Ice wind tunnel non-uniform particle size multi-particle cloud simulation method, device, equipment and medium

Abstract

The application discloses an ice wind tunnel non-uniform particle diameter multi-particle cloud simulation method, device, equipment and medium, and the method comprises the following steps: S1, grid division is performed on a calculation region in the ice wind tunnel; S2, air flow field calculation is performed by using the grid and convergence is calculated; S3, on the basis of the flow field calculation convergence, a discrete phase model is introduced, combined with a multi-nozzle incident source position, boundary characteristics of the non-uniform particle diameter multi-particle incidence, non-uniform particle diameter cloud multi-particle non-steady state motion heat transfer simulation is completed; S4, cloud particles are updated once when the flow field calculation in the iteration process is set to a step number; and S5, when the number of incident particles and the number of particles leaving the calculation domain reach a dynamic balance, if the evaluation of the particle diameter distribution and the cloud uniformity at the outlet section does not meet the requirements, the nozzle incident source position and the cloud particle boundary setting are redesigned, and the coupling iteration calculation of the cloud non-steady state is performed. The application can obtain more accurate distribution results of the cloud at each section position.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ice wind tunnel, in particular, relates to an ice wind tunnel non-uniform particle size multi-particle cloud simulation method, device, equipment and medium. BACKGROUND

[0002] The ice wind tunnel is a device for simulating high-altitude cloud icing environment on the ground. The cloud is generated by a spray system. Water mist is atomized and sprayed from multiple nozzles on the spray rake of the spray system, and diffuses and moves under the interaction of air in the ice wind tunnel. When it moves to the test section, the supercooled cloud collides with the surface of the test piece to produce icing, thereby reproducing high-altitude icing phenomena on the ground. Therefore, the uniformity and particle size distribution of the cloud in the test section are of great significance to the ground simulation of icing.

[0003] Since the particle size of the nozzle spray is not uniform, and has a certain diffusion angle, the spray diffuses and moves under the action of air flow. Therefore, there must be a certain distance between the nozzles. After a certain distance of movement or even through the contraction section, the spray can be mixed uniformly. Therefore, accurate cloud simulation, especially the setting of the boundary of the non-uniform particle size multi-particle nozzle cloud injection source, helps to analyze the movement, diffusion, and heat exchange of the cloud generated by the actual nozzle with air, and whether the particle size distribution and cloud uniformity requirements can be met when reaching the test section, thereby providing effective support for the design of the spray rake spacing and nozzle gauge.

[0004] The existing ice wind tunnel cloud simulation mostly uses the Euler method, which regards the cloud as a field, and sets the water droplet inlet as a uniform and uniform particle size inlet boundary at the inlet. Although the amount of cloud at each place can be obtained, the actual nozzle point injection boundary and the non-uniform particle size injection boundary are not considered, in addition, the influence of the spray generating device on the spray injection boundary is also not considered, which is not consistent with the liquid droplet inlet boundary of the actual ice wind tunnel. Therefore, it is also difficult to well evaluate the particle size distribution and cloud uniformity of the cloud at the test section of the ice wind tunnel, and it is difficult to provide help for the design of the spray system. SUMMARY

[0005] The present application provides an ice wind tunnel non-uniform particle size multi-particle cloud simulation method, which is used to solve the technical problem that the existing ice wind tunnel cloud simulation method is not consistent with the liquid droplet inlet boundary of the actual ice wind tunnel, and cannot well evaluate the particle size distribution and cloud uniformity of the cloud at the test section of the ice wind tunnel.

[0006] The present application is realized by the following scheme:

[0007] The ice wind tunnel non-uniform particle size multi-particle cloud simulation method comprises the following steps:

[0008] S1, grid division is performed on the calculation region in the ice wind tunnel;

[0009] S2. Calculate the airflow field using the divided grid and calculate the convergence.

[0010] S3. Based on the convergence of the flow field calculation, a discrete phase is introduced. By combining the spacing and row spacing in the multiple nozzle incident source positions and the boundary characteristics of the non-uniform particle size multi-particle incident, a discrete phase is set. The air is coupled to perform bidirectional heat and mass transfer coupling. The motion and heat transfer of air and non-uniform particle size droplets in the cloud are performed in an unsteady coupled iterative calculation to realize the unsteady motion heat transfer simulation of non-uniform particle size cloud multi-particles.

[0011] S4. Update the cloud particles once every set number of steps in the iterative flow field calculation.

[0012] S5. When the number of incident particles and the number of particles leaving the computational domain reach a dynamic equilibrium, evaluate the particle size distribution and cloud uniformity at the exit section. If the distribution and uniformity do not meet the requirements, adjust the spray rake spacing, readjust the nozzle spacing and cloud particle boundary settings at each incident source position, return to step S1 to perform unsteady coupled iterative calculation again, and use the calculation results to support the design of the ice wind tunnel spray system.

[0013] Furthermore, in step S1, when meshing the computational region inside the ice wind tunnel, an unstructured mesh combined with a local refinement strategy is adopted, including: generating prism layer meshes near the spray rake wall and the ice-sealed cave wall to resolve boundary layer details, and using polyhedral meshes in the remaining areas.

[0014] Furthermore, in step S2, when calculating the airflow field, the boundary conditions for the airflow field calculation adopt the inlet and outlet boundaries, matching the corresponding velocities and pressures. The wall is set to be adiabatic and non-slip, the semi-mode symmetry plane adopts a symmetric boundary, and the gravitational acceleration in the Z direction is set to -9.8 m / s². 2 The solution uses an implicit solution method, and the equations are discretized using a second-order upwind scheme. The governing equations for the airflow field calculation include:

[0015] Continuous phase equation: ;

[0016] Momentum equation: ;

[0017] Energy equation: ;

[0018] In the above formula, ρ It is air density; t It is time; It is a velocity vector; p It is static pressure; It is viscous stress tension; It's a volume force, but here it's gravity; EIt is the total energy per unit mass of fluid; k It is the thermal conductivity coefficient of air; T It's temperature. This represents the gradient operator.

[0019] Furthermore, in step S3, the discrete phase includes:

[0020] Discrete phase motion equations: ;

[0021] Mass conservation (equation of droplet mass change - evaporation / condensation): ;

[0022] Energy conservation (equation for droplet temperature change): ;

[0023] In the above formula, μ It is aerodynamic viscosity; It is particle density; It is the particle diameter; It is the particle Reynolds number; It is the drag coefficient; It is the particle velocity vector; It is gravitational acceleration; It is other forces; It refers to particle mass; Sh It is the Sherwood number, which characterizes the mass transfer rate; D ab It is the diffusion coefficient of water vapor in air; B m It is the Spalding number for mass transfer, which is the driving force for evaporation. ,in It is the mass fraction of vapor on the particle surface. It is the mass fraction of vapor in the distant air; It is the specific heat capacity of the particles; h It is the convective heat transfer coefficient; It is the surface area of ​​the particles; It is the air temperature at a distance; It is the particle temperature; h fg It is latent heat.

[0024] Furthermore, in step S3, setting the discrete phase includes:

[0025] The discrete and continuous phases are set to be bidirectionally coupled, and the calculation is set to be unsteady particles. The particle force and heat transfer state is updated iteratively once every set number of steps.

[0026] Set up a non-uniform particle size multi-particle incident source: Set the point injection source position according to the nozzle position, and this position must be in the non-return zone downstream of the spray rake;

[0027] Setting particle size distribution: Based on the particle size distribution and spray angle collected by the nozzle spray, the non-uniform particle size droplet size spectrum is set in a table format, and the particle incident source is set as a multi-particle cloud incident source with a certain spray angle and non-uniform particle size distribution.

[0028] Set DPM boundaries at the boundaries: the entrance, exit and walls are all set as particle escape boundaries, and particles leave the computational domain as soon as they arrive.

[0029] Furthermore, the set number of steps is 8 to 12, and the position is 3 to 7 cm downstream of the spray rake.

[0030] This application also provides a simulation device for non-uniform particle size multi-particle clouds and fog in ice wind tunnels, including:

[0031] The mesh generation module is used to mesh the computational region within the ice wind tunnel;

[0032] The airflow field calculation module is used to perform airflow field calculations and convergence calculations using a divided mesh.

[0033] The unsteady cloud and fog particle simulation module is used to introduce a discrete phase based on the convergence of flow field calculations. It combines the spacing and row spacing of the multi-nozzle incident source positions and the boundary characteristics of non-uniform particle size multi-particle incident to set the discrete phase, couple it with air to perform bidirectional heat and mass transfer coupling, and perform unsteady coupled iterative calculations on the motion and heat transfer of air and non-uniform particle size droplets in the cloud and fog to realize the unsteady motion and heat transfer simulation of non-uniform particle size cloud and fog multi-particles.

[0034] The cloud and fog particle update module is used to update the cloud and fog particles once every set number of steps in the iterative process flow field calculation.

[0035] The cloud and fog assessment module is used to evaluate the particle size distribution and cloud and fog uniformity at the exit section when the number of incident particles and the number of particles leaving the computational domain reach a dynamic equilibrium. If the distribution and uniformity do not meet the requirements, the spray rake spacing is adjusted, the nozzle spacing and cloud and fog particle boundary settings at each incident source position are readjusted, and the unsteady coupled iterative calculation is performed again. The calculation results are used to support the design of the ice wind tunnel spray system.

[0036] In another aspect, this 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 executes the computer program to implement the steps of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method.

[0037] This application also provides a storage medium including a stored program that, when the program is executed, controls the device where the storage medium is located to perform the steps of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method.

[0038] Compared with the prior art, this application has the following advantages:

[0039] This application proposes a method, apparatus, equipment, and medium for simulating multi-particle cloud fog with non-uniform particle size in ice wind tunnels. The basic principle of the method is to first solve a stable airflow field as a foundation, then inject a group of water droplets as a discrete phase, tracking their trajectory, heat transfer, and particle size evolution under the influence of airflow. Specifically, it proposes a cloud fog simulation of the non-uniform inlet boundary of an ice wind tunnel with multiple particle sizes based on a discrete phase method. A discrete phase model within a computational fluid dynamics (CFD) framework is used to achieve bidirectional coupling calculation of the gas-liquid two-phase flow. This technology also considers the influence of the spray generator and the heat and mass transfer between the spray and air, and can define a point incident source with non-uniform particle size based on the nozzle position of the ice wind tunnel. It also considers the influence of the spray rake. While mitigating aerodynamic influences, this method enables multi-particle-size cloud simulation, effectively improving the accuracy of cloud simulation and obtaining results such as particle size distribution characteristics and cloud uniformity at the test interface. This allows for a more realistic simulation of droplet movement within the ice wind tunnel, yielding more accurate cloud distribution results at various cross-sectional locations. Furthermore, the cloud simulation method of this application simulates actual spraying, considering the simultaneous entry of non-uniform particle-size clouds into the computational domain under each identical calculation condition. This means that the clouds entering the computational domain under the same calculation condition contain clouds of different particle sizes. It also considers the non-uniform particle size of multiple nozzles and obtains the uniformity of the outlet cloud, resulting in more realistic and reliable analysis results. This allows for timely optimization and adjustment of the nozzle layout, thus providing reliable technical support for ice wind tunnel spray systems.

[0040] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description

[0041] 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.

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0043] Figure 1This is a flowchart illustrating the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method according to a preferred embodiment of this application;

[0044] Figure 2 This is a schematic diagram of the overall mesh of the half-model, where blue represents the entrance and yellow represents the symmetry plane;

[0045] Figure 3 This is a schematic diagram of the wall grid, where blue represents the entrance and red represents the exit;

[0046] Figure 4 This is an enlarged schematic diagram of the spray rake and the grid on the wind tunnel wall;

[0047] Figure 5 It shows the symmetry plane and the air velocity cloud map at the inlet and outlet;

[0048] Figure 6 This is a schematic diagram of the downstream y-direction velocity of the spray device (the presence of negative values ​​indicates backflow).

[0049] Figure 7 It shows the symmetry plane and the temperature cloud map of the inlet and outlet air;

[0050] Figure 8 It is a cloud map showing the spray trajectory and temperature changes along the path;

[0051] Figure 9 It is a spray particle size profile along the spray path;

[0052] Figure 10 This is a schematic diagram showing the distribution of liquid water content at different cross sections;

[0053] Figure 11 This is a cloud map showing the distribution of liquid water content at the outlet.

[0054] Figure 12 This is the mass distribution of particle size at the outlet (horizontal axis unit: m).

[0055] Figure 13 It is a cloud map of the spray trajectory when the nozzle is located 2.5cm downstream of the spray device and there is backflow of particles (the velocity in the y direction is negative);

[0056] Figure 14 It is a cloud map of the spray trajectory when the nozzle is located 5.7cm downstream of the spray device and the particle outflow is normal (no negative value in the y-direction velocity);

[0057] Figure 15 This is a cloud map showing the distribution of liquid water content at the outlet of the ice wind tunnel when the geometric model does not consider the spray device;

[0058] Figure 16 This is a schematic diagram of a module for a non-uniform particle size multi-particle cloud and fog simulation device for an ice wind tunnel according to a preferred embodiment of this application;

[0059] Figure 17 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application;

[0060] Figure 18 This is an internal structural diagram of a computer device according to a preferred embodiment of this application. Detailed Implementation

[0061] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0062] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0063] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation device capable of achieving the above functions. The following description uses an ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation device as the executing entity to illustrate this embodiment and the following embodiments.

[0064] like Figure 1 As shown, a preferred embodiment of this application provides a method for simulating multi-particle clouds and fog with non-uniform particle size in ice wind tunnels, including the following steps:

[0065] S1. Mesh the computational region inside the ice tunnel. The geometry of the spray device is taken into account during meshing, including the shape of the spray rake and the row spacing.

[0066] S2. Calculate the airflow field and convergence using the divided grid. The specific steps include: based on typical working conditions, using the SST k-ω turbulence model and a density-based solver, numerically solve the airflow field in the half-mode ice wind tunnel with a spray rake structure. The typical working conditions include working conditions under typical velocity, typical temperature, and typical pressure.

[0067] S3. Based on the convergence of the flow field calculation, a discrete phase DPM model is introduced. By combining the spacing and row spacing in the multiple nozzle incident source positions and the boundary characteristics of non-uniform particle size multiple particle incident, a discrete phase is set, and bidirectional heat and mass transfer is coupled with air. The motion and heat transfer of air and non-uniform particle size cloud fog are calculated by DPM unsteady coupled iteration, realizing the simulation of unsteady motion heat transfer of non-uniform particle size cloud fog particles.

[0068] S4. Update the cloud particles once every set number of steps in the iterative flow field calculation.

[0069] S5. When the number of incident particles and the number of particles leaving the computational domain reach a dynamic equilibrium, it indicates that the cloud field is stabilizing. Evaluate the particle size distribution and cloud uniformity at the exit section. If the distribution and uniformity do not meet the requirements, adjust the spray rake spacing and readjust the nozzle spacing and cloud particle boundary settings at each incident source position. For example, by analyzing the particle spatial distribution, particle size spectrum and concentration field of the target section (such as the test section or the exit), evaluate whether the key parameters such as the liquid water content (LWC) and its uniformity, median particle size (MVD) of the cloud meet the cloud requirements. Return to step S1 to re-perform the unsteady coupled iterative calculation and use the calculation results to support the design of the ice wind tunnel spray system.

[0070] This embodiment proposes a method for simulating multi-particle cloud and fog in ice wind tunnels with non-uniform particle size. The basic principle of this method is to first solve for a stable airflow field as a foundation, then inject a group of water droplets as a discrete phase, tracking their trajectory, heat transfer, and particle size evolution under the influence of airflow. Specifically, it proposes a cloud and fog simulation method for the non-uniform inlet boundary of an ice wind tunnel with multiple particle sizes, using a discrete phase model within the framework of computational fluid dynamics (CFD). The DPM (Distributed Powder Milling) method enables bidirectional coupled calculation of gas-liquid two-phase flow. This technology considers the influence of the spray generator and heat and mass transfer with the air. It can define point incident sources with non-uniform particle sizes based on the nozzle position in the ice wind tunnel. While considering the aerodynamic effects of the spray rake, it can simulate multi-particle-size clouds, effectively improving the accuracy of cloud simulation. It can also obtain results such as particle size distribution characteristics and cloud uniformity at the test interface, providing a more realistic simulation of droplet movement within the ice wind tunnel and obtaining more accurate cloud distribution results at various cross-sectional positions. This provides reliable technical support for ice wind tunnel spray systems. Furthermore, the cloud simulation method in this application simulates actual spraying. Under each identical calculation condition, it considers non-uniform particle-size clouds entering the calculation domain simultaneously. That is, the clouds entering the calculation domain under the same calculation condition contain clouds with different particle sizes. It considers the non-uniform particle sizes of multiple nozzles and obtains the uniformity of the outlet clouds. The analysis results are more realistic and reliable, and the nozzle layout can be optimized and adjusted in a timely manner, thus providing reliable technical support for ice wind tunnel spray systems.

[0071] In this embodiment, when numerically solving the airflow field inside a semi-modal ice wind tunnel with a spray rake structure, the SST k-ω turbulence model, based on a density-based solver, is employed according to typical operating conditions. This SST k-ω turbulence model can accurately capture complex boundary layer flows and the transition process from laminar to turbulent flow, thus obtaining more reliable calculation results. Compared with other models, it has higher accuracy in complex boundary layers and adverse pressure gradient flows, avoiding the problems of premature prediction separation in fully turbulent models or inaccuracies in the near-wall region of standard models.

[0072] In summary, this embodiment can more realistically simulate cloud and fog based on nozzle layout and non-uniform particle size incident sources, obtain the movement of multi-particle-size cloud and fog droplets in the ice wind tunnel, and thus obtain more accurate distribution results of cloud and fog at various cross-sectional positions.

[0073] Preferably, in step S1, when dividing the computational region inside the ice wind tunnel into meshes, an unstructured mesh combined with a local refinement strategy is adopted, including: generating prism layer meshes on the spray rake wall and near the ice wind tunnel wall to resolve boundary layer details, and using polyhedral meshes in the remaining areas.

[0074] In this embodiment, when meshing the computational region inside the ice wind tunnel, an unstructured mesh combined with a local refinement strategy is adopted. For example, a wall-mounted prism layer mesh is generated near the spray rake wall and the ice wind tunnel wall to resolve boundary layer details, while a polyhedral mesh is used in other areas. The advantage of this approach is that near the wall, where the velocity gradient is large, the use of a dense boundary layer mesh (prism layer mesh) can ensure the capture of gradient changes. In areas where the velocity gradient does not change much in the spatial range, a relatively sparse polyhedral mesh can be used to resolve flow field changes, thereby balancing computational accuracy and efficiency.

[0075] In a preferred embodiment of this application, in step S2, when calculating the airflow field, the boundary conditions for the airflow field calculation are set as flow inlet and flow outlet boundaries, matching the corresponding velocity and pressure. The wall is set as adiabatic and slip-free, the semi-mode symmetry plane uses a symmetric boundary, and the gravitational acceleration in the Z direction is set to -9.8 m / s². 2 The solution uses an implicit solution method, and the equations are discretized using a second-order upwind scheme. The governing equations for the airflow field calculation include:

[0076] Continuous phase equation: ;

[0077] Momentum equation: ;

[0078] Energy equation: ;

[0079] In the above formula, ρ It is air density; t It is time; It is a velocity vector; p It is static pressure; It is viscous stress tension; It's a volume force, but here it's gravity; E It is the total energy per unit mass of fluid; k It is the thermal conductivity coefficient of air; T It's temperature. This represents the gradient operator.

[0080] In this embodiment, the airflow field calculation uses inlet and outlet boundaries as boundary conditions, matching the corresponding velocity and pressure. The wall is set to be adiabatic and non-slip, the semi-mode symmetry plane uses a symmetrical boundary, and the gravitational acceleration in the Z direction is set to -9.8 m / s². 2 The solution uses an implicit solution method, and the equations are discretized using a second-order upwind scheme. Furthermore, the governing equations for the airflow field calculation include the continuous phase equation, momentum equation, and energy equation. Therefore, it has the following advantages: First, the complete governing equations and reasonable boundary conditions can realistically simulate actual working conditions including gravity effects and heat exchange, ensuring the physical integrity of the flow field and cloud field simulation. Second, the implicit solution combined with the second-order upwind scheme guarantees the stability of the calculation process and the accuracy of the results, accurately capturing key flow and heat transfer characteristics, as well as the subsequent heat exchange between air and water droplets.

[0081] In a preferred embodiment of this application, step S3, the discrete phase DPM model includes:

[0082] Discrete phase motion equations: ;

[0083] Mass conservation equation (droplet mass change equation - evaporation / condensation): ;

[0084] Energy conservation equation (droplet temperature change equation): ;

[0085] In the above formula, μ It is aerodynamic viscosity; It is particle density; It is the particle diameter; It is the particle Reynolds number; It is the drag coefficient; It is the particle velocity vector; It is gravitational acceleration; It is other forces; It refers to particle mass; Sh It is the Sherwood number, which characterizes the mass transfer rate; D ab It is the diffusion coefficient of water vapor in air; B m It is the Spalding number for mass transfer, which is the driving force for evaporation. ,in It is the mass fraction of vapor on the particle surface. It is the mass fraction of vapor in the distant air; It is the specific heat capacity of the particles; h It is the convective heat transfer coefficient; It is the surface area of ​​the particles; It is the air temperature at a distance; It is the particle temperature; h fg It is latent heat.

[0086] This embodiment introduces a discrete-phase DPM model based on a converged airflow field, coupling it with air for bidirectional heat and mass transfer to simulate unsteady-state cloud and fog particles. The discrete-phase DPM model includes discrete-phase motion equations, mass conservation equations (droplet mass change equations - evaporation / condensation), and energy conservation equations (droplet temperature change equations). Therefore, it has the following advantages: it allows for setting the discrete-phase inlet boundary according to the actual particle distribution, accurately simulating the trajectory of cloud and fog particles and their heat exchange with the air. This method overcomes the limitations of single-diameter particles and unidirectional simulation, realistically reflecting the interaction between particles and airflow, providing crucial basis for analyzing the evolution of clouds and fog and optimizing related processes.

[0087] In a preferred embodiment of this application, step S3, setting the discrete phase, includes:

[0088] The discrete and continuous phases are set to bidirectional coupling, and the calculation is set to unsteady particles (time step 0.001s). The particle force and heat transfer state is updated iteratively once every set number of steps.

[0089] Set up a non-uniform particle size multi-particle incident source: Set the point injection source position according to the nozzle position. This position must be in the non-return zone downstream of the spray rake.

[0090] Setting particle size distribution: Based on the particle size distribution and spray angle collected by the nozzle spray, the non-uniform particle size droplet size spectrum is set in a table format, and the particle incident source is set as a multi-particle cloud incident source with a certain spray angle and non-uniform particle size distribution.

[0091] Set DPM boundaries at the boundaries: the entrance, exit and walls are all set as particle escape boundaries, and particles leave the computational domain as soon as they arrive.

[0092] The set number of steps is 8 to 12, preferably 10, and the position is 3 to 7 cm downstream of the spray rake to avoid particle backflow.

[0093] This embodiment sets the discrete phase by setting the bidirectional coupling between the discrete and continuous phases and the number of discrete phase update steps, setting the particle incident source, setting the particle size distribution, and setting the DPM boundary at the boundary. Therefore, it has the following beneficial effects: it can be set according to the actual spray speed, angle, particle size distribution, etc. at the nozzle outlet, thereby reproducing the actual spray pattern at the nozzle outlet.

[0094] Meanwhile, the number of steps will be set to 8-12, with 10 steps being preferred, to balance efficiency and accuracy. This position is 3-7cm downstream of the spray rake to avoid particle backflow.

[0095] The following is a specific example of cloud and fog simulation:

[0096] (1) Calculate the airflow field inside the ice wind tunnel based on typical ice wind tunnel operating conditions (velocity, temperature, pressure);

[0097] A mesh model of a semi-model of an ice tunnel is created, and the overall mesh is as follows: Figures 2 to 4 As shown, the mesh count is as follows: Cells: 356,987, Faces: 2,018,321, Nodes: 1,507,770. Figure 2 Overall mesh configuration of the half-model: blue represents the entrance, yellow represents the symmetry plane. Figure 3 Wall grid pattern: Blue indicates entrance, red indicates exit;

[0098] Figures 5 to 7 The diagram shows the air velocity and air temperature contours when the inlet pressure is 101325 Pa, the inlet velocity is 40 m / s, and the inlet temperature is -10℃, based on the calculated airflow field results inside the ice tunnel.

[0099] (2) The DPM unsteady-state algorithm is adopted to consider the bidirectional heat transfer coupling between air and clouds; according to the selected nozzle characteristics and nozzle layout, the DPM calculation incident source boundary is set as a point jet source, and the point source coordinates, jet source particle size distribution table (as shown in Table 1), jet temperature of 40℃ and velocity of 15m / s, nozzle flow rate of 1.1g / s, jet source jet angle of 35°, nozzle outlet orifice radius of 0.125mm, and the number of jet particles sprayed by each nozzle is 1000;

[0100] The particle size distribution of the jet source is shown in Table 1, with the particle size ranging from a minimum of 8.44 μm to a maximum of 131 μm;

[0101] Table 1. Nozzle jet source particle size distribution

[0102]

[0103] Figures 8 to 10 These are, respectively, the cloud map of the calculated spray trajectory and temperature variation along the path, the spray particle size along the path, and the LWC distribution cloud map for each cross section. Figure 8 It can be seen that the spray particles exchange heat with the air, cooling from 40°C at the outlet to the temperature of the outlet air, achieving a good degree of subcooling. Figure 9 The random distribution of particles and their movement within the ice tunnel, eventually reaching the exit, can be observed. Figure 10 and Figure 11It can be seen that the uniformity of liquid water content on the cross section becomes more and more uniform as the spray travel distance increases.

[0104] (3) Results of particle size distribution at the outlet

[0105] Figure 12 This is a schematic diagram of the particle size distribution at the outlet section. The minimum particle size at the outlet is 8.4 μm, the maximum particle size is 131 μm, and the average volumetric diameter is 44.3 μm. Since this calculation assumes a relative humidity of 100% in the ice tunnel and no evaporation, the maximum and minimum particle sizes at the outlet are consistent with those at the inlet.

[0106] This application can simulate and consider the particle size distribution of the jet source (set according to the particle size mass distribution table of the nozzle characteristic test), the trajectory of the particles in the ice wind tunnel, the temperature change of the particles along the path, and further obtain the uniformity of liquid water content distribution and particle distribution results of each section, such as the outlet section.

[0107] (4) Simulations were performed on clouds and fog at different nozzle positions, and it was found that... Figure 13 and Figure 14 The results show that when the spray position is close to the end of the spray device (e.g., 2.5 cm), the particles backflow phenomenon occurs, while when the spray position is 5.7 cm from the end of the spray device, the particle backflow phenomenon does not occur. Therefore, the spray position needs to be set in the downstream airflow velocity non-backflow area, generally between 3 and 7 cm.

[0108] (5) Without considering the geometry of the spray device, calculate the cloud and fog under the same operating conditions and spray incident source as before. Figure 15 This is a cloud map showing the distribution of liquid water content at the exit of the ice cave. Figure 15 It can be seen that the uniformity of the liquid water content distribution at the outlet was not considered when the spray device was not taken into account. This is because in the leeward low-speed zone without the spray device, it is difficult for the spray to spread after it is sprayed out, resulting in uneven cloud and fog at the outlet. Therefore, the influence of the geometry of the spray device needs to be considered when simulating cloud and fog in the ice wind tunnel.

[0109] like Figure 16 As shown, another preferred embodiment of this application also provides an ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation device, including:

[0110] The mesh generation module is used to mesh the computational region inside the ice wind tunnel. The mesh generation takes into account the geometry of the spray device, including the shape of the spray rake and the row spacing.

[0111] The airflow field calculation module is used to calculate and converge the airflow field using a divided grid. Specifically, it is used to: numerically solve the airflow field in a half-mode ice wind tunnel with a spray rake structure based on typical working conditions, using the SST k-ω turbulence model and a density-based solver. The typical working conditions include working conditions under typical velocity, typical temperature, and typical pressure.

[0112] The unsteady cloud and fog particle simulation module is used to introduce a discrete phase DPM model based on the convergence of flow field calculations. It combines the spacing and row spacing of the multi-nozzle incident source positions and the boundary characteristics of non-uniform particle size multi-particle incident to set the discrete phase, couple air for bidirectional heat and mass transfer coupling, and perform DPM unsteady coupled iterative calculations on the motion and heat transfer of air and non-uniform particle size cloud and fog, so as to realize the unsteady motion and heat transfer simulation of non-uniform particle size cloud and fog multi-particles.

[0113] The cloud and fog particle update module is used to update the cloud and fog particles once every set number of steps in the iterative process flow field calculation.

[0114] The cloud and fog assessment module is used to assess the particle size distribution and cloud and fog uniformity at the exit section when the number of incident particles and the number of particles leaving the computational domain reach a dynamic equilibrium. If the distribution and uniformity do not meet the requirements, the spray rake spacing is adjusted, the nozzle spacing and cloud and fog particle boundary settings at each incident source position are readjusted, and the unsteady coupled iterative calculation is performed again. The calculation results are used to support the design of the ice wind tunnel spray system.

[0115] The ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation device provided in this embodiment adopts the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method in the above embodiments, solving the technical problem that existing ice wind tunnel cloud and fog simulation methods do not match the droplet inlet boundary of the actual ice wind tunnel, and cannot effectively evaluate the particle size distribution and uniformity of the cloud and fog at the test section of the ice wind tunnel. Compared with the prior art, the beneficial effects of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation device provided in this application are the same as the beneficial effects of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method provided in the above embodiments, and other technical features in the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation device are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0116] like Figure 17 As shown, a preferred embodiment of this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method in the above embodiments.

[0117] This application provides an electronic device that employs the non-uniform particle size multi-particle cloud and fog simulation method for ice wind tunnels described in the above embodiments. This solves the technical problem that existing ice wind tunnel cloud and fog simulation methods do not match the actual droplet inlet boundary of the ice wind tunnel, and therefore cannot effectively assess the particle size distribution and uniformity of the cloud and fog at the test section of the ice wind tunnel. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the non-uniform particle size multi-particle cloud and fog simulation method for ice wind tunnels provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0118] like Figure 18 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 18 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned method for simulating non-uniform particle size multi-particle clouds and fog in ice wind tunnels.

[0119] Those skilled in the art will understand that Figure 18 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0120] The computer equipment provided in this application employs the non-uniform particle size multi-particle cloud and fog simulation method for ice wind tunnels described in the above embodiments. This addresses the technical problem that existing ice wind tunnel cloud and fog simulation methods do not accurately reflect the droplet inlet boundary of actual ice wind tunnels, thus failing to adequately assess the particle size distribution and uniformity of cloud and fog at the test section of the ice wind tunnel. Compared to the prior art, the beneficial effects of the computer equipment provided in this application are the same as those of the non-uniform particle size multi-particle cloud and fog simulation method for ice wind tunnels provided in the above embodiments. Furthermore, other technical features of the electronic equipment are the same as those disclosed in the methods of the above embodiments, and will not be elaborated upon here.

[0121] A preferred embodiment of this application also provides a storage medium, the storage medium including a stored program, which, when the program is executed, controls the device where the storage medium is located to perform the steps of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method in the above embodiments.

[0122] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0123] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this application's embodiments that contribute to the prior art or the technical solutions can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0124] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this 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 this application can be implemented in various computer languages, such as the object-oriented programming language C++ and the embedded programming language C.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart...Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method described above.

[0129] The computer program product provided in this application solves the technical problem that existing methods for simulating cloud and fog in ice wind tunnels do not match the droplet inlet boundary of actual ice wind tunnels, and therefore cannot effectively evaluate the particle size distribution and uniformity of cloud and fog in the test section of the ice wind tunnel. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the non-uniform particle size multi-particle cloud and fog simulation method for ice wind tunnels provided in the above embodiments, and will not be repeated here.

[0130] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0131] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for simulating non-uniform particle size multi-particle clouds and fog in ice wind tunnels, characterized in that, Including the following steps: S1. Grid the computational region inside the ice tunnel; S2. Calculate the airflow field using the divided grid and calculate the convergence. S3. Based on the convergence of the flow field calculation, a discrete phase is introduced. Combining the spacing and row spacing of the multi-nozzle incident source positions, and the boundary characteristics of the non-uniform particle size multi-particle incident, a discrete phase is set. This is coupled with air for bidirectional heat and mass transfer coupling. Unsteady-state coupled iterative calculations are performed on the motion and heat transfer of air and non-uniform particle size droplets in the cloud, realizing the unsteady motion and heat transfer simulation of non-uniform particle size cloud multi-particles. The discrete phase setting includes: The discrete and continuous phases are set to be bidirectionally coupled, and the calculation is set to be unsteady particles. The particle force and heat transfer state is updated iteratively once every set number of steps. Set up a non-uniform particle size multi-particle incident source: Set the point injection source position according to the nozzle position, and this position must be in the non-return zone downstream of the spray rake; Setting particle size distribution: Based on the particle size distribution and spray angle collected by the nozzle spray, the non-uniform particle size droplet size spectrum is set in a table format, and the particle incident source is set as a multi-particle cloud incident source with a certain spray angle and non-uniform particle size distribution. Set DPM boundaries at the boundaries: the entrance, exit and walls are all set as particle escape boundaries, and particles leave the computational domain as soon as they arrive at them; S4. Update the cloud particles once every set number of steps in the iterative flow field calculation. S5. When the number of incident particles and the number of particles leaving the computational domain reach a dynamic equilibrium, evaluate the particle size distribution and cloud uniformity at the exit section. If the distribution and uniformity do not meet the requirements, adjust the spray rake spacing, readjust the nozzle spacing and cloud particle boundary settings at each incident source position, return to step S1 to perform unsteady coupled iterative calculation again, and use the calculation results to support the design of the ice wind tunnel spray system.

2. The method for simulating non-uniform particle size multi-particle clouds and fog in ice wind tunnels according to claim 1, characterized in that, In step S1, when meshing the computational region inside the ice wind tunnel, an unstructured mesh combined with a local refinement strategy is adopted, including: generating prism layer meshes on the spray rake wall and near the ice wind tunnel wall to resolve boundary layer details, and using polyhedral meshes in the remaining areas.

3. The method for simulating non-uniform particle size multi-particle clouds and fog in ice wind tunnels according to claim 1, characterized in that, In step S2, when calculating the airflow field, the boundary conditions for the airflow field calculation are the inlet and outlet boundaries, matching the corresponding velocities and pressures. The wall is set to be adiabatic and slip-free, the semi-mode symmetry plane uses a symmetric boundary, and the gravitational acceleration in the Z direction is set to -9.8 m / s². 2 The solution uses an implicit solution method, and the equations are discretized using a second-order upwind scheme. The governing equations for the airflow field calculation include: Continuous phase equation: ; Momentum equation: ; Energy equation: ; In the above formula, ρ It is air density; t It is time; It is a velocity vector; p It is static pressure; It is viscous stress tension; It's a volume force, but here it's gravity; E It is the total energy per unit mass of fluid; k It is the thermal conductivity coefficient of air; T It's temperature. This represents the gradient operator.

4. The method for simulating non-uniform particle size multi-particle clouds and fog in ice wind tunnels according to claim 1, characterized in that, In step S3, the discrete phase includes: Discrete phase motion equations: ; mass conservation equation: ; Energy conservation expenditure: ; In the above formula, μ It is aerodynamic viscosity; It is particle density; It is the particle diameter; It is the particle Reynolds number; It is the drag coefficient; It is the particle velocity vector; It is gravitational acceleration; It is other forces; It refers to particle mass; Sh It is the Sherwood number, which characterizes the mass transfer rate; D ab It is the diffusion coefficient of water vapor in air; B m It is the Spalding number for mass transfer, which is the driving force for evaporation. ,in It is the mass fraction of vapor on the particle surface. It is the mass fraction of vapor in the distant air; It is the specific heat capacity of the particles; h It is the convective heat transfer coefficient; It is the surface area of ​​the particles; It is the air temperature at a distance; It is the particle temperature; h fg It is latent heat.

5. The method for simulating non-uniform particle size multi-particle clouds and fog in ice wind tunnels according to claim 1, characterized in that, The set number of steps is 8 to 12, and the position is 3 to 7 cm downstream of the spray rake.

6. A simulation device for non-uniform particle size multi-particle clouds and fog in an ice wind tunnel, characterized in that, include: The mesh generation module is used to mesh the computational region within the ice wind tunnel; The airflow field calculation module is used to perform airflow field calculations and convergence calculations using a divided mesh. The unsteady-state cloud and fog particle simulation module introduces a discrete phase based on the convergence of flow field calculations. It combines the spacing and row spacing of multiple nozzle incident sources with the boundary characteristics of non-uniform particle size multi-particle incidents to set the discrete phase. It couples the discrete phase with air for bidirectional heat and mass transfer coupling, performing unsteady coupled iterative calculations on the motion and heat transfer of air and non-uniform particle size cloud and fog particles. This achieves the simulation of unsteady motion and heat transfer of non-uniform particle size cloud and fog multi-particles. The discrete phase setting includes: The discrete and continuous phases are set to be bidirectionally coupled, and the calculation is set to be unsteady particles. The particle force and heat transfer state is updated iteratively once every set number of steps. Set up a non-uniform particle size multi-particle incident source: Set the point injection source position according to the nozzle position, and this position must be in the non-return zone downstream of the spray rake; Setting particle size distribution: Based on the particle size distribution and spray angle collected by the nozzle spray, the non-uniform particle size droplet size spectrum is set in a table format, and the particle incident source is set as a multi-particle cloud incident source with a certain spray angle and non-uniform particle size distribution. Set DPM boundaries at the boundaries: the entrance, exit and walls are all set as particle escape boundaries, and particles leave the computational domain as soon as they arrive at them; The cloud and fog particle update module is used to update the cloud and fog particles once every set number of steps in the iterative process flow field calculation. The cloud and fog assessment module is used to evaluate the particle size distribution and cloud and fog uniformity at the exit section when the number of incident particles and the number of particles leaving the computational domain reach a dynamic equilibrium. If the distribution and uniformity do not meet the requirements, the spray rake spacing is adjusted, the nozzle spacing and cloud and fog particle boundary settings at each incident source position are readjusted, and the unsteady coupled iterative calculation is performed again. The calculation results are used to support the design of the ice wind tunnel spray system.

7. 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 ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method as described in any one of claims 1 to 5.

8. 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 ice wind tunnel non-uniform particle size multi-particle cloud and fog simulation method as described in any one of claims 1 to 5.

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