Automated cfd solution method for dual-mode scramjet engine internal flowfield

Abstract

The application discloses a kind of dual-mode scramjet internal flow field automation CFD solving method, comprising: using Python script to build AnsysFluent automation solving environment, load the grid file of dual-mode scramjet model, using Python script to configure the physical model and boundary condition of dual-mode scramjet, using Python script to complete the setting of numerical discrete format, solving method and the flow field initial value construction of calculation domain;Flow field initial value is used as the starting point of iteration, using Python script to execute the dynamic climbing calculation under first-order accuracy, obtain stable initial flow field distribution;Stable initial flow field distribution is used as the initial value of calculation, using Python script to switch second-order high-precision calculation and carry out convergence determination.This method effectively solves the problems of artificial intervention tedious, shock capturing difficulty and convergence determination subject in high Mach number strong compressible flow calculation.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of aerospace power engineering and computational fluid dynamics (CFD), and in particular to an automated CFD solution method for the internal flow field of a dual-mode scramjet engine. Background Technology

[0002] In the field of aerospace propulsion engineering, the aerodynamic performance evaluation of supersonic inlets, Laval nozzles, and the external flow fields of hypersonic vehicles is a crucial aspect of engine design. These flow fields typically exhibit complex aerodynamic characteristics such as high Mach numbers, strong shock wave interference, and dramatic adverse pressure gradients. Numerical simulations of these problems using commercial software (such as ANSYS Fluent and CFX solvers) face significant challenges.

[0003] Specifically, the governing equations for hypersonic flow exhibit strong nonlinearity and rigidity. If a high-precision discretization scheme (such as a second-order upwind scheme) is directly used during the initialization phase of the calculation, it can easily lead to numerical oscillations near the shock wave in the flow field, which in turn can cause non-physical phenomena such as negative thermodynamic parameters, ultimately resulting in calculation divergence.

[0004] Existing mainstream solutions heavily rely on the human experience and real-time intervention of CFD engineers. Traditional calculations require engineers to manually set up first-order calculations to obscure the shock wave structure and maintain stability in the initial stages of the calculation. Once the residual curve drops to a certain level, the engineer needs to manually pause the calculation, switch the discretization scheme to a second-order upwind scheme, and try to increase the CFL number to accelerate convergence. During the second-order calculation, if a residual jump or flow field anomaly is detected, the engineer needs to immediately interrupt the calculation, roll back the parameters, and try again.

[0005] This traditional manual debugging approach has several significant drawbacks: it is inefficient and cannot be scaled up. Engineers need to constantly monitor the residual curves, resulting in a significant waste of manpower and time. Furthermore, it cannot meet the demands of automated batch calculations for hundreds or thousands of operating points within the flight envelope. Traditional methods often rely solely on whether the algebraic residuals decrease to the expected range to determine convergence, ignoring the actual steady-state of physical quantities (such as inlet and outlet flow rates). For complex flow fields with small-amplitude periodic oscillations, this approach is highly prone to misjudgment.

[0006] Although some commercial software provides simple batch scripts, these scripts have limited functionality and cannot dynamically adjust control parameters based on real-time feedback during the solution process. This results in extremely poor robustness of the scripts, which fail as soon as they encounter conditions where the shock wave position changes drastically. Summary of the Invention

[0007] The purpose of this application is to provide a method, apparatus, and electronic device to solve the problems in the related art, such as cumbersome manual intervention in hypersonic internal flow field calculation, easy divergence due to improper numerical format initialization, and strong subjectivity in convergence determination. It can effectively improve the solution efficiency, realize an unattended solution path, and thus improve the stability and reliability of high Mach number highly compressible flow calculation.

[0008] According to a first aspect of the embodiments of this application, an automated CFD solution method for the internal flow field of a dual-modal scramjet engine is provided, comprising:

[0009] An Automated Solver Environment was built using Python scripts to load the mesh file of the dual-modal scramjet engine model;

[0010] Based on the mesh file, the physical model and boundary conditions of the dual-modal scramjet engine are configured using a Python script;

[0011] Based on the configuration of the physical model and boundary conditions, and combined with the hypersonic flow characteristics of the dual-mode scramjet engine model, the numerical discretization scheme, solution method, and initial flow field construction of the computational domain are completed using Python scripts.

[0012] Using the initial flow field value as the starting point of the iteration, a dynamic ramp calculation with first-order accuracy is performed using a Python script to obtain a stable initial flow field distribution.

[0013] The stable initial flow field distribution is used as the initial value for calculation. A Python script is used to switch to second-order high-precision calculation, and the mass flow rate fluctuation is monitored in real time during the second-order high-precision calculation to determine convergence.

[0014] According to a second aspect of the embodiments of this application, an electronic device is provided, comprising:

[0015] One or more processors;

[0016] Memory, used to store one or more programs;

[0017] When the one or more programs are executed by the one or more processors, the one or more processors perform the method as described in the first aspect.

[0018] The technical solutions provided by the embodiments of this application may include the following beneficial effects:

[0019] This application employs Python scripts to fully automate the logical encapsulation and parameter mapping of the density-based solver architecture, ideal gas properties, and AUSM flux format. This eliminates the physical model deviations and calculation failures caused by human experience differences or operational oversights in traditional CFD processes, such as omitting zeroing the reference pressure or incorrectly selecting low-precision formats. This ensures the consistency and reliability of the aerodynamic performance evaluation of the dual-mode scramjet engine and provides an efficient automated tool for the construction of large-scale aerodynamic databases.

[0020] This application employs a hierarchical calculation strategy that switches from first-order accuracy to second-order accuracy, and combines it with a multi-stage linear parameter scheduling mechanism for Courant number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity. This effectively solves the problem of excessive numerical rigidity in the internal flow field of a dual-mode scramjet engine caused by shock wave / boundary layer interference and thermodynamic non-equilibrium phenomena. It suppresses severe residual oscillations and flow field parameter divergence in the early stages of calculation, thereby enabling the solver to move from a low-precision stable state to a high-precision convergent state. Without the need for manual intervention to adjust the relaxation factor, it ensures extremely high success rate and stability in hypersonic flow field calculations.

[0021] This application constructs a convergence determination program based on sliding window statistics. It quantifies the conservation stability of the flow field by calculating the numerical range of the net mass flow rate at the inlet and outlet within a fixed window in real time. This eliminates the subjective defects of relying on residual curves to judge convergence by visual inspection, overcomes the technical blind spot that a single residual standard cannot truly reflect whether physical quantities are stable, and avoids physical non-convergence caused by premature termination of calculation or waste of computing resources caused by over-computation. Thus, while ensuring the accuracy of mass and momentum conservation, it achieves the global optimal allocation of computing resources and intelligent termination determination that balances computational accuracy and efficiency. It is particularly suitable for the concurrent processing of massive tasks in high-performance computing cluster environments.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0024] Figure 1 This is a flowchart illustrating an automated CFD solution method for the internal flow field of a dual-modal scramjet engine, according to an exemplary embodiment.

[0025] Figure 2 This is a schematic diagram of a dual-mode scramjet engine according to an exemplary embodiment.

[0026] Figure 3This is an isometric view of a structured mesh model of a dual-modal scramjet engine, illustrated according to an exemplary embodiment.

[0027] Figure 4 This is a top view of a structured mesh model of a dual-modal scramjet engine, as illustrated in an exemplary embodiment.

[0028] Figure 5 This is a front view of a structured mesh model of a dual-modal scramjet engine, as illustrated in an exemplary embodiment.

[0029] Figure 6 This is a left view of a structured mesh model of a dual-modal scramjet engine, illustrated according to an exemplary embodiment.

[0030] Figure 7 This is a right view of a structured mesh model of a dual-modal scramjet engine, illustrated according to an exemplary embodiment.

[0031] Figure 8 This is a center-symmetric cross-sectional view of the flow channel of a structured mesh model of a dual-modal scramjet engine, illustrated according to an exemplary embodiment.

[0032] Figure 9 This is a diagram illustrating the boundary condition settings for the outer air domain of a dual-mode scramjet engine, according to an exemplary embodiment.

[0033] Figure 10 This is a comparison and verification diagram of the pressure distribution along the lower wall of the engine's internal flow field calculated by an automated CFD solution method for the internal flow field of a dual-mode scramjet engine, according to an exemplary embodiment, and NASA wind tunnel experimental data under the same conditions (NASA Technical Paper 350).

[0034] Figure 11 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0035] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0036] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used herein are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

[0037] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0038] This embodiment provides an automated CFD solution method for the internal flow field of a dual-modal scramjet engine. The execution of this embodiment relies on the interaction between a pre-built automated control script and the application programming interface (API) of commercial CFD software (AnsysFluent 2025 R2 version in this embodiment).

[0039] Figure 1 This is a flowchart illustrating an automated CFD solution method for the internal flow field of a dual-mode scramjet engine, according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps:

[0040] S1: Build the Ansys Fluent automated solver environment using a Python script and load the mesh file of the dual-modal scramjet engine model; this step includes the following sub-steps:

[0041] S11: Construct the startup configuration parameters of Ansys Fluent using a Python script. The startup configuration parameters include specifying the number of CPU physical cores for parallel computing and selecting a double-precision solution mode suitable for the hypersonic flow characteristics of a dual-mode scramjet engine. Instantiate a Fluent solver object based on the startup configuration parameters.

[0042] Specifically, Figure 2 This is a schematic diagram of a dual-mode scramjet engine according to an exemplary embodiment, wherein the engine width... ,high Intake duct compression angle Intake duct contraction angle Intake duct length Fairing length Length of isolation section ,high Combustion chamber expansion section angle Combustion chamber length Throttling plate length 1 indicates the direction of airflow; 2 indicates the air intake; 3 indicates the isolation section; 4 indicates the combustion chamber; 5 indicates the throttling device.

[0043] The Python script first defines a global configuration class (CaseConfig) that encapsulates the key environmental parameters required for the solution. The script specifies a double-precision solution mode to meet the numerical accuracy requirements for capturing small shock structures in high Mach number flows; it also sets parallel computing parameters, specifying the number of CPU physical cores (64 cores) to ensure computational efficiency for large-scale meshes. Subsequently, the script calls the launch_fluent interface in the ansys.fluent.core library, passing in the aforementioned configuration parameters, instantiating and starting a Fluent solver session object, and establishing a communication channel between the Python process and the CFD solver kernel.

[0044] S12: Use the Fluent solver object to read the mesh file of the dual-mode scramjet engine;

[0045] Specifically, during the mesh loading phase, the script executes `session.file.read` to read the set mesh file location and loads the structured mesh model file of the dual-modal scramjet engine, such as... Figures 3-8 As shown.

[0046] Furthermore, to ensure physical consistency of geometric scale, the script performs preprocessing operations immediately after the mesh is loaded. These operations include: calling the TUI command session.tui.mesh.scale to uniformly scale the mesh size units to the standard International System of Units (meter); and calling the mesh.check interface to perform mesh quality checks, focusing on checking for negative volumes and whether the orthogonality quality meets the calculation requirements, to prevent calculation divergence caused by mesh distortion.

[0047] S2: Based on the mesh file, configure the physical model and boundary conditions of the dual-modal scramjet engine using a Python script; this step includes the following sub-steps:

[0048] S21: Identify the fluid domain in the mesh file through script instructions, and change the density of the working fluid in the material tab to an ideal gas and the viscosity to Sutherland's law;

[0049] Specifically, the Python script accesses the Materials tab and sets the medium object to air. For the hypersonic compressible flow characteristics inside a dual-mode scramjet engine, the script sends a TUI command to overwrite the density calculation model of the fluid medium from the default constant to an ideal gas, thus establishing the thermodynamic basis of compressible flow. Simultaneously, it overwrites the viscosity calculation model with Sutherland's law to accurately describe the physical properties of air viscosity changing with temperature under hypersonic compressible flow.

[0050] S22: Based on the material configuration of the working fluid, activate the turbulence model as a physical model via script commands;

[0051] Specifically, the script further accesses the solver's physical model setup tree. The script activates the energy equation to solve the temperature field via Boolean commands and calls the viscous model configuration interface, passing in a specific model identifier, thereby activating the SST k-omega turbulence model. Leveraging the advantages of the SST k-omega model in near-wall and free shear flow, the script accurately captures the complex shock wave-boundary layer disturbance phenomena within the engine.

[0052] S23: According to the physical model, the gauge pressure and Mach number are set in the inlet boundary conditions by script instructions, the gauge pressure is set in the outlet boundary conditions, and the operating pressure in the boundary conditions is set to 0 Pa.

[0053] Specifically, based on Figure 9 The script demonstrates the boundary conditions for the outer air domain of the dual-mode scramjet engine. It sets "inlet", "upper1", "lower1", "z0", and "z1" as the pressure far field and "outlet" as the pressure outlet. Then, the script accesses the solver's global operating conditions node and sets the operating pressure parameter to 0 Pa. For the inlet boundary, the script iterates through the predefined list of inlet region names, extracts the gauge pressure (8729 Pa), Mach number (4.03), and temperature (300 K) values ​​from the predefined parameters, and writes them into the corresponding momentum and thermodynamic settings node of the solver. For the outlet boundary, the script writes the predefined outlet back pressure (101325 Pa) and recirculation temperature (300 K) values ​​into the corresponding boundary condition settings, thus completing the automated definition of the inlet and outlet states of the flow field.

[0054] S3: Based on the configuration of the physical model and boundary conditions, and combined with the hypersonic flow characteristics of the dual-mode scramjet engine model, the numerical discretization scheme, solution method, and initial flow field construction of the computational domain are completed using Python scripts; this step includes the following sub-steps:

[0055] S31: Based on the configuration of the physical model and boundary conditions, and combined with the hypersonic flow characteristics of the dual-mode scramjet engine model, the solver is configured as a steady-state, implicit density-based solver using script commands.

[0056] Specifically, for hypersonic compressible flow, the script sets the Fluent solver to a density-based implicit solver via the TUI command and accesses the underlying settings via the GUI Scheme command to set the solver method tab to implicit.

[0057] S32: According to the configuration requirements of the density-based solver, use script commands to modify the flux type to AUSM format, lock the gradient format to the least squares method based on the cell, set the momentum, turbulent kinetic energy and specific dissipation rate under the spatial discretization option in the solution method tab to the first-order upwind scheme, and execute the calculation initialization command to obtain the initial values ​​of the flow field in the computational domain.

[0058] Specifically, the script further configures the numerical discretization method via GUI Scheme commands, setting the flux type to AUSM format to improve shock wave capture accuracy and locking the spatial gradient algorithm to cell-based least squares. Secondly, the script sets the spatial discretization format of momentum, turbulent kinetic energy, and specific dissipation rate to first-order upwind scheme via GUI Scheme commands. Finally, the script calls the hybrid initialization interface via TUI commands to generate initial flow field values ​​in the computational domain, providing a convergence starting point for subsequent first-order dynamic ramp calculations.

[0059] S4: Using the initial flow field value as the starting point of the iteration, a dynamic ramp calculation with first-order accuracy is performed using a Python script to obtain a stable initial flow field distribution; this step includes the following sub-steps:

[0060] S41: Based on the initial value of the flow field as the iterative starting point for the dynamic ramp calculation under first-order accuracy, and maintaining the first-order discrete format and AUSM flux type, the script instruction will synchronously increase the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity to the set maximum value according to the preset step size.

[0061] Specifically, during the dynamic ramp calculation with first-order accuracy, the script first reads the preset control parameter range: the Courant number is set to increase from 1.0 to 4.0, the turbulent kinetic energy and specific dissipation are increased from 0.1 to 0.8, and the turbulent viscosity is increased from 0.1 to 0.6. Based on the globally configured total ramp order, the script constructs a multi-stage iterative loop. Within each ramp cycle, the script commands synchronously increase the values ​​of the Courant number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity at preset fixed iteration intervals (2000 steps), and continue to execute the flow field iterative calculation within that interval after the parameter update, until each parameter reaches the set maximum value.

[0062] S42: After the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity all reach the set maximum values ​​and the corresponding iterative calculations are completed, a stable initial flow field distribution is obtained. The script command then resets the values ​​of Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity to the set minimum values.

[0063] Specifically, once the dynamic ramp calculation at first-order accuracy is completed, the script pauses the calculation. To prevent divergence caused by excessively large control parameters when directly switching to the high-precision second-order format, the script performs parameter initialization operations, adjusting the Coulomb number back to 1, and the turbulent kinetic energy, specific dissipation rate, and turbulent viscosity back to 0.1.

[0064] S5: Using the stable initial flow field distribution as the initial value for calculation, a Python script is used to switch to second-order high-precision calculation, and mass flow rate fluctuations are monitored in real time during the second-order high-precision calculation to determine convergence; this step includes the following sub-steps:

[0065] S51: When the values ​​of Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity are set to the minimum, the script command will switch the momentum, turbulent kinetic energy, and specific dissipation rate under the spatial discretization option in the solution method tab to the second-order upwind scheme.

[0066] Specifically, with the control parameters reset to their minimum values, the script sets the spatial discretization format of momentum, turbulent kinetic energy, and specific dissipation rate to a second-order upwind scheme using the GUI Scheme command.

[0067] S52: When all parameters of the spatial discretization option are set to the second-order upwind mode, the script command will synchronously increase the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity to the set maximum value according to the preset step size to perform second-order high-precision calculation.

[0068] Specifically, during the second-order high-precision calculation, the script executes parameter scheduling logic similar to that of the first-order calculation, but applied to a second-order discrete format environment. The script first reads the preset control parameter range: the Courant number is increased from 1.0 to 4.0, the turbulent kinetic energy and specific dissipation rate are increased from 0.1 to 0.8, and the turbulent viscosity is increased from 0.1 to 0.6. Based on the globally configured total number of ramp orders, the script constructs a multi-stage iterative loop. Within each ramp cycle, the script instructions synchronously increase the values ​​of the Courant number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity at preset fixed iteration intervals (2000 steps), and continue to execute the flow field iterative calculation within that interval after the parameter update, until each parameter reaches its set maximum value.

[0069] S53: According to the second-order high-precision calculation process, a convergence determination procedure is activated during this process, and a convergence determination is made based on the fluctuation of mass flow rate.

[0070] Specifically, in the second-order high-precision calculation, the script immediately triggers a monitoring data extraction operation after each iteration block. The script accesses the mass flow rate in the surface report within the report definition tab, saving one surface mass flow rate data point every 10 iterations. This data is captured by the script and stored in a historical data list, serving as the sole numerical basis for convergence determination.

[0071] S54: During the convergence determination, the script command will construct a flow field stability monitoring window containing a preset fixed number of iterations, and extract historical iteration data within the monitoring window in real time to calculate the numerical range of net mass flow rates at the inlet and outlet.

[0072] Specifically, the script maintains a fixed-length sliding window. Whenever new data is generated, the script reads an array of nearly 100 mass flow rate data stored in the flow field stability monitoring window, and uses the numerical computing library NumPy to extract the maximum and minimum values ​​in the array, calculating the difference between them, i.e., the range.

[0073] S55: Compare the numerical range with the preset convergence threshold. If the numerical range is continuously less than the convergence threshold within the current window, determine that the internal flow field of the dual-mode scramjet engine has reached steady-state convergence and automatically save the current data, and terminate the Ansys Fluent solution process.

[0074] Specifically, the script compares the range with a preset convergence threshold. If the range is greater than the preset convergence threshold (0.01), Ansys Fluent continues the iterative calculation; if the range is less than the preset convergence threshold (0.01), Ansys Fluent automatically terminates and saves the data file. Furthermore, the script uses the pathlib library to clean file paths, automatically removing redundant suffixes to prevent file locking or write failures caused by filename conflicts.

[0075] In S41 and S52, the preset fixed number of iterations synchronously increases the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity. The initial value, maximum value, and total number of ramps of the control parameters are defined in the script. The parameters in the control tab are modified through API commands. The current Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity are increased by increment after the set number of iterations until the parameters reach their maximum values.

[0076] Specifically, in the method described in this embodiment, the preset fixed number of iteration steps is 2000, and the total number of climbing steps is...

[0077] The value is 6. The initial and maximum values ​​of the Courant number, control parameters, are defined in the script as 1 and 4 respectively. The initial and maximum values ​​of the turbulent kinetic energy and specific dissipation rate are defined in the script as 0.1 and 0.8 respectively. The initial and maximum values ​​of the turbulent viscosity are defined in the script as 0.1 and 0.6 respectively. That is, every 2000 iterations, the increments of the Courant number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity are 0.5, 0.116666667, 0.116666667, and 0.083333333 respectively, until the Courant number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity reach their maximum values. The formula for calculating the increment is:

[0078] ;

[0079] In the formula, Δ is the increment. As the initial value, To achieve the target maximum value, This represents the total number of steps in the hill climb.

[0080] To verify the effectiveness of the method described in this embodiment, a 2.5-inch short fairing with an inlet convergence angle β = 8.4° and an isolation section length L = 4.7H was selected under the condition of Mach 4 in NASA Technical Paper 3502 report. th To validate the model. For example... Figure 10 As shown, the friction pressure distribution along the lower wall calculated by the Pthyon script using Ansys Fluent is compared with NASA wind tunnel experimental data. The results show that the calculated data and experimental measurements are in excellent agreement without manual intervention, proving that this automated solution strategy not only ensures high stability but also possesses the computational accuracy required for engineering design.

[0081] Accordingly, this application also provides an electronic device, such as Figure 11 As shown, it includes: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the automated CFD solution method for the internal flow field of a dual-mode scramjet engine as described above.

[0082] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0083] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. An automated CFD solution method for the internal flow field of a dual-mode scramjet engine, characterized in that, include: An Automated Solver Environment was built using Python scripts to load the mesh file of the dual-modal scramjet engine model; Based on the mesh file, the physical model and boundary conditions of the dual-modal scramjet engine are configured using a Python script; Based on the configuration of the physical model and boundary conditions, and combined with the hypersonic flow characteristics of the dual-mode scramjet engine model, the numerical discretization scheme, solution method, and initial flow field construction of the computational domain are completed using Python scripts. Using the initial flow field value as the starting point of the iteration, a dynamic ramp calculation with first-order accuracy is performed using a Python script to obtain a stable initial flow field distribution. The stable initial flow field distribution is used as the initial value for calculation. The Python script is used to switch to second-order high-precision calculation, and the mass flow rate fluctuation is monitored in real time during the second-order high-precision calculation to determine convergence. Specifically, the initial flow field value is used as the starting point for iteration. A dynamic ramp calculation with first-order accuracy is performed using a Python script to obtain a stable initial flow field distribution, including: Using the initial flow field value as the starting point for the iteration of dynamic ramp calculation with first-order accuracy, and maintaining the first-order discrete format and AUSM flux type, the script command will synchronously increase the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity to the set maximum value according to the preset step size. Once the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity all reach their set maximum values ​​and the corresponding iterative calculations are completed, a stable initial flow field distribution is obtained. The script command then resets the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity values ​​to their set minimum values.

2. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 1, characterized in that, An SysFluent automated solver environment was built using Python scripts, and the mesh file of the dual-modal scramjet engine model was loaded, including: The startup configuration parameters of Ansys Fluent are constructed using a Python script. These startup configuration parameters include specifying the number of CPU physical cores for parallel computing and selecting a double-precision solution mode suitable for the hypersonic flow characteristics of a dual-mode scramjet engine. A Fluent solver object is then instantiated based on these startup configuration parameters. The Fluent solver object is used to read the mesh file of the dual-mode scramjet engine.

3. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 2, characterized in that, Also includes: The script commands are used to perform unit conversion and scaling on the mesh size in the mesh file, and to perform mesh negative volume and orthogonality quality checks.

4. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 1, characterized in that, Based on the mesh file, the physical model and boundary conditions of the dual-modal scramjet engine are configured using a Python script, including: The script commands identify the fluid domain in the mesh file and change the density of the working fluid in the material tab to an ideal gas and the viscosity to Sutherland's law. Based on the material configuration of the working fluid, the turbulence model is activated as a physical model via script commands; Based on the physical model, the inlet boundary conditions are set with gauge pressure and Mach number via script commands, the outlet boundary conditions are set with gauge pressure, and the operating pressure in the boundary conditions is set to 0 Pa.

5. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 1, characterized in that, Based on the physical model and boundary condition configuration, and considering the hypersonic flow characteristics of the dual-mode scramjet engine model, a Python script was used to complete the numerical discretization scheme, solution method setup, and initial flow field construction for the computational domain, including: Based on the configuration of the physical model and boundary conditions, and combined with the hypersonic flow characteristics of the dual-mode scramjet engine model, the solver is configured as a steady-state, implicit density-based solver using script commands. According to the configuration requirements of the density-based solver, the flux type is modified to AUSM format using script commands, the gradient format is locked to the least squares method based on the cell, the momentum, turbulent kinetic energy and specific dissipation rate under the spatial discretization option in the solver tab are uniformly set to the first-order upwind format, and the calculation initialization command is executed to obtain the initial values ​​of the flow field in the computational domain.

6. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 1, characterized in that, Using the stable initial flow field distribution as the initial value for calculation, a Python script is used to switch to second-order high-precision calculation. During the second-order high-precision calculation, mass flow rate fluctuations are monitored in real time for convergence determination, including: With the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity values ​​set to their minimum values, the script command will switch the momentum, turbulent kinetic energy, and specific dissipation rate under the spatial discretization option in the solution method tab to the second-order upwind scheme. When all parameters of the spatial discretization option are set to the second-order upwind mode, the script command will synchronously increase the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity to the set maximum value according to the preset step size to perform second-order high-precision calculation. According to the second-order high-precision calculation process, a convergence determination procedure is activated during this process, and convergence determination is performed based on the fluctuation of mass flow rate. During the convergence determination, the script commands will construct a flow field stability monitoring window containing a preset fixed number of iterations, and extract historical iteration data within the monitoring window in real time to calculate the numerical range of net mass flow rates at the inlet and outlet. The numerical range is compared with a preset convergence threshold. If the numerical range is continuously less than the convergence threshold within the current window, the internal flow field of the dual-mode scramjet engine is determined to have reached steady-state convergence, the current data is automatically saved, and the Ansys Fluent solution process is terminated.

7. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 1 or 6, characterized in that, The preset step size synchronously increases the Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity. The initial values, target maximum values, total number of ramps, and number of iterations of the control parameters are defined in the script. The parameters in the control tab are modified through API commands. The current Coulomb number, turbulent kinetic energy, specific dissipation rate, and turbulent viscosity are increased by increment after the set number of iterations until the parameters reach the target maximum value.

8. The automated CFD solution method for the internal flow field of a dual-mode scramjet engine according to claim 7, characterized in that, The formula for calculating the increment is: ； In the formula, Δ is the increment. As the initial value, To achieve the target maximum value, This represents the total number of steps in the hill climb.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-8.

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