multi-working condition training.

[0096] If not, execute S13 the second multi-operating condition verification.

[0097] S14 Second multi-operating condition verification: Execute S10 to verify the simulation result of each operating condition simulation result to determine whether the operating condition simulation results are consistent with the experimental data under the corresponding operating condition.

[0098] If so, the gas pressure pulsation model verified under the second multi-operating condition is used as a new verified gas pressure pulsation model.

[0099] If not, the difference between the operating condition simulation result and the corresponding experimental data is calculated and recorded as the first difference. The gas pressure pulsation model is optimized using the first difference, and then S12 multi-operating condition training is performed.

[0100] Embodiment 4: This embodiment discloses a gas generator gas pressure pulse fluctuation simulation system, the simulation system comprising: The data acquisition module is used to collect experimental data of the combustion chamber in the combustion generator.

[0101] A model module is established to construct a gas pressure pulsation model based on the combustion reaction, gas fluid process or sound wave propagation fluctuation in the combustion generator, wherein the gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model.

[0102] The meshing module is used to divide the combustion chamber into multiple network units and set physical properties and boundary conditions for each network unit.

[0103] Add perturbation module, used to add perturbation to the set boundary conditions or initial conditions, and use the boundary conditions after adding perturbation as new boundary conditions, and use the initial conditions after adding perturbation as new initial conditions.

[0104] The probability modeling module is used to select uncertain data in the experimental data, perform probability modeling on the uncertain data, and obtain the probability distribution of the uncertain data.

[0105] The uncertainty propagation module is used to perform sampling in the probability distribution of uncertain data using uncertainty quantification analysis methods, and the sampling values ​​are used as inputs of the gas pressure pulsation model.

[0106] The simulation solution module is used to solve the gas pressure pulsation model and output the simulation results; Set up a confidence interval module to calculate the uncertainty range of the simulation results and obtain the confidence interval of the simulation results; The coverage judgment module is used to determine whether all experimental data fall within the confidence interval of the simulation results: If yes, the verification simulation result module is triggered; If not, the deviation pattern between the simulation results and the experimental data is analyzed, and the gas pressure pulsation model is optimized according to the analysis results, and then the uncertainty propagation module is triggered.

[0107] The simulation result 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.

[0108] If the simulation results are consistent with the experimental data, the verified gas pressure pulsation model is obtained.

[0109] If the simulation results are inconsistent with the experimental data, the gas pressure pulsation model is optimized using the verification results, and then the simulation solution module is triggered.

[0110] In this embodiment, a model module is established to construct a gas pressure pulsation model based on the combustion reaction, gas fluid process or sound wave propagation fluctuation in the combustion generator. Based on the coupling of combustion chemical reaction with acoustic fluctuation and pressure wave propagation fluctuation, it can comprehensively reflect the interaction of different physical processes and their influence on gas pressure pulsation, thereby improving the accuracy of simulating gas pressure pulsation in the combustion chamber.

[0111] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for simulating gas pressure pulse fluctuations of a gas generator, characterized in that: include: Data collection: Collect experimental data of the combustion chamber in the combustion generator; Establishing a model: constructing a gas pressure pulsation model based on the combustion reaction, gas fluid process or acoustic wave propagation fluctuation in the combustion generator, wherein the gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model; Meshing: Divide the combustion chamber into multiple network units, and set physical properties, boundary conditions and initial conditions for each network unit; Simulation solution: Input boundary conditions and initial conditions into the gas pressure pulsation model, solve the gas pressure pulsation model, and output simulation results; Verify simulation results: Compare and verify the simulation results with experimental data to determine whether the simulation results are consistent with the experimental data; If so, the verified gas pressure pulsation model is obtained; If not, the gas pressure pulsation model is optimized according to the verification results and the simulation solution steps are performed.

2. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 1, characterized in that: After the meshing step and before the simulation solution step, the following steps are also included: Grid gradient calculation: collect the pressure, temperature and airflow velocity in each network unit, and calculate the pressure, temperature and airflow velocity gradient in space in each network unit by differential method; First judgment: judging whether the pressure gradient, temperature gradient and air flow velocity gradient in the network unit all exceed the corresponding preset thresholds; If yes, refine the size of the current network unit and shorten the time step; If not, keep the current network unit size and time step unchanged.

3. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 1, characterized in that: After the meshing step and before the simulation solution step, the following steps are also included: First processing: using the experimental data as the input of the combustion reaction sub-model to obtain first data; First coupling: inputting 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, record it as the first error, use the first error to optimize the model parameters, obtain the optimized gas pressure pulsation model, record it as the first optimization model, and use the first optimization model as the new gas pressure pulsation model.

4. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 3, characterized in that: After the meshing step and before the simulation solution step, the following steps are also included: Second coupling: inputting the first data into the acoustic wave sub-model to obtain third data; Second optimization: calculate the error between the third data and the experimental data, record it as the second error, use the second error to optimize the model parameters, and obtain the optimized gas pressure pulsation model, record it as the second optimization model, and use the second optimization model as the new gas pressure pulsation model.

5. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 4, characterized in that: After the meshing step and before the simulation solution step, the following steps are also included: Third coupling: inputting the second data into the acoustic wave sub-model to obtain fourth data; Third optimization: calculate the error between the fourth data and the experimental data, record it as the third error, use the third error to optimize the model parameters, and obtain the optimized gas pressure pulsation model, record it as the third optimization model, and use the third optimization model as the new gas pressure pulsation model.

6. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 1, characterized in that: After the meshing step and before the simulation solution step, the following steps are also included: Add perturbation: Add perturbation to the set boundary conditions or initial conditions, and use the boundary conditions after adding the perturbation as the new boundary conditions, and use the initial conditions after adding the perturbation as the new initial conditions.

7. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 1, characterized in that: After the meshing step and before the simulation solution step, the following steps are also included: Probabilistic modeling: Select uncertain data from experimental data, perform probabilistic modeling on the uncertain data, and obtain the probability distribution of the uncertain data; Uncertainty propagation: The uncertainty quantification analysis method is used to sample the probability distribution of uncertain data, and the sampled values ​​are used as the input of the gas pressure pulsation model.

8. The method for simulating gas pressure pulse fluctuation of a gas generator according to claim 7, characterized in that: After executing the simulation solution step and before executing the simulation result verification step, the method further includes: Set confidence interval: Calculate the uncertainty range of the simulation results and obtain the confidence interval of the simulation results; Coverage judgment: Determine whether all experimental data fall within the confidence interval of the simulation results: If yes, then the steps of verifying the simulation results are performed; If not, the deviation pattern between the simulation results and the experimental data is analyzed, and the gas pressure pulsation model is optimized according to the analysis results, and then the uncertainty propagation step is performed.

9. A gas generator gas pressure pulse fluctuation simulation system, the system is applicable to the method according to any one of claims 1 to 8, characterized in that: include: A data acquisition module, used to collect experimental data of the combustion chamber in the combustion generator; Establishing a model module for constructing a gas pressure pulsation model based on a combustion reaction, a gas fluid process or an acoustic wave propagation fluctuation in a combustion generator, wherein the gas pressure pulsation model includes a combustion reaction sub-model, a fluid fluctuation sub-model and an acoustic fluctuation sub-model; A meshing module is used to divide the combustion chamber into multiple network units and set physical properties and boundary conditions for each network unit; The simulation solution module is used to solve the gas pressure pulsation model and output the simulation results; The simulation result 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, the verified gas pressure pulsation model is obtained; If the simulation results are inconsistent with the experimental data, the gas pressure pulsation model is optimized using the verification results, and then the simulation solution module is triggered.

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