Hydrogen-doped natural gas mixer structure optimization method and system based on numerical simulation

Abstract

The present application relates to a hydrogen-doped natural gas mixer structure optimization method and system based on numerical simulation, comprising: establishing a parameterized geometric model of the hydrogen-doped natural gas mixer, the structure of the parameterized geometric model comprising a mixer shell; based on the parameterized geometric model, establishing a hydrogen-doped natural gas mixer CFD model; taking the best mixing uniformity of the outlet cross-section of the flow channel as the target, based on the numerical simulation of the hydrogen-doped natural gas mixer CFD model, optimizing the first group of design variables, including the length-diameter ratio of the shell cross-section, the number of mixing units, and the spacing of adjacent mixing units; under the constraint of the optimal value of the first group of design variables, taking the best mixing uniformity of the outlet cross-section of the flow channel and the minimum flow field pressure drop as the target, based on the numerical simulation of the hydrogen-doped natural gas mixer CFD model, optimizing the second group of design variables, including the length of the shell and the twist angle of the mixing unit. The present application reduces the calculation workload, improves the efficiency and accuracy of the optimization design process.

Description

Technical Field

[0001] This invention relates to the field of flow field numerical simulation technology, and in particular to a method and system for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation. Background Technology

[0002] The development of hydrogen energy relies on technological support across multiple stages, including production, transportation, storage, and utilization. Considering blending an appropriate proportion of hydrogen into existing natural gas networks to achieve hydrogen energy utilization through hydrogen-blended natural gas not only alleviates dependence on fossil fuels but also effectively reduces emissions of pollutants such as CO2, while providing technological reserves for the large-scale application of hydrogen energy. Currently, numerous demonstration projects for natural gas blending with hydrogen have been conducted both domestically and internationally, all demonstrating the feasibility of this approach.

[0003] The mixing of natural gas and hydrogen is a crucial technology for the transportation of hydrogen-blended natural gas (HCNG). A rational and advanced mixing structure and layout are key to ensuring uniform mixing. Hydrogen and methane have significantly different properties, especially in combustion performance. If the mixing uniformity of the two gases is low, or if stratification occurs in the downstream pipeline, it can lead to low burner thermal efficiency and even unsafe combustion. For carbon steel pipelines, it can easily cause hydrogen corrosion, hydrogen bulging, and hydrogen embrittlement. The mixing system consists of a long-tube trailer (hydrogen storage cylinder group), a hydrogen pressure reducing device, an H2 / CNG mixing unit, and an HCNG buffer tank. The H2 / CNG mixing unit consists of a hydrogen pipeline system, a CNG pipeline system, a static mixer, a pneumatic emergency shut-off valve, a pressure transmitter, instrument valves, a gas alarm, a hydrogen alarm, and a hydrogen analyzer.

[0004] Current optimization design methods for static mixers require repeated adjustments to design parameters to select the optimal one. This is labor-intensive and cannot accurately describe the relationship between design parameters and performance response, making it difficult to achieve the optimal design of natural gas hydrogen blending mixers. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation. The aim is to reduce computational workload and improve the efficiency and accuracy of the optimization design process.

[0006] The technical solution adopted in this invention is as follows:

[0007] This application also provides a method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation, including:

[0008] S1. Establish a parameterized geometric model of a hydrogen-blended natural gas mixer. The structure of the parameterized geometric model includes a mixer shell, in which a flow channel is formed. Several mixing units are provided in the flow channel. The mixing unit is formed by a rectangular blade twisting in the opposite direction with its center line perpendicular to the short side as the center line.

[0009] S2. Based on the parametric geometric model, establish a CFD model of the hydrogen-blended natural gas mixer;

[0010] S3. With the goal of achieving the best mixing uniformity at the outlet section of the flow channel, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables and obtain the optimal values ​​of the first set of design variables. The first set of design variables includes the aspect ratio of the shell cross section, the number of mixing units, and the spacing between adjacent mixing units.

[0011] S4. Under the constraint of the optimal values ​​of the first set of design variables, with the goal of achieving the best mixing uniformity at the outlet section of the flow channel and minimizing the pressure drop in the flow field, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables and obtain the optimal values ​​of the second set of design variables. The second set of design variables includes the length of the shell and the torsion angle of the mixing unit.

[0012] The further technical solution is as follows:

[0013] With the objectives of achieving optimal mixing uniformity and minimizing pressure drop at the outlet cross-section of the flow channel, numerical simulation calculations were performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables, including:

[0014] Set the constraint range of the second set of design variables, and select sample points by Latin hypercube sampling within the constraint range. Perform batch calculations on the sample points to obtain the corresponding simulation results, including the mixing uniformity index and flow field pressure drop of the channel outlet section.

[0015] Using response surface modeling tools, the relationship between the second set of design variables for the sample points and the corresponding simulation results is fitted to form a response surface.

[0016] The genetic algorithm is applied to find the optimal solution on the response surface. The optimal solution is obtained through iterative calculation, which yields the optimal values ​​of the second set of design variables corresponding to the optimal mixing uniformity index and the flow field pressure drop.

[0017] The response surface modeling tool is the Kriging model:

[0018] y(x) = μ + z(x)

[0019] In the above formula, y(x) is the response value of sample point x, μ represents the expected value of the response, and z(x) is a static random process with a mean of 0 and covariance, wherein the covariance is:

[0020] Cov[z(x i ),z(x j )]=σ 2 R(x i ,x j )

[0021] In the above formula, R(x) i ,x j () is a Gaussian exponential function:

[0022]

[0023] In the formula: n is the number of design variables, k is the design variable number, and x i For the i-th sample data, θ k and p k These are the hyperparameters obtained through algorithm optimization.

[0024] The constraint range for the second set of design variables is:

[0025] P1 min ≤P1≤P1 max

[0026] P2 min ≤P2≤P2 max

[0027] Wherein, P1 and P2 are the length of the shell and the torsion angle of the hybrid unit, respectively; P1 min P1 max These are the lower and upper limits of the shell length, respectively, P2. min P2 max These are the lower and upper limits of the torsion angle of the hybrid unit, respectively.

[0028] In step S3, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables, including:

[0029] With the same number of mixing units and other parameters, CFD models of mixers with different aspect ratios of shell cross sections were performed. Different shell cross sections were selected as monitoring sections, and the uniformity index of each monitoring section was read.

[0030]

[0031] In the above formula, γ a To monitor the surface uniformity index of the cross-section, φ iTo specify the field variable, i is the index of the grid face with n grid faces. The average value of the field variables across the entire surface:

[0032]

[0033] In the above formula, A i The surface area through which the specified field variable passes;

[0034] Calculate the average uniformity index of each monitoring section, and select the length-to-diameter ratio of the shell cross section corresponding to the maximum average uniformity index within the cost limit as the optimal length-to-diameter ratio.

[0035] In step S3, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables, which also includes:

[0036] Based on the determined optimal aspect ratio and other parameters, CFD models of mixers with different numbers of mixing units are performed. Different cross sections of the shell are selected as monitoring sections, and the uniformity index of each monitoring section is read. The average uniformity index of each monitoring section is calculated, and the number of mixing units corresponding to the maximum average uniformity index is selected as the optimal number of mixing units within the cost limit range.

[0037] Based on the determined optimal number of mixing units and other parameters being the same, CFD models of mixers with different mixing unit spacings are performed. Different cross sections of the shell are selected as monitoring sections, and the uniformity index of each monitoring section is read. The average uniformity index of each monitoring section is calculated, and the mixing unit spacing corresponding to the maximum average uniformity index within the cost limit is selected as the optimal mixing unit spacing.

[0038] The specified field variable includes the methane component flux.

[0039] The establishment of a CFD model for a hydrogen-blended natural gas mixer based on the parametric geometric model includes:

[0040] Fluid domain creation: In ANSYS Workbench, select the DM module, close the inlet and outlet of the hydrogen-blended natural gas mixer, fill the internal space of the mixer shell, and then remove the part of the fluid domain that overlaps with the solid of the mixing unit to obtain the final computational fluid domain and generate the computational fluid domain file.

[0041] Mesh the computational fluid dynamics domain file to generate a mesh file;

[0042] Import the mesh file into the simulation module, set the mathematical model, physical parameters and boundary conditions for the simulation, select the calculation method and detection variables, and set the number of iteration steps for the model.

[0043] Step S2 also includes building a data exchange simulation environment between the parametric geometric model building platform and the hydrogen-blended natural gas mixer CFD model building platform;

[0044] The mathematical model for the simulation includes a component transport model and a k-ε turbulence model; the physical properties include fluid density and dynamic viscosity; and the calculation method employs the finite volume method to solve the continuity and momentum equations.

[0045] This application also provides a numerical simulation-based system for optimizing the structure of a hydrogen-blended natural gas mixer, used to execute the numerical simulation-based method for optimizing the structure of a hydrogen-blended natural gas mixer.

[0046] The beneficial effects of this invention are as follows:

[0047] This invention optimizes a second set of design variables after optimizing the first set. The first set of design variables is optimized sequentially using a controlled variable method to obtain the aspect ratio, number of mixing units, and spacing parameters with optimal mixing performance. Then, the response surface is fitted using Kriging interpolation, and a multi-objective genetic algorithm is employed to rapidly optimize the second set of design variables. This two-stage optimization of the first and second sets of variables achieves a well-designed mixer structure. The optimization process is logically sound, easy to implement, and balances computational load and accuracy, resulting in relatively accurate design results.

[0048] This invention establishes a data-interoperable simulation environment between a parametric geometric model building platform (Solidworks) and a hydrogen-blended natural gas mixer CFD model building platform (Workbench). This avoids the hassle of repeatedly building geometric models and allows for the development of a suitable experimental design method. For each design scheme, Workbench returns the specific values ​​of the design variables to Solidworks to modify the mixer's geometric model, and then automatically performs simulation calculations. This process is repeated, resulting in less workload, saving time and effort, and shortening the product development cycle.

[0049] This invention obtains the coupling relationships among multiple structural optimization variables of a hydrogen-blended natural gas mixer and the correlation characteristics among the structural size optimization target variables by constructing a Kriging response surface surrogate model. The optimal Latin hypercube design method allows for manual control of the number of samples and improves the uniformity of sample point distribution, effectively reducing the number of simulation analyses. A multi-objective genetic algorithm is used to achieve rapid optimization of the structural optimization variables of the hydrogen-blended natural gas mixer.

[0050] Other features and advantages of the invention will be set forth in the following description or may be learned by practicing the invention. Attached Figure Description

[0051] Figure 1 This is a flowchart of a method according to an embodiment of the present invention.

[0052] Figure 2 This is a calculation scheme for the spacing of four different hybrid units designed when optimizing the first set of design variables in an embodiment of the present invention.

[0053] Figure 3 This is a schematic diagram of the mixer structure obtained after optimizing the first set of design variables in an embodiment of the present invention.

[0054] Figure 4 The response surface models of P3 and P4 with respect to P1 and P2 are constructed during the optimization of the second set of design variables in this embodiment of the invention. Detailed Implementation

[0055] The specific embodiments of the present invention are described below with reference to the accompanying drawings.

[0056] See Figure 1 This application discloses a method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation, comprising:

[0057] S1. Establish a parameterized geometric model of a hydrogen-blended natural gas mixer. The structure of the parameterized geometric model includes a mixer shell, in which a flow channel is formed. Several mixing units are provided in the flow channel. The mixing unit is formed by a rectangular blade twisting in the opposite direction with its center line perpendicular to the short side as the center line.

[0058] S2. Based on the parametric geometric model, establish a CFD model of the hydrogen-blended natural gas mixer;

[0059] S3. With the goal of achieving the best mixing uniformity at the outlet section of the flow channel, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables and obtain the optimal values ​​of the first set of design variables. The first set of design variables includes the aspect ratio of the shell cross section, the number of mixing units, and the spacing between adjacent mixing units.

[0060] S4. Under the constraint of the optimal values ​​of the first set of design variables, with the goal of achieving the best mixing uniformity at the outlet section of the flow channel and minimizing the pressure drop in the flow field, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables and obtain the optimal values ​​of the second set of design variables. The second set of design variables includes the length of the shell and the torsion angle of the mixing unit.

[0061] Based on the optimization of the first set of design variables, this application further optimizes the second set of design variables, which can obtain more accurate optimization results.

[0062] The following specific embodiments further illustrate the detailed implementation of the method for optimizing the structure of hydrogen-blended natural gas mixers based on numerical simulation.

[0063] S1. Establish a parametric geometric model of the SK-type hydrogen-blended natural gas static mixer in Solidworks, and set up a simulation environment for data exchange between Solidworks and ANSYS Workbench to implement a batch calculation scheme. Solidworks can transfer model information to Workbench, and Workbench can transfer design variables from the calculation scheme back to Solidworks, facilitating rapid modification of the mixer's 3D model.

[0064] S2. Based on the mixer output structure, establish a CFD model of the hydrogen-blended natural gas mixer:

[0065] S21. Solid Domain Creation: Obtain relevant 3D component models in modules such as "Part Design" in Solidworks, determine the interrelationships between components in the "Assembly Design" module, and complete the assembly.

[0066] S22. Fluid Domain Creation: In Workbench, select the DM module to close the inlet and outlet of the hydrogen-blended natural gas mixer. Use the Fill command to fill the internal space of the mixer. Then, use the "subtract" function of the Boolean operation to remove the part of the fluid domain that overlaps with the solid, and obtain the final computational fluid domain, generating the fluid domain file.

[0067] S23. Substitute the generated fluid domain file into the mesh processor in Workbench to draw the mesh. Select CFD as the mesh generation method and Fluent as the output software. Name the model's inlet, outlet, and wall as inlet_ch4, inlet_h2, outlet, and wall, respectively. Then generate the mesh. Check the mesh distortion level. If the mesh distortion level is concentrated below 0.7, the mesh quality is considered good. Otherwise, if the mesh quality is low, the mesh size parameters need to be modified and the mesh needs to be regenerated.

[0068] S24. In Workbench, call Fluent software to read the mesh file. After the mesh is loaded, check for negative mesh generation. If the mesh is normal, proceed to parameter settings. Set the mathematical model for the simulation (component transport model, k-ε turbulence model), physical properties (fluid density, dynamic viscosity), boundary conditions (inlet flow rate: methane flow rate 5000 Nm3 / h, hydrogen flow rate 1250 Nm3 / h, outlet pressure 0.28 MPa, wall conditions), select the calculation method, set the number of iteration steps for the model, and start the numerical simulation calculation. Define the detection variables, including the uniformity index of the monitoring points, pressure, etc.

[0069] In this embodiment, the gas-gas mixing process does not involve heat transfer, so there is no need to calculate the energy conservation equation. The continuity and momentum equations are solved using the finite volume method (FVM). During the flow of the mixed gas, the mass conservation equation satisfies:

[0070]

[0071] In the formula: S m For a user-defined generalized source term, ρ m Mixed phase density (kg / m³) 3 ), The average mixing phase velocity of natural gas and hydrogen.

[0072] The momentum conservation equation is expressed as follows:

[0073]

[0074]

[0075]

[0076] In the formula: ρ m Mixed phase density (kg / m³) 3 );v m The mixing phase velocity (m / s); The pressure loss (Pa) at the inlet and outlet of the mixed phase; g is the acceleration due to gravity (m / s²). 2 P is the resistance between the mixed phases (N); α k The volume fraction of phase k; μ m The dynamic viscosity of the mixed phase (Pa·s); v dr,k Let v be the drift velocity of phase k [m / s] (k is 1 or 2, k=1 indicates the first phase is methane, k=2 indicates the second phase is hydrogen). k Let μ be the velocity of phase k. k The dynamic viscosity is k phase.

[0077] S3. Determine the first set of design variables and optimization objectives for mixer optimization. The first set of design variables includes the aspect ratio of the mixing unit, the number of mixing units, and the spacing between mixing units. The optimization objective is to achieve the best mixing effect under a hydrogen doping ratio of 20%. The mixing effect is evaluated using the aforementioned uniformity index. Specifically, this includes:

[0078] S31. Selection of the aspect ratio of the hybrid unit: Two-unit SK-type static mixers with aspect ratios of 1:1, 3:2, 2:1, and 5:2 were selected for numerical simulation calculations, and the results were compared. During the simulation calculation, the mesh file was imported into the simulation module, and the mathematical model, physical property parameters, and boundary conditions of the simulation were set. The calculation method and detection variables were selected, and the number of iteration steps of the model was set. Specifically, monitoring sections were selected at 500mm, 1000mm, 1500mm, 2000mm, 2500mm, and 3000mm from the end of the last hybrid unit blade to read the uniformity index and conduct a uniformity study.

[0079] This embodiment uses the homogeneity index as an important parameter for evaluating gas mixing effects, describing the variation of a specified physical quantity on a given surface, with 1 being the maximum value. The homogeneity index can be measured by area or mass, with the area homogeneity index capturing changes in quantity (such as component concentration). Specified field variables. The area uniformity index γ a Calculate using the following formula:

[0080]

[0081] In the above formula, γ a To monitor the surface uniformity index of the cross-section, φ i To specify field variables, in this embodiment, the specified field variables include methane component flux, where i is the index of a grid face with n grid faces. The average value of the field variables across the entire surface:

[0082]

[0083] In the above formula, A i The surface area through which the specified field variable passes;

[0084] Calculate the average uniformity index of each monitoring section, and select the length-to-diameter ratio of the shell cross section corresponding to the maximum average uniformity index within the cost limit as the optimal length-to-diameter ratio.

[0085] S32. Selection of the Number of Hybrid Units: Based on the fixed length-to-diameter ratio selected in S31, the number of hybrid units is selected. A blade with an original length of 400mm is selected as one hybrid unit, and then combined into 2 or 3 hybrid units, with adjacent hybrid units arranged perpendicularly to each other at 90°. Using the same calculation and analysis method as in S31, the uniformity index of each monitoring section is read, and the average uniformity index of each monitoring section is calculated. Considering the application cost factor, the number of hybrid units corresponding to the maximum average uniformity index is selected as the optimal number of hybrid units. In this embodiment, the number of hybrid units is selected as 3.

[0086] S33. Selection of Mixing Unit Spacing: Based on the number of mixing units selected in S32, numerical simulation calculations are performed on the mixer CFD model with four different mixing unit spacings. Using the same calculation and analysis methods as in S31, the uniformity index of each monitoring section is read, the average value of the uniformity index of each monitoring section is calculated, and considering the application cost factors, the number of mixing units corresponding to the maximum average value of the uniformity index is selected as the optimal mixing unit spacing.

[0087] like Figure 2 As shown, this embodiment presents models for calculating the spacing between four different hybrid units. Figure 2 The model structures of schemes 1 to 4 are shown from top to bottom, and the design parameters of each scheme are as follows:

[0088] Option 1: ΔL1 = ΔL2 = 0;

[0089] Option 2: ΔL1 = 0, ΔL2 = 400mm;

[0090] Option 3: ΔL1 = 400mm, ΔL2 = 0;

[0091] Option 4: ΔL1 = 400mm, ΔL2 = 400mm.

[0092] ΔL1 and ΔL2 are the spacing between the first and second hybrid units (blades), and the spacing between the second and third hybrid units (blades), respectively. Based on the analysis of the simulation results, this embodiment selects the design parameters of Scheme 1, namely, the spacing between two hybrid units being 0, as the optimal value for the hybrid unit spacing.

[0093] S4. Under the constraints of the optimal values ​​of the first set of design variables obtained in S3, with the objectives of achieving optimal mixing uniformity (maximum uniformity index P3) and minimum pressure drop P4 at the outlet section of the flow channel, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables and obtain their optimal values. The second set of design variables includes the length P1 of the shell and the torsion angle P2 of the mixing unit. The length P1 of the shell and the torsion angle P2 of the mixing unit are as follows: Figure 3 As shown in (a) and (b).

[0094] Specifically, with the goal of achieving optimal mixing uniformity and minimizing pressure drop at the outlet cross-section of the flow channel, numerical simulation calculations were performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables, including:

[0095] Set the constraint range for the second set of design variables:

[0096] P1 min ≤P1≤P1 max

[0097] P2 min ≤P2≤P2 max

[0098] Wherein, P1 and P2 are the length of the shell and the torsion angle of the hybrid unit, respectively; P1 min P1 max These are the lower and upper limits of the shell length, respectively, P2. min P2 max These are the lower and upper limits of the torsion angle of the hybrid unit, respectively. In this embodiment, P1 min P1 max They are 12m and 20m respectively, P2 min P2 max The angles are 30° and 150° respectively. It can be understood that the twist angle refers to the angle of relative twist between the two parts of the hybrid unit blade about a centerline perpendicular to its short side.

[0099] Within the aforementioned constraints, Latin hypercube sampling is used to select sample points. Batch calculations are performed on these sample points to obtain the corresponding simulation results, including the mixing uniformity index and flow field pressure drop at the outlet section of the flow channel. Specifically, parameters P1 and P2, passed from Solidworks, are selected as input variables in the DM module, and the uniformity index P3 and flow field pressure drop P4 at the outlet section are selected as output variables in the Fluent results.

[0100] Using response surface modeling tools, the relationship between the second set of design variables for the sample points and the corresponding simulation results is fitted to form a response surface.

[0101] The genetic algorithm is applied to find the optimal solution on the response surface. The optimal solution is obtained through iterative calculation, which yields the optimal values ​​of the second set of design variables corresponding to the optimal mixing uniformity index and the flow field pressure drop.

[0102] In this embodiment, the Latin hypercube experimental design is specifically selected. The calculation scheme is arranged as evenly as possible within the constraints, and the calculation is performed in batches. The simulation software ANSYS Fluent is used to conduct numerical simulation analysis on mixers with different structures.

[0103] In this embodiment, the response surfaces of P3 and P4 with respect to P1 and P2 are constructed using the Kriging method in the Response Surface. Specifically, the Kriging model is selected as the response surface modeling tool. The Kriging surrogate model is an interpolation model, similar to a "black box" model, and can also be viewed as a Gaussian regression model. The Kriging interpolation process is a stochastic process; the value of a point in the design space is considered a separate random quantity related to all other design points. The response of the Kriging model consists of a constant μ and a random function z(x):

[0104] y(x) = μ + z(x)

[0105] In the above formula, y(x) is the response value of sample point x, μ represents the expected value of the response, and z(x) is a static random process with a mean of 0 and covariance, wherein the covariance is:

[0106] Cov[z(x i ),z(x j )]=σ 2 R(x i ,x j )

[0107] In the above formula, R(x) i ,x j ) is the relevant function.

[0108] The selection of the correlation function determines how the data is fitted in the surrogate model and reflects the local deviation from the global approximation. Generally, a relatively simple correlation coefficient is chosen. In this example, the Gaussian exponential function provided by ANSYS Workbench is selected:

[0109]

[0110] In the formula: n is the number of design variables, k is the design variable number, and x i For the i-th sample data, θ k and p k These are the hyperparameters obtained through algorithm optimization.

[0111] In this embodiment, the initial sample size is set to 40. The response surface models of P3 and P4 with respect to P1 and P2 are shown below. Figure 4 Then, in the optimization process, a genetic algorithm is applied to find the optimal solution on the response surface. After iterative calculation, the optimal solution is obtained, which is the optimal size parameter for the optimization objective (uniformity index, pressure loss). Finally, the optimal P1 = 13.03m, P2 = 121.37°, and the corresponding P3 = 0.99943, P4 = 264.5Pa are obtained.

[0112] Furthermore, to verify the accuracy of the results, the optimal solution set is analyzed, and the required optimal solution points are selected based on the actual engineering requirements. The obtained P1 and P2 values ​​are then returned to the geometric model for simulation verification to confirm the accuracy of the optimization results. If the error between the simulation and calculation results is within ±15%, and there is a significant improvement compared to the results before optimization, then the optimization results are considered valid.

[0113] In this embodiment, the obtained P1 and P2 values ​​are returned to the geometric model for verification through simulation, yielding P3 = 0.99931 and P4 = 264.2 Pa. This indicates that the optimal design variables found by the genetic algorithm on the response surface are rounded to P1 = 13m and P2 = 121°, resulting in P3 = 0.99939 and P4 = 264.3 Pa.

[0114] This application also provides a numerical simulation-based hydrogen-blended natural gas mixer structure optimization system for executing the numerical simulation-based hydrogen-blended natural gas mixer structure optimization method.

[0115] This application presents a method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation. This method requires less work, saves time and effort, and shortens the product development cycle.

[0116] It will be understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation, characterized in that, include: S1. Establish a parameterized geometric model of a hydrogen-blended natural gas mixer. The structure of the parameterized geometric model includes a mixer shell, in which a flow channel is formed. Several mixing units are provided in the flow channel. The mixing unit is formed by a rectangular blade twisting in the opposite direction with its center line perpendicular to the short side as the center line. S2. Based on the parametric geometric model, establish a CFD model of the hydrogen-blended natural gas mixer; S3. With the goal of achieving the best mixing uniformity at the outlet section of the flow channel, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables and obtain the optimal values ​​of the first set of design variables. The first set of design variables includes the aspect ratio of the shell cross section, the number of mixing units, and the spacing between adjacent mixing units. S4. Under the constraint of the optimal values ​​of the first set of design variables, with the goal of achieving the best mixing uniformity and the minimum pressure drop of the flow field at the outlet section of the flow channel, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables and obtain the optimal values ​​of the second set of design variables. The second set of design variables includes the length of the shell and the torsion angle of the mixing unit. The establishment of a CFD model for a hydrogen-blended natural gas mixer based on the parametric geometric model includes: Fluid domain creation: In ANSYS Workbench, select the DM module, close the inlet and outlet of the hydrogen-blended natural gas mixer, fill the internal space of the mixer shell, and then remove the part of the fluid domain that overlaps with the solid of the mixing unit to obtain the final computational fluid domain and generate the computational fluid domain file. Mesh the computational fluid dynamics domain file to generate a mesh file; Import the mesh file into the simulation module, set the mathematical model, physical parameters and boundary conditions for the simulation, select the calculation method and detection variables, and set the number of iteration steps for the model; Step S2 also includes building a data exchange simulation environment between the parametric geometric model building platform and the hydrogen-blended natural gas mixer CFD model building platform; The mathematical model for the simulation includes a component transport model and a k-ε turbulence model; the physical properties include fluid density and dynamic viscosity; and the calculation method employs the finite volume method to solve the continuity and momentum equations.

2. The method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation according to claim 1, characterized in that, In step S4, with the goal of achieving optimal mixing uniformity and minimizing pressure drop at the outlet cross-section of the flow channel, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the second set of design variables, including: Set the constraint range of the second set of design variables, and select sample points by Latin hypercube sampling within the constraint range. Perform batch calculations on the sample points to obtain the corresponding simulation results, including the mixing uniformity index and flow field pressure drop of the channel outlet section. Using response surface modeling tools, the relationship between the second set of design variables for the sample points and the corresponding simulation results is fitted to form a response surface. The genetic algorithm is applied to find the optimal solution on the response surface. The optimal solution is obtained through iterative calculation, which yields the optimal values ​​of the second set of design variables corresponding to the optimal mixing uniformity index and the flow field pressure drop.

3. The method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation according to claim 2, characterized in that, The response surface model tool is Kriging Model: ， In the above formula, y ( x ) is the sample point x The response value, μ To represent the mathematical expectation of the response value, z ( x Let be a static random process with a mean of 0 and a covariance, wherein the covariance is: ， In the above formula, For Gaussian exponential functions: ， In the formula: n To determine the number of design variables, k Number the design variables. For the first i One sample data, and These are the hyperparameters obtained through algorithm optimization.

4. The method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation according to claim 2, characterized in that, The constraint range for the second set of design variables is: ， ， in, These are the length of the shell and the torsion angle of the mixing unit, respectively. These are the lower and upper limits of the shell length, respectively. These are the lower and upper limits of the torsion angle of the hybrid unit, respectively.

5. The method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation according to claim 1, characterized in that, In step S3, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables, including: With the same number of mixing units and other parameters, CFD models of mixers with different aspect ratios of shell cross sections were performed. Different shell cross sections were selected as monitoring sections, and the uniformity index of each monitoring section was read. ， In the above formula, To monitor the surface uniformity index of the cross-section, To specify the field variable, i To have n The grid face index of each grid face. The average value of the field variables across the entire surface: ， In the above formula, The surface area through which the specified field variable passes; Calculate the average uniformity index of each monitoring section, and select the length-to-diameter ratio of the shell cross section corresponding to the maximum average uniformity index within the cost limit as the optimal length-to-diameter ratio.

6. The method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation according to claim 5, characterized in that, In step S3, numerical simulation calculations are performed based on the CFD model of the hydrogen-blended natural gas mixer to optimize the first set of design variables, which also includes: Based on the determined optimal aspect ratio and other parameters, CFD models of mixers with different numbers of mixing units are performed. Different cross sections of the shell are selected as monitoring sections, and the uniformity index of each monitoring section is read. The average uniformity index of each monitoring section is calculated, and the number of mixing units corresponding to the maximum average uniformity index is selected as the optimal number of mixing units within the cost limit range. Based on the determined optimal number of mixing units and other parameters being the same, CFD models of mixers with different mixing unit spacings are performed. Different cross sections of the shell are selected as monitoring sections, and the uniformity index of each monitoring section is read. The average uniformity index of each monitoring section is calculated, and the mixing unit spacing corresponding to the maximum average uniformity index within the cost limit is selected as the optimal mixing unit spacing.

7. The method for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation according to claim 5, characterized in that, The specified field variables include methane component flux.

8. A system for optimizing the structure of a hydrogen-blended natural gas mixer based on numerical simulation, characterized in that, Used to perform the numerical simulation-based method for optimizing the structure of a hydrogen-blended natural gas mixer as described in any one of claims 1-7.

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