Method and system for establishing nozzle profile ablation and regression prediction model based on deep learning

Abstract

The invention provides a deep learning-based method and system for establishing a nozzle profile ablation regression prediction model. According to the method, a nozzle profile ablation regression prediction model is constructed based on two-dimensional profile ablation data of an existing test nozzle and wall surface data obtained by numerical simulation of the corresponding test nozzle for ignition conditions, and the prediction time cost and the calculation cost are effectively reduced on the premise that the prediction precision is guaranteed; the invention further provides a method for extracting data points for a single two-dimensional ablated profile curve to form a data set. According to the method, points are taken according to the horizontal coordinate of the spray pipe, and a data set is formed by wall surface data obtained by extracting numerical simulation of points on a two-dimensional molded line of the corresponding horizontal coordinate and the wall surface retreat amount of an ignition experiment. According to the method, on the basis of limited experimental data and limited computing resources, the prediction time cost and the computing cost are reduced on the premise that the precision is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of nozzle profile prediction, and in particular to a method and system for establishing a nozzle profile ablation regression prediction model based on deep learning. Background Art

[0002] In the solid rocket propulsion system, the nozzle is one of the core components for thrust formation, and the shape of the nozzle profile directly affects the performance of the nozzle. Therefore, how to select materials and design the nozzle profile according to the needs is the main issue for nozzle designers. During the operation of the solid rocket, high-temperature and high-pressure gas is directly ejected from the nozzle by the combustion chamber, which makes the nozzle profile face the scouring of high-temperature and high-pressure gas. The gas discharged from the combustion chamber not only has the characteristics of high temperature and high pressure, but also contains a large amount of carbon monoxide, carbon dioxide, water vapor and solid particles. In order to reduce weight and improve structural strength, the nozzle profile material is mostly carbon-carbon composite material. This causes the nozzle profile material to undergo chemical reactions under the action of high-temperature gas, and the profile will also be subject to the material stripping caused by the collision of solid particles, so the nozzle profile will definitely change under working conditions. Predicting the changes in the nozzle profile can provide a reference for nozzle design and is an inevitable process in the nozzle design process.

[0003] The existing method for predicting nozzle profiles is mainly to write UDF to describe the relationship between the nozzle profile retreat and flow field parameters, embed numerical simulation software, and combine dynamic grids and numerical calculation methods to iteratively simulate the nozzle profile retreat. Simulation by numerical methods can effectively meet design requirements within a certain accuracy range. However, due to the limitations of computer hardware conditions and the contradiction between numerical calculation accuracy and computational complexity, numerical simulation methods face the problems of long simulation time and high computational cost. Therefore, how to provide a nozzle profile ablation retreat prediction method that can reduce prediction cost and time while ensuring prediction accuracy has become the focus of designers. Summary of the invention

[0004] The purpose of the present invention is to address the problems of long simulation time and high computational cost faced by nozzle surface erosion and regression prediction, and to propose a method and system for establishing a nozzle surface erosion and regression prediction model based on deep learning.

[0005] The present invention is implemented by the following technical scheme. The present invention proposes a method for establishing a nozzle profile ablation regression prediction model based on deep learning, and the method comprises the following steps:

[0006] Step 1: Obtain nozzle experimental data based on the experimental conditions and structure of the predicted nozzle, including geometric model, ignition conditions, and profile curve data before and after the test;

[0007] Step 2: Perform steady-state calculation based on the DPM model and ignition conditions to obtain the wall distribution;

[0008] Step 3: extracting data points from a single two-dimensional ablation profile curve to form a data set;

[0009] Step 4: training the nozzle profile ablation regression prediction model;

[0010] Step 5: Perform steady-state numerical calculation on the nozzle to be predicted, extract the numerical calculation results, form a verification set, and substitute it into the trained nozzle profile ablation regression prediction model to obtain prediction data.

[0011] Furthermore, in step one, with respect to the experimental conditions and structure of the predicted nozzle, four nozzle experimental data with similar experimental conditions and structures to the predicted object but different inlet pressures are obtained, including nozzle inlet pressure, inlet temperature, ambient pressure, ambient temperature, fuel gas relative molecular mass, fuel gas specific heat, total mass of fuel gas particles, fuel gas particle average diameter, maximum particle size, minimum particle size and nozzle profile curve data before and after the experiment; the nozzles are divided into high-pressure inlet and low-pressure inlet groups according to the inlet pressure, and steps two to three are all applied to the above four nozzles.

[0012] Furthermore, in step 2, based on the profile curve data and experimental data provided in step 1, Fluent is used to perform steady-state numerical calculations on the experimental nozzle based on the DPM model to obtain the nozzle wall temperature distribution, nozzle wall pressure distribution, nozzle wall shear force distribution and Oka erosion rate distribution.

[0013] Furthermore, the specific process of step 2 is as follows:

[0014] Step 21: Process the nozzle geometry model;

[0015] Step 22: Discretize the computational domain;

[0016] Step 23: Set the calculation conditions and perform steady-state numerical calculation;

[0017] Step 24: Adaptive mesh encryption;

[0018] Step 25: Superimpose particle flow for steady-state calculation.

[0019] Furthermore, in step three, the profile ablation regression amount and the numerical calculation data obtained in step two are extracted according to the structure of the experimental nozzle to form a data set. The specific process is as follows:

[0020] Step 31: Mark data points on the profile curve data graph before and after the nozzle experiment, and measure its radial ablation regression;

[0021] Step 32: interpolate to obtain a large number of sampling points and record the horizontal coordinates of the sampling points;

[0022] Step 33: Calculate the horizontal coordinate according to the numerical value extracted from the horizontal coordinate of the sampling point;

[0023] Steps 3 and 4: Form a training-testing data set; take nozzle wall temperature, wall pressure, wall shear force, nozzle material, Oka erosion rate, nozzle area ratio, nozzle throat diameter and nozzle position as training parameters, take nozzle ablation regression as the prediction object, and combine the experimental data of the four nozzles to form a high- and low-pressure mixed training-testing data set.

[0024] Furthermore, in step 4, the nozzle profile ablation and regression prediction model is trained based on the PyCharm platform equipped with PyTorch; the specific steps for generating the nozzle profile ablation and regression prediction model are as follows:

[0025] Step 41: Use the fully connected layer as the training network, select the initial parameters of the training network according to the size of the input data set and the number of input parameters, and obtain the fully connected layer architecture;

[0026] Step 42: Set the sample learning rate and the maximum number of training rounds, randomly select 80% of the data points as the training set to train the neural network, and train until the residual is less than 1×10 -4 ;

[0027] Step 43: Use the remaining 20% ​​of data points as the test set and calculate the average error, maximum error, and minimum error between the test set and the experimental data;

[0028] Step 44: Repeat step 42 and step 43 alternately until the maximum error is less than 4%, the average error is less than 0.5%, and the iteration converges, thereby obtaining the ablation regression prediction model.

[0029] Furthermore, in step five, the nozzle to be predicted is subjected to steady-state numerical calculation according to the process shown in step two, and the numerical calculation results of the position to be predicted are extracted according to step three to form a verification set, which is substituted into the nozzle profile ablation and regression prediction model obtained in step four to obtain prediction data.

[0030] The present invention also proposes a system for establishing a nozzle profile ablation regression prediction model based on deep learning, the system comprising:

[0031] Acquisition module: To predict the experimental conditions and structure of the nozzle, obtain the nozzle experimental data, including geometric model, ignition conditions, and profile curve data before and after the test;

[0032] Calculation module: Perform steady-state calculations based on the DPM model and ignition conditions to obtain wall distribution;

[0033] Data set forming module: extracting data points from a single two-dimensional ablation profile curve to form a data set;

[0034] Training module: training nozzle profile ablation regression prediction model;

[0035] Prediction module: Perform steady-state numerical calculation on the nozzle to be predicted, extract the numerical calculation results, form a verification set, and substitute it into the trained nozzle profile ablation and regression prediction model to obtain prediction data.

[0036] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for establishing a nozzle profile ablation and regression prediction model based on deep learning are implemented.

[0037] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of a method for establishing a nozzle profile ablation and regression prediction model based on deep learning.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention proposes a method for establishing a nozzle profile ablation regression prediction model based on deep learning. The method constructs a nozzle profile ablation regression prediction model based on the two-dimensional profile ablation data of the existing test nozzle and the wall data obtained by numerical simulation of the corresponding test nozzle under ignition conditions, which effectively reduces the prediction time cost and calculation cost under the premise of ensuring prediction accuracy; in this method, a method for extracting data points for a single two-dimensional ablation profile curve to form a data set is also proposed. The method takes points according to the horizontal coordinate of the nozzle, and forms a data set by extracting the wall data obtained by numerical simulation of the points on the two-dimensional profile line of the corresponding horizontal coordinate and the wall regression amount of the ignition experiment. Based on limited experimental data and limited computing resources, the present invention reduces the prediction time cost and calculation cost under the premise of ensuring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0041] Figure 1 It is a flow chart of a method for establishing a nozzle profile ablation and regression prediction model based on deep learning described in the present invention.

[0042] Figure 2 It is the steady-state numerical calculation flow chart.

[0043] Figure 3 It is a schematic diagram of the cross section of the calculation domain.

[0044] Figure 4 It is a flow chart of a method for extracting data points from a single two-dimensional ablation profile curve to form a data set.

[0045] Figure 5 It is a schematic diagram of marking and measurement methods.

[0046] Figure 6 This is a comparison diagram of the displacement-axial distance distribution before and after interpolation, where (a) is before interpolation and (b) is after interpolation.

[0047] Figure 7 It is a schematic diagram of the method of forming a sampling circle by slicing.

[0048] Figure 8 It is a flow chart of the ablation-regression model training method.

[0049] Fig. 9 This is a schematic diagram of the neural network structure used for training.

[0050] Fig.10 It is a schematic diagram of the residual curve.

[0051] Fig.11 It is a schematic diagram of error distribution.

[0052] Fig.12 It is a schematic diagram of the test set fitting situation.

[0053] Fig.13 It is a schematic diagram of the validation set fitting situation. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Combination Figure 1-Figure 13 The present invention proposes a method for establishing a nozzle profile ablation regression prediction model based on deep learning, and the method comprises the following steps:

[0056] Step 1: Obtain nozzle experimental data based on the experimental conditions and structure of the predicted nozzle, including geometric model, ignition conditions, and profile curve data before and after the test;

[0057] In step one, according to the experimental conditions and structure of the predicted nozzle, four nozzle experimental data with similar experimental conditions and structures to the predicted object but different inlet pressures are obtained, including nozzle inlet pressure, inlet temperature, ambient pressure, ambient temperature, fuel gas relative molecular mass, fuel gas specific heat, total mass of fuel gas particles, average fuel gas particle diameter, maximum particle size, minimum particle size and nozzle profile curve data before and after the experiment; the nozzles are divided into high-pressure inlet and low-pressure inlet groups according to the inlet pressure, and steps two to three are applied to the above four nozzles.

[0058] Step 2: Perform steady-state calculation based on the DPM model and ignition conditions to obtain the wall distribution;

[0059] In step 2, based on the profile curve data and experimental data provided in step 1, a steady-state numerical calculation of the experimental nozzle is performed using Fluent based on the DPM model to obtain the nozzle wall temperature distribution, nozzle wall pressure distribution, nozzle wall shear force distribution and Oka erosion rate distribution; the specific process of step 2 is:

[0060] Step 21: Process the nozzle geometry model; extract the internal closed geometry to extract the three-dimensional model of its internal flow field, take the calculation domain length as 10 times the nozzle length, the calculation domain radius as 10 times the nozzle outlet radius, and add an extension section with a length of 3 times the nozzle inlet radius at the nozzle inlet section. Considering the computational convergence, the large-scale geometric areas and small-scale geometric areas of the flow field are grouped, and a BOI encrypted domain is established in the center of the flow field to obtain the flow field structure;

[0061] Step 22: Discrete the computational domain; by simply estimating the velocity of the flow field in the nozzle, determine the height of the first layer of the wall boundary layer, use the unstructured meshing method to discretize the computational domain, and encrypt the mesh in the BOI encrypted domain. Then optimize the mesh using the orthogonal quality and aspect ratio of the mesh as indicators, and finally obtain the mesh of the flow field in the nozzle. The mesh quantity and mesh quality report of the four nozzles are shown in Table 1;

[0062] Table 1 Mesh quality report

[0063]

[0064] Step 2 and 3: Set the calculation conditions and perform steady-state numerical calculations; considering that the fluid inside the nozzle is a high-temperature and high-pressure gas, the material properties of the fluid are ideal gases considering the kinetic theory of molecules, and the viscosity of the gas fluid is calculated using the Sutherland model. Considering that the fluid inside the nozzle meets the compressible flow conditions, the pressure inlet is used as the inlet boundary condition, and the nozzle inlet pressure and inlet temperature are used as boundary condition parameters; the pressure outlet is used as the outlet boundary condition, and the ambient pressure and ambient temperature are used as boundary condition parameters. For the wall, a no-slip boundary condition is used. Considering that the main focus in the later stage is on the distribution of physical quantities on the nozzle wall, the k-ωSST turbulence model is used for calculation. The calculation results without considering particle erosion at a given inlet pressure and temperature are obtained through numerical calculation.

[0065] Step 24: Adaptive mesh encryption: Based on the flow field calculated in step 23, the computational domain mesh is manually adaptively encrypted using the shock wave capture method, and steady-state numerical calculations are performed on the flow field without changing the calculation settings to obtain more accurate calculation results.

[0066] Step 25: Superimpose particle flow rates for steady-state calculations. Considering that the nozzle fuel gas contains a large amount of solid particles, the DPM model is used for calculations based on the flow field calculated in step 24. Since a one-time input of a given particle flow rate will cause calculation divergence, the present invention uses a method of superimposing particle flow rates for numerical calculations. Specifically, 0.05 times the target particle flow rate is taken as the particle flow rate applied for the first time, and after the gas flow rate reaches a stable value, the particle flow rate is continuously superimposed with a step size of 0.05 times the target particle flow rate until the target value is reached.

[0067] Step 3: extracting data points from a single two-dimensional ablation profile curve to form a data set;

[0068] In step three, the surface ablation regression amount is extracted according to the structure of the experimental nozzle and the numerical calculation data obtained in step two to form a data set. The specific process is as follows:

[0069] Step 31: Mark the data points on the profile curve data graph before and after the nozzle test, and measure the radial ablation regression; the marking and measurement steps are as follows: Figure 5 shown.

[0070] Step 32: Interpolate to obtain a large number of sampling points and record the horizontal coordinates of the sampling points; sort out the data points in the previous step, and use the amount of retreat as the vertical coordinate, the nozzle center axis as the horizontal axis, the nozzle inlet as the starting point of the coordinate axis, and the airflow ejection direction as the positive direction of the horizontal coordinate to obtain a visual relationship between the amount of retreat and the horizontal coordinate. Since there are too few valid sample point data, linear interpolation is used to expand the number of samples to ensure that the number of samples for each material is greater than 100. The sample distribution before and after interpolation is as follows: Figure 6Recording the horizontal coordinates of all sample points facilitates the extraction of sample data from the numerical calculation results.

[0071] Step 33: Extract the numerical calculation coordinates according to the sampling point coordinates; import the numerical calculation results into CFD-Post, and generate a series of cross sections intersecting with the nozzle wall according to the recorded sample point coordinates, and obtain the following Figure 7 A series of sampling circles are shown. The temperature, pressure, Oka erosion rate, and wall shear force in the XYZ direction on the sampling circle are extracted, and the temperature, pressure, and erosion rate are linearly averaged. The axial, radial, and circumferential shear forces are obtained through coordinate transformation.

[0072] Steps 3 and 4: Form a training-testing data set; take nozzle wall temperature, wall pressure, wall shear force, nozzle material, Oka erosion rate, nozzle area ratio, nozzle throat diameter and nozzle position as training parameters, take nozzle ablation regression as the prediction object, and combine the experimental data of the four nozzles to form a high- and low-pressure mixed training-testing data set.

[0073] Step 4: training the nozzle profile ablation regression prediction model;

[0074] In step 4, the nozzle profile ablation regression prediction model is trained based on the PyCharm platform equipped with PyTorch; the specific steps for generating the nozzle profile ablation regression prediction model are as follows:

[0075] Step 41: Use the fully connected layer as the training network, select the initial parameters of the training network according to the size of the input data set and the number of input parameters, and obtain the fully connected layer architecture; Fig. 9 shown.

[0076] Step 42: Set the sample learning rate and the maximum number of training rounds, randomly select 80% of the data points as the training set to train the neural network, and train until the residual is less than 1×10 -4 ;

[0077] Step 43: Use the remaining 20% ​​of data points as the test set and calculate the average error, maximum error, and minimum error between the test set and the experimental data;

[0078] Step 44: Repeat step 42 and step 43 alternately until the maximum error is less than 4%, the average error is less than 0.5%, and the iteration converges, thereby obtaining the ablation regression prediction model.

[0079] Step 5: Perform steady-state numerical calculation on the nozzle to be predicted, extract the numerical calculation results, form a verification set, and substitute it into the trained nozzle profile ablation regression prediction model to obtain prediction data.

[0080] In step five, the nozzle to be predicted is subjected to steady-state numerical calculation according to the process shown in step two, and the numerical calculation results of the position to be predicted are extracted according to step three to form a verification set, which is substituted into the nozzle profile ablation regression prediction model obtained in step four to obtain prediction data. By combining the samples of high-pressure nozzles and low-pressure nozzles for training, a hybrid nozzle ablation prediction model is obtained. Figure 10-11 As shown, the iterative residual of the model is 10 -5 The following shows that the iteration has converged. The error of the test set on the model can be obtained, with a maximum error of 3.52% and a minimum error of 6.81×10 -5 , the average error is 0.32%. It can be considered that the model can fit the ablation trend of the samples used by the model. In order to verify its generalization performance, the test set and validation set are substituted into the model to obtain the following Figure 12-13 The results shown, compared with the actual experimental data, show that the overall predictions and trends of the model are correct.

[0081] The present invention also proposes a system for establishing a nozzle profile ablation regression prediction model based on deep learning, the system comprising:

[0082] Acquisition module: To predict the experimental conditions and structure of the nozzle, obtain the nozzle experimental data, including geometric model, ignition conditions, and profile curve data before and after the test;

[0083] Calculation module: Perform steady-state calculations based on the DPM model and ignition conditions to obtain wall distribution;

[0084] Data set forming module: extracting data points from a single two-dimensional ablation profile curve to form a data set;

[0085] Training module: training nozzle profile ablation regression prediction model;

[0086] Prediction module: Perform steady-state numerical calculation on the nozzle to be predicted, extract the numerical calculation results, form a verification set, and substitute it into the trained nozzle profile ablation and regression prediction model to obtain prediction data.

[0087] Relevant abbreviations and English meanings in this invention:

[0088]

[0089]

[0090] The present invention also proposes an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for establishing a nozzle profile ablation and regression prediction model based on deep learning are implemented.

[0091] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the steps of a method for establishing a nozzle profile ablation and regression prediction model based on deep learning.

[0092] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DRRAM). It should be noted that the memory of the method described in the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0093] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0094] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0095] It should be noted that the processor in the embodiment of the present application can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or the hardware and software modules in the decoding processor can be combined and performed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0096] The above is a detailed introduction to the method and system for establishing a nozzle profile erosion and regression prediction model based on deep learning proposed in the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for establishing a nozzle profile ablation regression prediction model based on deep learning, characterized in that: The method comprises the following steps: Step 1: Obtain nozzle experimental data based on the experimental conditions and structure of the predicted nozzle, including geometric model, ignition conditions, and profile curve data before and after the test; Step 2: Perform steady-state calculation based on the DPM model and ignition conditions to obtain the wall distribution; Step 3: extracting data points from a single two-dimensional ablation profile curve to form a data set; Step 4: training the nozzle profile ablation regression prediction model; Step 5: Perform steady-state numerical calculation on the nozzle to be predicted, extract the numerical calculation results, form a verification set, and substitute it into the trained nozzle profile ablation regression prediction model to obtain prediction data.

2. The method according to claim 1, characterized in that In step one, according to the experimental conditions and structure of the predicted nozzle, four nozzle experimental data with similar experimental conditions and structures to the predicted object but different inlet pressures are obtained, including nozzle inlet pressure, inlet temperature, ambient pressure, ambient temperature, fuel gas relative molecular mass, fuel gas specific heat, total mass of fuel gas particles, average fuel gas particle diameter, maximum particle size, minimum particle size and nozzle profile curve data before and after the experiment; the nozzles are divided into high-pressure inlet and low-pressure inlet groups according to the inlet pressure, and steps two to three are applied to the above four nozzles.

3. The method according to claim 2, characterized in that In step 2, based on the profile curve data and experimental data provided in step 1, Fluent is used to perform steady-state numerical calculations on the experimental nozzle based on the DPM model to obtain the nozzle wall temperature distribution, nozzle wall pressure distribution, nozzle wall shear force distribution, and Oka erosion rate distribution.

4. The method according to claim 3, characterized in that The specific process of step 2 is as follows: Step 21: Process the nozzle geometry model; Step 22: Discretize the computational domain; Step 23: Set the calculation conditions and perform steady-state numerical calculation; Step 24: Adaptive mesh encryption; Step 25: Superimpose particle flow for steady-state calculation.

5. The method according to claim 4, characterized in that In step three, the surface ablation regression amount is extracted according to the structure of the experimental nozzle and the numerical calculation data obtained in step two to form a data set. The specific process is as follows: Step 31: Mark data points on the profile curve data graph before and after the nozzle experiment, and measure its radial ablation regression; Step 32: interpolate to obtain a large number of sampling points and record the horizontal coordinates of the sampling points; Step 33: Calculate the horizontal coordinate according to the numerical value extracted from the horizontal coordinate of the sampling point; Step 3 and 4: Form a training-testing data set; The nozzle wall temperature, wall pressure, wall shear force, nozzle material, Oka erosion rate, nozzle area ratio, nozzle throat diameter and nozzle position are taken as training parameters, and the nozzle ablation regression is taken as the prediction object. The experimental data of four nozzles are combined to form a high- and low-pressure mixed training-testing dataset.

6. The method according to claim 5, characterized in that In step 4, the nozzle profile ablation regression prediction model is trained based on the PyCharm platform equipped with PyTorch; the specific steps for generating the nozzle profile ablation regression prediction model are as follows: Step 41: Use the fully connected layer as the training network, select the initial parameters of the training network according to the size of the input data set and the number of input parameters, and obtain the fully connected layer architecture; Step 42: Set the sample learning rate and the maximum number of training rounds, randomly select 80% of the data points as the training set to train the neural network, and train until the residual is less than 1×10 -4 ; Step 43: Use the remaining 20% ​​of data points as the test set and calculate the average error, maximum error, and minimum error between the test set and the experimental data; Step 44: Repeat step 42 and step 43 alternately until the maximum error is less than 4%, the average error is less than 0.5%, and the iteration converges, thereby obtaining the ablation regression prediction model.

7. The method according to claim 6, characterized in that In step five, the nozzle to be predicted is subjected to steady-state numerical calculation according to the process shown in step two, and the numerical calculation results of the position to be predicted are extracted according to step three to form a verification set, which is then substituted into the nozzle profile ablation regression prediction model obtained in step four to obtain prediction data.

8. A system for establishing a nozzle profile ablation regression prediction model based on deep learning, characterized in that: The system comprises: Acquisition module: To predict the experimental conditions and structure of the nozzle, obtain the nozzle experimental data, including geometric model, ignition conditions, and profile curve data before and after the test; Calculation module: Perform steady-state calculations based on the DPM model and ignition conditions to obtain wall distribution; Data set forming module: extracting data points from a single two-dimensional ablation profile curve to form a data set; Training module: training nozzle profile ablation regression prediction model; Prediction module: Perform steady-state numerical calculation on the nozzle to be predicted, extract the numerical calculation results, form a verification set, and substitute it into the trained nozzle profile ablation and regression prediction model to obtain prediction data.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images