A method for predicting the fluid dynamics of a rotating packed bed based on a porous media resistance model

Abstract

The application discloses a kind of based on porous medium resistance model's foam rotating packed bed fluid dynamics prediction method, it is related to gas-liquid mass transfer equipment technical field.The present application includes the following steps: constructing foam rotating packed bed geometric model and dividing grid;Establish the Euler porous medium framework;Embed new gas-liquid porous medium resistance model;Coupling three sub-models, including capillary pressure model, mechanical dispersion force model and effective interface area model;CFD simulation setting and solution are carried out;Prediction result output and analysis.The foam rotating packed bed fluid dynamics prediction method based on porous medium resistance model proposed in the present application is in good agreement with experimental data on two key flow indicators, gas phase pressure drop and liquid holdup, with high prediction accuracy, high calculation efficiency, and a wide range of applicable working conditions, providing reliable theoretical support and calculation tools for the industrial design, performance optimization and engineering application of foam packing rotating bed.

Description

Technical Field

[0001] This invention relates to the field of gas-liquid mass transfer equipment technology, and specifically to a method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model. Background Technology

[0002] A rotating packed bed (RPB) is a device that enhances gas-liquid mass transfer by relying on the strong centrifugal force generated by the rotation of the packing material. In a rotating packed bed, the liquid is broken into tiny droplets and forms a thin liquid film. The surface renewal rate is fast and the interphase transfer efficiency is high. Therefore, this device has been widely used in industrial processes such as absorption, hydrate formation, distillation, and mass and heat transfer enhancement.

[0003] Packing material is the core component of a rotating packed bed. Currently, commonly used packing types include wire mesh packing and open-cell foam packing. Experimental research and numerical simulation methods for wire mesh packing are relatively mature, while foam packing exhibits superior mass transfer performance due to its large specific surface area, high porosity, and the ability to further improve gas-liquid contact efficiency after surface modification. However, the flow behavior within foam packing is extremely complex, influenced by multiple physical mechanisms such as centrifugal force, porous media resistance, and liquid dispersion. Its liquid holdup and flow resistance are significantly higher than those of wire mesh packing. Existing research on rotating packed beds with foam packing is mostly limited to experimental measurements, lacking numerical models that can accurately describe its internal flow characteristics.

[0004] In large-scale numerical simulations of rotating packed beds, the Euler porous media method is the preferred approach due to its high computational efficiency and ability to effectively describe the macroscopic flow behavior of the packed zone. The key to this method lies in matching an accurate porous media resistance model. Currently, gas-liquid two-phase resistance models suitable for wire mesh packings are relatively mature and widely used. However, due to the unique skeletal structure, highly tortuous pore channels, and partially wetted surface of foam packings, existing resistance models cannot be directly applied.

[0005] Simulation studies of foam packings often employ pore-scale modeling methods. While these methods can capture local details, they are computationally intensive and costly, making them unsuitable for large-scale industrial applications. On the other hand, directly using conventional packing resistance models results in significantly lower prediction accuracy due to mismatches between model assumptions and the actual physical properties of the foam packing, failing to meet engineering application requirements. These issues severely limit the application of the Euler porous media method in rotating foam-filled beds.

[0006] In summary, foam-filled rotating beds have broad prospects in industrial applications, but currently there is a lack of suitable resistance models for gas-liquid two-phase porous media, and existing numerical simulation methods suffer from prominent problems such as high computational cost or low prediction accuracy.

[0007] Therefore, developing a fluid dynamics prediction method that can accurately predict the gas-liquid flow characteristics in foam packing while also taking into account computational efficiency is of great theoretical value and practical significance for the design optimization and engineering application of foam packing rotating beds. It is also a technical problem that general technicians in this field urgently need to solve.

[0008] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides a method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model, thereby resolving the issues raised in the background section.

[0010] This invention provides the following technical solution: a method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model, which can accurately predict the gas-liquid flow within the foam packing, comprising the following steps: Step 1: Construct the geometric model of the foam rotating filling bed and mesh it; Step 2: Construct an Euler porous media framework; Step 3: Embed a novel gas-liquid porous media resistance model; Step 4: Couple the three sub-models, including the capillary pressure model, the mechanical dispersion force model, and the effective interface area model; Step 5: Set up and solve the Computational Fluid Dynamics (CFD) simulation; Step 6: Output and analysis of prediction results.

[0011] Preferably, in step 1, a three-dimensional geometric model is established based on the size of the experimental device, and a mesh is generated.

[0012] Preferably, in step 2, the Euler two-fluid model is used to treat the foam packing as a homogeneous porous medium, and the gas and liquid phases are set to be incompressible and share a pressure field to construct the mass and momentum conservation control equations for the gas and liquid phases. The mass conservation equation is: ; in, t For time, Let represent the volume fraction of the i-th phase (i = G for gas phase, L for liquid phase). Let be the density of the i-th phase. Let i be the apparent velocity of the i-th phase; The momentum conservation equation is: ; ; in, For pressure, Let i be the corresponding force tensor. and These are the gas-solid and liquid-solid drag forces, respectively. For gas-liquid interfacial forces, The mechanical dispersion force acting on the i-th phase, This refers to capillary pressure.

[0013] Preferably, in step 3, a novel gas-liquid porous medium resistance model suitable for gas-liquid two-phase flow is established based on the single-phase gas pressure drop correlation. The correlation for the single-phase gas pressure drop is: ; ; in, For pressure drop, L As the head of the department, For tortuosity, Porosity For viscosity, For apparent speed, For gas density, The surface area per unit volume of a solid. These are geometric non-empirical constants determined by the cross-sectional shape of the support column; It should be noted that the formula ;middle, The actual surface area of ​​the skeleton in contact with the fluid was precisely quantified, and... Compared to foam fillers, it is more suitable for structures with fine skeletons and large pores; The introduction of this method matches the highly tortuous pore structure of the foam packing, correcting for the influence of the actual flow path. Therefore, this single-phase gas pressure drop correlation is used to establish a novel gas-liquid porous media resistance model suitable for gas-liquid two-phase flow.

[0014] It should be noted that, based on the formula and formula This extends the pressure drop correlation of single-phase gas to two-phase systems. Due to the partially wetting physical property of the foam filler surface, the percentage of wetting surface is introduced. , percentage of dry surface To distinguish between the wetted and dry areas on the packing surface; to reflect the actual resistance of the foam packing to the gas-liquid two-phase flow, Transform into and And the gas-solid resistance in single-phase flow is further expanded into gas-liquid relative motion resistance (…). ), gas-solid resistance of the dry surface ( ) and the liquid-solid resistance of wetted surfaces ( Simultaneously, gaseous properties are replaced with liquid properties to be applicable to the liquid phase, and apparent velocities are converted into physical velocities, thus obtaining a novel gas-liquid porous media resistance model. The governing equations of the novel gas-liquid porous media resistance model are: ; ; ; in, Let i be the physical velocity of phase i. is the phase momentum exchange coefficient.

[0015] Preferably, the expression for the coupled capillary pressure model in step 4 is: ; in, , , For surface tension, The equivalent diameter of the foam is... For the characteristic diameter, This is the density ratio correction function.

[0016] Preferably, the expression for the coupled mechanical dispersion force model in step 4 is: ; in, , , , The drift velocity of the i-th phase is determined by both the volume fraction gradient and the velocity gradient. and The momentum exchange coefficient, To spread factors, The phase volume fraction spatial gradient.

[0017] Preferably, the effective interface area model coupled in step 4 is: ; in, Experimental constants 205.6 m / s 2 , It is 0.0106 m / s. 3.35 10 -6 m 2 / s, It is 75°. Centrifugal acceleration, For the local liquid phase apparent velocity, For the kinematic viscosity of the liquid, This refers to the dynamic contact angle.

[0018] Preferably, in step 5, a three-dimensional transient solver is used for numerical simulation; the pressure-velocity coupling adopts the Phase Coupled SIMPLE algorithm; the pressure equation adopts a second-order discretization scheme; and the packing rotation region is simulated using a sliding mesh.

[0019] Preferably, in step 6, the simulated gas phase pressure drop, liquid phase holdup and their spatial distribution results are compared and verified with experimental measurements to confirm the accuracy and reliability of the prediction method.

[0020] Preferably, the capillary pressure model, mechanical dispersion force model, and effective interface area model are all embedded into the CFD solver through user-defined functions and solved in conjunction with the Euler two-fluid control equations.

[0021] The present invention provides a method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model, which has the following advantages: (1) This invention successfully solves the technical problem that the existing Euler porous media method cannot accurately predict the gas-liquid flow in foam packing due to the lack of a suitable resistance model. By using the single-phase gas pressure drop correlation, and considering the physical characteristics of partial wetting on the surface of the foam packing, the invention innovatively introduces the proportion of wetted surface and the proportion of dry surface, expanding the single-phase gas-solid resistance into three expressions: gas-liquid relative motion resistance, dry surface gas-solid resistance, and wet surface liquid-solid resistance, thus constructing a novel gas-liquid two-phase porous media resistance model suitable for foam packing rotating beds. This model fully considers the characteristics of the foam packing skeleton, large porosity, and highly tortuous structure, and can accurately capture the flow resistance characteristics within the foam packing.

[0022] (2) This invention achieves high-precision results in predicting gas phase pressure drop. For different experimental setups and a wide range of operating conditions, the gas phase pressure drop predicted by the model matches the experimental measurements well, with minimal prediction deviation. Compared with existing computational fluid dynamics studies, the prediction accuracy of this invention is significantly improved, proving that this single-phase resistance model can reliably predict the gas phase pressure drop of a single-layer foam packing.

[0023] (3) This invention also demonstrates excellent accuracy in predicting liquid holdup. For various configurations such as different rotational speeds, different liquid flow rates, and single-nozzle and multi-nozzle configurations, the simulated predicted radial distribution of liquid holdup is highly consistent with the experimental measurements in terms of morphology. Under normal operating conditions without flooding, the average and maximum deviations of the liquid holdup prediction are both controlled at low levels. The model successfully captures the characteristics of the local high liquid holdup zone caused by point source injection in a rotating bed and the circumferential uniform wetting distribution under a multi-nozzle structure, and accurately reproduces the physical law that liquid holdup decreases with increasing rotational speed.

[0024] (4) This invention overcomes the technical shortcomings of existing pore-scale modeling, such as large computational load, high cost, and difficulty in application to large-scale equipment. By adopting the Euler porous media framework to treat foam filler as a uniform porous medium, and combining it with the sliding mesh method to simulate the rotation of the filler region, the computational resource requirements are greatly reduced while ensuring computational accuracy. This provides an efficient and reliable computational tool for the design and optimization of industrial-scale foam rotating filling beds.

[0025] (5) This invention forms a complete prediction system describing the gas-liquid two-phase flow behavior within foam packing by coupling the capillary pressure model, the mechanical dispersion force model, and the effective interface area model. The capillary pressure model reasonably describes the gas-liquid interface pressure difference caused by surface tension, the mechanical dispersion force model effectively simulates the lateral diffusion behavior of the liquid caused by velocity gradient and turbulence, and the effective interface area model accurately calculates the ratio of the gas-liquid contact area to the total specific surface area of ​​the packing. The synergistic effect of the three sub-models enables the overall prediction method to comprehensively reflect the complex fluid dynamics characteristics within the foam packing. Attached Figure Description

[0026] Figure 1 This is a geometric model diagram drawn based on the experiments of Wojtasik-Malinowska and Pyka et al. in a specific embodiment of the present invention; Figure 2 This is a comparison chart of the experimentally measured values ​​and simulated predicted values ​​of gas phase pressure drop in a specific embodiment of the present invention; Figure 3 This is a flowchart illustrating the steps of a method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model, as described in this invention. Figure 4 This is a geometric model diagram drawn based on the experiments of Yang and Groß et al. in a specific embodiment of the present invention; Figure 5 A schematic diagram of the wetting and drying areas on the surface of the foam filler; Figure 6 This is a comparison diagram of the liquid holding capacity distribution in the Yang experiment and the liquid holding capacity distribution in different planar simulations in a specific embodiment of the present invention; Figure 7This is a comparison chart of experimental data and simulation results of the average liquid hold-up in the packing material at different rotation speeds in a specific embodiment of the present invention; Figure 8 This is a comparison chart of experimental data and simulation results of the average liquid hold-up in the packing material under different liquid flow rates in a specific embodiment of the present invention; Figure 9 This is a comparison chart of experimental data and simulation results of liquid holdup distribution in single-nozzle and multi-nozzle structures in a specific embodiment of the present invention; Figure 10 This is a comparison chart of experimental data and simulation results of the Groß experiment quantitatively comparing the liquid hold-up volume along the radial direction at different rotation speeds in a specific embodiment of the present invention.

[0027] in, Figure 1 In the middle: 1-Gas outlet 1; 2-Gas inlet 1; 3-Inner cavity area 1; 4-Packing area 1; 5-Outer cavity area 1 / elimination area 1.

[0028] Figure 4 In the middle: '1-Gas outlet two;'2-Gas inlet two;'3-Inner cavity zone two;'4-Packing zone two;'5-Outer cavity zone two / elimination zone two;'6-Liquid inlet two. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] To verify and analyze the effectiveness of the foam RPB fluid dynamics prediction method based on the porous media resistance model provided by this invention, the following two embodiments are selected to verify the effectiveness of the method of this invention.

[0031] Example 1

[0032] The gas phase flow resistance model was verified using the gas phase pressure drop data of foam packing measured by Wojtasik-Malinowska et al. and Pyka et al.

[0033] Step 1: Based on the experimental setups of Wojtasik-Malinowska et al. and Pyka et al., foam RPB models were established and meshed respectively.

[0034] To generate a structured mesh and reduce the number of meshes, the circular air inlet at the top of the casing was replaced with a square air inlet of equal cross-sectional area.

[0035] The specific simulation geometry and operating parameters of Wojtasik-Malinowska et al. are: inner diameter 0.146 m, outer diameter 0.6 m, porosity 0.922, specific surface area 2800 m² / m³; rotational speed 150-1500 rpm, gas flow rate 20-60 m³ / h; The specific simulation geometry and operating parameters of Pyka et al. were: inner diameter 0.166 m, outer diameter 0.58 m, porosity 0.92, specific surface area 1000 m² / m³; rotational speed 200-1200 rpm, gas flow rate 8.5-42.5 m³ / h.

[0036] The geometric models built by the above two simulations are as follows: Figure 1 As shown, you only need to modify the corresponding parameters.

[0037] All meshes are set using structured hexahedral meshes.

[0038] Step 2: Set the foam packing region as a porous medium model and the gas phase as a continuous fluid. Establish the following mass conservation equations and momentum conservation equations: (1); (2); in, t For time, This refers to the gas phase volume fraction. For gas phase density, For the apparent velocity of the gas phase, For pressure, For the gas-responsive force tensor, This represents the gas-solid resistance.

[0039] Step 3: Import the single-phase voltage drop correlation equation, as follows: (3); (4); in, For pressure drop, L As the head of the department, For tortuosity, Porosity For viscosity, For apparent speed, For gas density, The surface area per unit volume of a solid. The geometrical non-empirical constants determined by the cross-sectional shape of the support are 4.87, 5.62, and 6.49 for cylindrical, triangular, and concave triangular supports, respectively (in this embodiment). A value of 6.49 is chosen, which ensures a good match between the model's predicted results and the experimental pressure drop data.

[0040] Step 4: CFD simulation setup and solution. A three-dimensional transient solver is used for numerical simulation. The specific settings are as follows: (1) Solver settings: Pressure-based solver, absolute velocity format, double precision calculation is used; (2) Pressure-velocity coupling: Phase Coupled SIMPLE algorithm is used; (3) Spatial discretization: The pressure equation adopts a second-order scheme, and the momentum equation adopts a second-order upwind scheme; (4) Turbulence model: The Realizable k-ε model is selected; (5) Boundary conditions: The gas inlet is set as a mass flow inlet, the outlet is set as a pressure outlet, and all walls are set to no slip conditions; (6) Rotation simulation: The rotation of the packing zone is achieved using the sliding mesh method; (7) Time step and convergence criterion: The time step is set to 2×10 -4 - 5×10 -4 Seconds, each iteration does not exceed 20 times, and the residual convergence criterion is 1×10. -5 ; Step 5: Obtain the gas phase pressure drop inside the foam packing through simulation and compare it with the experimental measurement results to evaluate the accuracy and reliability of the technical solution proposed in this invention.

[0041] Figure 2 This is a comparison between experimentally measured and simulated predicted values ​​of gas phase pressure drop. Figure 2 As can be seen, almost all data points fall within the ±15% deviation band, which indicates that the pressure drop predicted by the model matches the experimental measurement well across the entire operating range, proving that the correlation can accurately capture the flow resistance within the foam packing.

[0042] but Figure 2 In (1), two data points deviated from the ±15% deviation range. The main reasons include: the actual foam has local structural irregularities, such as non-uniform compression and wall flow effect, while the homogeneous porous medium model cannot reflect these characteristics; the influence of the outer cavity region on pressure drop is ignored in the rotating packed bed geometric model; there are experimental errors in flow control and pressure measurement, which are more significant under low pressure drop conditions.

[0043] In fact, Wojtasik-Malinowska et al. also conducted related computational fluid dynamics research, and compared it with their experimental data, their absolute relative deviation was 11.46%, which is 1.6 times the deviation of the model in this study. Therefore, the model proposed in this study can reliably predict the gas phase pressure drop of monolayer foam packing under a wide range of operating conditions.

[0044] Example 2

[0045] The rotating bed experiments using foam-filled materials conducted by Yang et al. and Groß et al. were selected to verify the proposed porous media resistance model. The flowchart of the simulation method is shown below. Figure 3 As shown.

[0046] Step 1: Based on the experimental setups of Yang et al. and Groß et al., foam RPB models were established and meshed respectively.

[0047] Because the centrifugal force within the RPB is relatively large (7.2-286 times the gravity), and the liquid nozzles are symmetrically arranged along the plane of the packing, the flow field within the packing can be considered approximately symmetrical vertically. Therefore, this invention employs symmetrical boundary conditions, modeling only half of the bed, thus reducing computational costs while maintaining physical accuracy.

[0048] The specific simulation geometry and operating parameters of Yang et al. are as follows: inner diameter 0.042m, outer diameter 0.082m, porosity 0.8, specific surface area 1098 m² / m³, rotation speed 500-2500rpm, liquid flow rate 0.0657-0.1548 m³ / h, and number of nozzles is single nozzle; The specific simulation geometry and operating parameters of Groß et al. were: inner diameter 0.146 m, outer diameter 0.45 m, porosity 0.92, specific surface area 1000 m² / m³, rotation speed 300-1200 rpm, liquid flow rate 0.378 m³ / h, and number of nozzles: single / multiple nozzles.

[0049] The geometric models built by the above two simulations are as follows: Figure 4 As shown, you only need to modify the corresponding parameters.

[0050] Except for the multi-nozzle model, which does not use a hexahedral mesh, all other meshes use a structured hexahedral mesh.

[0051] Step 2: Based on the Euler two-fluid model, the foam packing region is set as a homogeneous porous medium model, with both the gas and liquid phases treated as continuous fluids sharing the same pressure field. The following mass conservation equations and momentum conservation equations are established: (5); (6); (7); in, t For time, Let represent the volume fraction of the i-th phase (i = G for gas phase, L for liquid phase). Let be the density of the i-th phase. Let i be the apparent velocity of the i-th phase. For pressure, Let i be the corresponding force tensor. and These are the gas-solid and liquid-solid drag forces, respectively. For gas-liquid interfacial forces, The mechanical dispersion force acting on the i-th phase, For capillary pressure (only included in the liquid phase momentum equation).

[0052] Step 3: Propose a resistance model for gas-liquid two-phase porous media suitable for foam packing.

[0053] This model is based on the single-phase gas pressure drop correlation proposed by Inayat et al., and extends the single-phase gas pressure drop correlation to two-phase systems. Due to the partial wetting physical characteristics of the foam filler surface, the proportion of the wetting surface is introduced. , percentage of dry surface To distinguish between the wetted and dry areas on the filler surface, such as Figure 5 As shown; to reflect the actual resistance of the foam packing to the gas-liquid two-phase flow, Transform into and And the gas-solid resistance in single-phase flow is further expanded into gas-liquid relative motion resistance (…). ), gas-solid resistance of the drying surface ( ) and the liquid-solid resistance of wetted surfaces ( Simultaneously, by replacing gaseous properties with liquid properties to suit the liquid phase and converting apparent velocity to physical velocity, a three-term resistance expression for gas-liquid two-phase flow is obtained, which is a novel gas-liquid porous media resistance model applicable to foam RPB. The corresponding governing equations are as follows: (8); (9); (10); in, Let i be the physical velocity of phase i. For tortuosity, For viscosity, The surface area per unit volume of a solid. is the phase momentum exchange coefficient.

[0054] Step 4: Couple the following three sub-models into the Euler porous media framework to fully describe the gas-liquid two-phase flow behavior within the foam packing: (1) Capillary pressure model: used to describe the pressure difference at the gas-liquid interface caused by surface tension, the expression is as follows: (11); in, , , For surface tension, The equivalent diameter of the foam is... For the characteristic diameter, This is the density ratio correction function.

[0055] (2) Mechanical dispersion force model: used to simulate the lateral diffusion behavior of liquid in the packing material due to velocity gradient and turbulence. The expression is as follows: (12); in, , , , The drift velocity of the i-th phase is determined by both the volume fraction gradient and the velocity gradient. and The momentum exchange coefficient, To spread factors, The phase volume fraction spatial gradient.

[0056] (3) Effective interface area model: used to calculate the wet surface ratio , defined as the ratio of the gas-liquid contact area to the total specific surface area of ​​the packing, is expressed as follows: (13); in, Experimental constants 205.6 m / s 2 , It is 0.0106 m / s. 3.35 10 -6 m 2 / s, It is 75°. Centrifugal acceleration, For the local liquid phase apparent velocity, For the kinematic viscosity of the liquid, This refers to the dynamic contact angle.

[0057] The aforementioned sub-model is embedded into the ANSYS Fluent solver through user-defined functions, achieving coupled solution with the main flow equations.

[0058] Step 5: CFD simulation setup and solution. A three-dimensional transient solver is used for numerical simulation. The specific settings are as follows: (1) Solver settings: Pressure-based solver, absolute velocity format, double precision calculation is used; (2) Pressure-velocity coupling: Phase Coupled SIMPLE algorithm is used; (3) Spatial discretization: The pressure equation adopts a second-order scheme, and the momentum equation adopts a second-order upwind scheme; (4) Turbulence model: The Realizable k-ε model is selected; (5) Boundary conditions: Both gas and liquid inlets are set as mass flow inlets, outlets are set as pressure outlets, and all wall surfaces are set to no-slip conditions; (6) Rotation simulation: The rotation of the packing zone is achieved using the sliding mesh method; (7) Time step and convergence criterion: The time step is set to 2 × 10 -4 - 5 × 10 -4 The time limit is 1 second, with no more than 20 iterations per step, and the residual convergence criterion is 1 × 10⁻⁶. -5 ; (8) Quasi-steady-state judgment: When the gas phase pressure and liquid phase holdup no longer change significantly, and all residuals are less than 2 × 10⁻⁶. -5 When the calculation reaches a quasi-steady state, it is determined that the calculation has reached a steady state.

[0059] Step 6: Obtain the liquid phase holdup distribution under different working conditions through simulation, and compare it with the experimental measurement results to evaluate the accuracy and reliability of the technical solution proposed in this invention.

[0060] The experimental and simulation results of Yang et al. are compared as follows: (1) Visual comparison of 1500 rpm and liquid flow rate of 0.043 m 3 At / s, Yang et al. experimentally measured the liquid holdup distribution map and simulated liquid distribution on different planes, such as Figure 6 As shown, the predicted radial distribution of liquid phase holdup is highly consistent with the experimental measurement results in terms of morphology.

[0061] (2) Secondly, the experimental data and simulation results of the average liquid holdup in the packing under different rotation speeds and liquid flow rates were quantitatively compared, such as Figure 7 and Figure 8 As shown. At 500 rpm and 0.043 m 3 Under the condition of / s, the predicted and measured liquid holdup deviates significantly. This is because the slightly lower porosity of the outer layer of foam and the rebound of droplets on the wall cause localized liquid enrichment in the external area, resulting in the largest deviation at the highest liquid flow rate. However, under most operating conditions, the deviation between the predicted and measured liquid holdup is less than 15%.

[0062] The experimental and simulation results of Groß et al. are compared as follows: (1) The liquid holdup distribution in single-nozzle and multi-nozzle structural configuration experiments and simulations was compared through visualization. The simulation cloud map of the single-nozzle model was extracted from the axial mid-surface corresponding to the nozzle outlet, located 0.005 m above the lower packing boundary, while that of the multi-nozzle model was located at 0.0075 m. Both the single-nozzle structural experiments and simulations showed obvious liquid jetting in the inner packing region, where the injected liquid accumulated and then diffused radially outward under the action of centrifugal force, such as... Figure 9 (1) and Figure 9 As shown in (2), this local high liquid holdup region is a typical feature of point source injection in a rotating bed, and the model captures it well; after the liquid enters the packing with the multi-nozzle structure, the initial distribution is immediately more uniform, and the circumferential arrangement of the nozzles eliminates the local jet phenomenon, such as Figure 9 (3) and Figure 9 As shown in (4) in the figure, a circumferentially uniform wetting distribution is formed on the z=0.0075m plane, and the simulation results can reproduce the characteristics of this uniform distribution with high accuracy.

[0063] (2) In addition, the radial liquid holdup distribution at different rotation speeds was quantitatively compared. For example... Figure 10 As shown, the multi-nozzle design makes the liquid distribution in the packing area more uniform, thus resulting in a higher overall liquid holdup. In addition, the liquid holdup decreases with increasing rotational speed because the increased centrifugal force promotes liquid discharge, a trend also reflected in the figure. It can also be seen that the predicted deviation from the experimental result is significantly larger at 300 rpm, because flooding occurs at 300 rpm. Under all other non-flooding conditions, the average deviation is less than 6%, and the maximum deviation does not exceed 15%.

[0064] Through visualization and quantitative comparison of the above two liquid holdup experiments and predicted values, a good match was observed, indicating that the simulation method based on the novel gas-liquid porous medium resistance model proposed in this invention can reliably predict the gas-liquid two-phase flow in foam RPB.

[0065] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model, which can accurately predict the gas-liquid flow within the foam packing, characterized in that... Includes the following steps: Step 1: Construct the geometric model of the foam rotating filling bed and mesh it; Step 2: Construct an Euler porous media framework; Step 3: Embed a novel gas-liquid porous media resistance model; Based on the single-phase gas pressure drop correlation, a novel gas-liquid porous media resistance model applicable to gas-liquid two-phase flow is established. The correlation for the single-phase gas pressure drop is: ； ； in, For pressure drop, L As the head of the department, For tortuosity, Porosity For viscosity, For apparent speed, For gas density, The surface area per unit volume of a solid. These are geometric non-empirical constants determined by the cross-sectional shape of the support column; Based on the single-phase gas pressure drop correlation, the wetted surface ratio is introduced. , percentage of dry surface ,Will Transform into and This expands the single-phase resistance into gas-liquid relative motion resistance. Gas-solid resistance on dry surface Liquid-solid resistance of wetted surfaces A novel gas-liquid porous media resistance model was obtained, and the governing equations of the novel gas-liquid porous media resistance model are as follows: ； ； ； in, Let i be the physical velocity of phase i. The phase-to-phase momentum exchange coefficient; Step 4: Couple the three sub-models, including the capillary pressure model, the mechanical dispersion force model, and the effective interface area model; Step 5: Set up and solve the CFD simulation; Step 6: Output and analysis of prediction results.

2. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, In step 1, a three-dimensional geometric model is established based on the dimensions of the experimental device, and a mesh is generated.

3. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, In step 2, the Euler two-fluid model is used to treat the foam packing as a homogeneous porous medium, and the gas and liquid phases are set to be incompressible and share a pressure field to construct the mass and momentum conservation control equations for the gas and liquid phases. The mass conservation equation is: ; in, t For time, Let i be the volume fraction of the i-th phase. Let be the density of the i-th phase. Let i be the apparent velocity of the i-th phase; The momentum conservation equation is: ； ； in, For pressure, Let i be the corresponding force tensor. and These are the gas-solid and liquid-solid drag forces, respectively. For gas-liquid interfacial forces, The mechanical dispersion force acting on the i-th phase, This refers to capillary pressure.

4. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, The expression for the coupled capillary pressure model in step 4 is as follows: ； in, , , For surface tension, The equivalent diameter of the foam is... For the characteristic diameter, This is the density ratio correction function.

5. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, The expression for the coupled mechanical dispersion force model in step 4 is as follows: ； in, , , , The drift velocity of the i-th phase is determined by both the volume fraction gradient and the velocity gradient. and The momentum exchange coefficient, To spread factors, The phase volume fraction spatial gradient.

6. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, The effective interface area model for coupling in step 4 is: ； in, Experimental constants 205.6 m / s 2 , It is 0.0106 m / s. 3.35 10 -6 m 2 / s, It is 75°. Centrifugal acceleration, For the local liquid phase apparent velocity, For the kinematic viscosity of the liquid, This refers to the dynamic contact angle.

7. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, In step 5, a three-dimensional transient solver is used for numerical simulation; the pressure-velocity coupling adopts the Phase Coupled SIMPLE algorithm; the pressure equation adopts a second-order discretization scheme; and the packing rotation region is simulated using a sliding mesh.

8. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, In step 6, the simulated gas phase pressure drop, liquid phase holdup and their spatial distribution results are compared with experimental measurements to verify the accuracy and reliability of the prediction method.

9. The method for predicting the fluid dynamics of a foam rotating packed bed based on a porous media resistance model according to claim 1, characterized in that, The capillary pressure model, mechanical dispersion force model, and effective interface area model are all embedded into the CFD solver through user-defined functions and coupled with the Euler two-fluid control equations for solution.

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