Multi-physics field coupled parameterized model establishment method

Abstract

The invention relates to the technical field of multi-physics coupling, in particular to a multi-physics field coupling parameterized model establishment method, which comprises the following steps of: establishing a basic equation of each physics field through a physical law and a mathematical principle, and embedding coupling parameters in the equation to describe an interaction relationship among the multiple physics fields to form a coupling equation set; further adopting a finite element method or a finite difference method to solve the model, improving the calculation precision of an interface area through an adaptive grid encryption technology, and combining sensitivity analysis, a gradient descent method or a genetic algorithm to carry out iterative optimization on key parameters; the accuracy of the model is verified through comparison of an experiment and a simulation result, and if a significant difference exists, nonlinear coupling parameters are dynamically adjusted, so that calibration and reliability improvement of the model are realized. Through the reasonable method and steps, an accurate and reliable parameterized model with practical application value is established, so that the complex multi-physics field coupling problem is better understood and solved, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-physics coupling, and specifically, to a method for establishing a parametric model of multi-physical field coupling. Background Art

[0002] Multi-physical fields refer to processes or systems in which multiple physical fields occur simultaneously, as well as the study of such processes and systems. As an interdisciplinary research field, multi-physical fields cover many disciplines in science and engineering, and are an applied topic that combines mathematics, physics, scientific and engineering applications, and numerical analysis. Among them, mathematics usually involves partial differential equations and tensor analysis, while physics refers to common types of physical fields or physical processes. The applications of multi-physical fields involve one or more physical processes or physical fields.

[0003] Parameterization is a necessary means for dealing with complex sub-processes in multi-physical systems and a key technology in modeling and simulation. Its core is to replace the detailed description of complex physical processes through simplified mathematical expressions or empirical relationships. By establishing a parametric model, while ensuring the reasonable accuracy of the model, the computational complexity of multi-physical systems is significantly reduced, and the efficiency is improved. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for establishing a parametric model of multi-physical field coupling to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides a method for establishing a parametric model of multi-physical field coupling, including:

[0006] 1) According to physical laws and mathematical principles, establish basic equations describing each physical field;

[0007] 2) Introduce coupling parameters into the basic equations to establish a system of equations for multi-physical field coupling, where these coupling parameters are used to describe the interaction relationships between different physical fields;

[0008] 3) Use solution methods such as the finite element method to reasonably simplify and assume the parameters;

[0009] 4) Verify and calibrate the model by comparing experimental results with simulation results to ensure the accuracy and reliability of the model. If there are significant differences between the simulation results and the experimental results, train and optimize the model.

[0010] In the step 2), the coupling parameters include but are not limited to the coefficient of thermal expansion, acoustic static flow resistivity, pore fluid parameters, or thermal conductivity parameters, and the coupling parameters are embedded into the control equations of different physical fields in the form of tensors.

[0011] In the specific implementation of the finite element method in step 3), it includes establishing a geometric model, setting material properties, dividing finite element meshes, applying boundary conditions, and setting solver parameters. Among them, adaptive mesh refinement technology is used for mesh division to improve the solution accuracy in the interface region.

[0012] In the model training and optimization in step 4), it includes using sensitivity analysis methods to determine key coupling parameters, and combining gradient descent methods or genetic algorithms to iteratively optimize the parameters until the error between the simulation results and the experimental data is less than a preset threshold.

[0013] When the coupling parameters are set as non-linear parameters that vary with the temperature field, stress field, or electromagnetic field intensity, the dynamic adjustment of the parameters is achieved by establishing a parameter interpolation function table or fitting an empirical formula.

[0014] In step 1), the basic equations include at least two or more of Fourier's law in the field of thermodynamics, the Naiver-Cauchy equation in the field of solid mechanics, and Maxwell's equations in the field of electromagnetics, and the equations are bidirectionally coupled through shared field variables.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0016] 1. Using the established parametric model for simulation and analysis to predict the behaviors and characteristics of multi-physical field coupling phenomena. This can provide a theoretical basis and technical support for solving practical engineering problems. For example, in the design of electronic devices, the multi-physical field coupling model can be used to optimize the performance and reliability of the devices; in structural mechanics analysis, the multi-physical field coupling model can be used to predict the stress and deformation of structures.

[0017] 2. By reasonable methods and steps, establishing an accurate, reliable, and practically applicable parametric model of multi-physical field coupling can better improve the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the process steps of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] In this embodiment, based on physical laws and mathematical principles, basic equations describing various physical fields are established. First, the types of physical fields and relevant conservation laws need to be clarified. For different physical fields, their core conservation laws are selected. For example, for the thermodynamic field, we usually choose the law of conservation of energy; for the solid mechanics field, we choose the law of conservation of momentum; and for the electromagnetic field, its evolution is described by Maxwell's equations.

[0021] Furthermore, for the thermodynamic field, we usually use the mathematical expression of Fourier's law, and for the solid mechanics field, we usually use the mathematical expression of the Naiver-Cauchy equation.

[0022] Furthermore, the equations are bidirectionally coupled through shared field variables.

[0023] The equations of different physical fields are linked through coupling parameters to form a system of equations for multi-physical field coupling. The coupling parameters are used to describe the interaction relationships between different physical fields.

[0024] Furthermore, the coupling parameters are set as non-linear parameters that vary with the temperature field, stress field, or electromagnetic field intensity, and the dynamic adjustment of the parameters is achieved by establishing a parameter interpolation function table or fitting an empirical formula.

[0025] Furthermore, the coupling parameters include, but are not limited to, the coefficient of thermal expansion, acoustic static flow resistivity, pore fluid parameters, or thermal conductivity parameters. Among them, thermo-mechanical coupling forms the thermal expansion effect, and acoustic-solid coupling forms the interaction between the sound field and the structure to achieve acoustic-solid coupling, and the structural deformation affects the distribution of the sound pressure level.

[0026] Furthermore, the equations are simplified according to the characteristics of the problem, and generally the finite element method is used to make reasonable simplifications and assumptions for the parameters.

[0027] The specific implementation of the finite element method includes establishing a geometric model, setting material properties, dividing the finite element mesh, applying boundary conditions, and setting solver parameters. Among them, adaptive mesh refinement technology is used for mesh division to improve the solution accuracy in the interface region.

[0028] Furthermore, establishing the geometric model specifically includes:

[0029] Create a three-dimensional geometric model of a target object through computer-aided software. In this process, it is necessary to ensure that the details and features of the model are accurately represented to obtain reliable results in subsequent finite element analysis.

[0030] Based on the three-dimensional geometric model of the target object, construct a finite element model of the target object. The finite element model consists of multiple interconnected elements and nodes, and each element has specific material properties and geometric shapes.

[0031] Furthermore, the acquisition of the geometric data of the above target object can be achieved through various methods, such as direct measurement, scanning, CAD software export, etc.;

[0032] In addition, CAD software or specialized modeling software is generally used for model construction.

[0033] Furthermore, boundary determination and mesh division specifically include:

[0034] Establish boundary conditions: Boundary conditions refer to the interaction methods between the model and the external environment, such as fixed constraints, load application, etc. These conditions need to be set according to the actual physical situation;

[0035] Mesh division: Mesh division is the process of discretizing a continuous three-dimensional geometric model into a finite number of elements and nodes. The quality and density of the mesh directly affect the accuracy of the analysis results and the calculation efficiency.

[0036] Furthermore, parametric design allows users to quickly generate different models and analysis scenarios by changing certain key parameters. In finite element analysis, some structural dimensions and material properties can be parameterized to study the impact of these parameter changes on the analysis results.

[0037] Furthermore, the model is verified and calibrated by comparing the experimental results with the simulation results to ensure the accuracy and reliability of the model. If there are significant differences between the simulation results and the experimental results, the model is trained and optimized.

[0038] Furthermore, model training and optimization include using sensitivity analysis methods to determine key coupling parameters, and combining gradient descent methods or genetic algorithms to iteratively optimize the parameters until the error between the simulation results and the experimental data is less than a preset threshold;

[0039] Furthermore, sensitivity analysis and genetic algorithms are illustrated with the example of a structural dynamics geometric model:

[0040] Preferably, by evaluating the sensitivity of experimental data (such as modal frequencies, vibration modes) to unknown parameters, and using experimental modal data to inversely deduce the actual parameters of the structure, the effect of locating the damaged area can be achieved in the field of damage detection;

[0041] Preferably, by finding out the parameter variables that are sensitive to the modal frequencies, the fundamental frequency of the target model is matched with the fundamental frequency of the modal test through optimization iteration.

[0042] This solution validates and calibrates the model by comparing the experimental results with the simulation results, and dynamically adjusts the finite element mesh. This dynamic adjustment strategy helps to improve the calculation efficiency while maintaining the calculation accuracy. Using the established parametric model for simulation and analysis to predict the behavior and characteristics of multi-physical field coupling phenomena can provide a theoretical basis and technical support for solving practical engineering problems.

[0043] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for establishing a parametric model of multi-physical field coupling, characterized in that It includes the following steps: 1) Establish the basic equations describing each physical field according to physical laws and mathematical principles; 2) Introduce coupling parameters into the basic equations to establish a system of equations for multi-physical-field coupling, where these coupling parameters are used to describe the interaction relationships between different physical fields; 3) Use solution methods such as the finite element method to reasonably simplify and make assumptions about the parameters; 4) Verify and calibrate the model by comparing experimental results with simulation results to ensure the accuracy and reliability of the model. If there are significant differences between the simulation results and the experimental results, train and optimize the model.

2. The method for establishing a multi-physical field coupling parameterization model according to claim 1, characterized in that: In step 2), the coupling parameters include but are not limited to the thermal expansion coefficient, acoustic static flow resistivity, pore fluid parameters, or thermal conductivity parameters, and the coupling parameters are embedded into the control equations of different physical fields in the form of tensors.

3. A method for establishing a multi-physical-field coupling parametric model according to claim 1, characterized in that: The specific implementation of the finite element method in step 3) includes establishing a geometric model, setting material properties, dividing finite element meshes, applying boundary conditions, and setting solver parameters and outputs, where adaptive mesh refinement technology is used for mesh division to improve the solution accuracy in the interface region.

4. A method for establishing a multi-physical field coupling parameterization model according to claim 1, characterized in that: The model training and optimization in step 4) include using sensitivity analysis methods to determine key coupling parameters, and iteratively optimizing the parameters in combination with the gradient descent method or genetic algorithm until the error between the simulation results and the experimental data is less than a preset threshold.

5. A method for establishing a multi-physical field coupling parameterized model according to claim 2, characterized in that: The coupling parameters are set as non-linear parameters that vary with the temperature field, stress field, or sound field intensity, and the dynamic adjustment of the parameters is achieved by establishing a parameter interpolation function table or fitting an empirical formula.

6. A method for establishing a multi-physical field coupling parametric model according to claim 1, characterized in that: In step 1), the basic equations include at least two or more of Fourier's law in the field of thermodynamics, the Naiver-Cauchy equation in the field of solid mechanics, and the wave equation of sound in the field of acoustics, and the equations are bidirectionally coupled through shared field variables.

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