Catalyst simulation method based on lattice Boltzmann model

Abstract

The invention discloses a catalyst simulation method based on a lattice Boltzmann model, and belongs to the field of water electrolysis hydrogen production. According to the invention, three typical catalyst forms are constructed, and two parameters of a bubble coverage area and an electrochemical activity surface area are abstracted from the three typical catalyst forms to describe the morphology of the catalyst; corresponding boundary conditions are set according to actual conditions, so that simulation results better conform to the actual conditions; by using the Stan-Chen multi-component lattice Boltzmann model, the bubble separation time, the diameter of generated bubbles and the bubble coverage range in different catalyst forms are analyzed. The invention discloses the relationship between the morphology of the catalyst and the bubble evolution, and can better guide the morphology optimization of the catalyst and promote the improvement of the performance of a water electrolysis hydrogen production system.

Description

Technical Field

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

[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 several major methods to produce high-purity (99.999%) hydrogen in a sustainable manner. There are three main water electrolysis technologies: alkaline water electrolysis (AWE), proton exchange membrane water electrolysis (PEMWE), and solid oxide electrolyzer (SOEC). Among them, AWE and PEMWE are mature technologies, and their technical feasibility has been verified by long-term operating demonstration projects. During the operation of AWE or PEMWE systems, 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 surface of the catalyst layer (CL), and its life cycle includes nucleation, growth, and detachment stages. The bubble evolution behavior affects the electrocatalytic process and thus the efficiency of water splitting. For example, the generation of bubbles covering the redox reaction sites may block the active electrode area, increase the ohmic resistance in the electrolyte, and produce undesirable concentration gradients. To this end, bubble entrapment within the CL may limit the maximum current density, thereby limiting the hydrogen productivity. Furthermore, with the help of high-speed cameras, it has been shown that electrodes with denser and smaller bubbles exhibit reduced overpotentials. Controlling the growth and departure of bubbles is therefore considered an important way to improve the energy efficiency of water electrolyzers.

[0003] In order to improve the bubble management performance, researchers have carried out a variety of optimization strategies for the catalytic layer (CL). For example, by introducing nanostructures (such as nanosheets, nanoparticles, nanocones and nanotubes) to increase the surface roughness and form a super-aerophobic surface, the bubble detachment performance is improved. At the same time, a hierarchical electrode structure was developed to grow smaller catalysts on the nanoarray to expand the reaction area and enhance the super-aerophobic performance. Studies have shown that the bubble contact angle on the nanostructured surface is significantly higher than that on the flat surface, resulting in a significant reduction in bubble size. The hierarchical nanocone structure composed of nickel and carbon nanotubes showed significant improvements in bubble detachment, with an extremely small water droplet contact angle, showing excellent hydrophobicity and better performance than traditional flat electrodes. In addition, these improved electrodes are able to support higher current densities without passing potential, showing excellent electrochemical performance. The designed nanotube array electrode shows a significantly low overpotential at a specific current density, further improving the overall performance of the electrode.

[0004] Studies have shown that super-aerophobic surfaces facilitate bubble detachment, which is mainly affected by the adhesion and buoyancy of the bubbles. Adhesion is directly related to surface roughness and can be described by the Cassie-Baxter equation, which shows that the smaller the contact area between the bubble and the surface, the larger the contact angle of the bubble. The buoyancy of the bubble is related to the 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 the bubble detachment time and diameter, which together affect the bubble coverage and the polarization of the CL.

[0005] Superaerophobic surfaces with variable morphology result in different bubble contact areas (BCA) and electrochemically active surface areas (ECSA) during bubble growth, convergence, and separation, resulting in significant buoyancy and adhesion forces. Qualitative analysis shows that BCA and ECSA increase with increasing adhesion and buoyancy, respectively. Some studies have pointed out that the effect of ECSA on bubble growth cannot be ignored, but others have shown that higher ECSA may lead to performance degradation due to differences in adhesion forces, and there may be a negative correlation between the aerophobicity of electrocatalysts and ECSA.

[0006] Although studies have been conducted to improve bubble detachment by increasing surface roughness, it remains unknown how to adjust the relative magnitude of buoyancy and adhesion by designing the morphology of the CL to avoid the formation of large bubbles and delay the simultaneous detachment of bubbles. Some studies have used macroscopic bubble dynamics models and electrochemical models to explore the evolution of bubbles on the CL, diffusion layer, and flow channel. However, these studies face certain challenges in revealing the variables that affect bubble growth and detachment, especially the morphology of the CL.

[0007] In summary, the influence of super-aerophobic surface morphology on bubble evolution behavior and polarization curve involves multiple aspects. Different surface morphologies may change the growth, movement and detachment process of bubbles. Surface roughness and geometric structure may affect the contact angle, adhesion and buoyancy of bubbles. How these factors work together on the formation and removal of bubbles is a key issue. Summary of the invention

[0008] In view of the key problem of super-aerophobic surface on bubble generation and removal in the prior art mentioned above, the present invention proposes a catalyst simulation method based on the lattice Boltzmann model. The method is applied to the field of hydrogen production by electrolysis of water from renewable energy. The problem of catalyst surface morphology on bubble generation and removal during the operation of the electrolyzer is revealed through the Shan-Chen multi-component lattice Boltzmann model, which guides the production of catalyst morphology.

[0009] The specific technical solutions adopted by the present invention are as follows:

[0010] The present invention provides a catalyst simulation method based on the lattice Boltzmann model, which is as follows:

[0011] S1. According to the actual situation, three typical super-aerophobic catalyst forms, namely nanoparticles (NP), nanorods (NR) and hierarchical nanostructures (HN), are constructed in the simulation area 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 different super-aerophobic catalyst morphologies constructed in S1, the two physical parameters of bubble contact area (BCA) and electrochemically active surface area (ECSA) were abstracted from the corresponding catalyst layer morphology for simulation;

[0013] S3. Based on the Shan-Chen multi-component lattice Boltzmann model and the two physical parameters abstracted in S2, different super-aerophobic catalyst morphologies are simulated to obtain the bubble detachment time, bubble detachment diameter and bubble coverage under the corresponding morphologies.

[0014] Preferably, in S1, the boundary condition setting method is as follows:

[0015] S11. The simulation area of ​​the catalyst layer is defined as solid, liquid and gas three phases. The velocity boundary condition is used as the upper boundary of the liquid phase. The specified velocity u is used at the liquid phase inlet. f The rebound formulation of the Dirichlet boundary condition is used. The catalyst surface as the solid boundary is arranged as a no-slip velocity boundary condition and an active site for hydrogen production, and the remaining boundary surfaces are considered to be open boundaries.

[0016] S12. Initially assume that the simulation region consists of only liquid phase, no gas phase exists, and the initial velocities of the liquid phase and the gas phase are both zero.

[0017] Furthermore, in 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 by using the Cassie-Baxter equation to describe the adhesion force.

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

[0020] S31. When solving, the simulation area of ​​the set catalyst layer is discretized, and the movement 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 speed;

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

[0022] S33, introduce the three interaction forces of Shan-Chen potential, fluid-solid surface tension and gravity to simulate the interaction between different fluid components, between fluid and solid and the gravity;

[0023] S34. In each time step, particles first collide on the discrete grid and then propagate to adjacent grid points; the collision process is implemented using the BGK model, and 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 with the local equilibrium distribution function.

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

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

[0027] The present invention constructs three typical catalyst forms, and abstracts two parameters, namely, bubble coverage area and electrochemical active surface area, to describe the catalyst morphology; the corresponding boundary conditions are set according to the actual situation to make the simulation results more in line with the actual situation; the use of the Shan-Chen multi-component lattice Boltzmann model analyzes the bubble separation time, the diameter of the generated bubbles, and the bubble coverage range in different catalyst forms. The present invention reveals the relationship between catalyst morphology and bubble evolution, which can better guide the morphological optimization of the catalyst and promote the performance improvement of the water electrolysis hydrogen production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a simulation flow chart of the method of the present invention.

[0029] Figure 2 Simplified structures and boundary conditions of NP, NR and HN morphologies in the present invention (for illustration purposes, particles and rods are uniformly distributed in the figure).

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

[0031] Figure 4(a) The relationship between the bubble coverage and the bubble detachment time in the embodiment of the present invention. (b) The change of the bubble coverage of NR, NP and HN morphologies over time. DETAILED DESCRIPTION

[0032] The present invention is further described and illustrated below in conjunction with the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention can be combined accordingly without conflicting with each other.

[0033] like Figure 1 As shown, a catalyst simulation method based on the lattice Boltzmann model provided by the present invention. In fact, super-aerophobic surface helps the detachment of bubbles, which is mainly affected by the adhesion and buoyancy of bubbles. Adhesion is directly related to surface roughness and can be described by the Cassie-Baxter equation, indicating that the smaller the bubble contact area (BCA), the larger the contact angle of the bubble. The buoyancy of the bubble is related to the bubble volume, and the volume is determined by the electrochemically active surface area (ECSA) of the catalyst layer (CL). Ultimately, the balance of adhesion and buoyancy determines the detachment behavior of the bubble, including detachment time and diameter, which jointly affect the coverage of the bubble and the polarization of the CL. Super-aerophobic surface with variable morphology can lead to different BCA and ECSA during bubble growth, convergence and separation, thereby generating significant buoyancy and adhesion. Qualitative analysis shows that BCA and ECSA increase with the increase of adhesion and buoyancy, respectively. Some studies have pointed out that the effect of ECSA on bubble growth cannot be ignored, but others have shown that higher ECSA may lead to performance degradation due to differences in adhesion and that there may be a negative correlation between the gas repellency of the electrocatalyst and ECSA.

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

[0035] S1. According to the actual situation, three typical super-aerophobic catalyst forms, namely nanoparticles, nanorods and layered nanostructures, are constructed in the simulation area of ​​the catalyst layer, and corresponding boundary conditions are set 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 simulation area of ​​the catalyst layer is defined as solid, liquid and gas three phases. The velocity boundary condition is used as the upper boundary of the liquid phase. The specified velocity u is used at the liquid phase inlet. f The rebound formulation of the Dirichlet boundary condition is used. The catalyst surface as the solid boundary is arranged as a no-slip velocity boundary condition and an active site for hydrogen production, and the remaining boundary surfaces are considered to be open boundaries.

[0038] S12. Initially, it is assumed that the simulation region is composed of only a liquid phase and no gas phase exists. 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 the liquid phase and the gas phase are both zero.

[0039] S2. Based on the different super-aerophobic catalyst forms constructed in S1, the two physical parameters of bubble contact area and electrochemically active surface area are abstracted from the corresponding catalyst layer morphology for simulation.

[0040] As 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 using the Cassie-Baxter equation to describe the adhesion force.

[0041] S3. Based on the Shan-Chen multi-component lattice Boltzmann model and the two physical parameters abstracted in S2, different super-aerophobic catalyst morphologies are simulated to obtain the bubble detachment time, bubble detachment diameter and bubble coverage 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. When solving, the simulation area of ​​the set catalyst layer is discretized, and the movement 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 speed;

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

[0045] S33, introduce the three interaction forces of Shan-Chen potential, fluid-solid surface tension and gravity to simulate the interaction between different fluid components, between fluid and solid and the gravity;

[0046] S34. In each time step, particles first collide on the discrete grid and then propagate to adjacent grid points; the collision process is implemented using the BGK model, and 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 with the local equilibrium distribution function.

[0048] The Shan-Chen multi-component lattice Boltzmann model in the present invention is described in detail below.

[0049] The present invention adopts a three-dimensional single relaxation time pseudopotential D3Q19 lattice Boltzmann (LB) model to study the gas precipitation behavior on the catalyst surface. The model can effectively calculate the two-phase flow of water and bubbles on a mesoscopic scale.

[0050] Governing equations - discrete velocity distribution function f i (x,t), also known as the particle group, is the 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 variable i ranges from 1 to 19. The mass density and momentum density can be calculated using f i The moment weighted sum calculation is:

[0051]

[0052]

[0053] Where ρ represents mass density, u represents velocity, c i represents the discrete velocity, x and t represent the position and time of the particle.

[0054] The lattice Boltzmann equation is obtained by discretizing the Boltzmann equation 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 means that in subsequent time steps, the particle is subject to the collision term Ω i and the source term S i The Bhatnagar-Gross-Krook (BGK) operator is used as the collision operator in the Navier-Stokes simulation:

[0057]

[0058] Where τ represents the relaxation time, f i eq represents the equilibrium distribution of particles and is determined by

[0059]

[0060] Among them, w i represents the weighting coefficient, represents the speed of sound in the isothermal model. The weight factor and discretized velocity of D3Q19 are provided:

[0061]

[0062]

[0063] Or, the source term S in equation (3) i Can be replaced by a force term:

[0064]

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

[0066]

[0067] Where F is the total external force acting on the fluid particles.

[0068] The Shan-Chen pseudopotential method is used to simulate the two-phase flow. The model calculates the forces interacting with each component in a multi-component system to produce a simulated flow as follows:

[0069]

[0070] Among them, F SC(σ) represents the Shan-Chen force density on fluid particles, represents the fluid σ and is a measure of the strength of the interaction between the components σ, while ψ(σ) represents the pseudopotential of the component σ. The equations proposed in the present invention describe the interaction between water and gas, which constitute the fluid components in the PEMWE system. It is worth recognizing that the model follows the individual f of equations (1)–(9) i (σ) to characterize the distribution of each particle. Unlike the single-component model, the equilibrium distribution of particles in the multi-component pseudopotential model 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. In addition, the state equation can be expressed as

[0075]

[0076] Where p represents the fluid pressure density.

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

[0078]

[0079] Among them, F s(σ) represents the surface tension density between the fluid and the solid, and the interaction strength between the fluid component and the solid boundary is represented by G σs In addition, there is an indicator function s(x) representing the 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 generated by the mixing of two fluids will be reflected in the gravity, thus producing a buoyancy effect. In summary, the fluid particles in the model are affected by three forces: the interface force between the fluids, the interface force between the solid and the fluid, and gravity:

[0083] F=F SC(σ) +F s(σ) +F g (16)

[0084] Equation (3) is usually divided into two different phases during execution. The initial phase involves collision:

[0085]

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

[0087] The subsequent phase is called streaming:

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

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

[0090] The present invention proposes three different components, namely, gas phase, liquid phase and solid phase, and their corresponding boundary conditions, such as Figure 2 shown.

[0091] Velocity boundary conditions ( Figure 2 The mark A) in the figure is used as the upper boundary of the liquid phase. The velocity u is used at the fluid inlet. f The rebound formula of the Dirichlet boundary condition is:

[0092]

[0093] in, Represents the particle distribution function after rebound.

[0094] The catalyst surface as the solid phase boundary ( Figure 2 The mark B in the figure is arranged as a no-slip velocity boundary condition and an active site for hydrogen production. For the no-slip velocity boundary condition, a mid-course rebound scheme is adopted as follows:

[0095]

[0096] in, Represents the original direction The opposite direction of velocity, x b represents the nodes in the fluid close to the interface between the solid phase and the fluid phase. In addition, considering the gas production area, the solid surface is subject to the following pressure boundary conditions:

[0097]

[0098] where u w Defined as:

[0099]

[0100] Solid surface density ρ w 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 are only applicable to the initial stage of the calculation to ensure that the gas is completely discharged from the catalyst surface. The boundary conditions on the catalyst surface are set as pressure boundary conditions for the hydrogen production stage (0-0.6ms), and no boundary conditions are applied to the hydrogen-free stage (0.6-1.2ms). Please note that the segmentation of these stages is determined based on the preliminary simulation results.

[0103] The remaining boundary surfaces ( Figure 2 Marked in C) is considered an open boundary.

[0104] Initial Conditions - Initially assume that the model consists of only a liquid phase, with no gas phase present. Specifically, set the density of the liquid phase to 1 and the density of the gas phase to 0. The initial velocities of both phases are zero.

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

[0106] Unit conversion - The calculation of LB simulation involves the use of lattice units. Therefore, the conversion of physical parameters to lattice units significantly affects the accuracy, validity and consistency. Since the capillary force in the microscopic pores is dominant, thus replacing the influence of viscous forces, the viscosity ratio between oxygen and water is ignored in this invention. The density, dynamic viscosity and surface tension of liquid water at room temperature are ρ p =997.043kg / m 3 , ν p =8.964×10 -7 m 2 / s、σ p =0.072N / m. From the length scale C l The value of the time scale C t Derivation of:

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

[0108] Pressure scale C p It can be derived from the Euler number (Eu = p / ρu 2 ) and Weber number (We=ρu 2 The dimensionless number of l / σ) is obtained:

[0109] C p =σ p / (C l σ l ) (25)

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

[0111] Numerical process - The present invention implements the simulation of the Shan-Chen multi-component lattice Boltzmann model through self-written Python code, which runs in parallel on the NVIDIA GeForce RTX 3090 GPU with a compute unified device architecture.

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

[0113] S1. Construct three typical super-aerophobic catalyst forms, namely nanoparticles, nanorods and layered nanostructures, in the simulation area of ​​the catalyst layer according to the actual situation, and set corresponding boundary conditions to make the simulation results more consistent with the actual situation;

[0114] S2, based on the different super-aerophobic catalyst morphologies constructed in S1, 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 in S2, different super-aerophobic catalyst morphologies are simulated to obtain the bubble detachment time, bubble detachment diameter and bubble coverage under the corresponding morphologies.

[0116] In order to verify the accuracy of the lattice Boltzmann model constructed by the present invention, it was compared with the 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, namely, small bubbles slowly detach, large bubbles quickly detach and small bubbles quickly detach.

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

[0118] Depend on Figure 4It can be seen 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 an increase in bubble diameter will lead to an increase in bubble coverage. In the NR morphology, when the bubble diameter increases from 120μm to 207μm, the bubble coverage increases from 6.2% to 31.7%. In the HN morphology, when the bubble diameter increases to 201μm, the bubble coverage increases significantly to 39.0%. The experimental results may underestimate the actual bubble coverage because only the coverage on the 2D image is calculated. In terms of the effect of bubble coverage on the polarization curve, the experimental results show that the polarization curves under different solid forms indicate that the polarization loss is mainly caused by the activation loss caused by the bubble blocking the reaction sites, especially when the current density is lower than 1.0A / cm 2 Moreover, the HN morphology tested by the empirical model showed better bubble coverage and activation loss, thus demonstrating its superior electrochemical performance.

[0119] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A catalyst simulation method based on a lattice Boltzmann model, characterized in that: The details are as follows: S1. Construct three typical super-aerophobic catalyst forms, namely nanoparticles, nanorods and layered nanostructures, in the simulation area of ​​the catalyst layer according to the actual situation, and set corresponding boundary conditions to make the simulation results more consistent with the actual situation; S2, based on the different super-aerophobic catalyst morphologies constructed in S1, the two physical parameters of bubble contact area and electrochemical active surface area were 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, different super-aerophobic catalyst morphologies are simulated to obtain the bubble detachment time, bubble detachment diameter and bubble coverage under the corresponding morphologies.

2. A catalyst simulation method based on a lattice Boltzmann model as claimed in claim 1, characterized in that: In S1, the boundary condition setting method is as follows: S11. The simulation area of ​​the catalyst layer is defined as solid, liquid and gas three phases. The velocity boundary condition is used as the upper boundary of the liquid phase. The specified velocity u is used at the liquid phase inlet. f The rebound formulation of the Dirichlet boundary condition is used. The catalyst surface as the solid boundary is arranged as a no-slip velocity boundary condition and an active site for hydrogen production, and the remaining boundary surfaces are considered to be open boundaries. S12. Initially assume that the simulation region consists of only liquid phase, no gas phase exists, and the initial velocities of the liquid phase and the gas phase are both zero.

3. A catalyst simulation method based on a lattice Boltzmann model as claimed in claim 2, characterized in that: In the above S12, the density of the liquid phase is set to 1, and the density of the gas phase is set to 0.

4. A catalyst simulation method based on a lattice Boltzmann model as claimed in claim 1, characterized in that: In S2, the bubble contact area refers to the solid area in contact with the bubble, which is obtained by using the Cassie-Baxter equation to describe the adhesion force.

5. The catalyst simulation method based on the lattice Boltzmann model according to claim 1, characterized in that: 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 movement 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 speed; S32. When dealing with gas-liquid two-phase flow, the gas and liquid phases have different particle distribution functions on the discrete grid; different components of the gas-liquid two-phase flow have different physical properties and are coupled through specific interaction forces; S33, introduce the three interaction forces of Shan-Chen potential, fluid-solid surface tension and gravity to simulate the interaction between different fluid components, between fluid and solid and the gravity; S34. In each time step, particles first collide on the discrete grid and then propagate to adjacent grid points; the collision process is implemented using the BGK model, and the propagation process is completed according to the discrete velocity direction; S35. At each grid point, the particle distribution function is updated by the difference with the local equilibrium distribution function.

6. A catalyst simulation method based on a lattice Boltzmann model as claimed in claim 5, characterized in that: In the S32, the physical properties include speed, density, pressure, and viscosity.

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