Method and device for calculating resistance coefficient of porous medium, medium and equipment

Abstract

The application discloses a porous medium resistance coefficient calculation method, device, medium and equipment. The method comprises the following steps: constructing a three-dimensional geometric model of a target device, determining a simulation area of the porous medium in the three-dimensional geometric model, performing a simulation experiment on the target device, and obtaining an import and export experimental pressure difference; constructing an optimization model based on the import and export experimental pressure difference and an import and export simulation pressure difference, wherein the import and export simulation pressure difference is obtained by performing fluid simulation calculation on the three-dimensional geometric model based on a CFD control equation, a continuity equation, a CFD momentum conservation equation, a viscous resistance coefficient and an inertial resistance coefficient; taking the optimization model as a target function in a population algorithm, and taking a position variable corresponding to an individual in the population algorithm as a multidirectional viscous resistance coefficient and a multidirectional inertial resistance coefficient; and solving the optimization model based on the population algorithm to obtain an optimal viscous resistance coefficient and an optimal inertial resistance coefficient. The application improves the accuracy of inertial resistance coefficient and viscous resistance coefficient identification.

Description

Technical Field

[0001] This invention relates to the field of resistance coefficient measurement technology, and in particular to a method, apparatus, medium and equipment for calculating the resistance coefficient of porous media. Background Technology

[0002] Porous media, due to their unique pore structure and excellent heat transfer, filtration, and reaction properties, are widely used in various thermal equipment, such as industrial dryers, catalytic reactors, and heat exchange systems. In these applications, porous media typically function as functional fillers. When fluid flows through their complex internal channels, significant pressure losses occur, primarily caused by the combined effects of viscous and inertial drag. Accurately understanding the drag characteristics of porous media is crucial for optimizing equipment performance, reducing energy consumption, and ensuring operational stability.

[0003] Currently, obtaining the resistance characteristics of porous media mainly relies on empirical estimation or repeated experimental parameter tuning. Empirical estimation usually determines the resistance coefficient by looking up tables or applying semi-empirical correlations based on the media type and porosity, while experimental methods use pressure drop-flow rate relationships measured and back-calculated using the Darcy-Forchheimer model.

[0004] However, empirical estimation methods have poor adaptability and cannot reflect the diversity of the microstructure of actual media; repeated experimental parameter adjustment methods are time-consuming, costly, and difficult to dynamically respond to changes in working conditions. Therefore, there is an urgent need for an accurate and engineering-applicable method for identifying porous media parameters. Summary of the Invention

[0005] In view of this, the present invention provides a method, apparatus, medium and equipment for calculating the resistance coefficient of porous media, the main purpose of which is to solve the problem of low accuracy of current porous media parameter identification methods.

[0006] According to one aspect of this application, a method for calculating the drag coefficient of a porous medium is provided, the method comprising:

[0007] A three-dimensional geometric model of the target device is constructed, the simulation region of the porous medium in the three-dimensional geometric model is determined, and the CFD control equation, continuity equation and CFD momentum conservation equation of the simulation region are constructed. The drag source term is added to the CFD momentum conservation equation.

[0008] A simulation experiment was conducted on the target equipment according to the preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment.

[0009] An optimization model is constructed based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference. The inlet and outlet simulated pressure difference is obtained by performing fluid simulation calculations on the three-dimensional geometric model based on the CFD control equation, the continuity equation, the CFD momentum conservation equation, the multi-directional viscous drag coefficient, the multi-directional inertial drag coefficient, and preset experimental parameters.

[0010] The optimization model is used as the objective function in the population algorithm, where the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient.

[0011] The optimization model is solved using the population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient.

[0012] Optionally, the step of solving the optimization model based on the population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient includes:

[0013] An initial population is randomly generated, and the position variable corresponding to each individual in the initial population is input into the three-dimensional geometric model.

[0014] Based on preset experimental parameters, positional variables corresponding to each individual, the CFD control equations, the continuity equation, and the CFD momentum conservation equation, fluid simulation is performed on the three-dimensional geometric model to obtain the simulated inlet and outlet pressure differences of the three-dimensional geometric model for each individual:

[0015] Substitute the simulated pressure difference between inlet and outlet and the experimental pressure difference between inlet and outlet for each individual into the objective function to obtain the fitness value for each individual. Based on the fitness value for each individual, determine the minimum fitness value.

[0016] The population is evolved to obtain evolved individuals. Based on the evolved individuals, the fitness value corresponding to the evolved individuals is obtained. The minimum fitness value after evolution is determined. The difference between the minimum fitness values ​​of two adjacent rounds is calculated. When the difference between the minimum fitness values ​​of two adjacent rounds is less than a set threshold or the number of iterations reaches a preset upper limit, the position variable of the individual corresponding to the minimum fitness value of the last round is taken as the optimal multidirectional viscous drag coefficient and multidirectional inertial drag coefficient.

[0017] Optionally, constructing a three-dimensional geometric model of the target device and determining the simulation region of the porous medium in the three-dimensional geometric model includes:

[0018] A three-dimensional geometric model of the target device is constructed, wherein the three-dimensional geometric model includes an inner cavity, an outer shell, inlet and outlet channels, and a filling region, wherein the filling region is a simulated region of porous media in the three-dimensional geometric model;

[0019] The three-dimensional geometric model is meshed and boundary conditions are set, wherein the boundary conditions include inlet boundary, outlet boundary, wall boundary and porous medium region.

[0020] Optionally, the optimization model is:

[0021]

[0022] in, Let X be the simulated pressure difference between the inlet and outlet in the i-th simulation experiment, and let X be the matrix composed of the multidirectional viscous drag coefficient and the multidirectional inertial drag coefficient. Let N be the pressure difference between the inlet and outlet in the i-th simulation experiment, and N be the number of simulation experiments.

[0023] Optionally, the CFD momentum conservation equation is:

[0024]

[0025] in, γ Porosity For dynamic viscosity, Let p be the density and p be the pressure. τ Let v be the shear stress introduced by fluid molecules due to viscosity, v be the velocity, and g be the acceleration due to gravity. S External volume force;

[0026]

[0027] in, Let represent the external volume force of viscous drag in the k-th direction, where k=1 represents the x-direction, k=2 represents the y-direction, and k=3 represents the z-direction. Let j represent the inertial drag in the j-th direction, where j=1 represents the x-direction, j=2 represents the y-direction, and j=3 represents the z-direction. For absolute speed, Let be the coefficient of viscous drag in the k-th direction and inertial drag in the j-th direction. Let be the coefficient of inertial drag in the j-th direction and viscous drag in the k-th direction. Let be the velocity in the j-th direction of the inertial drag.

[0028] Optionally, the process of population evolution includes:

[0029] Individuals are selected using a roulette wheel selection mechanism, offspring are generated using a two-point crossover strategy, and the mutation probability is dynamically adjusted through adaptive mutation operations.

[0030] According to another aspect of this application, a device for calculating the drag coefficient of porous media is provided, comprising:

[0031] The three-dimensional model construction module is used to construct a three-dimensional geometric model of the target device, determine the simulation region of the porous medium in the three-dimensional geometric model, and construct the CFD control equation, continuity equation, and CFD momentum conservation equation of the simulation region. The CFD momentum conservation equation includes a drag source term.

[0032] The simulation experiment module is used to conduct a simulation experiment on the target equipment according to preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment.

[0033] The optimization model construction module is used to construct an optimization model based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference. The inlet and outlet simulated pressure difference is obtained by performing fluid simulation calculations on the three-dimensional geometric model based on the CFD control equation, the continuity equation, the CFD momentum conservation equation, the multi-directional viscous drag coefficient, the multi-directional inertial drag coefficient, and preset experimental parameters.

[0034] The objective function determination module is used to use the optimization model as the objective function in the population algorithm, wherein the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient.

[0035] The solution module is used to solve the optimization model based on the population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient.

[0036] Optionally, the solution module is further configured to:

[0037] An initial population is randomly generated, and the position variable corresponding to each individual in the initial population is input into the three-dimensional geometric model.

[0038] Based on preset experimental parameters, positional variables corresponding to each individual, the CFD control equations, the continuity equation, and the CFD momentum conservation equation, fluid simulation is performed on the three-dimensional geometric model to obtain the simulated inlet and outlet pressure differences of the three-dimensional geometric model for each individual:

[0039] Substitute the simulated pressure difference between inlet and outlet and the experimental pressure difference between inlet and outlet for each individual into the objective function to obtain the fitness value for each individual. Based on the fitness value for each individual, determine the minimum fitness value.

[0040] The population is evolved to obtain evolved individuals. Based on the evolved individuals, the fitness value corresponding to the evolved individuals is obtained. The minimum fitness value after evolution is determined. The difference between the minimum fitness values ​​of two adjacent rounds is calculated. When the difference between the minimum fitness values ​​of two adjacent rounds is less than a set threshold or the number of iterations reaches a preset upper limit, the position variable of the individual corresponding to the minimum fitness value of the last round is taken as the optimal multidirectional viscous drag coefficient and multidirectional inertial drag coefficient.

[0041] Optionally, the 3D model building module is further used for:

[0042] A three-dimensional geometric model of the target device is constructed, wherein the three-dimensional geometric model includes an inner cavity, an outer shell, inlet and outlet channels, and a filling region, wherein the filling region is a simulated region of porous media in the three-dimensional geometric model;

[0043] The three-dimensional geometric model is meshed and boundary conditions are set, wherein the boundary conditions include inlet boundary, outlet boundary, wall boundary and porous medium region.

[0044] Optionally, the optimization model is:

[0045]

[0046] in, Let X be the simulated pressure difference between the inlet and outlet in the i-th simulation experiment, and let X be the matrix composed of the multidirectional viscous drag coefficient and the multidirectional inertial drag coefficient. Let N be the pressure difference between the inlet and outlet in the i-th simulation experiment, and N be the number of simulation experiments.

[0047] Optionally, the CFD momentum conservation equation is:

[0048]

[0049] in, γ Porosity For dynamic viscosity, Let p be the density and p be the pressure. τ Let v be the shear stress introduced by fluid molecules due to viscosity, v be the velocity, and g be the acceleration due to gravity. S External volume force;

[0050]

[0051] in, Let represent the external volume force of viscous drag in the k-th direction, where k=1 represents the x-direction, k=2 represents the y-direction, and k=3 represents the z-direction. Let j represent the inertial drag in the j-th direction, where j=1 represents the x-direction, j=2 represents the y-direction, and j=3 represents the z-direction. For absolute speed, Let be the coefficient of viscous drag in the k-th direction and inertial drag in the j-th direction. Let be the coefficient of inertial drag in the j-th direction and viscous drag in the k-th direction. Let be the velocity in the j-th direction of the inertial drag.

[0052] Optionally, the solution module is further configured to:

[0053] Individuals are selected using a roulette wheel selection mechanism, offspring are generated using a two-point crossover strategy, and the mutation probability is dynamically adjusted through adaptive mutation operations.

[0054] According to another aspect of this application, a storage medium is provided that stores at least one executable instruction, which causes a processor to perform an operation corresponding to the above-described method for calculating the drag coefficient of a porous medium.

[0055] According to another aspect of this application, a computer device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0056] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described method for calculating the resistance coefficient of porous media.

[0057] By employing the above-described technical solutions, the technical solutions provided by the embodiments of the present invention have at least the following advantages:

[0058] This application provides a method, apparatus, equipment, and medium for calculating the drag coefficient of porous media. The method involves determining the simulation region of the porous media in a three-dimensional geometric model, constructing the CFD governing equation, continuity equation, and CFD momentum conservation equation for the simulation region, incorporating a drag source term into the CFD momentum conservation equation, constructing an optimization model, using the optimization model as the objective function in a population algorithm, and using the multi-directional viscous drag coefficient and multi-directional inertial drag coefficient as the position variables corresponding to individuals in the population algorithm. A simulation experiment is conducted on the target equipment to obtain the inlet and outlet experimental pressure difference. Based on the position variables corresponding to individuals in the population algorithm, the CFD governing equation, continuity equation, and CFD momentum conservation equation, the three-dimensional geometric model is simulated to obtain the simulated inlet and outlet pressure difference. The fitness value in the population algorithm is calculated based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference. The objective function is solved using the population algorithm to obtain the optimal multi-directional viscous coefficient and the optimal multi-directional inertial coefficient. This method achieves high-precision inversion of the inertial drag coefficient and viscous drag coefficient in porous media, has engineering applicability, and improves the accuracy of identifying the inertial drag coefficient and viscous drag coefficient.

[0059] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0060] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0061] Figure 1 A flowchart illustrating a method for calculating the drag coefficient of a porous medium according to an embodiment of this application is shown;

[0062] Figure 2 This paper presents another flowchart illustrating a method for calculating the drag coefficient of a porous medium according to an embodiment of this application.

[0063] Figure 3 This paper presents another flowchart illustrating a method for calculating the drag coefficient of a porous medium according to an embodiment of this application.

[0064] Figure 4 The figure shows the effect of the number of test points on the inversion results of another method for calculating the drag coefficient of porous media provided in this application embodiment;

[0065] Figure 5 This diagram shows a block diagram of a device for calculating the drag coefficient of a porous medium according to an embodiment of this application.

[0066] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention is shown.

[0067] in,

[0068] Figure 5 Chinese module: 502 - 3D model construction module; 504 - Simulation experiment module; 506 - Optimization model construction module; 508 - Objective function determination module; 510 - Solution module;

[0069] Figure 6 In Chinese: 602 - Processor; 604 - Communication interface; 606 - Memory; 608 - Communication bus; 610 - Program. Detailed Implementation

[0070] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other.

[0071] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific embodiments, structures, features, and effects according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "an embodiment" or "an embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0072] To address the low accuracy of current methods for identifying porous media parameters, this application provides a method for calculating the drag coefficient of porous media, such as... Figure 1 As shown, the method includes:

[0073] 102: Construct a three-dimensional geometric model of the target device, determine the simulation region of the porous medium in the three-dimensional geometric model, and construct the CFD governing equation, continuity equation, and CFD momentum conservation equation for the simulation region. Among them, the drag source term is added to the CFD momentum conservation equation.

[0074] 104: Conduct a simulation experiment on the target equipment according to the preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment;

[0075] 106: An optimization model is constructed based on the experimental pressure difference between inlet and outlet and the simulated pressure difference between inlet and outlet. The simulated pressure difference between inlet and outlet is obtained by fluid simulation calculation of the three-dimensional geometric model based on the CFD control equation, continuity equation, CFD momentum conservation equation, multi-directional viscous drag coefficient, multi-directional inertial drag coefficient and preset experimental parameters.

[0076] 108: The optimization model is used as the objective function in the population algorithm, where the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient;

[0077] 110: The optimization model is solved based on the population algorithm to obtain the optimal multi-directional viscous drag coefficient and the optimal multi-directional inertial drag coefficient.

[0078] Specifically, a simplified three-dimensional geometric model is constructed based on the actual geometric dimensions of the target equipment. The model includes an inner cavity, an outer shell, inlet and outlet channels, and a packing area. The packing part is simplified as a uniformly distributed porous medium structure, and the packing area is used as the simulation area of ​​the porous medium in the three-dimensional geometric model.

[0079] The main parameters of porous media include the viscous drag coefficient and the inertial drag coefficient. The viscosity coefficient directly corresponds to the drag characteristics generated by the interaction between intramolecular friction (viscous force) and the pore walls of the medium's skeleton when the fluid flows at low speeds in a porous medium. The smaller the pores and the more tortuous the pore channels, the larger the contact area between the fluid and the wall, and the stronger the viscous drag. In the same medium, the higher the fluid viscosity, the stronger the drag dominated by viscous forces. The inertial coefficient corresponds to the additional drag characteristics generated by the interaction between the fluid's inertial force (momentum exchange caused by velocity changes) and the medium's skeleton (such as sudden expansion, contraction, or tortuosity of pores) when the fluid flows at high speeds in a porous medium. The more irregular the pore structure (e.g., the presence of narrow throats, sudden bifurcation / merging), the more drastic the abrupt change in velocity direction / magnitude when the fluid flows through it, and the stronger the additional drag dominated by inertial forces.

[0080] The CFD control equations, continuity equations, and CFD momentum conservation equations for the simulation region are constructed. The CFD momentum conservation equations include a drag source term, which includes multi-directional viscous drag coefficients and multi-directional inertial drag coefficients.

[0081] The governing equations, continuity equations, and momentum conservation equations used in fluid simulations using 3D geometric models are as follows: The primary premise of fluid flow is that "mass cannot be increased or decreased out of thin air." The continuity equation (essentially a fluid dynamics expression of the law of mass conservation) mathematically constrains the rate of mass change of fluid within any infinitesimal element in 3D space to be equal to the mass difference between the inflow and outflow from that element. Fluid motion (such as acceleration, deflection, and slowdown) is determined by the forces acting on it. The momentum conservation equation mathematically quantifies the balance between pressure, gravity (or other volume forces), and fluid momentum changes in 3D space. This application introduces a drag coefficient into the momentum conservation equation, quantifying the relationship between inertial and viscous forces and fluid momentum changes, and introducing the influence of the drag coefficient on the fluid.

[0082] Multiple simulation experiments were conducted on the target equipment according to the preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment. The three-dimensional geometric model was simulated multiple times according to the same experimental parameters to obtain the inlet and outlet simulated pressure difference of the three-dimensional geometric model. Based on the inlet and outlet simulated pressure and the inlet and outlet experimental pressure difference, an optimization model was constructed.

[0083] Using the multi-directional viscous drag coefficients (x, y, and z directions) and multi-directional inertial drag coefficients (x, y, and z directions) as the position variables of individuals in the swarm optimization algorithm, and the optimization model as the objective function in the swarm optimization algorithm, an improved genetic algorithm is used to perform parameter inversion on the objective function to obtain the optimal multi-directional viscous drag coefficients and multi-directional inertial drag coefficients.

[0084] This application provides a method for calculating the drag coefficient of porous media. Compared with existing technologies, it constructs a three-dimensional geometric model of the target device, determines the simulation region of the porous medium in the three-dimensional geometric model, constructs the CFD control equation, continuity equation, and CFD momentum conservation equation of the simulation region, adds a drag source term to the CFD momentum conservation equation, constructs an optimization model, uses the optimization model as the objective function in the swarm optimization algorithm, and uses the multi-directional viscous drag coefficient and multi-directional inertial drag coefficient as the position variables corresponding to individuals in the swarm optimization algorithm. A simulation experiment is conducted on the target device to obtain the inlet and outlet experimental pressure difference. Based on the position variables corresponding to individuals in the swarm optimization algorithm, the CFD control equation, continuity equation, and CFD momentum conservation equation, the three-dimensional geometric model is simulated to obtain the simulated inlet and outlet pressure difference. Based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference, the fitness value in the swarm optimization algorithm is calculated. Based on the swarm optimization algorithm, the objective function is solved to obtain the optimal multi-directional viscous coefficient and the optimal multi-directional inertial coefficient. This achieves high-precision inversion of the inertial drag coefficient and viscous drag coefficient in porous media, improving the accuracy of identifying the inertial drag coefficient and viscous drag coefficient.

[0085] In another embodiment of the invention, for further definition and explanation, such as Figure 2 As shown, the optimization model is solved using a population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient, including:

[0086] 202: Randomly generate an initial population and input the position variable corresponding to each individual in the initial population into the three-dimensional geometric model;

[0087] 204: Based on preset experimental parameters, position variables corresponding to each individual, CFD governing equations, continuity equations, and CFD momentum conservation equations, fluid simulation is performed on the three-dimensional geometric model to obtain the simulated inlet and outlet pressure differences of the three-dimensional geometric model for each individual.

[0088] 206: Substitute the simulated pressure difference between inlet and outlet and the experimental pressure difference between inlet and outlet for each individual into the objective function to obtain the fitness value for each individual. Based on the fitness value for each individual, determine the minimum fitness value.

[0089] 208: Perform population evolution to obtain evolved individuals. Based on the evolved individuals, obtain the fitness value corresponding to the evolved individuals, determine the minimum fitness value after evolution, calculate the difference between the minimum fitness values ​​of two adjacent rounds, and when the difference between the minimum fitness values ​​of two adjacent rounds is less than a set threshold or the number of iterations reaches a preset upper limit, take the position variable of the individual corresponding to the minimum fitness value of the last round as the optimal multidirectional viscous drag coefficient and multidirectional inertial drag coefficient.

[0090] Specifically, an improved genetic algorithm is used for parameter inversion, such as... Figure 3 As shown, the process includes:

[0091] 1) Initial population generation: The position variables corresponding to each individual are the multidirectional viscosity coefficient and the multidirectional inertia coefficient. For example, 30 individuals are randomly generated to form the initial solution set.

[0092] 2) Fitness assessment: based on the objective function value J(X) As an evaluation standard, the smaller the error, the higher the fitness.

[0093]

[0094] in, Let X be the simulated pressure difference between the inlet and outlet in the i-th simulation experiment, and let X be the matrix composed of the multidirectional viscous drag coefficient and the multidirectional inertial drag coefficient. Let N be the pressure difference between the inlet and outlet in the i-th simulation experiment, and N be the number of simulation experiments.

[0095] The position variables corresponding to each individual are input into a 3D geometric model. Based on the CFD governing equations, continuity equations, CFD momentum conservation equations, multi-directional viscous drag coefficients, multi-directional inertial drag coefficients, and preset experimental parameters, fluid simulation calculations are performed on the 3D geometric model to obtain the simulated inlet and outlet pressures. The difference between the simulated inlet and outlet pressures is taken as the simulated inlet and outlet pressure difference. Multiple simulation experiments are conducted based on preset experimental data to obtain the simulated inlet and outlet pressure difference under each experiment. The simulated inlet and outlet pressure difference under each experiment and the simulated inlet and outlet experimental pressure difference under each experiment are substituted into the above objective function to calculate the fitness value corresponding to each individual, and the minimum fitness value is obtained. The individuals in the population are then evolved. After evolution, the position variables corresponding to each individual are input into the 3D geometric model again, and the fitness value corresponding to each individual is recalculated.

[0096] 3) The following methods are mainly used when conducting population evolution:

[0097] When selecting individuals: a roulette wheel selection mechanism is used.

[0098] When generating offspring individuals: a two-point crossover strategy is adopted.

[0099] When adjusting the mutation probability: an adaptive mutation probability adjustment strategy is adopted.

[0100] 4) Convergence Criterion: When the increase in the minimum fitness value between two adjacent rounds is less than the set threshold or the number of iterations reaches the preset upper limit (e.g., 10 generations), it is considered to have converged. The position variable of the individual corresponding to the minimum fitness value in the last round is taken as the optimal multidirectional viscosity coefficient and multidirectional inertia coefficient.

[0101] Specifically, in each iteration, the FLUENT simulation platform is invoked to complete automatic simulation tasks under five sets of flow conditions, and the calculated pressure difference is extracted as the input to the objective function. MATLAB is used to automatically control the log files, achieving full coupling of the FLUENT parameter writing, calculation submission, and result reading processes.

[0102] Optimal solution output and inversion parameter verification: Parameters of the final output X =[ R cx , R cy , R cz , R dx , R dy , R dz This can be used for predicting flow in porous media (typically perpendicular to the flow direction). R cy， R dy (Value is 0), and verify it in the following way:

[0103] Pressure difference fitting accuracy: The inversion parameters are input into the three-dimensional geometric model and compared with the measured pressure difference; the optimal parameter combination obtained from the final inversion is then calculated. X The data was substituted into FLUENT for whole-machine simulation, and the results were compared with the measured differential pressure curves. The results show:

[0104] The maximum differential pressure error is less than 5%, and the velocity vector distribution shows that the velocity is significantly reduced in the porous area. The vertical flow (z-axis) is relatively smooth, while the horizontal resistance is significant, which is consistent with the understanding of actual packing structure.

[0105] Furthermore, the simulation results show good agreement with the velocity field distribution under both filled and unfilled conditions, further demonstrating the accuracy of the inversion parameters.

[0106] Flow field structure comparison: Simulate the pressure and velocity fields with and without porous structures to confirm the physical rationality of the inversion parameters;

[0107] Sensitivity analysis at experimental points: Sensitivity analysis at experimental points used 1 to 5 sets of experimental data for inversion. The results showed that when N When =1, the parameters overfit a single point, resulting in a large overall error. When N When the value is ≥4, the error converges and stabilizes, and the inversion parameters can be generalized. Compared to using all 5 sets of data, only 4 sets are needed to obtain results with similar accuracy, saving approximately 20% of simulation resources. Figure 4 As shown.

[0108] In this embodiment, five sets of existing experimental data were used to measure the inlet and outlet pressure difference of the target equipment under different inlet flow rates (40~80 kg / h). Using the same experimental data, fluid simulation was performed on the three-dimensional geometric model to obtain simulation data for the three-dimensional near-model. Based on this data, a least-squares objective function was constructed as the optimization model:

[0109] .

[0110] In one embodiment, the CFD momentum conservation equation is:

[0111]

[0112] in, γ Porosity For dynamic viscosity, Let p be the density and p be the pressure. τ Let v be the shear stress introduced by fluid molecules due to viscosity, v be the velocity, and g be the acceleration due to gravity. S External volume force;

[0113]

[0114] in, Let represent the external volume force of viscous drag in the k-th direction, where k=1 represents the x-direction, k=2 represents the y-direction, and k=3 represents the z-direction. Let j represent the inertial drag in the j-th direction, where j=1 represents the x-direction, j=2 represents the y-direction, and j=3 represents the z-direction. For absolute speed, Let be the coefficient of viscous drag in the k-th direction and inertial drag in the j-th direction. Let be the coefficient of inertial drag in the j-th direction and viscous drag in the k-th direction. Let be the velocity in the j-th direction of the inertial drag.

[0115] Specifically, the simulation domain includes both the interior of the porous medium and the region without porous media. The simulation domain employs the governing equations for the incompressible steady-state flow field, considering the volumetric source terms in the porous region. The treatment of the momentum equation in the porous medium involves modifying the diffusion and source terms.

[0116]

[0117] In the formula τ Shear stress is introduced by fluid molecules due to viscosity. p Where ρ is pressure and ρ is density. γ Porosity S This is an external volume force. In porous media, k direction(k External volume forces (representing x, y, or z) can be expressed as:

[0118]

[0119] In the formula, the first term on the left is the viscous loss term, and the term on the right is the inertial loss term. D and C These are the matrices of viscous drag and inertial drag coefficients, respectively. μ For dynamic viscosity, | v | represents absolute velocity.

[0120] The negative source term leads to a pressure drop in the porous medium region. The pressure drops in the three directions caused by viscous drag and inertial drag in the porous medium can be expressed as follows:

[0121]

[0122]

[0123] In the formula 1 / a kj Coefficient matrix D , n k This represents the thickness of the porous medium in three coordinate directions. (Matrix) C and D Inertial drag coefficients in the three coordinate directions R cx , R cy , R cz and viscous drag coefficient R dx , R dy , R dz These are the parameters that need to be inverted. The dryer model uses a standard... k-ε In the turbulence model, since the porous medium region has high permeability and the geometric scale of the medium does not interact with the scale of the turbulent eddies, the solid matrix in the porous medium has no effect on the generation and dissipation of turbulence.

[0124] When performing fluid simulations on a three-dimensional geometric model, the addition of viscous drag and inertial drag terms to the momentum conservation equation allows us to observe their influence on the three-dimensional geometric model. Based on the simulation data and actual experimental data of the three-dimensional geometric model, we can solve the objective function and inversely obtain the optimal viscous drag coefficient and inertial drag coefficient.

[0125] In one embodiment of the present invention, a three-dimensional geometric model of the target device is constructed, and the simulation region of the porous medium in the three-dimensional geometric model is determined, including:

[0126] A three-dimensional geometric model of the target device is constructed. The three-dimensional geometric model includes an inner cavity, an outer shell, inlet and outlet channels, and a packing region. The packing region is the simulation area of ​​porous media in the three-dimensional geometric model.

[0127] The three-dimensional geometric model is meshed and boundary conditions are set. The boundary conditions include inlet boundary, outlet boundary, wall boundary, and porous medium region.

[0128] Specifically, a three-dimensional geometric model is constructed. In this model, the packing region is simplified into a porous media structure, physically representing an equivalent flow resistance source, with the porosity set as [value missing]. γ The material is an anisotropic homogeneous medium, and different types of porous materials have different drag coefficients and viscosity coefficients. Taking a typical thermal equipment such as a dryer as the research object, a simplified three-dimensional geometric model of the dryer is constructed based on its actual geometric dimensions. The model includes an inner cavity, an outer shell, inlet and outlet channels, and a packing region. The packing part is simplified as a uniformly distributed porous medium structure.

[0129] Typically, the three-dimensional geometric model is a three-dimensional CAD model, which is then imported into the FLUENT simulation software for fluid domain partitioning.

[0130] First, mesh generation is performed using unstructured tetrahedral meshes, with a total of 15 million meshes to ensure computational accuracy in complex structural regions. Mesh quality is controlled to within Skewness 0.66.

[0131] Then, the boundary conditions are set as follows:

[0132] Inlet boundary: set as velocity inlet, air temperature 190℃, mass flow rate 60 kg / h, corresponding velocity 21.5 m / s;

[0133] Outlet boundary: Set to atmospheric pressure outlet.

[0134] Wall boundary: Set to adiabatic wall.

[0135] Porous media region: The porosity is set at 85.3%, and an anisotropic resistance model is adopted.

[0136] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this invention provides a device for calculating the drag coefficient of porous media, such as... Figure 5As shown, the device includes:

[0137] The 3D model construction module 502 is used to construct a 3D geometric model of the target device, determine the simulation region of the porous medium in the 3D geometric model, and construct the CFD control equation, continuity equation, and CFD momentum conservation equation of the simulation region. Among them, the drag source term is added to the CFD momentum conservation equation.

[0138] The simulation experiment module 504 is used to conduct a simulation experiment on the target equipment according to preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment.

[0139] The optimization model construction module 506 is used to construct an optimization model based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference. The inlet and outlet simulated pressure difference is obtained by performing fluid simulation calculations on the three-dimensional geometric model based on the CFD control equation, continuity equation, CFD momentum conservation equation, multi-directional viscous drag coefficient, multi-directional inertial drag coefficient and preset experimental parameters.

[0140] The objective function determination module 508 is used to use the optimization model as the objective function in the population algorithm, wherein the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient.

[0141] The solver module 510 is used to solve the optimization model based on the population algorithm to obtain the optimal multi-directional viscous drag coefficient and the optimal multi-directional inertial drag coefficient.

[0142] This application provides a device for calculating the drag coefficient of porous media. Compared with existing technologies, it constructs a three-dimensional geometric model of the target device, determines the simulation region of the porous media in the three-dimensional geometric model, constructs the CFD control equation, continuity equation, and CFD momentum conservation equation of the simulation region, adds a drag source term to the CFD momentum conservation equation, constructs an optimization model, uses the optimization model as the objective function in the swarm optimization algorithm, uses the multi-directional viscous drag coefficient and multi-directional inertial drag coefficient as the position variables corresponding to individuals in the swarm optimization algorithm, conducts a simulation experiment on the target device, obtains the inlet and outlet experimental pressure difference of the target device, simulates the three-dimensional geometric model based on the position variables corresponding to individuals in the swarm optimization algorithm, obtains the inlet and outlet simulated pressure difference, calculates the fitness value in the swarm optimization algorithm based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference, solves the objective function based on the swarm optimization algorithm, and obtains the optimal multi-directional viscous coefficient and the optimal multi-directional inertial coefficient, realizing high-precision inversion of the inertial drag coefficient and viscous drag coefficient in porous media, and improving the accuracy of identifying the inertial drag coefficient and viscous drag coefficient.

[0143] In one embodiment, the solver module is further configured to:

[0144] An initial population is randomly generated, and the positional variable corresponding to each individual in the initial population is input into the three-dimensional geometric model.

[0145] Based on preset experimental parameters, position variables corresponding to each individual, CFD governing equations, continuity equations, and CFD momentum conservation equations, fluid simulation is performed on the three-dimensional geometric model to obtain the simulated inlet and outlet pressure differences of the three-dimensional geometric model for each individual:

[0146] Substitute the simulated pressure difference between inlet and outlet and the experimental pressure difference between inlet and outlet for each individual into the objective function to obtain the fitness value for each individual. Based on the fitness value for each individual, determine the minimum fitness value.

[0147] The population is evolved to obtain evolved individuals. Based on the evolved individuals, the fitness value corresponding to the evolved individuals is obtained. The minimum fitness value after evolution is determined. The difference between the minimum fitness values ​​of two adjacent rounds is calculated. When the difference between the minimum fitness values ​​of two adjacent rounds is less than a set threshold or the number of iterations reaches a preset upper limit, the position variable of the individual corresponding to the minimum fitness value of the last round is taken as the optimal multidirectional viscous drag coefficient and multidirectional inertial drag coefficient.

[0148] In one embodiment, the 3D model building module is also used for:

[0149] A three-dimensional geometric model of the target device is constructed. The three-dimensional geometric model includes an inner cavity, an outer shell, inlet and outlet channels, and a packing region. The packing region is the simulation area of ​​porous media in the three-dimensional geometric model.

[0150] The three-dimensional geometric model is meshed and boundary conditions are set. The boundary conditions include inlet boundary, outlet boundary, wall boundary, and porous medium region.

[0151] In one embodiment, the optimization model is:

[0152]

[0153] in, Let X be the simulated pressure difference between the inlet and outlet in the i-th simulation experiment, and let X be the matrix composed of the multidirectional viscous drag coefficient and the multidirectional inertial drag coefficient. Let N be the pressure difference between the inlet and outlet in the i-th simulation experiment, and N be the number of simulation experiments.

[0154] In one embodiment, the CFD momentum conservation equation is:

[0155]

[0156] in, γ Porosity For dynamic viscosity, Let p be the density and p be the pressure. τ Let v be the shear stress introduced by fluid molecules due to viscosity, v be the velocity, and g be the acceleration due to gravity. S External volume force;

[0157]

[0158] in, Let represent the external volume force of viscous drag in the k-th direction, where k=1 represents the x-direction, k=2 represents the y-direction, and k=3 represents the z-direction. Let j represent the inertial drag in the j-th direction, where j=1 represents the x-direction, j=2 represents the y-direction, and j=3 represents the z-direction. For absolute speed, Let be the coefficient of viscous drag in the k-th direction and inertial drag in the j-th direction. Let be the coefficient of inertial drag in the j-th direction and viscous drag in the k-th direction. Let be the velocity in the j-th direction of the inertial drag.

[0159] In one embodiment, the solver module is further configured to:

[0160] Individuals are selected using a roulette wheel selection mechanism, offspring are generated using a two-point crossover strategy, and the mutation probability is dynamically adjusted through adaptive mutation operations.

[0161] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, which is capable of executing the method for calculating the resistance coefficient of porous media in any of the above method embodiments.

[0162] Figure 6 The diagram illustrates a structural schematic of a computer device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computer device.

[0163] like Figure 6 As shown, the computer device may include: a processor 602, a communications interface 604, a memory 606, and a communications bus 608.

[0164] The processor 602, communication interface 604, and memory 606 communicate with each other via communication bus 608.

[0165] Communication interface 604 is used to communicate with other network elements such as clients or other servers.

[0166] The processor 602 is used to execute program 610, specifically the relevant steps in the above embodiment of the method for calculating the resistance coefficient of porous media.

[0167] Specifically, program 610 may include program code that includes computer operation instructions.

[0168] Processor 602 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computer device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0169] Memory 606 is used to store program 610. Memory 606 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0170] Specifically, program 610 can be used to cause processor 602 to perform the following operations:

[0171] A three-dimensional geometric model of the target device is constructed, the simulation region of the porous medium in the three-dimensional geometric model is determined, and the CFD governing equation, continuity equation and CFD momentum conservation equation of the simulation region are constructed. Among them, the drag source term is added to the CFD momentum conservation equation.

[0172] A simulation experiment was conducted on the target equipment according to the preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment.

[0173] An optimization model is constructed based on the experimental pressure difference between inlet and outlet and the simulated pressure difference between inlet and outlet. The simulated pressure difference between inlet and outlet is obtained by fluid simulation calculation of the three-dimensional geometric model based on the CFD control equation, continuity equation, CFD momentum conservation equation, multi-directional viscous drag coefficient, multi-directional inertial drag coefficient and preset experimental parameters.

[0174] The optimization model is used as the objective function in the population algorithm, where the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient.

[0175] The optimization model is solved using a population algorithm to obtain the optimal multi-directional viscous drag coefficient and the optimal multi-directional inertial drag coefficient.

[0176] It will be apparent to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. In one embodiment, they can be implemented using device-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular hardware and software combination.

[0177] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method for calculating the drag coefficient of porous media, characterized in that, include: A three-dimensional geometric model of the target device is constructed, the simulation region of the porous medium in the three-dimensional geometric model is determined, and the CFD control equation, continuity equation and CFD momentum conservation equation of the simulation region are constructed. The drag source term is added to the CFD momentum conservation equation. A simulation experiment was conducted on the target equipment according to the preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment. An optimization model is constructed based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference. The inlet and outlet simulated pressure difference is obtained by performing fluid simulation calculations on the three-dimensional geometric model based on the CFD control equation, the continuity equation, the CFD momentum conservation equation, the multi-directional viscous drag coefficient, the multi-directional inertial drag coefficient, and preset experimental parameters. The optimization model is used as the objective function in the population algorithm, where the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient. The optimization model is solved using the population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient.

2. The method for calculating the resistance coefficient of porous media as described in claim 1, characterized in that, The step of solving the optimization model based on the population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient includes: An initial population is randomly generated, and the position variable corresponding to each individual in the initial population is input into the three-dimensional geometric model. Based on preset experimental parameters, positional variables corresponding to each individual, the CFD control equations, the continuity equation, and the CFD momentum conservation equation, fluid simulation is performed on the three-dimensional geometric model to obtain the simulated inlet and outlet pressure differences of the three-dimensional geometric model for each individual: Substitute the simulated pressure difference between inlet and outlet and the experimental pressure difference between inlet and outlet for each individual into the objective function to obtain the fitness value for each individual. Based on the fitness value for each individual, determine the minimum fitness value. The population is evolved to obtain evolved individuals. Based on the evolved individuals, the fitness value corresponding to the evolved individuals is obtained. The minimum fitness value after evolution is determined. The difference between the minimum fitness values ​​of two adjacent rounds is calculated. When the difference between the minimum fitness values ​​of two adjacent rounds is less than a set threshold or the number of iterations reaches a preset upper limit, the position variable of the individual corresponding to the minimum fitness value of the last round is taken as the optimal multidirectional viscous drag coefficient and multidirectional inertial drag coefficient.

3. The method for calculating the resistance coefficient of porous media as described in claim 1, characterized in that, The construction of a three-dimensional geometric model of the target device, and the determination of the simulation region of the porous medium in the three-dimensional geometric model, includes: A three-dimensional geometric model of the target device is constructed, wherein the three-dimensional geometric model includes an inner cavity, an outer shell, inlet and outlet channels, and a filling region, wherein the filling region is a simulated region of porous media in the three-dimensional geometric model; The three-dimensional geometric model is meshed and boundary conditions are set, wherein the boundary conditions include inlet boundary, outlet boundary, wall boundary and porous medium region.

4. The method for calculating the resistance coefficient of porous media as described in claim 1, characterized in that, The optimization model is as follows: in, Let X be the simulated pressure difference between the inlet and outlet in the i-th simulation experiment, and let X be the matrix composed of the multidirectional viscous drag coefficient and the multidirectional inertial drag coefficient. Let N be the pressure difference between the inlet and outlet in the i-th simulation experiment, and N be the number of simulation experiments.

5. The method for calculating the resistance coefficient of porous media as described in claim 1, characterized in that, The CFD momentum conservation equation: in, γ Porosity For dynamic viscosity, Let p be the density and p be the pressure. τ Let v be the shear stress introduced by fluid molecules due to viscosity, v be the velocity, and g be the acceleration due to gravity. S External volume force; in, Let represent the external volume force of viscous drag in the k-th direction, where k=1 represents the x-direction, k=2 represents the y-direction, and k=3 represents the z-direction. Let j represent the inertial drag in the j-th direction, where j=1 represents the x-direction, j=2 represents the y-direction, and j=3 represents the z-direction. For absolute speed, Let be the coefficient of viscous drag in the k-th direction and inertial drag in the j-th direction. Let be the coefficient of inertial drag in the j-th direction and viscous drag in the k-th direction. Let be the velocity in the j-th direction of the inertial drag.

6. The method for calculating the resistance coefficient of porous media as described in claim 2, characterized in that, The process of population evolution includes: Individuals are selected using a roulette wheel selection mechanism, offspring are generated using a two-point crossover strategy, and the mutation probability is dynamically adjusted through adaptive mutation operations.

7. A device for calculating the resistance coefficient of porous media, characterized in that, include: The three-dimensional model construction module is used to construct a three-dimensional geometric model of the target device, determine the simulation region of the porous medium in the three-dimensional geometric model, and construct the CFD control equation, continuity equation, and CFD momentum conservation equation of the simulation region. The CFD momentum conservation equation includes a drag source term. The simulation experiment module is used to conduct a simulation experiment on the target equipment according to preset experimental parameters to obtain the inlet and outlet experimental pressure difference of the target equipment. The optimization model construction module is used to construct an optimization model based on the inlet and outlet experimental pressure difference and the inlet and outlet simulated pressure difference. The inlet and outlet simulated pressure difference is obtained by performing fluid simulation calculations on the three-dimensional geometric model based on the CFD control equation, the continuity equation, the CFD momentum conservation equation, the multi-directional viscous drag coefficient, the multi-directional inertial drag coefficient, and preset experimental parameters. The objective function determination module is used to use the optimization model as the objective function in the population algorithm, wherein the position variables corresponding to individuals in the population algorithm are the multi-directional viscous drag coefficient and the multi-directional inertial drag coefficient. The solution module is used to solve the optimization model based on the population algorithm to obtain the optimal multidirectional viscous drag coefficient and the optimal multidirectional inertial drag coefficient.

8. The apparatus for calculating the resistance coefficient of porous media as described in claim 7, characterized in that, The solution module is also used for: An initial population is randomly generated, and the position variable corresponding to each individual in the initial population is input into the three-dimensional geometric model. Based on preset experimental parameters, positional variables corresponding to each individual, the CFD control equations, the continuity equation, and the CFD momentum conservation equation, fluid simulation is performed on the three-dimensional geometric model to obtain the simulated inlet and outlet pressure differences of the three-dimensional geometric model for each individual: Substitute the simulated pressure difference between inlet and outlet and the experimental pressure difference between inlet and outlet for each individual into the objective function to obtain the fitness value for each individual. Based on the fitness value for each individual, determine the minimum fitness value. The population is evolved to obtain evolved individuals. Based on the evolved individuals, the fitness value corresponding to the evolved individuals is obtained. The minimum fitness value after evolution is determined. The difference between the minimum fitness values ​​of two adjacent rounds is calculated. When the difference between the minimum fitness values ​​of two adjacent rounds is less than a set threshold or the number of iterations reaches a preset upper limit, the position variable of the individual corresponding to the minimum fitness value of the last round is taken as the optimal multidirectional viscous drag coefficient and multidirectional inertial drag coefficient.

9. A storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the method for calculating the resistance coefficient of a porous medium as described in any one of claims 1-6.

10. A computer device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method for calculating the drag coefficient of porous media as described in any one of claims 1-6.

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