Multi-physics field coupling Fluent simulation analysis integrated system

Abstract

The invention belongs to the technical field of multi-physics field coupling simulation, and particularly relates to a multi-physics field coupling Fluent simulation analysis integrated system, which comprises a data preprocessing module used for carrying out data integration, grid division and boundary condition setting on a multi-physics field coupling problem; the multi-physical field coupling module comprises a fluid-structure coupling sub-module, a thermal-force coupling sub-module, an electromagnetic-fluid coupling sub-module and the like, and interaction simulation among different physical fields is realized respectively; the calculating and solving module is used for calculating and solving a multi-physics field coupling problem based on a Fluent software platform and coordinating the calculating sequence and the time step length of each physics field; and the post-processing and visualization module is used for performing post-processing analysis and visualization display on the simulation result. The method effectively solves the problems that a traditional system is low in data interaction efficiency, insufficient in grid precision and poor in calculation coordination, and has the advantages that simulation precision is improved, resource consumption is reduced, and system expansibility is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-physics field coupling simulation, and in particular relates to a multi-physics field coupling Fluent simulation analysis integrated system. Background Art

[0002] In modern engineering, multiphysics coupling is a widespread problem in complex systems such as aerospace, energy and power, and electronic equipment cooling. With the advancement of computer technology, Fluent software has become a crucial tool for physics simulations, including fluid dynamics. However, existing multiphysics coupling simulation systems still face numerous challenges: Traditional systems lack real-time data synchronization and format mapping mechanisms for multiphysics data exchange, making it difficult to achieve efficient collaboration between Fluent and other solvers. Meshing often uses a fixed pattern, making it impossible to dynamically adjust mesh density based on field gradients, resulting in insufficient computational accuracy and wasted computing resources in critical areas. During multiphysics coupling, the computational order and time step size of each field are difficult to coordinate, resulting in low pressure-velocity coupling efficiency in commonly used algorithms, impacting overall computational efficiency. Post-processing and visualization capabilities are limited, preventing unified analysis and coupling evaluation of multi-field coupling results. Furthermore, the system architecture lacks openness and scalability, making it difficult to quickly integrate third-party solvers to meet diverse simulation needs. Therefore, there is an urgent need to develop an integrated multiphysics coupling Fluent simulation and analysis system that can effectively address these challenges. Summary of the Invention

[0003] The purpose of this application is to provide a multi-physics field coupling Fluent simulation analysis integrated system and its data coupling module, adaptive meshing unit, post-processing and visualization module, physical field interaction configuration unit, modular architecture, simulation process template library, automated script engine, adaptive meshing algorithm, multi-field interaction model, and improved pressure-velocity coupling algorithm, which have the advantages of improving the efficiency and accuracy of multi-physics field coupling simulation, optimizing computing resource allocation and enhancing system scalability.

[0004] This application provides a multi-physics field coupling Fluent simulation analysis integrated system, the technical solution is as follows:

[0005] A multi-physics field coupling Fluent simulation analysis integrated system, characterized in that the system includes the following modules:

[0006] Data preprocessing module: used for data integration, meshing and boundary condition setting for multi-physics field coupling problems;

[0007] Multi-physics coupling module: includes fluid-structure coupling submodule, thermal-mechanical coupling submodule, electromagnetic-fluid coupling submodule, etc., which respectively realize the interaction simulation between different physical fields;

[0008] Computational solution module: Based on the Fluent software platform, it performs computational solutions to multi-physics coupling problems and coordinates the calculation order and time step of each physical field;

[0009] Post-processing and visualization module: used for post-processing analysis and visualization of simulation results;

[0010] System management module: provides a user interface to configure, monitor and store the entire simulation results.

[0011] Furthermore, the present application also proposes a data coupling module, which realizes real-time data synchronization and format mapping between Fluent and other solvers for the calculation and solution module through a real-time data bus and a heterogeneous data conversion unit.

[0012] Furthermore, the present application also proposes that the grid division adopts an adaptive grid division unit, dynamically adjusts the local grid density based on the gradient change of the physical field, and adopts an error estimation encryption algorithm to realize the grid reconstruction of the key area.

[0013] Furthermore, this application also proposes that the post-processing and visualization module is used for unified analysis, visualization output and coupling degree evaluation of multi-field coupling simulation results, including streamline diagrams, isosurface diagrams, and three-dimensional model reconstruction functions.

[0014] Furthermore, the present application also proposes that the data preprocessing module also includes a physical field interaction configuration unit, which establishes the fluid-solid coupling interface displacement transfer rules and the heat flux density threshold of the heat-fluid coupling interface through a parameterized boundary condition definition tool.

[0015] Furthermore, this application also proposes that the system adopts a modular architecture, each module communicates with the main control program through a standardized API interface, and supports plug-and-play integration of third-party solvers; the API interface is defined based on the RESTful protocol, allowing third-party solvers to be encapsulated as dynamic link libraries or containerized services through the SDK.

[0016] Furthermore, the present application also proposes that the system management module has built-in:

[0017] A simulation process template library with pre-set standardized parameter configurations for aircraft engine thermal-fluid-solid coupling and battery thermal-fluid-electric coupling scenarios;

[0018] The automated scripting engine supports Python scripts to extend multi-field coupling logic and customize physical quantity mapping rules.

[0019] Furthermore, the present application also proposes that the data preprocessing module adopts an error-based adaptive meshing algorithm (Error-Based Adaptive Meshing), the core formula of which is:

[0020]

[0021] Among them, h new and h old Represent the new and old grid step sizes respectively, ε is the preset error threshold, η is the calculation error of the current grid, and n is the adaptive adjustment index.

[0022] Furthermore, the present application also proposes that the multi-physics field coupling module adopts a multi-field interaction model (Multi-Field Interaction Model), which specifically includes the following formula:

[0023] Fluid-structure coupling: The fluid momentum equation and the structural motion equation exchange force and displacement information through boundary conditions;

[0024] F fluid =ρ f ∫ A (v f -v S )·ndA

[0025] Among them, F fluid is the force exerted by the fluid on the structure, ρ f is the fluid density, v f and v S are the velocities of the fluid and structure respectively, and n is the normal unit vector.

[0026] Thermal-mechanical coupling: the interaction between the heat conduction equation and the mechanical response equation through temperature and stress;

[0027]

[0028] Where q is the heat flow rate, k is the thermal conductivity, T is the temperature, ρ and c p are density and specific heat capacity, respectively.

[0029] Electromagnetic-fluid coupling: interaction between electromagnetic forces and fluid forces;

[0030]

[0031] Among them, F em is the electromagnetic force density, σ is the electrical conductivity, H and B are the magnetic field intensity and magnetic induction intensity respectively.

[0032] Furthermore, the present application also proposes that the calculation and solution module adopts an improved pressure-velocity coupling algorithm (iPVCA), the core formula of which is as follows:

[0033]

[0034] Where ρ is density, v is velocity vector, P is pressure, μ is dynamic viscosity, F ext For external force.

[0035] As can be seen from the above, compared with the prior art, the present invention has the following beneficial effects:

[0036] The present application provides a multi-physics field coupling Fluent simulation analysis integrated system and its data coupling module, adaptive meshing unit, post-processing and visualization module, physical field interaction configuration unit, modular architecture, simulation process template library, automated script engine, adaptive meshing algorithm, multi-field interaction model, and improved pressure-velocity coupling algorithm. The system realizes multi-physics field data integration and dynamic meshing through the data preprocessing module, coordinates the interaction between different physical fields by using the multi-physics field coupling module, optimizes the solution efficiency by combining the calculation solution module and unifies the analysis results by the post-processing module, effectively solving the problems of inefficient data interaction, insufficient mesh accuracy and poor calculation coordination in traditional systems, and has the advantages of improving simulation accuracy, reducing resource consumption and enhancing system scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system block diagram of the present invention. DETAILED DESCRIPTION

[0038] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] In existing technologies, multi-physics coupled simulation systems generally face problems such as imperfect data exchange mechanisms, insufficient meshing flexibility, and inefficient computational coordination. For example, in the thermal-fluid-solid coupled simulation of an aircraft engine combustion chamber, the lack of a real-time data synchronization mechanism leads to delayed transfer of boundary conditions between the fluid and solid domains. Fixed meshing prevents automatic refinement of high-temperature regions, affecting the accuracy of thermal stress calculations. The inefficient pressure-velocity coupling algorithm prolongs iteration cycles, hindering design verification progress.

[0040] To address these issues, the research and development process discovered that the closed nature of traditional system architectures hindered multi-physics collaborative optimization. Analysis revealed that achieving multi-field coupling requires building a unified data exchange framework, establishing a dynamic mesh adjustment mechanism, and optimizing solver collaboration. Based on this, the team proposed decomposing the system into functional modules that can operate independently and collaboratively, enabling efficient communication between modules through standardized interfaces.

[0041] Therefore, Figure 1 As described, this application proposes a multi-physics field coupling Fluent simulation analysis integrated system, which includes a data preprocessing module, a multi-physics field coupling module, a calculation and solution module, a post-processing and visualization module, and a system management module;

[0042] Among them, the data preprocessing module refers to the functional unit for data integration and meshing of multi-physics field coupling problems. Specifically, the parameterized boundary condition definition tool can be used to establish physical field interaction rules, such as realizing the data docking of fluid and structure through the fluid-solid coupling interface displacement transfer rules.

[0043] The multi-physics coupling module refers to a functional unit that realizes the interaction between different physical fields. Specifically, the fluid-structure coupling sub-module can be used to transmit force and displacement parameters. For example, bidirectional coupling can be achieved by exchanging boundary conditions between the momentum equation and the structural motion equation.

[0044] The calculation and solution module refers to the functional unit that performs numerical calculations based on Fluent software. Specifically, an improved pressure-velocity coupling algorithm can be used to coordinate the calculation timing. For example, the convergence speed can be improved by optimizing the number of iterations of the pressure correction equation.

[0045] The post-processing and visualization module refers to the functional unit for processing simulation results. Specifically, it can adopt multi-dimensional data fusion technology, for example, through the superposition analysis of streamline diagrams and isosurface diagrams to reveal the correlation between flow field and temperature field.

[0046] The system management module refers to the functional unit that controls the simulation process. Specifically, a graphical interface can be used to integrate the operations of each module. For example, the thermal-fluid-solid coupling parameters of an aircraft engine can be quickly configured through a preset template library.

[0047] Specifically, the data preprocessing module standardizes the geometric models of different physical fields and generates grid files that conform to the Fluent format. The multi-physics field coupling module establishes a mapping relationship between fluid pressure and structural deformation based on preset coupling rules, and transmits boundary parameters through real-time data channels. The computational solution module calls the Fluent solver to perform iterative calculations, and dynamically adjusts the time step of each physical field according to the convergence status. The post-processing and visualization module reconstructs data such as the velocity field and temperature field in three dimensions and generates a comprehensive evaluation report. The system management module displays the operating status of each module through the process monitoring interface and saves key calculation results.

[0048] Compared to existing technologies, this solution achieves flexible solver expansion through a modular architecture, overcoming the limitations of traditional system interfaces, which are often closed. Compared to fixed meshing methods, adaptive meshing technology can automatically refine key areas based on flow gradients, such as implementing local mesh reconstruction in shock wave regions. Compared to traditional hard-coded methods, the standardized API interface design supports rapid integration of third-party solvers through containerized services, such as encapsulating electromagnetic field solvers as independent service components.

[0049] Through the above technical solutions, this application realizes real-time interaction and format compatibility of multi-physics field data, avoiding the accuracy loss caused by traditional system data conversion; the dynamic grid adjustment mechanism optimizes the allocation of computing resources and balances simulation accuracy and computing efficiency; the modular architecture design enhances the system expansion capability and provides customizable solutions for different engineering scenarios.

[0050] The present application further proposes to include a data coupling module, which realizes real-time data synchronization and format mapping between Fluent and other solvers for the calculation and solution module through a real-time data bus and a heterogeneous data conversion unit.

[0051] Among them, the real-time data bus refers to a transmission channel that supports multi-node high-speed communication. Specifically, it can be implemented using a message middleware based on the publish-subscribe model, such as using the DDS protocol or ROS framework to build a distributed communication network. Its function is to eliminate the data transmission delay of the traditional file exchange model and ensure the real-time data exchange between multi-physics solvers. The heterogeneous data conversion unit refers to a conversion device that can parse the data structures of different solvers. Specifically, it can be implemented using a format mapping engine based on XSLT or JSON Schema. By establishing bidirectional conversion rules between Fluent's CAS / DAT file format and the input and output of third-party solvers, the information exchange barriers caused by data format differences are resolved. Real-time data synchronization refers to different solvers maintaining data consistency within the same simulation step. Specifically, it can be achieved through timestamp matching and verification mechanisms. For example, a data buffer queue is set at the coupling interface and an interpolation algorithm is used to compensate for time step differences, thereby avoiding the accumulation of computational errors caused by data asynchrony. Format mapping refers to the process of converting the data structures of different solvers into a unified intermediate format. Specifically, it can be achieved by combining a field mapping table with a unit conversion module. For example, the vector field data of the electromagnetic field solver can be converted into scalar field distribution data that can be recognized by Fluent, ensuring the semantic consistency of data across physical fields.

[0052] When the data coupling module is operational, the real-time data bus first establishes a communication link between the Fluent solver and an external structural mechanics or electromagnetic field solver, for example, through the TCP / IP protocol stack for cross-process data transmission. When the pressure field data generated by the fluid calculation needs to be passed to the structural solver, the heterogeneous data conversion unit converts the pressure distribution data output by Fluent from a control volume-based mesh format to the nodal force load format required by the structural solver, automatically converting units from Pascals to Newtons. In thermal-fluid coupling scenarios, before the temperature field data is transmitted to the thermodynamic solver via the bus, the conversion unit remaps the unstructured mesh data to a structured mesh coordinate system according to preset mapping rules. For coupled calculations with inconsistent time steps, the data synchronization mechanism ensures the continuity of transient data by dynamically adjusting the buffer capacity and applying a cubic spline interpolation algorithm. This entire process eliminates the need for manual file export and format conversion, achieving closed-loop data interaction between multiphysics solvers.

[0053] This application realizes online data interaction between Fluent and third-party solvers, solving the problem of low collaborative efficiency caused by data format differences and transmission delays in traditional systems. In fluid-solid coupling simulation, structural deformation data can be fed back to the fluid domain in real time for mesh update, avoiding distortion of flow field calculations caused by data asynchrony. In the electromagnetic-fluid coupling scenario, magnetic field distribution data is directly applied to the fluid motion equation through automatic format conversion, reducing the risk of errors introduced by manual data processing. At the same time, the standardized data interface design shortens the integration cycle of new solvers, meeting the needs of multi-tool collaboration in complex engineering simulations.

[0054] This application further proposes that mesh division adopts adaptive mesh division unit, dynamically adjusts local mesh density based on physical field gradient changes, and uses error estimation encryption algorithm to achieve mesh reconstruction in key areas;

[0055] An adaptive meshing unit is a computational module that automatically adjusts the mesh distribution based on the gradient changes of physical field variables and error estimates during the computational process. This is achieved through a combination of dynamic mesh reconstruction technology and an error feedback mechanism. By monitoring areas of real-time physical field gradient changes, such as sudden changes in flow velocity or significant temperature gradients, this unit triggers local mesh refinement or thinning, thereby improving computational accuracy in critical areas.

[0056] The error estimation and refining algorithm is a mathematical method for refining the grid based on the current computational error distribution. This method can be implemented using a posteriori error estimation method combined with a grid refinement strategy. By calculating the error metrics of physical quantities within each grid cell, such as velocity residuals or temperature gradient deviations, the algorithm automatically identifies areas requiring refining and reconstructs the grid, ensuring that resources are concentrated in critical areas with large errors.

[0057] During the simulation process, the gradient changes of the physical field variables are first monitored. For example, when the fluid flow shows significant velocity or temperature changes in the boundary layer or near the heat source, the adaptive meshing unit triggers mesh adjustment according to the gradient threshold. Subsequently, the error estimation encryption algorithm evaluates the computational error of the current mesh, for example, by comparing the differences in physical quantities between adjacent mesh units to generate an error distribution map. Based on this distribution map, the meshing unit locally encrypts areas where the error exceeds the preset threshold, such as subdividing the triangular mesh into smaller units, while maintaining or reducing the mesh density in areas with lower errors. Through multiple iterative adjustments, the mesh distribution is gradually optimized, eventually forming a high-density mesh in key areas and reducing unnecessary computational load in non-critical areas.

[0058] This application effectively resolves the contradiction between accuracy and efficiency caused by the rigid meshing of traditional simulation systems. In electronic equipment heat dissipation simulation, areas with drastic temperature gradient changes near heat sources can be automatically identified and encrypted, accurately capturing the heat flux distribution. In aerodynamic noise analysis, areas with significant sound pressure fluctuations are reconstructed through error estimation to avoid loss of acoustic features. While maintaining computational accuracy, this technical solution significantly reduces the number of redundant meshes and improves the overall efficiency of multi-physics field coupling simulations.

[0059] This application further proposes a post-processing and visualization module for unified analysis, visualization output and coupling degree evaluation of multi-field coupling simulation results, including streamline maps, isosurface maps, and three-dimensional model reconstruction functions.

[0060] Unified analysis refers to the correlation integration and cross-validation of simulation data from different physical fields. This can be achieved by using a multi-source data fusion algorithm or a collaborative processing framework to solve the problem of low analysis efficiency caused by data dispersion in traditional methods. Visual output refers to the display of multi-field coupling results through graphical tools such as streamlines and isosurfaces. This can be achieved by using OpenGL or VTK graphics libraries to facilitate intuitive observation of the distribution patterns of physical fields. Coupling degree assessment refers to the quantitative analysis of the interaction strength of multiple fields. This can be achieved by using indicators such as energy exchange rate or the proportion of stress field overlap areas to identify key coupling areas. Three-dimensional model reconstruction refers to the generation of a three-dimensional dynamic model based on simulation data. This can be achieved by using ParaView or Blender rendering engines, supporting multi-perspective observation of the physical field coupling process.

[0061] Specifically, the post-processing and visualization module receives the multi-field coupling simulation results from the computational solution module through the data interface. During the unified analysis process, data such as the fluid field, temperature field, and structural field are synchronously loaded into a unified database, and the time step differences are eliminated through the data alignment algorithm. For example, the interpolation method is used to synchronously match the time series data of different physical fields. In the visualization output stage, streamlines are used to display the distribution of the fluid velocity field, and isosurfaces are used to display temperature gradients or stress concentration areas. The three-dimensional model reconstruction function converts discrete grid data into surface geometry, such as extracting isosurfaces based on the Marching Cubes algorithm and generating a visualization model. The coupling degree is evaluated through preset physical quantity association rules, such as superimposing the spatial distribution of the fluid pressure field and the structural strain field to generate a coupling action thermal map to quantify the interaction intensity.

[0062] The present application further proposes that the data preprocessing module also includes a physical field interaction configuration unit, which establishes the fluid-solid coupling interface displacement transfer rules and the heat flux density threshold of the heat-fluid coupling interface through a parameterized boundary condition definition tool.

[0063] The physical field interaction configuration unit refers to a logical component used to define the interaction rules between different physical fields. Specifically, it can be implemented using an interface data mapping algorithm based on a mathematical model. By setting displacement transfer functions and heat flux density constraints, data interaction between fluids and solids, and between thermal fields and flow fields can be achieved.

[0064] The parameterized boundary condition definition tool is a visual interface that allows users to set physical field interaction rules through configurable parameters. This can be achieved by combining graphical drag-and-drop components with numerical input boxes, supporting dynamic adjustment of the interface displacement transfer coefficient and heat flux density critical value.

[0065] In fluid-solid coupling scenarios, the physics interaction configuration unit uses parameterized tools to establish interface displacement transfer functions. For example, a linear interpolation algorithm is used to convert fluid domain pressure loads into displacement constraints for solid boundary nodes. In thermal-fluid coupling scenarios, the configuration unit automatically determines the heat flux transfer path based on a preset heat flux density threshold, such as through a thermal resistance network model. When the interface heat flux exceeds the set value, a local mesh refinement mechanism is triggered. This unit connects to the multiphysics coupling module through a standardized data interface, ensuring that boundary conditions between different solvers are updated synchronously.

[0066] Through the above technical solution, this application effectively improves the accuracy of interface data transmission in multi-physics field coupling simulation, supports the automated configuration requirements of complex fluid-solid coupling deformation analysis and thermal-fluid coupling heat transfer optimization, and at the same time reduces the operational complexity of multi-physics field coupling simulation through parametric tools.

[0067] This application further proposes a multi-physics field coupling Fluent simulation analysis integrated system, which adopts a modular architecture. Each module communicates with the main control program through a standardized API interface, supporting plug-and-play integration of third-party solvers; the API interface is defined based on the RESTful protocol, allowing third-party solvers to be encapsulated as dynamic link libraries or containerized services through SDK.

[0068] A modular architecture divides system functionality into independent modules, with each module implementing data interaction through interface specifications. This can be achieved using a layered design pattern, for example, by decoupling functional modules such as data preprocessing and computational solution, and scheduling the operation of each module through a master control program, thereby improving the flexibility and scalability of the system. A standardized API interface refers to a program interface that follows a unified communication protocol and parameter specification. Specifically, data exchange standards can be defined in JSON or XML format. For example, by defining the data structure and calling method of input and output parameters, seamless integration between the master control program and each module can be achieved. The RESTful protocol is a lightweight interface specification based on the HTTP protocol. Specifically, cross-platform communication can be achieved through request methods such as GET and POST. For example, the master control program sends computation instructions to a third-party solver through a RESTful interface and receives return results. SDK encapsulation as a dynamic link library or containerized service refers to encapsulating the functions of a third-party solver into an independently deployable software package. Specifically, this can be achieved using Docker container technology or a dynamic link library (DLL) file. For example, the computational logic of a third-party solver can be encapsulated as a containerized service through an SDK toolkit, enabling it to interact with the master control program through an API interface.

[0069] The modular architecture connects the main control program to each functional module through standardized API interfaces, enabling independent development and deployment of functional modules. The main control program sends calculation requests to third-party solvers via the RESTful protocol. After being encapsulated as a dynamic link library or containerized service via the SDK, the third-party solver can directly respond to the main control program's call instructions and return calculation results. For example, when integrating an electromagnetic field solver, simply encapsulate the solver's core algorithm as a Docker container and communicate with the main control program via the RESTful interface, completing functional expansion without modifying the original system architecture.

[0070] This application further proposes a built-in simulation process template library and automated scripting engine within the system management module. The simulation process template library pre-sets standardized parameter configurations for aircraft engine thermal-fluid-solid coupling and battery thermal-fluid-electric coupling scenarios; the automated scripting engine supports Python scripts to extend multi-field coupling logic and customize physical quantity mapping rules.

[0071] A simulation process template library is a database that stores preset parameter configurations for typical engineering scenarios. This library can be implemented using XML or JSON-based configuration files to reduce repetitive parameter settings. For aircraft engine thermal-fluid-solid coupled scenarios, this template library can preset combustion chamber wall thermal conductivity ranges, such as a 5-50 W / (m·K) thermal conductivity parameter range. For battery thermal-fluid-electric coupled scenarios, it can also configure reference values for electrode material expansion coefficients.

[0072] An automated scripting engine is an execution environment that supports dynamic loading of user-defined logic. This can be achieved by integrating a Jython or CPython interpreter, allowing field coupling relationships to be defined through Python scripts. The engine can call system APIs to register custom electromagnetic-thermal coupling mapping rules, for example, to pass Lorentz force calculation results to the thermodynamic solver in real time.

[0073] During the thermal-fluid-solid coupling analysis of aircraft engines, the simulation process template library automatically loads the turbine blade material parameters and aerodynamic load boundary conditions, and simultaneously calls the adaptive mesh algorithm to generate the initial calculation domain. The automated script engine executes the user-written unsteady-state coupling control script and dynamically adjusts the time step synchronization strategy between the flow field and the structural field. In the battery pack thermal management simulation, the template library provides preset values for the electrolyte flow boundary conditions, and the script engine can expand the electro-thermal coupling logic in thermal runaway scenarios, for example, defining the automatic triggering of the short-circuit protection mechanism when the temperature exceeds 80°C.

[0074] This application further proposes that the data preprocessing module adopts an adaptive grid partitioning algorithm based on error evaluation, and its core formula is:

[0075]

[0076] Among them, h new and h old Represent the new and old grid step sizes respectively, ε is the preset error threshold, η is the calculation error of the current grid, and n is the adaptive adjustment index.

[0077] Among them, h new and h old It refers to the old and new grid step sizes, which can be achieved by the step size difference between two adjacent grid divisions during the grid iterative calculation process. It is used to describe the local density changes before and after the grid adjustment.

[0078] Here, ε refers to a preset error threshold, which can be implemented by a numerical range pre-set by the user according to the simulation accuracy requirements, and is used to control the triggering conditions for grid adjustment.

[0079] Here, η refers to the calculation error of the current mesh, which can be achieved by physical field gradient data obtained through finite element analysis or residual calculation, and is used to quantify the impact of the current mesh division on the simulation results.

[0080] Here, n refers to the adaptive adjustment index, which can be implemented by a positive integer parameter calibrated by experiments, and is used to control the nonlinear relationship between the amplitude of the grid step adjustment and the error change.

[0081] Specifically, during the simulation calculation process, the system monitors the calculation error η of the current grid in real time and compares it with the preset error threshold ε. When η exceeds ε, according to the formula Dynamically adjust the local mesh step size. For example, in high-gradient regions near the fluid-structure interface, when the error η increases significantly, the algorithm will reduce the mesh step size to increase local mesh density. In areas where the physical field changes more slowly, the mesh step size is increased to reduce redundant calculations. This cyclical execution of error evaluation and step size adjustment ultimately achieves mesh reconstruction in critical areas and optimizes computational resource allocation.

[0082] This application further proposes a multi-field interaction model for the multi-physics coupling module, specifically including the following formulas: In fluid-structure coupling, the fluid momentum equation and the structural motion equation exchange force and displacement information through boundary conditions;

[0083] F fluid =ρ f ∫ A (v f -v s )·ndA

[0084] Among them, F fluid is the force exerted by the fluid on the structure, ρ f is the fluid density, v f and v S are the velocities of the fluid and structure respectively, and n is the normal unit vector.

[0085] The interaction between the heat conduction equation and the mechanical response equation in thermal-mechanical coupling through temperature and stress;

[0086]

[0087] Where q is the heat flow rate, k is the thermal conductivity, T is the temperature, ρ and c p are density and specific heat capacity, respectively.

[0088] Interaction between electromagnetic force and fluid force in electromagnetic-fluid coupling;

[0089]

[0090] Among them, F em is the electromagnetic force density, σ is the electrical conductivity, H and B are the magnetic field intensity and magnetic induction intensity respectively.

[0091] The force transfer equation for fluid-structure coupling establishes a correlation between the force exerted by the fluid on the structure and the velocity field, enabling bidirectional data exchange between fluid pressure and solid deformation. Specifically, this involves using an integral calculation at the fluid-solid interface to convert the pressure distribution in the fluid domain into a load input for the structural domain.

[0092] The interaction equation for thermal-mechanical coupling constructs a bidirectional coupling relationship between the temperature field and the stress field. The finite element method can be used to solve the thermoelastic equation, using the thermal expansion caused by the temperature gradient as the boundary condition for the mechanical analysis.

[0093] The electromagnetic force density equation for electromagnetic-fluid coupling embeds the Lorentz force generated by the electromagnetic field into the governing fluid equations by linking the Maxwell stress tensor with the conservation of fluid momentum. Specifically, the electromagnetic force term is discretized using the finite volume method and then added to the source term of the fluid momentum equation.

[0094] During fluid-structure coupling, the pressure distribution in the fluid domain is transferred to the structural domain through the fluid-solid interface, and the deformed geometry of the structure is used to inversely update the fluid mesh. Thermal-mechanical coupling employs a step-by-step iterative approach, first solving for the temperature field distribution and then calculating the thermal stresses. Iterative convergence is controlled by setting a relaxation factor. Electromagnetic-fluid coupling achieves bidirectional coupling by adding an electromagnetic force source term to the momentum equation, and an implicit algorithm is used to synchronously update the electromagnetic and flow field parameters.

[0095] Compared with existing technologies, traditional methods typically rely on one-way data transfer or weak coupling, enabling only simple parameter transfer between physical fields. This solution establishes rigorous bidirectional coupling equations to achieve precise conservation of energy and mass across multiple physical fields, overcoming the error accumulation problem caused by data asynchrony in traditional methods.

[0096] Through the above technical solution, this application can accurately describe the interaction mechanism between electromagnetic fields and flow fields, avoiding the traditional method of ignoring the influence of electromagnetic forces on flow characteristics. In scenarios involving electromagnetic-thermal-fluid coupling, such as battery thermal management systems, it can accurately predict the impact of electrode surface current density distribution on coolant flow patterns.

[0097] This application further proposes that the calculation solution module adopts an improved pressure-velocity coupling algorithm, the core formula of which is as follows:

[0098]

[0099] Where ρ is density, v is velocity vector, P is pressure, μ is dynamic viscosity, F ext For external force.

[0100] Among them, the improved pressure-velocity coupling algorithm refers to a numerical calculation method for solving the fluid pressure field and velocity field by coupling the continuity equation and the momentum equation. Specifically, it can be implemented using an iterative solver based on the finite volume method, and dynamic response to complex flow problems can be achieved by introducing external force terms.

[0101] Among them, external forces refer to volume forces other than pressure gradient and viscous force. They can be implemented through user-defined functions or external data interface inputs to characterize electromagnetic forces, gravity, or other multi-physics field coupling effects.

[0102] Specifically, the improved pressure-velocity coupling algorithm achieves synchronous updates of the pressure and velocity fields by alternately iteratively solving the momentum equation and the pressure correction equation. During each iteration, external force terms are dynamically introduced into the momentum equation to ensure real-time transmission of external coupling forces such as electromagnetic or structural fields. The pressure correction equation adjusts the pressure distribution using mass conservation conditions and performs feedback corrections with the velocity field. An implicit time discretization scheme is used to process transient terms during the calculation process, and the iterative convergence rate is controlled by a relaxation factor.

[0103] Through the above technical solution, this application can effectively solve the problem of low pressure-velocity field iteration efficiency during multi-physics field coupling, reducing the convergence difficulty of solving nonlinear equations. By dynamically integrating external force terms, it ensures real-time data synchronization between the flow field and other physical fields such as electromagnetic fields and structural fields, improving the calculation accuracy and efficiency in complex coupling scenarios.

[0104] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A multi-physics field coupling Fluent simulation analysis integrated system, characterized in that: The system includes the following modules: Data preprocessing module: used for data integration, meshing and boundary condition setting for multi-physics field coupling problems; Multi-physics coupling module: includes fluid-structure coupling submodule, thermal-mechanical coupling submodule, electromagnetic-fluid coupling submodule, etc., which respectively realize the interaction simulation between different physical fields; Computational solution module: Based on the Fluent software platform, it performs computational solutions to multi-physics coupling problems and coordinates the calculation order and time step of each physical field; Post-processing and visualization module: used for post-processing analysis and visualization of simulation results; System management module: provides a user interface to configure, monitor and store the entire simulation results.

2. A multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: It also includes a data coupling module, which realizes real-time data synchronization and format mapping between Fluent and other solvers for the calculation and solution module through a real-time data bus and a heterogeneous data conversion unit.

3. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The grid division adopts adaptive grid division unit, dynamically adjusts the local grid density based on the gradient change of the physical field, and uses the error estimation encryption algorithm to realize the grid reconstruction of the key area.

4. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The post-processing and visualization module is used for unified analysis, visualization output and coupling degree evaluation of multi-field coupling simulation results, including streamline diagrams, isosurface diagrams, and 3D model reconstruction functions.

5. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The data preprocessing module also includes a physical field interaction configuration unit, which establishes the fluid-solid coupling interface displacement transfer rules and the heat flux density threshold of the heat-fluid coupling interface through parameterized boundary condition definition tools.

6. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The system adopts a modular architecture, and each module communicates with the main control program through a standardized API interface, supporting plug-and-play integration of third-party solvers; the API interface is defined based on the RESTful protocol, allowing third-party solvers to be encapsulated as dynamic link libraries or containerized services through the SDK.

7. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The system management module has built-in: A simulation process template library with pre-set standardized parameter configurations for aircraft engine thermal-fluid-solid coupling and battery thermal-fluid-electric coupling scenarios; The automated scripting engine supports Python scripts to extend multi-field coupling logic and customize physical quantity mapping rules.

8. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 3, characterized in that: The data preprocessing module adopts an error-based adaptive meshing algorithm (Error-Based AdaptiveMeshing), and its core formula is: Among them, h new and h old Represent the new and old grid step sizes respectively, ε is the preset error threshold, η is the calculation error of the current grid, and n is the adaptive adjustment index.

9. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The multi-physics field coupling module adopts a multi-field interaction model, which specifically includes the following formula: Fluid-structure coupling: The fluid momentum equation and the structural motion equation exchange force and displacement information through boundary conditions; F fluid =ρ f ∫ A (v f -v s )·ndA Among them, F fluid is the force exerted by the fluid on the structure, ρ f is the fluid density, v f and v S are the velocities of the fluid and structure respectively, and n is the normal unit vector. Thermal-mechanical coupling: the interaction between the heat conduction equation and the mechanical response equation through temperature and stress; Where q is the heat flow rate, k is the thermal conductivity, T is the temperature, ρ and c p are density and specific heat capacity, respectively. Electromagnetic-fluid coupling: interaction between electromagnetic forces and fluid forces; Among them, F em is the electromagnetic force density, σ is the electrical conductivity, H and B are the magnetic field intensity and magnetic induction intensity respectively.

10. The multi-physics field coupling Fluent simulation analysis integrated system according to claim 1, characterized in that: The calculation and solution module adopts the improved pressure-velocity coupling algorithm (iPVCA), and its core formula is as follows: Where ρ is density, v is velocity vector, P is pressure, μ is dynamic viscosity, F ext For external force.

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