A space flow field display method and system based on a three-dimensional engine particle system

Abstract

This application discloses a spatial flow field display method and system based on a 3D engine particle system, belonging to the field of spatial flow field display. The method includes: acquiring raw flow field data from a turboshaft engine test stand, preprocessing and standardizing it to construct a database that can be efficiently read by a 3D engine; in the 3D scene construction stage, performing polygon simplification and topology optimization on the physical model in the flow field to ensure that the engine can maintain high-performance operation while maintaining visual effects; and establishing a mapping mechanism of spatial coordinates-particle trajectory-particle dynamic binding, enabling abstract flow field data to accurately drive the motion trajectory of each particle in the particle system, achieving a precise correspondence between data and visual effects. Through frame update scripts, this application allows the particle system to respond in real time to data changes in the database, dynamically adjusting its motion state and vividly displaying the instantaneous characteristics of the flow field, providing a powerful and convenient interactive tool for flow field analysis.

Description

Technical Field

[0001] This application belongs to the field of spatial flow field display, and specifically relates to a spatial flow field display method and system based on a three-dimensional engine particle system. Background Technology

[0002] In the research and development and testing of turboshaft engines, the accurate monitoring and visualization of the flow field state on the test bench (such as velocity distribution, temperature changes, airflow trajectory, etc.) is a key link in evaluating engine performance, optimizing test bench structural design, and ensuring test safety. With the development of digital testing technology, the industry's demand for spatial flow field visualization has shifted from traditional static data charts to dynamic, intuitive, and highly realistic three-dimensional displays. Currently, the flow field analysis of turboshaft engine test benches mainly relies on numerical simulation software to obtain flow field data. However, how to transform these complex numerical data into three-dimensional visualization results that can be interactive in real time and accurately reflect the dynamic changes of the flow field has become a core demand of the industry. Especially in multi-condition (high, medium, and low intake flow) tests, it is necessary to clearly present complex flow field characteristics such as airflow separation zone, recirculation zone, and vortex structure to provide engineers with intuitive support for judging flow mechanisms, optimizing measurement point layout, and predicting test risks.

[0003] However, the existing technology has the following drawbacks:

[0004] First, the flow field display is static, and flow field parameters can only be presented in static forms such as cloud maps and tables. It is impossible to dynamically simulate the airflow trajectory. Engineers find it difficult to intuitively perceive the real-time flow process of airflow in space, such as the airflow circulation path in the recirculation zone and the dynamic changes of vortex structures.

[0005] Secondly, there is a lack of interactivity. The existing display methods cannot support users to focus on key flow field areas (such as the throat of the engine intake and the inlet of the ejector tube) through operations such as adjusting the view and filtering parameters, making it difficult to analyze the details of the local flow field in depth.

[0006] Third, the flow field data is disconnected from the visualization model. Existing technologies have not achieved real-time binding of flow field data and three-dimensional scenes. When flow field parameters (such as airflow) change dynamically, the visualization results cannot be updated synchronously, and the dynamic response of the flow field under different working conditions cannot be simulated.

[0007] Fourth, the particle simulation accuracy is insufficient. If particle display is involved, the existing technology has not optimized the particle emitter parameters and life cycle control for industrial scenarios (such as the complex structure of the test bench). The particle trajectory deviates greatly from the real flow field and cannot accurately reflect the spatial distribution differences of airflow speed and direction.

[0008] Therefore, achieving high-precision visualization of the spatial flow field on the turboshaft engine test stand through a synergistic combination of flow field data processing, 3D scene construction, particle control, and interactive control is a feasible and preferred approach. Summary of the Invention

[0009] To address the aforementioned issues, this application provides a spatial flow field display method and system based on a three-dimensional engine particle system. Through the coordinated combination of flow field data processing, three-dimensional scene construction, particle control, and interactive control, a high-precision visualization of the spatial flow field on a turboshaft engine test stand is achieved.

[0010] This application provides a method for displaying spatial flow fields based on a three-dimensional engine particle system, the method comprising:

[0011] The raw data of the flow field on the test bench of the turboshaft engine are acquired, and the raw data are preprocessed and standardized in sequence to generate a standardized flow field database that can be read by the three-dimensional engine.

[0012] Based on the spatial coordinate information in the standardized flow field database, the initial test bench physical model is simplified by polygons and optimized by topology to obtain the target test bench physical model. The target test bench physical model is then imported into the 3D engine to construct the 3D engine scene.

[0013] Based on the real-time communication between the 3D engine scene and the standardized flow field database, and the reading results of the standardized flow field database, a mapping relationship of spatial coordinates-particle trajectory-particle dynamic binding is established.

[0014] Based on the established particle dynamic binding relationship, the dynamic changes of the standardized flow field database are read in real time through the frame update script during the particle's life cycle, and the particle motion state is adjusted according to the dynamic changes. Specifically, when the dynamic change is a change in airflow speed, the particle color parameter is changed to correct the particle color. When the dynamic change is a change in airflow trajectory, the particle coordinate position is updated in real time. When the particle moves to the flow field boundary, the collision detection logic is triggered.

[0015] A view control function is developed based on the particle dynamic binding relationship. The view control function presets multiple key observation views and includes a working condition switching function. After selecting the target working condition, the corresponding flow field data is automatically loaded from the standardized flow field database, triggering a real-time communication mechanism and particle state update.

[0016] Furthermore,

[0017] The raw flow field data of the turboshaft engine test bench is acquired, and the raw data is preprocessed and standardized sequentially to generate a standardized flow field database that can be read by the 3D engine. Specifically, this includes:

[0018] Obtain raw data of the flow field on the turboshaft engine test bench, including velocity, temperature and airflow coordinate data under different operating conditions;

[0019] Data cleaning removes outliers from the original data, and interpolation algorithms are used to fill in missing data in the original data to obtain preprocessed data.

[0020] The preprocessed data is standardized and stored in the format of spatial coordinates-flow field parameters-operating condition identifiers to generate the standardized flow field database that can be read by the 3D engine.

[0021] Furthermore,

[0022] Based on the spatial coordinate information in the standardized flow field database, the initial test bench physical model is simplified by polygons and optimized by topology to obtain the target test bench physical model. The target test bench physical model is then imported into the 3D engine to construct a 3D engine scene, specifically including:

[0023] Import the initial test bench physical model;

[0024] Based on the spatial coordinate information, the initial test bench physical model is simplified with polygons and optimized with topology, while retaining core structural details.

[0025] Physical rendering technology is used to create materials, and the optimized physical model of the target test bench is imported into a 3D engine. A particle system is created in the 3D engine, a particle emitter preform is defined, and the initial properties of the particle emitter preform are set to construct the 3D engine scene.

[0026] Furthermore,

[0027] Based on the real-time communication between the 3D engine scene and the standardized flow field database, and the reading results from the standardized flow field database, a mapping relationship between spatial coordinates, particle trajectories, and dynamic particle binding is established, specifically including:

[0028] When reading flow field data at any spatial coordinate in the standardized flow field database, flow field trajectory lines are generated in the corresponding area of ​​the three-dimensional engine scene, and a particle system is bound according to the flow field trajectory lines.

[0029] Secondly, based on the same inventive concept, this application provides a spatial flow field display system based on a three-dimensional engine particle system.

[0030] The system includes:

[0031] The flow field data processing unit acquires the raw data of the flow field on the turboshaft engine test bench, performs preprocessing and standardization storage on the raw data in sequence, and generates a standardized flow field database that can be read by the three-dimensional engine.

[0032] The three-dimensional scene construction unit performs polygon simplification and topology optimization on the initial test bench physical model based on the spatial coordinate information in the standardized flow field database to obtain the target test bench physical model. The target test bench physical model is then imported into the three-dimensional engine to construct the three-dimensional engine scene.

[0033] The particle control unit establishes a mapping relationship between spatial coordinates, particle trajectory, and particle dynamic binding based on real-time communication between the 3D engine scene and the standardized flow field database, as well as the reading results from the standardized flow field database. Based on the established particle dynamic binding relationship, it reads the dynamic changes of the standardized flow field database in real time through a frame update script during the particle's life cycle and adjusts the particle's motion state according to the dynamic changes. Specifically, when the dynamic change is a change in airflow velocity, the particle color parameter is changed to correct the particle color; when the dynamic change is a change in airflow trajectory, the particle coordinate position is updated in real time; and when the particle moves to the flow field boundary, collision detection logic is triggered.

[0034] The interactive control unit develops a perspective control function based on the particle dynamic binding relationship. The perspective control function presets multiple key observation perspectives and includes a working condition switching function. After selecting the target working condition, it automatically loads the corresponding flow field data from the standardized flow field database, triggering a real-time communication mechanism and particle state update.

[0035] Furthermore,

[0036] The flow field data processing unit includes a data acquisition module, a data cleaning module, an interpolation and completion module, and a standardized storage module;

[0037] The data acquisition module uses the Fluent numerical simulation software interface, the data cleaning module has a built-in outlier identification algorithm, the interpolation completion module integrates the Kriging interpolation algorithm library, and the standardized storage module exports and saves comma-separated value files.

[0038] Furthermore,

[0039] The 3D scene construction unit includes a model optimization module, a material rendering module, an engine import module, and a lighting and collision configuration module;

[0040] The model optimization module uses a 3D modeling software algorithm plugin, the material rendering module integrates a physically based rendering material editor, the engine import module is a 3D engine interface, and the lighting and collision configuration module has a built-in high dynamic range image environment texture library and a 3D engine collision component.

[0041] Furthermore,

[0042] The particle control unit includes: a particle emitter preform, a data communication module, a parameter mapping module, and a life cycle control module;

[0043] The particle emitter preform is formed by the particle emitter of the 3D engine; the data communication module reads comma-separated value files; the parameter mapping module integrates a C# script color mapping algorithm; and the lifecycle control module has a built-in frame update C# script.

[0044] Furthermore,

[0045] The interactive control unit includes a view adjustment module and a working condition switching module;

[0046] The perspective adjustment module is developed based on the camera function of the 3D engine, and the working condition switching module is based on the switching logic of the standardized flow field database.

[0047] Compared with the prior art, this application has the following advantages:

[0048] 1. By combining flow field data with the 3D engine particle system, the speed and direction of the particle emitter are dynamically adjusted according to the flow field parameters to reproduce the continuous movement of airflow and break through the limitations of static display.

[0049] 2. Develop multi-dimensional interactive functions, including multiple preset key perspectives, to support quick focusing on local flow fields and improve analysis efficiency;

[0050] 3. Comma-separated value file reading is used to link flow field data with 3D model, and particle state is updated synchronously when the working condition is switched, thus solving the problem of "data-model" disconnect;

[0051] 4. Generate streamline trajectories based on the calculated flow field data, match the airflow cycle to set the particle life cycle, and combine the collision and reflection algorithm to make the particle trajectory deviate little from the real flow field.

[0052] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0053] 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, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1A flowchart illustrating a spatial flow field display method based on a three-dimensional engine particle system according to an embodiment of this application is shown.

[0055] Figure 2 The diagram shows the simulated airflow field distribution under low operating conditions according to an embodiment of this application.

[0056] Figure 3 The diagram shows the simulated airflow field distribution under medium operating conditions in an embodiment of this application.

[0057] Figure 4 The diagram shows the simulated airflow field distribution under high operating conditions according to an embodiment of this application;

[0058] Figure 5 A visualization of the 3D engine in an embodiment of this application is shown. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] Figure 1 This paper illustrates a method for displaying spatial flow fields based on a three-dimensional engine particle system according to an embodiment of this application. For example... Figure 1 As shown in the figure, the spatial flow field display method based on a three-dimensional engine particle system according to an embodiment of this application includes the following steps:

[0061] S1. Obtain the raw data of the flow field on the turboshaft engine test bench, and preprocess and standardize the raw data in sequence to generate a standardized flow field database that can be read by the three-dimensional engine.

[0062] S2, Based on the spatial coordinate information in the standardized flow field database, the initial test bench physical model is simplified by polygons and optimized by topology to obtain the target test bench physical model. The target test bench physical model is then imported into the 3D engine to construct the 3D engine scene.

[0063] S3, Based on the real-time communication between the 3D engine scene and the standardized flow field database, and the reading results from the standardized flow field database, establish a mapping relationship between spatial coordinates, particle trajectories, and dynamic particle binding.

[0064] S4. Based on the established particle dynamic binding relationship, the dynamic changes of the standardized flow field database are read in real time through the frame update script during the particle life cycle, and the particle motion state is adjusted according to the dynamic changes.

[0065] S5. Develop a perspective control function based on the particle dynamic binding relationship. The perspective control function presets multiple key observation perspectives and includes a working condition switching function. After selecting the target working condition, it automatically loads the corresponding flow field data from the standardized flow field database, triggering a real-time communication mechanism and particle state update.

[0066] In the embodiments of this application, step S1 specifically includes:

[0067] S11, acquire raw data of the flow field on the turboshaft engine test bench, including velocity, temperature and airflow coordinate data under different operating conditions;

[0068] S12, based on data cleaning, outliers in the original data are removed, and interpolation algorithms are used to fill in missing data in the original data to obtain preprocessed data;

[0069] S13, the preprocessed data is standardized and stored in the format of spatial coordinates-flow field parameters-operating condition identifier to generate the standardized flow field database that can be read by the 3D engine.

[0070] In the specific implementation process, raw flow field data of the turboshaft engine test bench calculated by numerical simulation software such as Fluent is obtained, including velocity, temperature, and airflow coordinate data under different operating conditions, such as... Figure 2 , Figure 3 , Figure 4 As shown, the operating conditions cover low, medium and high intake flow rates. Outliers are removed through data cleaning, missing data are filled in using interpolation algorithms, and the flow field data is standardized in the format of "spatial coordinates-flow field parameters-operating condition identifier" to generate a flow field database that can be read by the 3D engine.

[0071] In this embodiment, Fluent parameters are configured as follows: a simplified test bench model is imported (simplified using SpaceClaim (3D modeling and geometry processing software), removing holes and slots with diameters <0.01m); the RNG k-ε turbulence model (Renormalization Group k-epsilon turbulence model, an improved k-ε two-equation turbulence model based on renormalization group theory) is selected (to adapt to complex backflow field simulation), the SIMPLE algorithm (Semi-Implicit Method for Pressure-Linked Equations) (pressure-velocity coupling), and a first-order upwind discretization scheme (to improve computational stability); boundary conditions are set, with the inlet of the air tower being a pressure inlet (total pressure 0Pa), the outlet of the exhaust tower being a pressure outlet (gauge pressure 0Pa), and the test bench wall being a non-slip wall; an unstructured mesh is generated (total number 7,435,452, number of nodes 1,463,571, mesh independence verification shows that the relative deviation of mass flow rate under this mesh is <1%).

[0072] Calculate the flow field under high (inlet flow rate 7.7 kg / s), medium (inlet flow rate 6.2 kg / s), and low (inlet flow rate 4.1 kg / s) operating conditions respectively, and output the raw data of 12,000 spatial coordinate points (including X / Y / Z coordinates, velocity, airflow direction vector, and temperature), in Fluent native .dat file format;

[0073] Data Export: Using Fluent's TUI command / file / export / data, export the flow field data as a CSV file (Comma-Separated Values) in "coordinate-parameter" format. One CSV file (approximately 500KB / file) is generated for each working condition, containing the following fields: X(m), Y(m), Z(m), Velocity(m / s), Dir_X, Dir_Y, Dir_Z, Temperature(°C).

[0074] Data cleaning: Assuming the flow field parameters follow a normal distribution, calculate the mean μ and standard deviation σ of these parameters. Data exceeding the interval [μ-3σ, μ+3σ] are identified as outliers (probability ≤ 0.1%, meeting the accuracy requirements for industrial data cleaning). The formula is as follows:

[0075]

[0076]

[0077] in, Here, n represents the parameter value of the i-th data point, and n is the total number of data points. In this embodiment, n = 12000.

[0078] The CSV file is read using Python, and NumPy is used to calculate μ and σ for each parameter (e.g., the intake velocity of a high-performance engine is μ=42.0m / s, σ=3.5m / s). Outliers with velocities >42.0+3×3.5=52.5m / s or <42.0-3×3.5=31.5m / s are removed. In this example, 12 outliers were removed, accounting for 0.1%. A cleaned CSV file is generated, with fields consistent with the original file.

[0079] Interpolation completion: For missing data areas such as the intake tower inlet (X=0-2m, Z=15-17.2m) and exhaust tower outlet (X=20-22.5m, Z=20-22.35m), Kriging interpolation with a Gaussian semivariogram is used to predict missing values ​​based on the spatial correlation of known data points. The formula is as follows:

[0080] Semi-mutation function:

[0081] Interpolated estimates:

[0082] Where h is the data point spacing, A is the sill value (reflecting the total data variation), and a is the range (spatial correlation distance). These are the weighting coefficients. Here, m represents the known data point values, and m represents the number of known data points.

[0083] The interpolation model was initialized using the KrigingRegressor class from Scikit-learn, with the semi-variogram function set to Gaussian and the range a = 0.5m (optimized based on the test bench structure). The coordinates (X / Y / Z) and corresponding parameters (velocity, temperature) of known data points were input. After training the model, the parameter values ​​for the missing regions were predicted (e.g., the velocity at the inlet of the air tower at (1.5, 5, 16.5)m, predicted value = 0.68m / s). The total number of data points after completion was restored to 12,000. Interpolation accuracy was verified by randomly selecting 100 known points and covering them with the model for prediction; the velocity error was ≤0.02m / s, and the temperature error was ≤5℃.

[0084] In the embodiments of this application, step S2 specifically includes:

[0085] S21, Import the initial test bench physical model;

[0086] S22, Based on the spatial coordinate information, the initial test bench physical model is simplified by polygons and optimized by topology, while retaining core structural details;

[0087] S23. Materials are created using physical rendering technology, and the optimized physical model of the target test bench is imported into the 3D engine. A particle system is created in the 3D engine, a particle emitter preform is defined, and the initial properties of the particle emitter preform are set to construct the 3D engine scene.

[0088] In the specific implementation process, in step 2, based on the initial test bench physical model of the turboshaft engine generated by UG (Unigraphics), polygon simplification and topology optimization are performed using 3D modeling software, while retaining core structural details such as the air intake tower, exhaust tower, engine body, and ejector tube; materials are created using PBR (Physically Based Rendering) technology, and the optimized model is imported into the 3D engine to complete scene lighting settings and collision body configuration; a particle system is created in the 3D engine, particle emitter prefabs are defined, and initial attributes of the particle emitter prefabs are set, including particle size, particle color, lifespan, particle material, and emitter shape.

[0089] Specifically, it includes:

[0090] Original model import and preprocessing: Export the test bench model in STEP format from UG, including core components (intake tower, engine body, first stage ejector tube, etc.); import it into 3D modeling software, delete non-critical structures (such as bolts with diameter <0.05m, supports without flow field influence), and retain details such as pipe interfaces and engine combustion chamber;

[0091] Polygon simplification and topology optimization: The mesh optimization plugin was enabled, the target number of faces was set, and the boundary preservation mode was selected to ensure that the dimensions of key structures such as pipe interfaces and ejector inlets remained unchanged; the plugin automatically reconstructed triangular faces into quadrilateral meshes, merged coplanar polygons, and simplified areas with gentle curvature, resulting in an 80% reduction in the number of faces in the final model; dimensional verification was performed using 3D modeling software measurement tools to check the diameter of the first-stage ejector inlet (design value 0.55m, measured value 0.5502m, error ≤0.01m) and the diameter of the engine intake throat (design value 0.2m, measured value 0.2001m);

[0092] UV unwrapping and texture baking: For complex parts, the UV unwrapping tool automatically splits the UVs according to material partitions, and manually corrects overlapping areas to ensure that the UV mesh is evenly distributed in the 0-1 space; baking normal maps and AO maps maps high-poly details to low-poly details, reducing the amount of real-time rendering geometry calculations by 70%;

[0093] PBR Material Creation: Create materials according to component type in the PBR material creation plugin, simulate different lighting in the plugin's real-time rendering window, and observe the material reflection effect: metal pipes show clear highlight bands under top lighting, cement floors have no obvious highlights, etc., which are consistent with real physical properties.

[0094] 3D Engine Scene Integration: Import the optimized model from the 3D modeling software into the 3D engine in FBX format, and divide it into layers according to functional systems: scene root node - each process system - process system components, to facilitate the subsequent display of particles in the scene; add mesh colliders to the main structures that particles can touch, so that collision detection is triggered when particles flow in space; enable the static batch processing function of the 3D engine to mark non-interactive parts as static, optimize performance and reduce resource usage; run the 3D engine, check whether the scene loading time is ≤15 seconds, check whether the rendering frame rate is ≥60fps, and whether the resolution is 3840x2160, to ensure that there are no problems such as model clipping or material distortion.

[0095] In the embodiments of this application, step S3 specifically includes:

[0096] When reading flow field data at any spatial coordinate in the standardized flow field database, flow field trajectory lines are generated in the corresponding area of ​​the three-dimensional engine scene, and a particle system is bound according to the flow field trajectory lines.

[0097] In the specific implementation process, in step S3, a C# script is written to realize real-time communication between the 3D engine and the flow field database, and the flow field data is read using a CSV file; a mapping relationship of "spatial coordinates-particle trajectory-dynamic particle binding" is established. When the flow field data of a certain spatial coordinate is read, the flow field trajectory lines in the corresponding area are generated, and the particle system is dynamically bound according to the trajectory lines. The particle color is set according to the flow field velocity (particles in the low-speed area are blue, particles in the high-speed area are red, and the flow field velocity range corresponds to the color gradient range).

[0098] CSV file reading: Write the CSVFileSelector.cs and CSVLoader.cs scripts, attach them to the empty object in the 3D engine, click the streamline display button to call the method, select the CSV file generated under different working conditions in the pop-up window, and parse and load the corresponding CSV data;

[0099] Flow trajectory generation: Write the CSVViewerUI.cs script, attach it to the empty object in the 3D engine, and generate flow trajectory lines based on the parsed CSV data, converting wind speed values ​​into trace color values;

[0100] Dynamic particle binding: While generating the trace, the trace object is dynamically bound to the 3D engine particle system. The initial parameters of the particle system are set through the script CustomParticleSettings.cs.

[0101] In the embodiments of this application, step S4 specifically includes:

[0102] Based on the dynamic change of airflow velocity, the particle color parameter is changed to correct the particle color.

[0103] Based on the dynamic change of the airflow trajectory, the particle coordinate position is updated in real time, and when the particle moves to the flow field boundary, the collision detection logic is triggered.

[0104] In the specific implementation process, within the particle life cycle (set to 2 seconds to match the flow cycle of the airflow in the test stand), the dynamic changes of the flow field database are read in real time through the frame update script (such as the update of flow field parameters when the operating condition is switched), and the particle motion state is adjusted: when the airflow speed changes, the particle color is corrected by changing the particle color parameter; when the airflow trajectory changes (such as the airflow in the exhaust tower being deflected by the guide vanes), the particle coordinate position is updated in real time; when the particle moves to the flow field boundary (such as the test stand wall), the collision detection logic is triggered to simulate the reflection or attenuation of the airflow after hitting the wall.

[0105] In the embodiments of this application, step S5 specifically includes:

[0106] In step 5, a view control function is developed, allowing users to rotate the view using the right mouse button, zoom using the scroll wheel, and pan using the middle mouse button. The right mouse button rotation angle range is ±90° (vertical) and 360° (horizontal), the middle mouse button movement speed is 2x, and the scroll wheel zoom range is 0.1-5x. Multiple key observation views, such as "guide frame view" and "ejector section view," are preset, and clicking the view button allows for smooth switching (transition time 2 seconds). A working condition switching function is also developed; after the user selects a target working condition (high / medium / low), the corresponding flow field data is automatically loaded, and the particle system synchronously updates its color and motion trajectory. The final effect is as follows. Figure 5 As shown.

[0107] Secondly, based on the same inventive concept, this application provides a spatial flow field display system based on a three-dimensional engine particle system.

[0108] The system includes:

[0109] The flow field data processing unit acquires the raw data of the flow field on the turboshaft engine test bench, performs preprocessing and standardization storage on the raw data in sequence, and generates a standardized flow field database that can be read by the three-dimensional engine.

[0110] The three-dimensional scene construction unit performs polygon simplification and topology optimization on the initial test bench physical model based on the spatial coordinate information in the standardized flow field database to obtain the target test bench physical model. The target test bench physical model is then imported into the three-dimensional engine to construct the three-dimensional engine scene.

[0111] The particle control unit establishes a mapping relationship between spatial coordinates, particle trajectory, and particle dynamic binding based on the real-time communication between the 3D engine scene and the standardized flow field database, as well as the reading results of the standardized flow field database. Based on the established particle dynamic binding relationship, it reads the dynamic changes of the standardized flow field database in real time through the frame update script during the particle's life cycle and adjusts the particle motion state according to the dynamic changes.

[0112] The interactive control unit develops a perspective control function based on the particle dynamic binding relationship. The perspective control function presets multiple key observation perspectives and includes a working condition switching function. After selecting the target working condition, it automatically loads the corresponding flow field data from the standardized flow field database, triggering a real-time communication mechanism and particle state update.

[0113] Specifically, the flow field data processing unit includes a data acquisition module, a data cleaning module, an interpolation and completion module, and a standardized storage module;

[0114] The data acquisition module uses the Fluent numerical simulation software interface, the data cleaning module has a built-in outlier identification algorithm, the interpolation completion module integrates the Kriging interpolation algorithm library, and the standardized storage module exports and saves comma-separated value files.

[0115] In the specific implementation process, the flow field data processing unit acquires the original flow field data of the turboshaft engine test bench, removes outliers with deviations >5% through the data cleaning module, completes missing data in areas such as the inlet of the air intake tower and the outlet of the exhaust tower through the interpolation completion module, and stores the data in the format of "spatial coordinates-flow field parameters-operating condition identifier" through the standardization storage module, generating a standardized flow field database that can be read by the three-dimensional engine.

[0116] Specifically, the 3D scene construction unit includes a model optimization module, a material rendering module, an engine import module, and a lighting and collision configuration module;

[0117] The model optimization module uses a 3D modeling software algorithm plugin, the material rendering module integrates a physically based rendering material editor, the engine import module is a 3D engine interface, and the lighting and collision configuration module has a built-in high dynamic range image environment texture library and a 3D engine collision component.

[0118] In the specific implementation process, the three-dimensional scene construction unit takes the physical model of the test bench generated by UG as input, the model optimization module simplifies it to 80% of the original number of faces, and retains the core structures such as the air intake tower and the first stage ejector tube; the material rendering module configures PBR parameters for different parts; the engine import module imports the optimized model into the three-dimensional engine, the lighting module simulates real lighting based on HDRI (High Dynamic Range Image Environment Map Library) textures, and the collision module adds three-dimensional engine collision components to key parts to build a three-dimensional scene that matches the real test bench 1:1.

[0119] Specifically, the particle control unit includes: a particle emitter preform, a data communication module, a parameter mapping module, and a life cycle control module;

[0120] The particle emitter preform is formed by the particle emitter of the 3D engine; the data communication module reads comma-separated value files; the parameter mapping module integrates a C# script color mapping algorithm; and the lifecycle control module has a built-in frame update C# script.

[0121] In the specific implementation process, the data communication module communicates with the standardized database of the flow field data processing unit in real time to read the flow field parameters corresponding to the spatial coordinates; the parameter mapping module adjusts the transmitter speed according to the velocity value, sets the initial motion direction of the particles according to the airflow direction vector, and maps the wind speed value to the particle color (blue for low speed and red for high speed) through C# script; the life cycle control module sets the particle life cycle to 2 seconds, corrects the particle speed, position and trajectory in real time, and simulates the real airflow motion.

[0122] Specifically, the interactive control unit includes a view adjustment module and a working condition switching module;

[0123] The perspective adjustment module is developed based on the camera function of the 3D engine, and the working condition switching module is based on the switching logic of the standardized flow field database.

[0124] In the specific implementation process, the perspective adjustment module supports right-click rotation, middle-click displacement, and scroll wheel zoom, presets multiple key perspectives and achieves smooth switching in 2 seconds; after receiving the user's selection, the working condition switching module triggers the flow field data processing unit to call the corresponding working condition database and synchronously update the trajectory parameters of the particle control unit.

[0125] In this embodiment, the data connection is as follows: the standardized storage module of the flow field data processing unit establishes bidirectional data interaction with the data communication module of the particle control unit through CSV file reading, and transmits flow field parameters and particle state feedback data in real time.

[0126] Scene support connection: The engine import module of the 3D scene building unit outputs the 3D scene to the 3D engine, providing a spatial carrier for the particle emitter preform of the particle control unit. The position of the particle emitter preform is precisely bound to the flow field trajectory line generated in the scene.

[0127] Control command connection: The view adjustment module and working condition switching module of the interactive control unit send view switching commands and working condition update commands to the particle control unit through the 3D engine event system. The particle control unit adjusts the particle rendering angle and parameters according to the commands to realize the linkage between particle display and interaction.

[0128] In this embodiment, the collaborative workflow of the combination unit specifically includes:

[0129] Phase 1: Data preparation and scene building collaboration: The flow field data processing unit initiates the data acquisition, cleaning, and standardization process to generate the database. At the same time, the 3D scene building unit simultaneously performs model optimization, material rendering, and engine import. After both are completed in parallel, the 3D scene building unit feeds back the scene space coordinate range to the flow field data processing unit to ensure that the flow field data matches the scene coordinates.

[0130] Phase 2: Particle Simulation and Data Synchronization and Collaboration: The data communication module of the particle control unit reads the standardized data from the flow field data processing unit. The parameter mapping module and the life cycle control module collaborate to generate particle trajectories that match the flow field in the 3D scene. If the flow field data is updated, the flow field data processing unit pushes the new data in real time, and the particle control unit completes parameter adjustment within 1 second to achieve synchronous changes between particles and the flow field.

[0131] Phase 3: Coordination of Interactive Operation and Particle Response: When the user triggers a viewpoint switch through the interactive control unit, the viewpoint adjustment module sends viewpoint parameters to the 3D engine, and the 3D engine adjusts the particle rendering viewpoint synchronously; when a working condition switch is triggered, the working condition switch module first instructs the flow field data processing unit to call the corresponding database, and then instructs the particle control unit to reset the emitter parameters, ensuring seamless connection between interactive operation and flow field display.

[0132] By combining the above four units, the entire process of "data processing - scene construction - particle simulation - interactive control" is coordinated, which solves the shortcomings of existing technologies: real-time data connection between the flow field data processing unit and the particle control unit overcomes the problem of flow field data being disconnected from the visualization model; spatial binding between the 3D scene and the particle system, combined with parameter mapping and life cycle control, improves the accuracy of particle simulation; and the command linkage between the interactive control unit and other units realizes multi-dimensional interactive functions, which greatly improves the efficiency and flexibility of flow field analysis compared with the existing static display.

[0133] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for displaying spatial flow fields based on a three-dimensional engine particle system, characterized in that, The method includes: The raw data of the flow field on the test bench of the turboshaft engine are acquired, and the raw data are preprocessed and standardized in sequence to generate a standardized flow field database that can be read by the three-dimensional engine. Based on the spatial coordinate information in the standardized flow field database, the initial test bench physical model is simplified by polygons and optimized by topology to obtain the target test bench physical model. The target test bench physical model is then imported into the 3D engine to construct the 3D engine scene. Based on the real-time communication between the 3D engine scene and the standardized flow field database, and the reading results of the standardized flow field database, a mapping relationship of spatial coordinates-particle trajectory-particle dynamic binding is established. Based on the established particle dynamic binding relationship, the dynamic changes of the standardized flow field database are read in real time through the frame update script during the particle's life cycle, and the particle motion state is adjusted according to the dynamic changes. Specifically, when the dynamic change is a change in airflow speed, the particle color parameter is changed to correct the particle color. When the dynamic change is a change in airflow trajectory, the particle coordinate position is updated in real time. When the particle moves to the flow field boundary, the collision detection logic is triggered. A view control function is developed based on the particle dynamic binding relationship. The view control function presets multiple key observation views and includes a working condition switching function. After selecting the target working condition, the corresponding flow field data is automatically loaded from the standardized flow field database, triggering a real-time communication mechanism and particle state update.

2. The method according to claim 1, characterized in that, The raw flow field data of the turboshaft engine test bench is acquired, and the raw data is preprocessed and standardized sequentially to generate a standardized flow field database that can be read by the 3D engine. Specifically, this includes: Obtain raw data of the flow field on the turboshaft engine test bench, including velocity, temperature and airflow coordinate data under different operating conditions; Data cleaning removes outliers from the original data, and interpolation algorithms are used to fill in missing data in the original data to obtain preprocessed data. The preprocessed data is standardized and stored in the format of spatial coordinates-flow field parameters-operating condition identifiers to generate the standardized flow field database that can be read by the 3D engine.

3. The method according to claim 2, characterized in that, Based on the spatial coordinate information in the standardized flow field database, the initial test bench physical model is simplified by polygons and optimized by topology to obtain the target test bench physical model. The target test bench physical model is then imported into the 3D engine to construct a 3D engine scene, specifically including: Import the initial test bench physical model; Based on the spatial coordinate information, the initial test bench physical model is simplified with polygons and optimized with topology, while retaining core structural details. Physical rendering technology is used to create materials, and the optimized physical model of the target test bench is imported into a 3D engine. A particle system is created in the 3D engine, a particle emitter preform is defined, and the initial properties of the particle emitter preform are set to construct the 3D engine scene.

4. The method according to claim 3, characterized in that, Based on the real-time communication between the 3D engine scene and the standardized flow field database, and the reading results from the standardized flow field database, a mapping relationship between spatial coordinates, particle trajectories, and dynamic particle binding is established, specifically including: When reading flow field data at any spatial coordinate in the standardized flow field database, flow field trajectory lines are generated in the corresponding area of ​​the three-dimensional engine scene, and a particle system is bound according to the flow field trajectory lines.

5. A spatial flow field display system based on a three-dimensional engine particle system, characterized in that, The system includes: The flow field data processing unit acquires the raw data of the flow field on the turboshaft engine test bench, performs preprocessing and standardization storage on the raw data in sequence, and generates a standardized flow field database that can be read by the three-dimensional engine. The three-dimensional scene construction unit performs polygon simplification and topology optimization on the initial test bench physical model based on the spatial coordinate information in the standardized flow field database to obtain the target test bench physical model. The target test bench physical model is then imported into the three-dimensional engine to construct the three-dimensional engine scene. The particle control unit establishes a mapping relationship between spatial coordinates, particle trajectory, and particle dynamic binding based on real-time communication between the 3D engine scene and the standardized flow field database, as well as the reading results from the standardized flow field database. Based on the established particle dynamic binding relationship, it reads the dynamic changes of the standardized flow field database in real time through a frame update script during the particle's life cycle and adjusts the particle's motion state according to the dynamic changes. Specifically, when the dynamic change is a change in airflow velocity, the particle color parameter is changed to correct the particle color; when the dynamic change is a change in airflow trajectory, the particle coordinate position is updated in real time; and when the particle moves to the flow field boundary, collision detection logic is triggered. The interactive control unit develops a perspective control function based on the particle dynamic binding relationship. The perspective control function presets multiple key observation perspectives and includes a working condition switching function. After selecting the target working condition, it automatically loads the corresponding flow field data from the standardized flow field database, triggering a real-time communication mechanism and particle state update.

6. The system according to claim 5, characterized in that, The flow field data processing unit includes a data acquisition module, a data cleaning module, an interpolation and completion module, and a standardized storage module; The data acquisition module uses the Fluent numerical simulation software interface, the data cleaning module has a built-in outlier identification algorithm, the interpolation completion module integrates the Kriging interpolation algorithm library, and the standardized storage module exports and saves comma-separated value files.

7. The system according to claim 6, characterized in that, The 3D scene construction unit includes a model optimization module, a material rendering module, an engine import module, and a lighting and collision configuration module; The model optimization module uses a 3D modeling software algorithm plugin, the material rendering module integrates a physically based rendering material editor, the engine import module is a 3D engine interface, and the lighting and collision configuration module has a built-in high dynamic range image environment texture library and a 3D engine collision component.

8. The system according to claim 7, characterized in that, The particle control unit includes: a particle emitter preform, a data communication module, a parameter mapping module, and a life cycle control module; The particle emitter preform is formed by the particle emitter of the 3D engine; the data communication module reads comma-separated value files; the parameter mapping module integrates a C# script color mapping algorithm; and the lifecycle control module has a built-in frame update C# script.

9. The system according to claim 8, characterized in that, The interactive control unit includes a view adjustment module and a working condition switching module; The perspective adjustment module is developed based on the camera function of the 3D engine, and the working condition switching module is based on the switching logic of the standardized flow field database.

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