Freezing wind tunnel test section flow field multi-dimensional simulation method, device, equipment and medium

Abstract

The invention discloses an icing wind tunnel test section flow field multi-dimensional simulation method, device and equipment and a medium. The method comprises the following steps: S1, establishing a geometric model according to an actual structure of a test section; S2, carrying out grid division on the geometric model; s3, determining a control equation and selecting a physical model; S4, defining boundary conditions of a test section computational domain; s5, utilizing aero-engine performance analysis or test data to construct an aero-engine performance agent model to be coupled with the CFD solver; and S6, transmitting data between the CFD solver and the agent model by adopting a time step-by-step iteration mode, and carrying out coupling calculation to realize multi-dimensional simulation. According to the method, the aero-engine performance agent model is introduced into icing wind tunnel flow field simulation, a high-precision and efficient numerical simulation method is developed, and then prediction and analysis of an aero-engine icing meteorological condition test simulation result are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of icing wind tunnel test, in particular, relates to an icing wind tunnel test section flow field multi-dimensional simulation method, device, equipment and medium. BACKGROUND

[0002] The existing aero-engine icing wind tunnel test section flow field analysis method includes experimental measurement method and numerical simulation method, and the above methods have the following disadvantages: first, the test measurement means is limited and the cost is high, the wind tunnel test is limited by the size of the cabin and the measurement means, the test data is limited, and the three-dimensional gas-liquid two-phase flow field characteristics cannot be fully reflected. Second, although numerical simulation can provide more flow field information, at present, three independent modules of air flow, droplet motion and icing state are combined, there is lack of bidirectional coupling with the aero-engine performance model, and it is difficult to balance the calculation accuracy and efficiency.

[0003] In summary, the existing test measurement means and numerical simulation method have disadvantages, and cannot meet the demand of rapid, accurate and comprehensive evaluation of the aero-engine icing wind tunnel test section flow field. SUMMARY

[0004] The present application provides an icing wind tunnel test section flow field multi-dimensional simulation method, which realizes rapid, accurate and comprehensive evaluation of the aero-engine icing wind tunnel test section flow field.

[0005] The present application is realized by the following scheme: The icing wind tunnel test section flow field multi-dimensional simulation includes the following steps: S1, a three-dimensional modeling software is used to establish a three-dimensional geometric model including a test cabin rear cabin, a corner section, an engine inlet duct, a free jet nozzle, a test stand, an exhaust diffuser, an aero-engine and a transmission shaft according to the actual structure of the test section; S2, after completing the geometric modeling of the test section, the test section calculation domain is meshed, and a boundary identifier is introduced in the meshing process, which is used for subsequent physical model setting and proxy model coupling; S3, the control equation is determined and the physical model is selected, including the gas phase flow control equation, the droplet dynamics model, the droplet transformation strategy and the interphase coupling between the droplets and the gas flow; S4, the boundary conditions of the test section calculation domain are defined, including the test section inlet boundary condition, the test section outlet boundary condition, the boundary condition at the outlet of the spray device, the wall boundary condition and the aerodynamic interface between the aero-engine and the test cabin rear cabin; S5, the aero-engine performance calculation or test data is used to establish an aero-engine performance proxy model through machine learning means, the trained proxy model is packaged as a general executable file, and the code is coupled with the CFD solver; S6, the information is transmitted between the CFD solver and the surrogate model in a time step iteration manner, multi-dimensional coupling calculation is carried out, all time step calculation is completed, and multi-dimensional simulation of the flow field of the icing wind tunnel test section is realized.

[0006] Further, in the step S1, when the aero-engine geometric model is established, a simplified shape contour model is used to reduce the calculation amount, and an interface for interaction with the surrogate model is reserved.

[0007] Further, in the step S2, during the mesh division process, a hybrid mesh is selected, wherein local encryption or unstructured mesh is used for key areas including nozzle exit, engine inlet and outlet, and exhaust diffuser inlet to capture the flow details of the gas flow field and cloud field, and the mesh quality is ensured by checking the indexes of cell distortion, orthogonality and volume distribution.

[0008] Further, in the step S3, the gas phase flow control equation uses the incompressible or weakly compressible Navier-Stokes equation set as the control equation of the gas phase, including the mass conservation equation, the momentum equation and the energy equation, and an appropriate solver is selected according to the incoming flow Mach number; the turbulence simulation adopts the Reynolds average (RANS) method or the large eddy simulation (LES) method; the energy equation considers the latent heat exchange during heat transfer and phase change; When setting the droplet dynamics model, the droplets are described by Lagrange, and the mass, position, speed and temperature of each droplet or droplet group are tracked over time, and the equation for calculating the droplet acceleration is: ; wherein m p is the droplet mass, v p is the droplet speed, is the droplet acceleration, is the droplet drag force, is the lift, is the gravity, is the pressure gradient force, the drag force is the product of the drag coefficient and the speed difference, the drag coefficient can be obtained according to the Reynolds number and the droplet shape by using the Schiller-Naumann model or the Oseen model; collision and aggregation use the O'Rourke model, and breaking uses the Kelvin-Helmholtz and Rayleigh-Taylor instability criterion; wall collision is judged according to the elastic coefficient and the critical Weber number to determine rebound, wetting or breaking; The droplet transformation strategy comprises: when the droplet diameter is less than a threshold value or the local droplet volume fraction is lower than a threshold value, the Lagrangian particle model is continuously used; when the local volume fraction is higher than the threshold value, to avoid excessive single particle tracking leading to a sharp increase in calculation amount, the droplet group is regarded as a continuous medium, a Smoothed Particle Hydrodynamics (SPH) or a Discrete Element Method (DPM) is used to describe the group behavior, and a group model describes the momentum and energy exchange between the droplet group and the gas phase through a volume fraction equation and a group dynamics equation; The interphase coupling between the droplets and the airflow is specifically: the reaction of the droplet movement on the gas phase is added to the gas phase control equation through a source term, including a momentum source term and an energy source term, which expresses the influence of the droplets on the resistance and heat exchange of the airflow; the CFD solver realizes the two-phase coupling by cyclically updating the interphase source term and the droplet trajectory.

[0009] Further, in the step S4, the inlet boundary condition of the test section includes an incoming flow velocity, a pressure, a temperature, a relative humidity and a turbulence intensity; The outlet boundary condition of the test section includes a set pressure outlet or a total pressure outlet boundary condition; The boundary condition at the outlet of the spray device is set by using an atomizing nozzle model, the droplet size distribution and the initial velocity are calculated according to the nozzle structure and the water supply and air supply pressure, the droplet size is commonly expressed by a logarithmic normal distribution or a Rosin-Rammler distribution, and the liquid water content (LWC) and the median volume diameter (MVD) are set; The wall boundary condition includes that the outer surface of the transmission shaft is set as a rotating wall, and other walls of the test section are set as a no-slip solid wall, and the influence of the wall temperature, roughness and ice layer growth on the surface friction is considered; The aerodynamic interface between the aero-engine and the rear cabin of the test cabin includes an engine inlet section and an outlet section, and the dynamic updating of the aerodynamic interface parameters is realized by coupling calculation of the aero-engine performance proxy model and the test section CFD model.

[0010] Further, the step S5 specifically comprises the steps of: S51, experimental design and data sampling: based on a space-filling strategy, sample points are selected in the entire working envelope of the engine, the sample input variables include a flight height, a flight Mach number, an inlet total temperature, a rotation speed or a fuel flow, and the output variables include engine inlet flow and outlet flow, temperature and pressure; the collected samples come from aero-engine performance calculation or test data; S52, feature processing and model selection: the input and output data are normalized, and a suitable proxy model structure is selected, including a polynomial response surface proxy model, a radial basis function proxy model, a Kriging proxy model or a deep neural network proxy model; S53, model training and verification; train the surrogate model using cross-validation method, and verify it with data not involved in training; evaluate the model performance according to the prediction error and correlation coefficient indicators, and optimize the model by increasing the sample or adjusting the model structure; S54, embedding and coupling of the surrogate model; encapsulate the trained surrogate model as a general executable file, and couple it with the CFD solver through code; in the model coupling calculation process, the data exchange rule between models is that data exchange is performed before the start of each iteration step.

[0011] Further, the step S6 specifically comprises the steps of: S61, initialization stage: import geometry and grid, set initial temperature field, pressure field, velocity field and cloud field; initialize engine operating condition and call the surrogate model to calculate the inlet flow and outlet flow, temperature and pressure of the engine under the set rotating speed or fuel flow; S62, CFD solving stage: solve the gas phase and droplet motion equation under the given boundary condition, calculate the instantaneous distribution of the gas flow field and cloud field, and update the interphase coupling source term; solve the flow field by using the pressure-based coupling algorithm (SIMPLE, PISO, etc.), and use the set time step to ensure numerical stability; S63, surrogate model calculation stage: extract the total temperature and total pressure of the engine inlet cross section and the exhaust environment static pressure, input the surrogate model to calculate the inlet flow and outlet flow, temperature and pressure of the engine under the set rotating speed or fuel flow; the surrogate model output is used to update the test section CFD calculation boundary condition; S64, iteration and convergence judgment: compare whether the CFD calculation result in the current time step and the surrogate model output meet the convergence condition, if not, return the surrogate model output to the CFD solver and continue iteration; if yes, enter the next time step; S65, ice layer growth: if the growth of the wall ice layer needs to be simulated, the ice layer thickness is calculated according to the wall water collection rate, heat transfer amount, freezing rate, etc. in each time period, and the wall shape is updated; by embedding the grid dynamic deformation or local reconstruction module, the grid is adjusted accordingly, and in the iteration process, the fixed geometry shape is used to ignore the ice layer change or updated once in the set time scale; S66, post-processing and data output: after completing all time step calculations, the flow field data is statistically analyzed and visualized, and the velocity vector diagram, pressure contour, temperature contour, droplet concentration distribution, particle size distribution, total pressure / total temperature non-uniformity, liquid water content distribution, etc. are output.

[0012] Another aspect of the present application also provides a multi-dimensional simulation system for the flow field of the icing wind tunnel test section, comprising: A geometry model construction module is configured to establish a three-dimensional geometry model including a test cabin rear cabin, a corner section, an engine inlet duct, a free jet nozzle, a test stand, an exhaust diffuser, an aero-engine and a transmission shaft by using a three-dimensional modeling software according to an actual structure of the test section; A mesh division and preprocessing module is configured to divide a mesh of a calculation domain of the test section after completing geometry modeling of the test section, and introduce a boundary identifier in the mesh division process for subsequent physical model setting and proxy model coupling. A control equation definition and physical model selection module is configured to determine a control equation and select a physical model, including a gas phase flow control equation, a droplet dynamics model, a droplet conversion strategy and interphase coupling between droplets and gas flow. A boundary condition setting module is configured to define boundary conditions of the calculation domain of the test section, including test section inlet boundary conditions, test section outlet boundary conditions, boundary conditions at the outlet of the spray device, wall boundary conditions and an aerodynamic interface between the aero-engine and the test cabin rear cabin. An aero-engine performance proxy model construction and coupling module is configured to establish an aero-engine performance proxy model by machine learning using aero-engine performance calculation or test data, encapsulate the trained proxy model as a general executable file and couple the code with a CFD solver. A multi-dimensional coupling calculation module is configured to transfer information between the CFD solver and the proxy model in a time step by time step iteration manner, perform multi-dimensional coupling calculation, complete all time step calculation and realize multi-dimensional simulation of the icing tunnel test section flow field.

[0013] Another aspect of the present application also provides an electronic device including a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the multi-dimensional simulation method of the icing tunnel test section flow field when executing the computer program.

[0014] Another aspect of the present application also provides a storage medium including a stored program, wherein the program controls a device where the storage medium is located to perform the steps of the multi-dimensional simulation method of the icing tunnel test section flow field when running.

[0015] Compared with the prior art, the present application has the following beneficial effects: The application provides a method, device, equipment and medium for multi-dimensional simulation of a flow field of an icing wind tunnel test section. The method for multi-dimensional simulation of the flow field of the icing wind tunnel test section solves the problem that existing test methods and numerical simulation methods cannot quickly, accurately and comprehensively evaluate whether the flow field of the test section meets the icing test requirements of an aero-engine. On the one hand, a three-dimensional gas-liquid two-phase flow model coupled with an aero-engine performance proxy model is constructed based on an Euler-Lagrange framework and a droplet transformation sub-model, and prediction and analysis of the simulation results of the engine icing meteorological condition test are realized. The method makes up for the defect of limited test data, provides multi-dimensional data such as the speed, pressure, temperature, liquid water content and droplet size distribution of the test section, and provides reliable support for accurate evaluation of the flow field of the test section.

[0016] On the other hand, the application solves the core challenge of insufficient calculation accuracy caused by fixed boundary conditions and low efficiency of full three-dimensional simulation. Current numerical simulation has two drawbacks: one is to ignore the coupling effect of the aero-engine working condition and the test section flow field, and to set the engine inlet and outlet as fixed boundary conditions, which leads to distorted simulation results; the other is to adopt full three-dimensional simulation of the test section and the engine, which brings great calculation difficulty and amount, and cannot adapt to the engineering application scene which needs rapid iteration. The application proposes a coupling calculation strategy of an aero-engine performance proxy model and a test section CFD. Through the aero-engine performance proxy model, the total temperature and total pressure of the engine inlet section and the exhaust environment static pressure are read from the test section CFD calculation results at the beginning of each iteration step, the inlet flow of the engine under the set speed or fuel flow is predicted, and the outlet flow, temperature and pressure are predicted, and the test section CFD calculation boundary conditions are updated in real time. The strategy takes into account the calculation efficiency, effectively avoids the resource bottleneck of full three-dimensional simulation and the model error introduced by simplified boundary, realizes the collaborative optimization of calculation accuracy and efficiency, and maps the complex coupling relationship between the flow in the test section and the engine performance. The high-credibility simulation results output by the coupling model provide a reliable basis for the optimization of the aerodynamic layout of the test section and the accurate regulation and control of the engine icing test environment parameters.

[0017] In addition to the purposes, features and advantages described above, the application has other purposes, features and advantages. The application will be described in further detail below with reference to the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the application and, together with the specification, serve to explain the principles of the application.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, those skilled in the art can obtain other drawings from these drawings without any creative effort. Figure 1 is a flowchart of the icing wind tunnel test section flow field multi-dimensional simulation method of the preferred embodiment of the present application; Figure 2 is a schematic diagram of the icing wind tunnel test section flow field calculation region; Figure 3 is a schematic diagram of the test section geometric model; Figure 4 is a schematic diagram of the tetrahedral unstructured grid; Figure 5 is a schematic diagram of data transmission among the CFD calculation, the mesh screen cross section condition change and the aero-engine performance proxy model; Figure 6 is a schematic diagram of the construction process of the coupling algorithm; Figure 7 is a schematic diagram of the serial coupling and parallel coupling schemes; Figure 8 is a schematic diagram of the icing wind tunnel test section flow field multi-dimensional simulation system module of the preferred embodiment of the present application; Figure 9 is a schematic block diagram of the electronic device entity of the preferred embodiment of the present application; Figure 10 is an internal structure diagram of the computer device of the preferred embodiment of the present application.

[0020] In the drawings: 1, transmission shaft; 2, spraying device; 3, test cabin rear cabin; 4, aero-engine; 5, exhaust diffuser; 6, test stand; 7, free jet nozzle; 8, engine inlet duct; 9, corner section. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and are not used to limit the present application.

[0022] In order to better understand the technical solutions of the present application, the following will be described in detail in combination with the accompanying drawings and specific embodiments.

[0023] Term explanation: Icing wind tunnel: Icing wind tunnel is a test facility specially used for simulating the running state of aircraft and engine in high-altitude icing environment. It can generate low-temperature and high-humidity airflow in a closed cabin, and form a cloud field by injecting liquid water droplets into the airflow through a misting nozzle. During the test, the incoming flow velocity, temperature, pressure, liquid water content, and liquid droplet median volume diameter are usually controlled to approach the real atmospheric conditions.

[0024] Non-uniformity: refers to the uniformity of the distribution of a certain physical quantity in the cross section, commonly represented by the formula δ = (x max −x min ) / x avg . One of the design goals of the icing wind tunnel test section is to ensure that the non-uniformity of the engine inlet and outlet flow field is below a certain threshold, to simulate real flight conditions and ensure test accuracy.

[0025] Surrogate model: an approximate model trained based on high-fidelity simulation results or test data through machine learning, response surface method, etc. Surrogate model can quickly predict the output of complex systems with extremely low computational cost. The performance surrogate model of an aero-engine predicts the engine inlet flow and outlet flow, temperature, and pressure according to the engine inlet total pressure, inlet total temperature, exhaust environment static pressure, and speed, which can replace complex engine performance calculation and save simulation time.

[0026] CFD, i.e. Computational Fluid Dynamics, is a computer-aided engineering technology that analyzes and predicts systems involving fluid flow, heat conduction, and related physical phenomena through numerical calculation methods and computer simulation. The basic principle is to discretize the continuous fluid motion control equation (such as the Navier-Stokes equation) in space and time domain, and then build a corresponding numerical model. By iteratively solving these discretized algebraic equations, the quantitative distribution and evolution of key physical parameters such as velocity, pressure, and temperature can be obtained under specific boundary conditions and initial conditions.

[0027] It should be noted that the execution subject of the present embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a icing wind tunnel test section flow field multi-dimensional simulation device capable of realizing the above functions. The following takes the icing wind tunnel test section flow field multi-dimensional simulation device as an example to illustrate the present embodiment and the following embodiments.

[0028] As shown in Figure 1 , the preferred embodiment of the present application provides a method for simulating the flow field of an icing wind tunnel test section, comprising the steps of: S1, according to the actual structure of the test section (see Figure 2A geometric model of the test section was established using 3D modeling software (see...). Figure 3 To ensure the smoothness and rationality of the geometric components; the actual structure of the test section includes 1. drive shaft, 2. spray device, 3. rear chamber of the test cabin, 4. aero engine, 5. exhaust diffuser, 6. test bench, 7. free jet nozzle, 8. engine intake duct, and 9. corner section. S2. After completing the geometric modeling of the test section, the computational domain of the test section is meshed. During the meshing process, boundary markers are introduced for subsequent physical model settings and surrogate model coupling. Figure 4 This is a tetrahedral unstructured mesh generated using mesh generation software. S3. Determine the governing equations and select the physical model, including the gas phase flow governing equations, droplet dynamics model, droplet transformation strategy, and interphase coupling between droplets and gas flow. S4. Define the boundary conditions of the test section computational domain, including the test section inlet boundary conditions, the test section outlet boundary conditions, the boundary conditions at the spray device outlet, the wall boundary conditions, and the aerodynamic interface between the aero-engine and the rear compartment of the test cabin. S5. Using aero-engine performance calculation or test data, establish an aero-engine performance proxy model through machine learning, encapsulate the trained proxy model into a general executable file, and couple it with the CFD solver through code. S6. By adopting a time-step iterative approach to transfer information between the CFD solver and the surrogate model, multi-dimensional coupled calculations are performed to complete all time-step calculations and realize multi-dimensional simulation of the flow field in the icing wind tunnel test section.

[0029] This embodiment proposes a multi-dimensional simulation method for the flow field of an icing wind tunnel test section. This method solves the problem that existing experimental testing methods and numerical simulation methods cannot quickly, accurately, and comprehensively assess whether the flow field of the test section meets the requirements of aero-engine icing tests. On the one hand, based on the Eulerian-Lagrange framework and the droplet transformation sub-model, a three-dimensional gas-liquid two-phase flow model coupled with an aero-engine performance proxy model is constructed, realizing the predictive analysis of the simulation results of engine icing meteorological conditions. On the other hand, this method compensates for the limitation of experimental testing data, providing multi-dimensional data such as velocity, pressure, temperature, liquid water content, and droplet size distribution of the test section, providing reliable support for the accurate evaluation of the flow field of the test section.

[0030] In another aspect, the embodiment solves the core challenge of insufficient calculation accuracy caused by fixed boundary conditions and low efficiency of full three-dimensional simulation. Current numerical simulation has two drawbacks: one is to ignore the coupling of engine working conditions and test section flow field, set the engine inlet and outlet as fixed boundary conditions, which leads to distorted simulation results; the second is to take full three-dimensional simulation of the test section and the engine, which brings great calculation difficulty and amount, and cannot adapt to the engineering application scenarios that need rapid iteration. The embodiment proposes a coupling calculation strategy of an aero-engine performance proxy model and a test section CFD. Through the aero-engine performance proxy model, the total temperature and total pressure of the engine inlet section and the exhaust environment static pressure are read from the test section CFD calculation results before the start of each iteration step, the inlet flow rate of the engine under the set speed or fuel flow rate, and the outlet flow rate, temperature and pressure are predicted, and the test section CFD calculation boundary conditions are updated in real time. This strategy takes into account the calculation efficiency while effectively avoiding the resource bottleneck of full three-dimensional simulation and the model error introduced by simplified boundary, realizing the coordinated optimization of calculation accuracy and efficiency. The iterative solution of the coupled model can truly map the complex coupling relationship between the test section flow and the engine performance, and the high-confidence simulation results provide a reliable basis for the optimization of the test section aerodynamic layout and the accurate regulation of the engine icing test environment parameters.

[0031] Preferably, in step S1, when establishing the aero-engine geometric model, a simplified shape contour model is used to reduce the calculation amount, and an interface for interacting with the proxy model is reserved.

[0032] Preferably, in the grid division process of step S2, the grid type is selected as a hybrid grid, wherein local encryption or unstructured grid is used for key areas including nozzle exit, engine inlet and outlet, exhaust diffuser inlet, etc. to capture the flow details of the gas flow field and cloud field, and the grid quality is ensured by checking the indexes of unit distortion, orthogonality and volume distribution.

[0033] Preferably, in step S3, the gas phase flow control equation uses the incompressible or weakly compressible Navier-Stokes equation set as the control equation of the gas phase, including the mass conservation equation, the momentum equation and the energy equation, and an appropriate solver is selected according to the incoming flow Mach number; the turbulence simulation adopts the Reynolds average (RANS) method or the large eddy simulation (LES) method; the energy equation considers the latent heat exchange during heat transfer and phase change; When setting the droplet dynamics model, the droplets are described by Lagrange, and the mass, position, speed and temperature of each droplet or droplet group are tracked over time. The equation for calculating the droplet acceleration is: ; where m p is the droplet mass, v p is the droplet speed, For droplet acceleration, The drag force acting on the droplet, For lift, For gravity, The pressure gradient force is the drag force, which is the product of the drag coefficient and the velocity difference. The drag coefficient can be determined using the Schiller-Naumann model or the Oseen model based on the Reynolds number and droplet shape. Collision aggregation uses the O'Rourke model, and breakup uses the Kelvin-Helmholtz and Rayleigh-Taylor instability criteria. Wall collision is determined by the elastic coefficient and the critical Weber number to determine whether it is rebound, wetting, or breakup. The droplet transformation strategy includes: when the droplet diameter is less than a threshold or the local droplet volume fraction is below a threshold, the Lagrange particle model continues to be used; when the local volume fraction is higher than the threshold, to avoid excessive single-particle tracking leading to a surge in computational load, the droplet swarm is treated as a continuous medium, and the swarm behavior is described using Smoothed Particle Hydrodynamics (SPH) or Discrete Element Method (DPM). The swarm model describes the momentum and energy exchange between the droplet swarm and the gas phase through the control volume fraction equation and the swarm dynamics equation. The interphase coupling between droplets and gas flow is specifically as follows: the reaction of droplet motion on the gas phase is added to the gas phase control equation through source terms, including momentum source terms and energy source terms, to express the droplet's drag and heat transfer effects on the gas flow; the CFD solver achieves two-phase coupling by cyclically updating the interphase source terms and droplet trajectory.

[0034] Preferably, in step S4, the boundary conditions of the test section inlet (section A) include the incoming flow velocity, pressure, temperature, relative humidity, and turbulence intensity. The boundary conditions at the test section outlet (sections E and F) include set pressure outlet or full pressure outlet boundary conditions. The boundary conditions at the outlet of the spray device (section B) are set using an atomizing nozzle model. The droplet size distribution and initial velocity are calculated based on the nozzle structure and the water and air supply pressures. The droplet size is usually represented by a log-normal distribution or a Rosner-Marcano distribution. The liquid water content (LWC) and median volume diameter (MVD) are also set. The wall boundary conditions include setting the outer surface of the drive shaft as a rotating wall and setting other walls in the test section as non-slip solid walls, taking into account the influence of wall temperature, roughness and ice layer growth on surface friction; The aero-engine and the rear compartment of the test module include the engine inlet section (C section) and the outlet section (D section). The aero-engine performance proxy model and the test section CFD model are coupled for calculation to realize the dynamic update of the aero-engine interface parameters. Preferably, the operating state of the aero-engine has a significant impact on the flow field of the test section, and directly taking the full three-dimensional coupling simulation of the test section and the engine will bring great calculation difficulty and calculation amount. Therefore, the embodiment adopts the proxy model method, and establishes an aero-engine performance proxy model by machine learning means using aero-engine performance calculation or test data. As shown in the figure, the step S5 specifically includes the steps of: Figure 5 S51, experimental design and data sampling: based on the space filling strategy, sample points are selected in the entire working envelope of the engine, the sample input variables include flight altitude, flight Mach number, inlet total temperature, rotation speed or fuel flow, and the output variables include engine inlet flow and outlet flow, temperature and pressure; the collected samples come from aero-engine performance calculation or test data; S52, feature processing and model selection: the input and output data are normalized, and the applicable proxy model structure is selected, including polynomial response surface proxy model, radial basis function (RBF) proxy model, Kriging (Kriging) proxy model or deep neural network proxy model; for strong nonlinear relationship, a hybrid proxy model or an integrated learning method can be used to improve the prediction accuracy; wherein: The polynomial response surface proxy model (Polynomial Response Surface Model, PRSM) approximates the mapping relationship between the input variables and the system response by constructing a low-order polynomial function, and the typical form is a second-order polynomial containing linear terms, square terms and cross terms. The model has the advantages of high calculation efficiency and strong parameter interpretability, and is especially suitable for rapid analysis of low-dimensional linear or weak nonlinear problems, such as parameter sensitivity research in the early stage of engineering design; however, its limitation is that it is not suitable for fitting high-dimensional complex nonlinear systems, and is prone to overfitting due to high-order terms, resulting in increased extrapolation prediction error; therefore, PRSM is mostly used in optimization scenarios with less than 10 dimensions or as an initialization tool for other high-precision models; the commonly used form is a second-order polynomial: ; In the formula, , , , are polynomial coefficients, y is the dependent variable, x is the independent variable, n is the polynomial order, and i and j take values in the range of 1 to n.

[0035] ​Radial Basis Function Model (RBF) is based on the radial symmetry function of spatial distance. Its strong nonlinear fitting ability and adaptability to high-dimensional data make it outstanding in the reconstruction of complex system response surface, especially when the data distribution is irregular or there are local mutations. RBF can adapt flexibly by adjusting the center and shape parameters of the basis function. However, the model is sensitive to noisy data and prone to overfitting, which can lead to a decline in generalization performance. The selection strategy of basis function center (such as random sampling, clustering optimization) directly affects the prediction accuracy. This model is widely used in multidisciplinary optimization, real-time control, and other fields, especially in scenarios that require fast interpolation and high dimensionality (such as robot path planning, fluid dynamics parameter inversion). Based on the radial symmetry function (such as Gaussian function, multi-quadratic function), an interpolation model is constructed: ; where, is the radial basis function, is the weight coefficient, y(x) is the objective function, x is the independent variable, N is the interpolation order, i ranges from 1 to n, and x i is the interpolation point.

[0036] Kriging Model combines global trend function and local Gaussian random process. Its core advantage is to quantify the prediction uncertainty (such as 95% confidence interval) and capture spatial correlation through covariance function (such as Matern kernel), thereby accurately describing the response characteristics of nonlinear and non-stationary systems. Kriging Model can still maintain high accuracy with small sample data, and is particularly suitable for the substitution and high-fidelity optimization design of expensive simulation models (such as CFD), but the computational complexity increases cubically with the sample size (O(N 3 ), and the optimization of covariance function hyperparameters has a significant impact on model performance, often requiring maximum likelihood estimation or Bayesian methods for parameter tuning.

[0037] ; where, is the zero-mean Gaussian process, y(x) is the objective function, and f(x) is the global trend function.

[0038] S53, model training and verification; use cross-validation to train the surrogate model and verify it with data not involved in training; evaluate model performance based on prediction error and correlation coefficient indicators, and optimize the model by increasing the sample size or adjusting the model structure; S54, the agent model embedding and coupling; the trained agent model is packaged as a general executable file, and is coupled with the CFD solver through the code. In the model coupling calculation process, the data exchange rule between models is: data exchange is carried out before the start of each iteration step. The model coupling calculation process consists of three main stages: 1. The program code initializes its data and initializes the coupling module.

[0039] 2. The coupling module establishes the connection between the code association and the neighborhood search. Each code calculates the problem of its own part during iteration, and exchanges data at a specific time.

[0040] 3. End the calculation by disconnecting the code and stopping all codes and coupling modules.

[0041] In the iteration process, how many iteration steps a transient problem contains, data is exchanged several times, and the coupling module cannot control the simulation process of the coupling program itself. Therefore, when one party of the coupling program sends data, the other party should be ready to receive.

[0042] For the transient solution problem of joint simulation, the two-way coupling method is adopted, different simulation models send and receive data, and each model performs data exchange before the start of each iteration step, that is, exchange before iteration. Figure 6 The solution exchange process construction process of two coupled codes is shown. ①~⑨ represent the serial number of each data processing process, wherein code A exchanges data before solving, and code B exchanges data after solving. For code A, the box is located at the end of the time step, and the code B box is always located at the beginning of the time step. The first box in each code represents the initial transmission. From left to right, find the first sending operation x in code A. Here starts a transmission line, connected to the r end of code B. Then find the first receiving operation in code B, which is also the first box r. The first transmission of the code is generated. Now continue to the next sending operation, the starting position is in the second box in code B, then connect it to the first box of code A, and so on. In this way, the final algorithm described in Figure 6 The final algorithm described in can be identified as a sequential coupling algorithm. However, note that not all initial transmission combinations can produce a reasonable coupling algorithm.

[0043] In most coupled simulations, data transfer is bidirectional, and the coupled algorithm depends on the order of the solution and transfer and the initial data transfer method chosen. The accuracy and convergence rate of the solution also depend on the choice of the code coupling method. A code exchanges after iteration and a code exchanges before iteration produce a variety of algorithms. However, in practice, only a few algorithms are recommended. To add more algorithms, a dummy "idle program" can be added to the post-solution code exchange, i.e., the two codes are not synchronized. One code always leads the other code by one time step. In general, the serial code coupling method is slower but easier to converge, while the code parallel coupling method is faster but more difficult to converge. For one-way transfer and bidirectional transfer, as the name implies, in some coupled applications, only one-way transfer is used, i.e., one code only sends data to the other code, and the other code only receives data, which reduces the possible number of coupled algorithms. In fact, there is only one basic coupled algorithm. The following describes all possible code coupling methods based on the pre-solution transfer of parameters: First, take the first segment of the coupling code as an example: Figure 7 "serial coupling" indicates serial coupling. In this code, data is first sent from code B to code A, i.e., process ① in the figure; then code A starts iteration calculation and obtains the calculation result, corresponding to process ②; the calculation result is sent to code B through process ③, and B performs iteration calculation through process ④ to obtain the calculation result; the data is transmitted to code A through process ⑤, and this is a loop. The loop continues until the required calculation time is reached. For the "parallel coupling" process, take the first segment of the parallel coupling code as an example. In this code, data is sent from code A to code B at the same time as code B sends data to code A, and both processes are performed simultaneously, and then the two codes start their respective solution processes. At the next time point, data transfer and exchange are simultaneously started, and this continues until the calculation is complete.

[0044] In the data transfer and exchange process of three-dimensional CFD calculation programs and engine proxy model calculation programs, convergence control and parameter range limitation are required. In order to avoid unrealistic numerical values in the data transfer process, the Relaxion Factor convergence control method is used. The Relaxion Factor method is used to under-relax or over-relax the numerical value sent to the receiving party: ; wherein, is the new value used to start the next iteration after relaxation factor control; a is the relaxation factor, is the value of the variable newly calculated by the solver in the current iteration step, is the value of the variable from the previous iteration.

[0045] Relaxion Factor also is the relaxation factor, in the multi-dimensional joint simulation, is a kind of commonly used convergence control method, can control the reasonable and accurate data transmission and exchange of the physical quantity of the coupling boundary. Less than 1 under relaxation may lead to some problems more stable solution, for example, when the transmission and exchange process of the coupled process data has a large number of 'numerical spikes'. However, for some steady coupling cases, over relaxation factor method may be more appropriate, which can improve the convergence of some slow convergence problems.

[0046] Preferably, the multi-dimensional simulation of the embodiment transmits information between the CFD solver and the surrogate model in a time step by step iteration manner, therefore, the step S6 specifically comprises the steps of: S61, initialization stage: import geometry and grid, set initial temperature field, pressure field, velocity field and cloud field; initialize engine operating condition and call surrogate model to calculate the inlet flow and outlet flow, temperature and pressure of the engine under the set speed or fuel flow; S62, CFD solving stage: solve the gas phase and droplet motion equation under the given boundary condition, calculate the instantaneous distribution of the gas flow field and cloud field, and update the interphase coupling source term, solve the flow field by using the pressure-based coupling algorithm (SIMPLE, PISO, etc.), and use the set time step to ensure numerical stability; S63, surrogate model calculation stage: extract the total temperature and total pressure of the engine inlet cross section (C cross section) and the exhaust environment static pressure, input the surrogate model to calculate the inlet (C cross section) flow and outlet (D cross section) flow, temperature and pressure of the engine under the set speed or fuel flow; the surrogate model outputs are used to update the test section CFD calculation boundary conditions; S64, iteration and convergence judgment: compare whether the CFD calculation results and the surrogate model output in the current time step meet the convergence conditions, if not, return the surrogate model output to the CFD solver and continue iteration; if yes, enter the next time step; S65, ice layer growth: if the growth of the wall ice layer needs to be simulated, the ice layer thickness is calculated according to the wall water collection rate, heat transfer amount and freezing rate in each time period, and the wall shape is updated; the grid is adjusted correspondingly by embedding the grid dynamic deformation or local reconstruction module, since the embodiment mainly focuses on the distribution of airflow parameters and cloud parameters, the fixed geometry shape is used to ignore the ice layer change or updated once in the set time scale during the iteration process; S66、Post-processing and data output: After completing all time step calculations, statistical and visual analysis is performed on the flow field data, and velocity vector diagrams, pressure contour maps, temperature contour maps, liquid droplet concentration distribution, particle size distribution, total pressure / total temperature non-uniformity, liquid water content distribution, etc. are output.

[0047] In summary, the above-mentioned embodiment provides a multi-dimensional simulation method for the flow field of the icing wind tunnel test section considering real-time calculation of the performance of the aero-engine. Based on the geometric modeling and grid partitioning of the test section, a three-dimensional gas-liquid two-phase flow model is constructed. The momentum, heat and mass exchange between air and liquid droplets are described by the Euler-Lagrange framework and the liquid droplet transformation sub-model. The performance of the aero-engine is calculated in real time by introducing an aero-engine performance proxy model to calculate the inlet flow rate and outlet flow rate, temperature and pressure of the engine. The CFD solver and the proxy model exchange data before the start of each iteration step, update the boundary conditions in real time, and realize high-fidelity two-way coupling simulation of the test section flow field and the performance of the engine. The simulation results obtained include multi-dimensional data such as test section velocity, pressure, temperature, liquid water content and liquid droplet size distribution, which can be used for test section aerodynamic layout optimization and precise regulation of engine icing test environment parameters. At the same time, the above-mentioned embodiment provides a coupling calculation strategy for the aero-engine performance proxy model and the test section CFD. Through the pre-trained aero-engine performance proxy model, the total inlet pressure, total inlet temperature and exhaust environment static pressure and other parameters are read from the test section CFD calculation results before the start of each iteration step, the inlet flow rate of the engine under the set speed or fuel flow rate is predicted, and the outlet flow rate, temperature and pressure are predicted, and the test section CFD calculation boundary conditions are updated in real time. The strategy iterates at each time step until convergence, ensuring the accuracy and computational efficiency of the coupling of the engine performance and the test section flow field, and providing a general data exchange framework for multidisciplinary simulation.

[0048] It can be seen that, in order to solve the problem that the existing test test means and numerical simulation method cannot quickly, accurately and comprehensively evaluate whether the test section flow field meets the aero-engine icing test requirements, the application provides a high-precision simulation technology capable of truly simulating the gas-liquid two-phase flow field in the test section and the coupling of the gas-liquid two-phase flow field with the performance of the engine. The high-precision simulation technology introduces an aero-engine performance proxy model and a liquid droplet dynamics model in three-dimensional CFD simulation, realizes efficient coupling and prediction of complex gas-liquid two-phase flow and engine performance.

[0049] As shown in Figure 8 Another preferred embodiment of the application also provides a multi-dimensional simulation system for the flow field of the icing wind tunnel test section, comprising: A geometric model construction module is used to establish a three-dimensional geometric model including the test cabin rear cabin, corner section, engine inlet duct, free jet nozzle, test stand, exhaust diffuser, aero-engine and transmission shaft by using a three-dimensional modeling software according to the actual structure of the test section; A meshing and preprocessing module is configured to perform meshing on a calculation domain of the test section after completing geometric modeling of the test section, and introduce boundary identification in the meshing process for subsequent physical model setting and proxy model coupling; A control equation definition and physical model selection module is configured to determine control equations and select physical models, including gas flow control equations, droplet dynamics models, droplet conversion strategies, and phase coupling between droplets and gas flow; A boundary condition setting module is configured to define boundary conditions of the calculation domain of the test section, including test section inlet boundary conditions, test section outlet boundary conditions, boundary conditions at the outlet of the spray device, wall boundary conditions, and aerodynamic interfaces between the aero-engine and the rear cabin of the test cabin; An aero-engine performance proxy model establishment and coupling module is configured to use aero-engine performance calculation or test data to establish an aero-engine performance proxy model through machine learning, encapsulate the trained proxy model as a general executable file, and couple the code with a CFD solver; A multi-dimensional coupling calculation module is configured to perform information transmission between the CFD solver and the proxy model in a time step-by-time step iteration manner, perform multi-dimensional coupling calculation, complete all time step calculation, and realize multi-dimensional simulation of the flow field of the icing wind tunnel test section.

[0050] The icing wind tunnel test section flow field multi-dimensional simulation system provided by the embodiment solves the problem that the existing test and numerical simulation methods cannot quickly, accurately and comprehensively evaluate whether the flow field of the test section meets the icing test requirements of the aero-engine. Compared with the prior art, the icing wind tunnel test section flow field multi-dimensional simulation system provided by the present application has the same beneficial effects as the icing wind tunnel test section flow field multi-dimensional simulation method provided by the above-mentioned embodiment, and other technical features in the icing wind tunnel test section flow field multi-dimensional simulation system are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0051] As Figure 9 shown, the preferred embodiment of the present application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize the steps of the icing wind tunnel test section flow field multi-dimensional simulation method in the above-mentioned embodiment.

[0052] The electronic device provided in the application adopts the icing wind tunnel test section flow field multi-dimensional simulation method in the above embodiment, and solves the problem that the existing test test means and numerical simulation method cannot quickly, accurately and comprehensively evaluate whether the test section flow field meets the icing test requirements of the aero-engine. Compared with the prior art, the electronic device provided in the application has the same beneficial effects as the icing wind tunnel test section flow field multi-dimensional simulation method provided in the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0053] As shown in Figure 10 The preferred embodiment of the application further provides a computer device, which can be a terminal or a living body detection server, and an internal structure diagram thereof can be as shown in Figure 10 The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is used to communicate with other computer devices outside through network connection. The computer program is executed by the processor to implement the steps of the icing wind tunnel test section flow field multi-dimensional simulation method.

[0054] Those skilled in the art can understand that Figure 10 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the application, and does not constitute a limitation on the computer device to which the scheme of the application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0055] The computer device provided in the application adopts the icing wind tunnel test section flow field multi-dimensional simulation method in the above embodiment, and solves the problem that the existing test test means and numerical simulation method cannot quickly, accurately and comprehensively evaluate whether the test section flow field meets the icing test requirements of the aero-engine. Compared with the prior art, the computer device provided in the application has the same beneficial effects as the icing wind tunnel test section flow field multi-dimensional simulation method provided in the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0056] The preferred embodiment of the application further provides a storage medium, which includes a stored program. When the program runs, it controls the device where the storage medium is located to perform the steps of the icing wind tunnel test section flow field multi-dimensional simulation method in the above embodiment.

[0057] It is to be understood that the steps illustrated in the flowchart of the drawings can be performed in a computer system such as a set of computer readable instructions executed by a computer system and while logic associated with the steps is being executed, processes can inadvertently be performed in an order other than that described herein. However, it is the results that are important for the aspects of the described embodiments.

[0058] If the functions described in the method of the embodiments are implemented in software, and the software is sold or used as an independent product, the software can be stored in one or more computer-readable storage media. Based on such an understanding, the part of the present application embodiments that contribute to the prior art or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium, and includes a number of instructions for causing one or more computers to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk, and various other media that can store program codes.

[0059] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming language C++ and embedded programming language C.

[0060] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The device that implements the functions specified in one flow or multiple flows and / or blocks.

[0061] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 The flow or flows and / or blocks Figure 1 The function specified in the flow or flows and / or blocks

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 The flow or flows and / or blocks Figure 1 The function specified in the flow or flows and / or blocks

[0063] The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method for multi-dimensional simulation of the flow field of an icing wind tunnel test section as described above.

[0064] The computer program product provided by the present application solves the problem that the existing test and test methods and numerical simulation methods cannot quickly, accurately and comprehensively evaluate whether the flow field of the test section meets the icing test requirements of the aero-engine. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the method for multi-dimensional simulation of the flow field of an icing wind tunnel test section provided by the above-mentioned embodiments, and will not be repeated here.

[0065] Although preferred embodiments of the present application have been described, those skilled in the art, once aware of the basic inventive concept, can make additional changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0066] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application also intends to include these modifications and variations.

Claims

1. A multi-dimensional simulation method for the flow field in an icing wind tunnel test section, characterized in that, Including the following steps: S1. Based on the actual structure of the test section, a three-dimensional geometric model was established using three-dimensional modeling software, including the rear compartment of the test chamber, corner section, engine air intake duct, free jet nozzle, test bench, exhaust diffuser, aero-engine and drive shaft. S2. After completing the geometric modeling of the test section, the computational domain of the test section is meshed. During the meshing process, boundary markers are introduced for subsequent physical model settings and surrogate model coupling. S3. Determine the governing equations and select the physical model, including the gas phase flow governing equations, droplet dynamics model, droplet transformation strategy, and interphase coupling between droplets and gas flow. S4. Define the boundary conditions of the test section computational domain, including the test section inlet boundary conditions, the test section outlet boundary conditions, the boundary conditions at the spray device outlet, the wall boundary conditions, and the aerodynamic interface between the aero-engine and the rear compartment of the test cabin. S5. Using engine performance calculation or test data, establish an aero-engine performance proxy model through machine learning, encapsulate the trained proxy model into a general executable file, and couple it with the CFD solver through code. S6. By adopting a time-step iterative approach to transfer information between the CFD solver and the surrogate model, multi-dimensional coupled calculations are performed to complete all time-step calculations and realize multi-dimensional simulation of the flow field in the icing wind tunnel test section.

2. The multi-dimensional simulation method for the flow field of an icing wind tunnel test section according to claim 1, characterized in that, In step S1, when establishing the geometric model of the aero-engine, a simplified outline model is used to reduce the amount of computation, and an interface for interaction with the proxy model is reserved.

3. The multi-dimensional simulation method for the flow field of an icing wind tunnel test section according to claim 1, characterized in that, In step S2, during the mesh generation process, a hybrid mesh type is selected. Specifically, for key areas including the nozzle outlet, engine inlet and outlet, and exhaust diffuser inlet, locally densified or unstructured meshes are used to capture the flow details of the airflow field and cloud field. The mesh quality is ensured by checking the indicators of cell distortion, orthogonality, and volume distribution.

4. The multi-dimensional simulation method for the flow field of an icing wind tunnel test section according to claim 1, characterized in that, In step S3, the gas phase flow control equations adopt the incompressible or weakly compressible Navier-Stokes equations as the control equations for the gas phase, including the mass conservation equation, momentum equation and energy equation, and an appropriate solver is selected according to the incoming Mach number; the turbulence simulation adopts the Reynolds-averaged method or the large eddy simulation method; the energy equations consider heat transfer and latent heat exchange during phase change. When setting up the droplet dynamics model, the droplets are described using the Lagrangian method. The changes in mass, position, velocity and temperature of each droplet or droplet group are tracked over time. The main forces acting on the droplets include drag force, gravity, buoyancy, pressure gradient force, lift force and virtual mass force. The equation for calculating droplet acceleration is: ； Where, m p v is the mass of the droplet. p For droplet velocity, For droplet acceleration, The drag force acting on the droplet, For lift, For gravity, The pressure gradient force is the drag force, which is the product of the drag coefficient and the velocity difference. The drag coefficient can be determined using the Schiller-Naumann model or the Oseen model based on the Reynolds number and droplet shape. Collision aggregation uses the O'Rourke model, and breakup uses the Kelvin-Helmholtz and Rayleigh-Taylor instability criteria. Wall collision is determined by the elastic coefficient and the critical Weber number to determine whether it is rebound, wetting, or breakup. The droplet conversion strategy includes: when the droplet diameter is less than the threshold or the local droplet volume fraction is lower than the threshold, the Lagrange particle model is continued; when the local volume fraction is higher than the threshold, the droplet swarm is regarded as a continuous medium, and the swarm behavior is described by smooth particle hydrodynamics or discrete element method. The swarm model describes the momentum and energy exchange between the droplet swarm and the gas phase by controlling the volume fraction equation and the swarm dynamics equation. The interphase coupling between droplets and gas flow is specifically as follows: the reaction of droplet motion on the gas phase is added to the gas phase control equation through source terms, including momentum source terms and energy source terms, to express the droplet's drag and heat transfer effects on the gas flow; the CFD solver achieves two-phase coupling by cyclically updating the interphase source terms and droplet trajectory.

5. The multi-dimensional simulation method for the flow field of an icing wind tunnel test section according to claim 1, characterized in that, In step S4, the inlet boundary conditions of the test section include the incoming flow velocity, pressure, temperature, relative humidity, and turbulence intensity. The test section outlet boundary conditions include set pressure outlet or full pressure outlet boundary conditions. The boundary conditions at the outlet of the spray device are set using an atomizing nozzle model. The droplet size distribution and initial velocity are calculated based on the nozzle structure and the water and air supply pressures. The droplet size is usually represented by a log-normal distribution or a Rosner-Marcano distribution. The liquid water content and median volume diameter are also set. The wall boundary conditions include setting the outer surface of the drive shaft as a rotating wall and setting other walls in the test section as non-slip solid walls, taking into account the influence of wall temperature, roughness and ice layer growth on surface friction; The aerodynamic interface between the aero-engine and the rear compartment of the test module includes the engine inlet section and the outlet section. The aerodynamic interface parameters are dynamically updated by constructing an aero-engine performance proxy model and coupling it with the test section CFD model.

6. The multi-dimensional simulation method for the flow field of an icing wind tunnel test section according to claim 1, characterized in that, Step S5 specifically includes the following steps: S51. Experimental Design and Data Sampling: Based on the space filling strategy, sample points are selected within the entire working envelope of the engine. The sample input variables include flight altitude, flight Mach number, total intake temperature, engine speed or fuel flow rate, and the output variables include engine inlet and outlet flow rates, temperature and pressure. The collected samples are from engine performance calculations or experimental data. S52. Feature Processing and Model Selection: Normalize the input and output data and select a suitable surrogate model structure, including multinomial response surface surrogate model, radial basis function surrogate model, kriging surrogate model, and deep neural network surrogate model. S53. Model Training and Validation: Train the surrogate model using cross-validation and validate it with data not used in the training; evaluate model performance based on prediction error and correlation coefficient, and optimize the model by increasing the sample size or adjusting the model structure. S54. Proxy Model Embedding and Coupling: The trained proxy model is encapsulated into a general executable file and coupled with the CFD solver through code. During the model coupling calculation process, the data exchange rule between models is: data exchange is performed before the start of each iteration step.

7. The multi-dimensional simulation method for the flow field of an icing wind tunnel test section according to claim 1, characterized in that, Step S6 specifically includes the following steps: S61. Initialization Phase: Import geometry and mesh, set initial temperature field, pressure field, velocity field and cloud field; initialize engine operating conditions and call the proxy model to calculate the engine inlet and outlet flow rates, temperature and pressure at set speed or fuel flow rate; S62, CFD solution stage: Under given boundary conditions, solve the motion equations of the gas phase and droplets, calculate the instantaneous distribution of the airflow field and cloud field, update the interphase coupling source terms, use a pressure-based coupling algorithm to solve the flow field, and use a set time step to ensure numerical stability. S63, Proxy Model Calculation Stage: Extract the total temperature and pressure of the engine inlet section and the static pressure of the exhaust environment, and input them into the proxy model to calculate the inlet flow rate, outlet flow rate, temperature and pressure of the engine at a set speed or fuel flow rate; the output of the proxy model is used to update the boundary conditions of the CFD calculation of the test section. S64. Iteration and Convergence Judgment: Compare the CFD calculation results and the surrogate model output in the current time step to see if they meet the convergence condition. If they do not meet the condition, return the surrogate model output to the CFD solver and continue iterating. If they meet the condition, proceed to the next time step. S65, Ice Layer Growth: If it is necessary to simulate the growth of ice layer on the wall, the ice layer thickness is calculated based on the wall water collection rate, heat transfer rate, and freezing rate at each time period, and the wall shape is updated. The mesh is adjusted accordingly by embedding a dynamic mesh deformation or local reconstruction module. During the iteration process, a fixed geometry is used to ignore the ice layer change or it is updated once at a set time scale. S66. Post-processing and data output: After completing the calculation of all time steps, perform statistical and visual analysis on the flow field data, and output velocity vector map, pressure cloud map, temperature cloud map, droplet concentration distribution, particle size distribution, total pressure / total temperature non-uniformity, and liquid water content distribution.

8. A multi-dimensional simulation system for the flow field of an icing wind tunnel test section, characterized in that, include: The geometric model building module is used to create a three-dimensional geometric model based on the actual structure of the test section using three-dimensional modeling software. The model includes the rear compartment of the test chamber, corner section, engine air intake duct, free jet nozzle, test bench, exhaust diffuser, aero-engine and drive shaft. The mesh generation and preprocessing module is used to mesh the computational domain of the test section after completing the geometric modeling of the test section. During the mesh generation process, boundary markers are introduced for subsequent physical model settings and surrogate model coupling. The module for defining governing equations and selecting physical models is used to determine governing equations and select physical models, including gas phase flow governing equations, droplet dynamics models, droplet transformation strategies, and interphase coupling between droplets and gas flow. The boundary condition setting module is used to define the boundary conditions of the test section computational domain, including the test section inlet boundary conditions, the test section outlet boundary conditions, the boundary conditions at the spray device outlet, the wall boundary conditions, and the aerodynamic interface between the aero-engine and the rear compartment of the test cabin. The module for constructing and coupling the performance proxy model of aero-engines is used to build a performance proxy model of aero-engines using aero-engine performance calculation or test data and machine learning methods. The trained proxy model is encapsulated into a general executable file and coupled with the CFD solver through code. The multi-dimensional coupled calculation module is used to transfer information between the CFD solver and the surrogate model in a time-step iterative manner, perform multi-dimensional coupled calculations, complete all time-step calculations, and realize multi-dimensional simulation of the flow field in the icing wind tunnel test section.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the multi-dimensional simulation method for the flow field of the icing wind tunnel test section as described in any one of claims 1 to 7.

10. A storage medium comprising a stored program, characterized in that, When the program is running, it controls the device containing the storage medium to perform the steps of the multi-dimensional simulation method for the flow field of the icing wind tunnel test section as described in any one of claims 1 to 7.

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