A catalyst simulation method based on lattice boltzmann model

Abstract

The application discloses a catalyst simulation method based on a lattice Boltzmann model and belongs to the field of hydrogen production by water electrolysis. Three typical catalyst morphologies are constructed, and two parameters, namely, a bubble coverage area and an electrochemical active surface area, are abstracted from the three morphologies to describe the catalyst morphology; corresponding boundary conditions are set according to actual conditions, so that the simulation results are more in line with actual conditions; and the use of a Shan-Chen multi-component lattice Boltzmann model analyzes the bubble separation time, the diameter of the generated bubble and the bubble coverage range in different catalyst morphologies. The application reveals the relationship between the catalyst morphology and the bubble evolution, can better guide the optimization of the catalyst morphology and promote the improvement of the performance of the water electrolysis hydrogen production system.

Description

Technical Field

[0001] This invention belongs to the field of hydrogen production by water electrolysis, and specifically relates to a catalyst simulation method based on the lattice Boltzmann model. Background Technology

[0002] Hydrogen is considered an ideal energy carrier for the future energy society due to its high energy density and zero carbon emissions. Water electrolysis is one of the main methods for sustainably producing high-purity (99.999%) hydrogen. There are three main water electrolysis technologies: alkaline water electrolysis (AWE), proton exchange membrane water electrolysis (PEMWE), and solid oxide electrolyzers (SOEC). Among these, AWE and PEMWE are mature technologies, and their feasibility has been verified through long-term operational demonstration projects. During the operation of an AWE or PEMWE system, water is continuously decomposed into hydrogen and oxygen products at the cathode and anode, respectively. Their accumulation leads to the formation of bubbles on the catalyst bed (CL) surface, whose life cycle includes nucleation, growth, and desorption phases. The bubble evolution behavior affects the electrocatalytic process, thus affecting the efficiency of water splitting. For example, the generation of bubbles covering redox reaction sites may block the active electrode region, increase the ohmic resistance in the electrolyte, and generate undesirable concentration gradients. Therefore, bubble trapping within the CL may limit the maximum current density, thereby limiting hydrogen production. Furthermore, with the aid of high-speed cameras, it has been shown that electrodes with denser and smaller bubbles exhibit reduced overpotential. Therefore, controlling bubble growth and exit is considered an important approach to improving the energy efficiency of water electrolyzers.

[0003] To improve bubble management performance, researchers employed various optimization strategies for the catalyst layer (CL). For example, they increased surface roughness by introducing nanostructures (such as nanosheets, nanoparticles, nanocones, and nanotubes) to create a superhydrophobic surface, thereby improving bubble detachment performance. Simultaneously, a layered electrode structure was developed, growing smaller catalysts on nanoarrays to expand the reaction area and enhance superhydrophobic properties. Studies showed that the bubble contact angle on nanostructured surfaces was significantly higher than on flat surfaces, resulting in a significant reduction in bubble size. A layered nanocone structure composed of nickel and carbon nanotubes exhibited significant improvements in bubble detachment, with an extremely small water droplet contact angle, demonstrating excellent hydrophobicity and better performance compared to traditional flat electrodes. Furthermore, these improved electrodes were able to support higher current densities without overpotential, exhibiting superior electrochemical performance. The designed nanotube array electrode showed significantly low overpotential at specific current densities, further improving the overall electrode performance.

[0004] Studies have shown that superhydrophobic surfaces facilitate bubble detachment, primarily influenced by bubble adhesion and buoyancy. Adhesion is directly related to surface roughness and can be described by the Cassie-Baxter equation, indicating that a smaller contact area between the bubble and the surface results in a larger contact angle. Buoyancy, on the other hand, is related to bubble volume, which is determined by the electrochemically active surface area (ECSA) of the catalytic layer (CL). Ultimately, the balance between adhesion and buoyancy determines the bubble detachment behavior, including detachment time and diameter, which collectively influence the bubble coverage and CL polarization.

[0005] Superhydrophobic surfaces with variable morphologies result in varying bubble contact areas (BCA) and electrochemically active surface areas (ECSA) during bubble growth, convergence, and separation, leading to significant buoyancy and adhesion. Qualitative analysis shows that BCA and ECSA increase with increasing adhesion and buoyancy, respectively. Some studies indicate that the impact of ECSA on bubble growth is not negligible, but others suggest that higher ECSA may lead to performance degradation due to differences in adhesion, and there may be a negative correlation between the gas-phobicity of the electrocatalyst and ECSA.

[0006] While existing research has explored ways to improve bubble detachment by increasing surface roughness, the question of how to adjust the relative magnitudes of buoyancy and adhesion forces by designing the morphology of the flow channel (CL) to avoid the formation of large bubbles and delay their simultaneous detachment remains unanswered. Some studies have employed macroscopic bubble dynamics and electrochemical models to investigate bubble evolution within the CL, diffusion layer, and flow channel. However, these studies face challenges in revealing the variables influencing bubble growth and detachment, particularly the morphology of the CL.

[0007] In summary, the influence of superhydrophobic surface morphology on bubble evolution behavior and polarization curves involves multiple aspects. Different surface morphologies can alter the bubble growth, migration, and detachment processes. Surface roughness and geometry can affect bubble contact angle, adhesion, and buoyancy; how these factors interact in bubble formation and removal is a key question. Summary of the Invention

[0008] To address the critical issue of superhydrophobic surfaces affecting bubble generation and removal in existing technologies, this invention proposes a catalyst simulation method based on a lattice Boltzmann model. This method is applied to the field of renewable energy water electrolysis for hydrogen production. During electrolyzer operation, the catalyst surface morphology affects bubble generation and removal. Using the Shan-Chen multi-component lattice Boltzmann model, the principle of bubble generation and removal is revealed, guiding the fabrication of catalyst morphologies.

[0009] The specific technical solution adopted in this invention is as follows:

[0010] This invention provides a catalyst simulation method based on the lattice Boltzmann model, as detailed below:

[0011] S1. Based on the actual situation, three typical superhydrophobic catalyst morphologies, namely nanoparticles (NP), nanorods (NR) and hierarchical nanostructures (HN), are constructed in the simulated region of the catalyst layer, and corresponding boundary conditions are set to make the simulation results more consistent with the actual situation.

[0012] S2. Based on the superhydrophobic catalyst morphologies constructed in S1, the bubble contact area (BCA) and electrochemical active surface area (ECSA) of each catalyst layer were abstracted from their respective physical parameters for simulation.

[0013] S3. Based on the Shan-Chen multi-component lattice Boltzmann model and the two physical parameters abstracted from S2, simulations were performed on different superhydrophobic catalyst morphologies to obtain the bubble detachment time, bubble detachment diameter, and bubble coverage range under the corresponding morphologies.

[0014] Preferably, the boundary conditions in step S1 are set as follows:

[0015] S11. The simulated region of the catalyst layer is defined as a three-phase system consisting of solid, liquid, and gas. The velocity boundary condition is used as the upper boundary of the liquid phase, and a specified velocity u is used at the liquid phase inlet. f The rebound formula for the Dirichlet boundary condition is that the catalyst surface, as the solid boundary, is arranged as a no-slip velocity boundary condition and hydrogen production active site, while the remaining boundary surfaces are considered as open boundaries.

[0016] S12. It is initially assumed that the simulation region consists only of the liquid phase and there is no gas phase, and that the initial velocities of both the liquid and gas phases are zero.

[0017] Furthermore, in step S12, the density of the liquid phase is set to 1, and the density of the gas phase is set to 0.

[0018] Preferably, in S2, the bubble contact area refers to the solid area in contact with the bubble, which is obtained during the process of describing the adhesion force using the Cassie-Baxter equation.

[0019] Preferably, in S3, the Shan-Chen multi-component lattice Boltzmann model is as follows:

[0020] S31. During the solution process, the simulation region of the catalyst layer is discretized, and the fluid motion is described by the particle distribution function on the discrete grid; it is assumed that each particle can only move along a finite direction and velocity.

[0021] S32. When dealing with gas-liquid two-phase flow, the gas and liquid phases have different particle distribution functions on the discrete lattice; different components of the gas-liquid two-phase flow have different physical properties and are coupled through specific interaction forces.

[0022] S33. Introduce three interaction forces: Shan-Chen potential, fluid-solid surface tension, and gravity, to simulate the interaction between different fluid components, between fluid and solid, and between the forces exerted by gravity.

[0023] S34. Within each time step, particles first collide on the discrete lattice and then propagate to adjacent lattice points; the collision process is implemented using the BGK model, while the propagation process is completed according to the discrete velocity direction.

[0024] S35. At each grid point, the particle distribution function is updated by the difference between it and the local equilibrium distribution function.

[0025] Furthermore, in S32, the physical properties include velocity, density, pressure, and viscosity.

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] This invention constructs three typical catalyst morphologies and abstracts two parameters—bubble coverage area and electrochemically active surface area—to describe catalyst morphology. Appropriate boundary conditions are set according to actual conditions to make the simulation results more realistic. The Shan-Chen multi-component lattice Boltzmann model is used to analyze bubble separation time, bubble diameter, and bubble coverage in different catalyst morphologies. This invention reveals the relationship between catalyst morphology and bubble evolution, which can better guide catalyst morphology optimization and promote the improvement of the performance of water electrolysis hydrogen production systems. Attached Figure Description

[0028] Figure 1 This is a simulation flowchart of the method of the present invention.

[0029] Figure 2 The simplified structures and boundary conditions for the NP, NR, and HN morphologies in this invention are shown in the figure (for illustrative purposes, the particles and rods are uniformly distributed in the figure).

[0030] Figure 3 (a) The average detachment diameter of bubbles on hydrophobic and hydrophilic surfaces in NR, NP, and HN morphologies, as described in the embodiments of the present invention. (b) The change in bubble height over time and detachment time in NR, NP, and HN morphologies.

[0031] Figure 4This invention presents (a) the relationship between bubble coverage and bubble detachment time in the embodiments of the present invention, and (b) the change in bubble coverage over time for NR, NP, and HN morphologies. Detailed Implementation

[0032] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention can be combined accordingly, provided that there is no mutual conflict.

[0033] like Figure 1 The diagram illustrates a catalyst simulation method based on a lattice Boltzmann model provided by this invention. In practice, superhydrophobic surfaces facilitate bubble detachment, primarily influenced by bubble adhesion and buoyancy. Adhesion is directly related to surface roughness and can be described by the Cassie-Baxter equation, indicating that a smaller bubble contact area (BCA) results in a larger bubble contact angle. Buoyancy is related to bubble volume, determined by the electrochemically active surface area (ECSA) of the catalyst layer (CL). Ultimately, the balance between adhesion and buoyancy determines the bubble detachment behavior, including detachment time and diameter, which collectively influence the bubble coverage and CL polarization. Superhydrophobic surfaces with variable morphologies lead to different BCA and ECSA during bubble growth, convergence, and detachment, resulting in significant buoyancy and adhesion. Qualitative analysis shows that BCA and ECSA increase with increasing adhesion and buoyancy, respectively. Some studies indicate that the impact of ECSA on bubble growth cannot be ignored, but other studies have shown that higher ECSA may lead to performance degradation due to differences in adhesion, and there may be a negative correlation between the gas-repellency of the electrocatalyst and ECSA.

[0034] Based on the above principles, the catalyst simulation method based on the lattice Boltzmann model of the present invention is as follows:

[0035] S1. Based on the actual situation, construct three typical superhydrophobic catalyst morphologies—nanoparticles, nanorods, and layered nanostructures—within the simulated region of the catalyst layer, and set corresponding boundary conditions to make the simulation results more consistent with the actual situation.

[0036] As a preferred embodiment of the present invention, the boundary condition setting method is as follows:

[0037] S11. The simulated region of the catalyst layer is defined as a three-phase system consisting of solid, liquid, and gas. The velocity boundary condition is used as the upper boundary of the liquid phase, and a specified velocity u is used at the liquid phase inlet. f The rebound formula for the Dirichlet boundary condition is that the catalyst surface, as the solid boundary, is arranged as a no-slip velocity boundary condition and hydrogen production active site, while the remaining boundary surfaces are considered as open boundaries.

[0038] S12. Initially, it is assumed that the simulation region consists only of the liquid phase and that there is no gas phase. Specifically, the density of the liquid phase is set to 1, and the density of the gas phase is set to 0. Furthermore, the initial velocities of both the liquid and gas phases are zero.

[0039] S2. Based on S1, different superhydrophobic catalyst morphologies were constructed, and the two physical parameters of bubble contact area and electrochemical active surface area were abstracted from the corresponding catalyst layer morphology for simulation.

[0040] In a preferred embodiment of the present invention, the bubble contact area refers to the solid area in contact with the bubble, which is obtained in the process of describing the adhesion force using the Cassie-Baxter equation.

[0041] S3. Based on the Shan-Chen multi-component lattice Boltzmann model and the two physical parameters abstracted from S2, simulations were performed on different superhydrophobic catalyst morphologies to obtain the bubble detachment time, bubble detachment diameter, and bubble coverage range under the corresponding morphologies.

[0042] As a preferred embodiment of the present invention, the Shan-Chen multi-component lattice Boltzmann model is as follows:

[0043] S31. During the solution process, the simulation region of the catalyst layer is discretized, and the fluid motion is described by the particle distribution function on the discrete grid; it is assumed that each particle can only move along a finite direction and velocity.

[0044] S32. When dealing with gas-liquid two-phase flow, the gas and liquid phases have different particle distribution functions on the discrete lattice; different components of the gas-liquid two-phase flow have different physical properties (including velocity, density, pressure, and viscosity), and are coupled through specific interaction forces.

[0045] S33. Introduce three interaction forces: Shan-Chen potential, fluid-solid surface tension, and gravity, to simulate the interaction between different fluid components, between fluid and solid, and between the forces exerted by gravity.

[0046] S34. Within each time step, particles first collide on the discrete lattice and then propagate to adjacent lattice points; the collision process is implemented using the BGK model, while the propagation process is completed according to the discrete velocity direction.

[0047] S35. At each grid point, the particle distribution function is updated by the difference between it and the local equilibrium distribution function.

[0048] The Shan-Chen multi-component lattice Boltzmann model in this invention will be described in detail below.

[0049] This invention employs a three-dimensional single-relaxation time pseudopotential D3Q19 lattice Boltzmann (LB) model to study gas evolution behavior on catalyst surfaces. This model can effectively calculate the two-phase flow of water and bubbles at the mesoscale.

[0050] Governing equations – Discrete velocity distribution function f i (x,t), also known as the particle swarm, is a core parameter of the Lattice Boltzmann Method (LBM). The variable i in the function represents the potential direction of the particle velocity. In the D3Q19 model, the value of variable i ranges from 1 to 19. Mass density and momentum density can be calculated using f... i Moment weighted sum calculation:

[0051]

[0052]

[0053] Where ρ represents mass density, u represents velocity, and c i The discrete velocity is represented by x and t, which represent the particle's position and time.

[0054] The lattice Boltzmann equations are obtained by discretizing the Boltzmann equations in terms of velocity, physical space, and time:

[0055] f i (x+c i Δt, t+Δt)=f i (x,t)+Ω i (x,t)+S i (x,t) (3)

[0056] This indicates that, in subsequent time steps, the particle is subjected to the collision term Ω. i and source item S i The influence of these factors causes them to move to adjacent positions at a specific speed. The Bhatnagar-Gross-Krook (BGK) operator is used as the collision operator in Navier-Stokes simulations:

[0057]

[0058] Where τ represents the relaxation time, f i eq The equilibrium distribution of particles is represented by the following formula.

[0059]

[0060] Among them, w i Indicates the weighting coefficient. This represents the speed of sound in the isothermal model. The weighting factors and discrete velocities for D3Q19 are provided:

[0061]

[0062]

[0063] Alternatively, the source term S in equation (3) i It can be replaced by the force term:

[0064]

[0065] Guo's method defines the force term as:

[0066]

[0067] Where F is the net external force acting on the fluid particle.

[0068] The Shan-Chen pseudopotential method is used to simulate two-phase flow. This model calculates the forces interacting with each component in the multi-component system to generate the simulated flow, as shown below:

[0069]

[0070] Among them, F SC(σ) This represents the Shan-Chen force density experienced by fluid particles. Representing fluid σ and The interaction strength between the components is measured, and ψ(σ) represents the pseudopotential of component σ. The equations proposed in this invention describe the interaction between water and gas, which constitute the fluid components within the PEMWE system. It is worth acknowledging that this model follows individual f... i (σ) This is used to characterize the distribution of each particle. Unlike the single-component model, in the multi-component pseudopotential model, the equilibrium distribution of particles is determined by the center-of-mass velocity:

[0071]

[0072] The pseudopotential is defined as:

[0073] ψ(ρ)=ρ0[1-exp(-ρ / ρ0)] (12)

[0074] Where ρ0 represents the reference density. Furthermore, the equation of state can be expressed as:

[0075]

[0076] Where p represents the fluid pressure density.

[0077] In addition to the interaction between fluids, the surface tension between a fluid and a solid can be described by the same equation:

[0078]

[0079] Among them, F s(σ) The surface tension density represents the interaction strength between the fluid and the solid boundary, denoted as G. σs In addition, there is an indicator function s(x) representing fluid and solid nodes, with a value of 1 for fluid and 0 for solid.

[0080] The gravitational density can be obtained by multiplying the gravitational acceleration g by the fluid density.

[0081] F g =ρg (15)

[0082] The density gradient created by the mixing of two fluids is reflected in gravity, thus producing a buoyancy effect. In summary, the fluid particles in the model are affected by three forces: interfacial forces between the fluids, interfacial forces between the solid and the fluid, and gravity.

[0083] F = F SC(σ) +F s(σ) +F g (16)

[0084] Equation (3) is typically divided into two distinct phases during execution. The initial phase involves collisions:

[0085]

[0086] Among them, f i * This represents the particle distribution function after the collision.

[0087] The subsequent stage is called streaming transmission:

[0088] f i (x+c i Δt, t+Δt)=f i * (x,t) (18)

[0089] Geometry and Boundary Conditions—The focus of this invention is to evaluate the influence of CL morphology on bubble evolution. To simplify the calculations, the morphology of the CL is simplified. Nanoparticles are represented as spheres, nanorods as cuboids, and layered nanostructures as combinations of cuboids and spheres, such as... Figure 2 As shown. These structures have representative specific surface areas, which is helpful for morphological analysis in this invention. Due to the inherent randomness of the nanoarray distribution, the model also randomly distributes simplified structures.

[0090] This invention proposes three distinct components: the gas phase, the liquid phase, and the solid phase, along with their corresponding boundary conditions, such as... Figure 2 As shown.

[0091] Velocity boundary conditions Figure 2 Marker A) is used as the upper boundary of the liquid phase. A specified velocity u is used at the fluid inlet. f The bounce formula for the Dirichlet boundary conditions:

[0092]

[0093] in, This represents the particle distribution function after the bounce.

[0094] The catalyst surface as a solid-phase boundary ( Figure 2 Marker B) is positioned as the no-slip velocity boundary condition and the active site for hydrogen production. For the no-slip velocity boundary condition, a mid-course bounce scheme is adopted as follows:

[0095]

[0096] in, Indicates the original direction In the opposite direction of velocity, x b This represents the node near the interface between the solid and fluid phases in the fluid. Furthermore, considering the gas-generating area, the solid surface is subjected to the following pressure boundary conditions:

[0097]

[0098] Where u w Defined as:

[0099]

[0100] solid surface density ρ w It is proportional to the input current density:

[0101]

[0102] Where M represents the molar mass of the gas, j represents the current density, and F represents the Faraday constant. It should be noted that the above boundary conditions apply only to the initial stage of the calculation to ensure that the gas is completely expelled from the catalyst surface. The boundary conditions for the catalyst surface are set as pressure boundary conditions for the hydrogen production stage (0–0.6 ms), and no boundary conditions are applied for the hydrogen-free stage (0.6–1.2 ms). Please note that the segmentation of these stages is determined based on preliminary simulation results.

[0103] Other boundary surfaces ( Figure 2 The marker C) in the diagram is considered an open boundary.

[0104] Initial conditions—It is initially assumed that the model consists only of a liquid phase and no gas phase. Specifically, the density of the liquid phase is set to 1, and the density of the gas phase is set to 0. The initial velocities of both phases are zero.

[0105] Model Validation – This invention synthesized CL with morphology similar to NR and HN, and conducted related experiments. The accuracy of the model was verified by observing the diameter of the bubbles detached from the catalyst surface.

[0106] Unit Conversion—LB simulations involve the application of lattice units. Therefore, the conversion of physical parameters to lattice units significantly affects accuracy, validity, and consistency. Since capillary forces dominate within the micropores, thus replacing the influence of viscous forces, this invention neglects the viscosity ratio between oxygen and water. The density, dynamic viscosity, and surface tension of liquid water at room temperature are ρ0, ... p = 997.043 kg / m 3 ν p =8.964×10 -7 m 2 / s、σ p = 0.072 N / m. (Based on length scale C) l The value can be obtained for the time scale C. t Derivation:

[0107] C t =C l 2 (τ-0.5)c s 2 / ν p (twenty four)

[0108] Pressure scale C p From including Euler number (Eu=p / ρu) 2 ) and the Weiber number (We = ρu) 2 The dimensionless number of l / σ is obtained as follows:

[0109] C p =σ p / (C l σ l (25)

[0110] In this invention, the length dimension C l The lattice unit σ is 1 μm, and is selected according to the Laplace test. l Given a surface tension of 0.075, the pressure scale C is calculated. p and time scale C t The values ​​are 9.60 × 10 5 Pa and 1.86×10 -7 s.

[0111] Numerical Process – This invention uses self-written Python code to simulate the Shan-Chen multi-component lattice Boltzmann model, which runs in parallel on an NVIDIA GeForce RTX 3090 GPU with a unified computing device architecture.

[0112] In this embodiment, based on the aforementioned Shan-Chen multi-component lattice Boltzmann model, the method for analyzing the influence of catalyst layer morphology on bubble generation and detachment in the electrolyzer specifically includes the following steps:

[0113] S1. Based on the actual situation, construct three typical superhydrophobic catalyst morphologies—nanoparticles, nanorods, and layered nanostructures—within the simulated region of the catalyst layer, and set corresponding boundary conditions to make the simulation results more consistent with the actual situation.

[0114] S2. Based on S1, different superhydrophobic catalyst morphologies were constructed, and the two physical parameters of bubble contact area and electrochemical active surface area were abstracted from the corresponding catalyst layer morphology for simulation.

[0115] S3. Based on the Shan-Chen multi-component lattice Boltzmann model and the two physical parameters abstracted from S2, simulations were performed on different superhydrophobic catalyst morphologies to obtain the bubble detachment time, bubble detachment diameter, and bubble coverage range under the corresponding morphologies.

[0116] To verify the accuracy of the lattice Boltzmann model constructed in this invention, it was compared with actual experimental results. NP (low BCA and low ECSA), NR (high BCA and high ECSA), and HN (low BCA and high ECSA) correspond to the three different bubble evolution modes observed above: slow detachment of small bubbles, fast separation of large bubbles, and rapid separation of small bubbles.

[0117] Depend on Figure 3 It can be seen that the bubble detachment time and diameter are affected not only by surface tension and contact area, but also by current density, surface tension, and surface morphology. Specifically, the detachment height of bubbles on non-hydrophilic surfaces is greater than that on hydrophilic surfaces, and the detachment time on non-hydrophilic surfaces is shorter. Furthermore, by changing the morphology and the coefficient of the Shan-Chen force to adjust the bubble diameter, the diameter variation of bubbles on different types of surfaces (i.e., hydrophilic and non-hydrophilic surfaces) was obtained. Simulation results show that the average bubble diameter is affected by the surface tension of the solid surface and the morphology of the contact layer (CL). On surfaces with the same morphology, the bubble diameter on non-hydrophilic surfaces is on average 20% smaller than that on hydrophilic surfaces. The HN morphology, due to its high specific surface area and small surface contact area, exhibits the shortest detachment time.

[0118] Depend on Figure 4It is known that the bubble detachment time is inversely proportional to the bubble coverage; the shorter the detachment time, the higher the bubble coverage. Both experimental results and theoretical simulations show that increasing the bubble diameter leads to an increase in bubble coverage. In the NR morphology, when the bubble diameter increases from 120 μm to 207 μm, the bubble coverage increases from 6.2% to 31.7%. In the HN morphology, when the bubble diameter increases to 201 μm, the bubble coverage significantly increases to 39.0%. The experimental results may underestimate the true bubble coverage because they only calculated the coverage on the 2D image. Regarding the effect of bubble coverage on the polarization curve, experimental results show that the polarization loss is mainly caused by activation loss due to bubble blockage of reaction sites, especially at current densities below 1.0 A / cm². 2 The mass transfer loss is minimal at this time. Furthermore, empirical model tests of the HN morphology show better bubble coverage and activation loss performance, thus demonstrating its superior electrochemical performance.

[0119] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for simulating a catalyst based on a lattice-Boltzmann model, characterized by, Specifically as follows: S1, according to the actual situation, construct three typical super gas repellent catalyst morphologies of nanoparticles, nanorods and layered nanostructures in the simulation area of the catalyst layer, and set the corresponding boundary conditions to make the simulation results more consistent with the actual situation; S2, based on the different super gas repellent catalyst morphologies constructed in S1, the two physical parameters of bubble contact area and electrochemical active surface area are abstracted from the corresponding catalyst layer morphology for simulation; S3, based on the Shan-Chen multi-component lattice Boltzmann model and the two physical parameters abstracted in S2, simulate different super gas repellent catalyst morphologies to obtain the bubble detachment time, bubble detachment diameter and bubble coverage range under the corresponding morphology; In S1, the boundary condition setting method is as follows: S11, the simulation area of the catalyst layer is specified as a solid-liquid-gas three-phase, the velocity boundary condition is used as the upper boundary of the liquid phase, the rebound formula of the Dirichlet boundary condition with a specified velocity is used at the liquid phase inlet, the catalyst surface as the solid phase boundary is arranged as a no-slip velocity boundary condition and a hydrogen-producing active site, and the remaining boundary surface is considered as an open boundary; S12, initially assume that the simulation area is only composed of a liquid phase, there is no gas phase, and the initial velocities of the liquid phase and the gas phase are both zero; In S2, the bubble contact area refers to the solid area in contact with the bubble, which is obtained in the process of describing the adhesion force using the Cassie-Baxter equation; In S3, the Shan-Chen multi-component lattice Boltzmann model is as follows: S31, when solving, the simulation area of the set catalyst layer is discretized, and the motion of the fluid is described by the particle distribution function on the discrete grid; it is assumed that each particle can only move along a limited direction and velocity; S32, when processing gas-liquid two-phase flow, the gas-liquid two-phase has different particle distribution functions on the discrete lattice; different components of gas-liquid two-phase flow have different physical properties, which are coupled through specific interaction forces; S33, introduce Shan-Chen potential, fluid-solid surface tension, and gravity as three interaction forces to simulate the interaction between different fluid components, fluid and solid, and gravity; S34, within each time step, particles first collide on the discrete lattice and then propagate to adjacent grid points; the collision process uses the BGK model, and the propagation process is completed according to the discrete velocity direction; S35, at each grid point, the distribution function of the particle is updated by the difference with the local equilibrium distribution function.

2. The method of claim 1, wherein the lattice-Boltzmann model-based catalyst simulation method is characterized by, In S12, the density of the liquid phase is set to 1, and the density of the gas phase is set to 0.

3. The method of claim 1, wherein the lattice-Boltzmann model-based catalyst simulation method is characterized by, In S32, the physical properties include velocity, density, pressure, and viscosity.

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