An ultrasonic gas meter flow channel optimization method and system based on acoustic streaming coupling

Abstract

The application relates to the technical field of ultrasonic gas meter optimization, and particularly provides an ultrasonic gas meter flow channel optimization method and system based on acoustic flow coupling. The method comprises the following steps: a parameterized geometric model of an ultrasonic gas meter is established; N groups of design parameter values are generated based on flow channel design parameters and constraint conditions, and corresponding flow channel geometric models are generated based on the parameterized geometric model; flow field and acoustic field simulation is carried out on the flow channel geometric models, so that flow field performance and acoustic field performance index values of the flow channel geometric models are obtained; a multi-objective optimization algorithm is adopted to optimize the flow channel design parameters, so that optimized design parameter values are generated, with the minimum flow field performance index and acoustic field performance index as the target; and it is judged whether the optimized design parameter values meet a convergence condition; if yes, a Pareto optimal solution is output as an optimal flow channel design. Through parameterized modeling, acoustic flow simulation and multi-objective optimization, the optimization design of the flow channel can be realized, and the time and labor cost of flow channel design are effectively shortened.

Description

Technical Field

[0001] This invention belongs to the field of ultrasonic gas meter optimization technology, and particularly relates to an ultrasonic gas meter flow channel optimization method and system based on acoustic-flow coupling. Background Technology

[0002] Ultrasonic gas meters, as a high-precision, wear-free intelligent metering instrument, have been widely used in the field of gas metering in recent years. Their core metering principle is based on calculating gas flow rate according to the propagation time difference of ultrasound waves in flowing gas. Therefore, the geometric characteristics of the flow channel structure directly determine the distribution characteristics of the internal flow field, thus affecting the propagation path and signal quality of the ultrasound waves. Specifically, geometric parameters such as the length, radius, curvature, rectification structure, and transducer installation position of the flow channel all significantly affect the stability of the flow field, pressure loss, turbulence intensity, and sound wave attenuation, ultimately limiting the metering consistency, accuracy, and long-term robustness of the gas meter.

[0003] Currently, computational fluid dynamics simulation and acoustic simulation technologies have become increasingly mature and are widely used in the design and analysis of fluid machinery and instrumentation flow channels. In the research and development of products such as gas meters, water meters, and petrochemical flow meters, researchers typically use simulation methods to analyze the flow or acoustic characteristics inside the flow channels to assess the feasibility of the design. These simulation methods provide important theoretical basis for understanding the laws of fluid motion and the mechanism of sound wave propagation, significantly improving the scientific rigor and efficiency of the design process.

[0004] However, existing flow channel design methods still have the following obvious limitations:

[0005] First, in current design practices, flow characteristic analysis and acoustic characteristic analysis within the flow channel are usually conducted separately. Researchers may optimize the flow field through simulation and then separately build an acoustic model to verify signal strength, failing to integrate both into a unified quantitative analysis framework. This separation of fields limits the accurate description of the interaction between the flow field and the acoustic field. For example, gas velocity distribution and turbulent fluctuations affect the actual propagation path and energy attenuation of ultrasonic waves, while the scattering effect of the flow channel geometry on sound waves, in turn, restricts the quality of the received signal. Therefore, existing designs struggle to fundamentally balance flow performance and acoustic performance.

[0006] Secondly, traditional flow channel design often employs an experience-based design and experimental verification model. Designers typically base their designs on existing product configurations, making limited adjustments to local geometric dimensions based on experience. After generating a small number of candidate solutions, they then build prototypes for experimental testing. This process requires significant manual intervention in geometric modeling, acoustic and flow field simulation, and experimental verification, resulting in long design iteration cycles and high labor costs. Furthermore, this method struggles to achieve globally optimal design when facing wide flow ranges, complex operating conditions, or multiple performance objectives. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides an ultrasonic gas meter flow channel optimization method based on acoustic-fluid coupling, comprising the following steps:

[0008] Step S1: Establish a parametric geometric model of the ultrasonic gas meter. The parametric geometric model includes a fixed template, flow channel design parameters, constraints, and optimization objectives.

[0009] Step S2: Based on the flow channel design parameters and constraints, generate N sets of design parameter values, and generate the corresponding flow channel geometric model based on the parameterized geometric model;

[0010] Step S3: Perform flow field simulation and acoustic field simulation on the flow channel geometric model to obtain the flow field performance index value and acoustic field performance index value of the flow channel geometric model;

[0011] Step S4: With the goal of minimizing the flow field performance index and the acoustic field performance index, a multi-objective optimization algorithm is used to optimize the flow channel design parameters and generate optimized design parameter values.

[0012] Step S5: Determine whether the optimized design parameter values ​​meet the convergence condition. If they do, output the Pareto optimal solution as the optimal flow channel design. If they do not meet the condition, establish the flow channel geometric model based on the optimized design parameter values ​​and return to step S3 for iteration.

[0013] Preferably, the flow channel design parameters are the flow channel geometric design variables to be optimized, including at least the flow channel length, flow channel radius, flow channel spatial position, flow channel rectifier ring radius, flow channel inlet length, flow channel inlet arc, flow channel inlet arc radius, number of flow channel inlet grilles, and distance from the inlet transducer to the inlet. The fixed template is a gas meter component whose structure remains unchanged during the optimization process. The constraints limit the range of values ​​for the flow channel design parameters and the geometric and manufacturing relationships between the parameters.

[0014] Based on the above scheme, the flow field simulation adopts a realizable k-ε turbulence model. The flow field performance indicators include the turbulence intensity at the maximum flow point, the pressure drop at the maximum flow point, and the sum of squared residuals obtained by fitting the flow correction coefficients at multiple flow points. The sound field performance indicators include the acoustic attenuation of the backflow at the maximum flow point.

[0015] Specifically, the residual sum of squares is obtained through the following steps:

[0016] Under multiple preset flow rate points, flow field simulation is performed on the flow channel geometry model to obtain the flow rate correction coefficient corresponding to each flow rate point;

[0017] The least squares method is used to fit each flow point and flow correction coefficient to a linear function, and the sum of squared residuals between each flow point and the linear function is calculated.

[0018] Based on the above scheme, the multi-objective optimization algorithm in step S4 is the NSGA-II evolutionary algorithm, and step S4 specifically includes:

[0019] The current population is merged with the population selected in the previous round. Based on the flow field performance index and sound field performance index corresponding to each set of design parameter values, the merged design parameter values ​​are sorted in a non-dominated manner.

[0020] Based on the non-dominated layer priority, crowding degree, and elite strategy, N sets of design parameter values ​​are selected from the merged population as the optimal design parameter values.

[0021] Specifically, the constraints include:

[0022] The flow channel length L satisfies: ,in, , These are the minimum and maximum values ​​of the flow channel length, respectively;

[0023] Flow channel radius satisfy: ,in, , These are the minimum and maximum values ​​of the flow channel radius, respectively;

[0024] Flow channel spatial location ( , )satisfy: , ,in, , These are the minimum and maximum x-coordinates of the flow channel center, respectively. , These are the minimum and maximum values ​​of the y-coordinate of the flow channel center, respectively;

[0025] Flow channel rectifier ring radius satisfy: ,in, The minimum radius of the rectifier ring is given by , and D is the sum of the inner wall thickness and the minimum manufacturable inter-wall distance.

[0026] Inlet length satisfy: ,in, , These are the minimum and maximum values ​​of the air intake length, respectively;

[0027] Inlet curvature satisfy: ,in, , These are the minimum and maximum values ​​of the air intake curvature, respectively;

[0028] Inlet radius satisfy: ,in, , These are the minimum and maximum values ​​of the air intake arc radius, respectively;

[0029] The number N of air intake grilles satisfies: ,in, , These represent the minimum and maximum values ​​for the number of air intake grilles, respectively.

[0030] Distance from imported transducer to air inlet satisfy: ,in, , These are the minimum and maximum distances from the inlet transducer to the air inlet, respectively.

[0031] The radius at the maximum diameter of the inlet transducer is ≤ the inner radius of the flow channel inlet - 2mm;

[0032] The outer diameter of the flow channel inlet is ≤ the distance from the front and back of the case and the bottom to the center axis of the flow channel -5mm.

[0033] Preferably, step S3 specifically includes:

[0034] S31: Use flow field simulation software to read the flow channel geometry model, and adopt the realizable k-ε turbulence model to obtain the flow correction coefficient at each flow point, the turbulence intensity and pressure drop at the maximum flow point;

[0035] S32: Use acoustic field simulation software to read the flow channel geometry model, set the ultrasonic propagation medium to be in the reverse flow state at the maximum flow point, and obtain the acoustic attenuation of the reverse flow at the maximum flow point.

[0036] Preferably, during the flow field simulation and sound field simulation in step S3, when simulation divergence or stagnation is detected, the mesh generation parameters are automatically adjusted or the solution settings are changed and then restarted until the simulation is completed or the preset number of restarts is reached; if the number of restarts is reached, the flow channel geometry model is assigned an elimination optimization target value, and the model is eliminated through step S4.

[0037] On the other hand, the present invention provides an ultrasonic gas meter flow channel optimization system based on acoustic-fluid coupling, comprising:

[0038] The parametric modeling module is used to establish a parametric geometric model of the ultrasonic gas meter. The parametric geometric model includes a fixed template, flow channel design parameters, constraints, and optimization objectives.

[0039] The model generation module is used to generate N sets of design parameter values ​​based on the flow channel design parameters and constraints, and to generate the corresponding flow channel geometric model based on the parameterized geometric model.

[0040] The coupled simulation module is used to perform flow field simulation and acoustic field simulation on the flow channel geometric model to obtain the flow field performance index value and acoustic field performance index value of the flow channel geometric model;

[0041] The optimization module is used to optimize the flow channel design parameters with the goal of minimizing the flow field performance index and the sound field performance index, and to generate optimized design parameter values.

[0042] The output judgment module is used to determine whether the optimized design parameter values ​​meet the convergence condition. If they do, the Pareto optimal solution is output as the optimal flow channel design. If they do not meet the condition, the flow channel geometric model is established based on the optimized design parameter values, and the simulation module is returned to perform a loop.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. This invention uses both flow field performance indicators and sound field performance indicators as optimization targets. The output flow channel structure effectively reduces sound wave attenuation while ensuring low pressure loss, thereby improving the metering accuracy and reliability of the ultrasonic gas meter.

[0045] 2. This invention constructs a complete closed-loop process from model generation to result output through parametric modeling, automatic simulation, and multi-objective optimization, which can realize the optimized design of flow channels and greatly shorten the time and manpower cost of flow channel design;

[0046] 3. By embedding geometric logic constraints and manufacturability constraints, we ensure that the optimization algorithm always searches within the engineering-feasible parameter space while exploring the geometric structure. Furthermore, the output Pareto optimal solution set is not only a multi-objective optimal solution in a mathematical sense, but also meets assembly requirements and manufacturing processes to ensure engineering feasibility. Attached Figure Description

[0047] Figure 1 This is an overall flowchart of the optimization method of the present invention;

[0048] Figure 2 This is a flowchart illustrating the optimization method of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0050] Example 1

[0051] This embodiment provides a method for optimizing the flow channel of an ultrasonic gas meter based on acoustic-fluid coupling, such as... Figure 1 and Figure 2 As shown, it includes the following steps:

[0052] Step S1: Establish a parametric geometric model of the ultrasonic gas meter;

[0053] First, a baseline model of the ultrasonic gas meter is established using 3D modeling software (such as Solidworks, UG, SpaceClaim, etc.). This model adopts a hybrid modeling method that combines a feature-based fixed template with parameterized flow channel design parameters.

[0054] In this embodiment, the fixed template consists of gas meter components whose structure remains unchanged during the optimization process, including the meter casing, gas inlet and outlet, valves, etc. Based on this fixed template, parametric modeling of the flow channel design parameters is performed to construct a geometric model of the variable parts of the flow channel. The flow channel design parameters are the geometric design variables of the flow channel to be optimized, including the flow channel length L, flow channel radius R, and flow channel spatial position (…). , ), Flow channel rectifier ring radius Length of air inlet Flow channel air inlet curvature Flow channel air inlet arc radius The number of air inlet grilles N and the distance from the inlet transducer to the air inlet. Factors that affect the flow of the medium.

[0055] Furthermore, the constraints for the design parameters of each flow channel are defined as follows:

[0056] The flow channel length L satisfies: ,in, , These are the minimum and maximum values ​​of the flow channel length, respectively;

[0057] Flow channel radius satisfy: ,in, , These are the minimum and maximum values ​​of the flow channel radius, respectively;

[0058] Flow channel spatial location ( , )satisfy: , ,in, , These are the minimum and maximum x-coordinates of the flow channel center, respectively. , These are the minimum and maximum values ​​of the y-coordinate of the flow channel center, respectively;

[0059] Flow channel rectifier ring radius satisfy: ,in, The minimum radius of the rectifier ring is given by , and D is the sum of the inner wall thickness and the minimum manufacturable inter-wall distance.

[0060] Inlet length satisfy: ,in, , These are the minimum and maximum values ​​of the air intake length, respectively;

[0061] Inlet curvature satisfy: ,in, , These are the minimum and maximum values ​​of the air intake curvature, respectively;

[0062] Inlet radius satisfy: ,in, , These are the minimum and maximum values ​​of the air intake arc radius, respectively;

[0063] The number N of air intake grilles satisfies: ,in, , These represent the minimum and maximum values ​​for the number of air intake grilles, respectively.

[0064] Distance from imported transducer to air inlet satisfy: ,in, , These are the minimum and maximum distances from the inlet transducer to the air inlet, respectively.

[0065] The radius at the maximum diameter of the inlet transducer is ≤ the inner radius of the flow channel inlet - 2mm;

[0066] The outer diameter of the flow channel inlet is ≤ the distance from the front and back of the case and the bottom to the center axis of the flow channel -5mm.

[0067] The constraints that the above-mentioned flow channel design parameters must meet are used to limit the value range of each flow channel design parameter, as well as the geometric logic and manufacturability between parameters, to ensure that the design scheme is within the parameter space that is feasible for engineering.

[0068] Furthermore, step S1 defines an optimization objective to clarify the performance metrics that need to be evaluated in subsequent simulations. This optimization objective includes at least flow field performance metrics and acoustic field performance metrics, specifically the following four minimized metrics:

[0069] ;

[0070] Among them, the residual sum of squares (SSE) characterizes the linearity deviation of the flow correction coefficient K at different flow points; turbulence intensity (TI) characterizes the stability of the flow field at the maximum flow point; pressure drop (PD) characterizes the pressure loss of gas flowing through the channel at the maximum flow point; and acoustic attenuation (SD) characterizes the signal attenuation of ultrasonic waves propagating in the reverse flow at the maximum flow point.

[0071] According to this embodiment, the model established in step S1 needs to be verified, as follows:

[0072] The parametric geometric model is imported into the flow field simulation software and the sound field simulation software to complete the basic mesh generation and solver settings. Basic mesh generation refers to using conventional mesh generation techniques in this field to set the mesh type, size and boundary layer parameters according to the geometric characteristics to generate a computational mesh that meets the computational accuracy requirements. Solver settings refer to selecting the solver type, physical model, boundary conditions and solution control parameters according to the flow characteristics to establish a numerical model that can be solved correctly.

[0073] The model reliability was then verified by comparing the simulation data such as flow velocity distribution and pressure drop with existing literature data or physical experimental test results to determine whether the deviation was within an acceptable range. Then, the mesh independence was verified by setting different mesh densities and conducting simulations to observe the changing trends of flow velocity, pressure drop or turbulence intensity in the pipe with the mesh after the results converged. The most suitable mesh density was obtained when the mesh density had no effect on the results.

[0074] Step S2: Based on the flow channel design parameters and constraints in step S1, generate N sets of design parameter values, with each set of flow channel design parameters corresponding to a specific set of flow channel design parameter values;

[0075] In this embodiment, N sets of binary design parameter values ​​are randomly generated within the parameter space, and then compiled into N sets of readable design parameter values ​​according to the parameter space range.

[0076] Then, the N sets of readable design parameter values ​​are input into the parametric geometric model in step S1 to generate the corresponding flow channel geometric model.

[0077] Step S3: Perform flow field simulation and acoustic field simulation on the flow channel geometric model to obtain the flow field performance index value and acoustic field performance index value of the flow channel geometric model. The flow field performance index includes the residual sum of squares, the turbulence intensity at the maximum flow point, and the turbulence pressure drop at the maximum flow point. The acoustic field performance index includes the acoustic attenuation of the backflow at the maximum flow point.

[0078] Step S3 of this embodiment simulates the real flow field and sound field using simulation software, receives the flow channel geometry model, and performs multiphysics coupling simulation at preset flow points, specifically including:

[0079] S31. Using flow field simulation software (such as ANSYS Fluent, COMSOL, etc.), the flow channel geometry model is read, adaptive mesh generation and adaptive boundary condition setting are completed, and a realizable k-ε turbulence model is used to analyze 5 preset flow points. The flow behavior of the medium inside the ultrasonic gas meter was analyzed to obtain the volume average velocity along the sound wave transmission path at each flow point, thereby determining the flow correction coefficients (K1, K2, K3, K4, K5) and the turbulence intensity at the maximum flow point. and voltage drop PD.

[0080] According to this embodiment, the flow field performance indicators include the turbulence intensity at the maximum flow rate point, the pressure drop at the maximum flow rate point, and the residual sum of squares obtained by fitting the flow rate correction coefficient K at multiple flow rate points; the residual sum of squares is obtained through the following steps:

[0081] At multiple preset flow points, flow field simulation is performed on the flow channel geometry model to obtain the volume average velocity on the acoustic wave transmission path corresponding to each flow point, and then the flow correction coefficient is obtained.

[0082] The least squares method is used to fit each flow point and flow correction coefficient to a linear function, and the sum of squared residuals between each flow point and the linear function is calculated.

[0083] Those skilled in the art should understand that the volume average velocity in this invention is the average velocity obtained by weighting the volume occupied by each finite element within a volume, and the flow correction coefficient is the ratio between the flow rate obtained based on the measured volume average velocity and the actual flow rate of the passage.

[0084] S32 uses acoustic field simulation software (ANSYS Sound, COMSOL, etc.) to read the flow channel geometry model, establish a finite element simulation model based on the coupling of fluid dynamics and acoustics, complete adaptive mesh generation and adaptive boundary condition setting, set the ultrasonic propagation medium to be in the reverse flow state at the maximum flow point, and obtain the acoustic attenuation SD of the reverse flow at the maximum flow point.

[0085] Since the flow field has a significant impact on the propagation of ultrasonic waves, this embodiment considers the unidirectional coupling of the flow field to the sound field. During the sound field simulation, the maximum flow rate is set so that the ultrasonic propagation medium is in a reverse flow state, and the acoustic attenuation of the ultrasonic signal received by the receiving transducer at this time is detected.

[0086] Based on the above steps, obtain N sets of optimization target values ​​corresponding to N sets of design parameter values, and input the target values ​​into step S4.

[0087] Based on this invention, the above-mentioned mesh generation, solver settings, and result post-processing can be standardized and encapsulated to form an adaptive execution module. This module automatically completes mesh generation, solution calculation, and result post-processing according to the input parametric geometric model. The result post-processing further processes the volume average velocity at each flow point in the flow tube obtained after acoustic-flow coupling simulation to obtain the residual sum of squares of the flow correction coefficient K and the actual flow at each flow point. It also saves the residual sum of squares, the pressure loss at the maximum flow, the turbulence intensity at the maximum flow, and the acoustic attenuation results of the ultrasonic backflow at the maximum flow point.

[0088] Preferably, this method also includes a fault tolerance and restart mechanism. During the flow field simulation and sound field simulation in step S3, convergence conditions and the maximum number of iterations are set, and the simulation is monitored in real time for divergence or stagnation. If this occurs, the mesh generation parameters are automatically adjusted, including adjusting the minimum mesh size, maximum mesh size, boundary layer thickness, number of boundary layers, and growth rate, or the solver settings are changed, including changing the turbulence model, solver order, and initialization. Then, the simulation is restarted until it is successfully completed or the preset number of restarts is reached. If the restart limit is reached, a larger optimization target value is given to the model, and the model is automatically eliminated using the optimization algorithm. This ensures that the optimization loop is not interrupted and reduces the failure rate of the automated process under extreme geometries.

[0089] Those skilled in the art should understand that, since the optimization objective is to minimize the flow field performance index and the acoustic field performance index, by assigning a larger optimization objective value to the flow channel geometry model with abnormal structure, it can be automatically screened and eliminated in the subsequent multi-objective optimization process, thus ensuring the sustainability of the simulation process.

[0090] Step S4: With the goal of minimizing the flow field performance index and the acoustic field performance index, a multi-objective optimization algorithm is used to optimize the flow channel design parameters and generate optimized design parameter values.

[0091] According to this embodiment, the multi-objective optimization algorithm is optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an elitist strategy, and the N sets of optimization objective values ​​obtained in the above steps are used as input.

[0092] It should be noted that during the first optimization iteration, there is no offspring population yet, so sorting and screening are not performed. Instead, the following steps are executed directly: the N sets of design parameter values ​​are binary encoded, and based on the preset crossover and mutation rates, crossover and mutation operations are used to generate an offspring population containing the N sets of design parameter values. This offspring population is then used as the simulation object and returned to step S3 for simulation.

[0093] When the number of iterations is greater than 1, multi-objective optimization specifically includes:

[0094] The current population and the population selected from the previous generation are merged to obtain 2N sets of design parameter values;

[0095] For each group of flow channel design parameters in the merged population, a fast non-dominated sort is performed according to the optimization objective value, and each group of flow channel design parameters is assigned to a different non-dominated layer.

[0096] The congestion degree of the flow channel design parameters within the same undominated layer is calculated, and the top N sets of design parameter values ​​are selected as the optimized design parameter values ​​based on the undominated layer priority, congestion degree, and elitist strategy.

[0097] Step S5: Determine whether the optimized design parameter values ​​meet the convergence conditions. Perform a convergence check on the optimized design parameter values ​​and check whether the results satisfy the condition that all simulation results are uniformly distributed on the Pareto front in the solution space or that the maximum number of iterations has been reached.

[0098] If the convergence condition is met, the optimization ends and the Pareto optimal solution is output as the optimal flow channel design; if the condition is not met, the flow channel geometric model is established based on the optimized design parameter values, and the process returns to step S3 to continue the loop.

[0099] Finally, based on the output Pareto optimal solution and the optimization objective value, further selection is made to obtain the optimal flow channel design scheme.

[0100] Example 2

[0101] This embodiment provides an ultrasonic gas meter flow channel optimization system based on acoustic-fluid coupling, used to implement the method described in Embodiment 1. The system includes:

[0102] The parametric modeling module is used to establish a parametric geometric model of the ultrasonic gas meter. The parametric geometric model includes a fixed template, flow channel design parameters, constraints, and optimization objectives.

[0103] The model generation module is used to generate N sets of design parameter values ​​based on the flow channel design parameters and constraints, and to generate the corresponding flow channel geometric model based on the parametric geometric model.

[0104] The coupled simulation module is used to perform flow field simulation and acoustic field simulation on the flow channel geometry model, and obtain the flow field performance index value and acoustic field performance index value of the flow channel geometry model;

[0105] The optimization module is used to optimize the flow channel design parameters with the goal of minimizing the flow field performance index and the sound field performance index, and to generate optimized design parameter values.

[0106] The output module determines whether the optimized design parameter values ​​meet the convergence conditions. If they do, it outputs the Pareto optimal solution as the optimal flow channel design. If not, it builds a flow channel geometric model based on the optimized design parameter values ​​and returns to the simulation module for iteration.

[0107] The specific implementation method of this embodiment is the same as that in Embodiment 1, and will not be repeated here.

[0108] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0109] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the flow channel of an ultrasonic gas meter based on acoustic-fluid coupling, characterized in that, Includes the following steps: Step S1: Establish a parametric geometric model of the ultrasonic gas meter. The parametric geometric model includes a fixed template, flow channel design parameters, constraints, and optimization objectives. The fixed template is a component of the gas meter whose structure remains unchanged during the optimization process. Step S2: Based on the flow channel design parameters and constraints, generate N sets of design parameter values, and generate the corresponding flow channel geometric model based on the parameterized geometric model; Step S3: Perform flow field simulation and sound field simulation on the flow channel geometric model to obtain the flow field performance index value and sound field performance index value of the flow channel geometric model; the flow field performance index includes the residual sum of squares obtained by fitting the flow correction coefficient at each flow point, the turbulence intensity at the maximum flow point and the pressure drop at the maximum flow point, and the sound field performance index includes the sound attenuation of the backflow at the maximum flow point. Specifically, it includes: S31: Use flow field simulation software to read the flow channel geometry model, complete adaptive mesh generation and adaptive boundary condition setting, and use the realizable k-ε turbulence model to obtain the flow correction coefficient at each flow point, the turbulence intensity and pressure drop at the maximum flow point; S32: Use acoustic field simulation software to read the flow channel geometry model, establish a finite element simulation model based on the coupling of fluid dynamics and acoustics, complete adaptive mesh generation and adaptive boundary condition setting, set the ultrasonic propagation medium to be in the reverse flow state at the maximum flow point, and obtain the acoustic attenuation of the reverse flow at the maximum flow point. Step S4: With the goal of minimizing the flow field performance index and the acoustic field performance index, a multi-objective optimization algorithm is used to optimize the flow channel design parameters and generate optimized design parameter values. Step S5: Determine whether the optimized design parameter values ​​meet the convergence condition. If they do, output the Pareto optimal solution as the optimal flow channel design. If they do not meet the condition, establish the flow channel geometric model based on the optimized design parameter values ​​and return to step S3 for iteration.

2. The ultrasonic gas meter flow channel optimization method based on acoustic-fluid coupling according to claim 1, characterized in that, The flow channel design parameters are the geometric design variables of the flow channel to be optimized, including at least the flow channel length, flow channel radius, flow channel spatial position, flow channel rectifier ring radius, flow channel inlet length, flow channel inlet arc, flow channel inlet arc radius, number of flow channel inlet grilles, and distance from the inlet transducer to the inlet. The constraints limit the range of values ​​of the flow channel design parameters and the geometric and manufacturing relationships between the parameters.

3. The ultrasonic gas meter flow channel optimization method based on acoustic-fluid coupling according to claim 1, characterized in that, The sum of squared residuals is obtained through the following steps: Under multiple preset flow rate points, flow field simulation is performed on the flow channel geometry model to obtain the flow rate correction coefficient corresponding to each flow rate point; The least squares method is used to fit each flow point and flow correction coefficient to a linear function, and the sum of squared residuals between each flow point and the linear function is calculated.

4. The ultrasonic gas meter flow channel optimization method based on acoustic-fluid coupling according to claim 1, characterized in that, The multi-objective optimization algorithm in step S4 is the NSGA-II evolutionary algorithm, and step S4 specifically includes: The current population is merged with the population selected in the previous round. Based on the flow field performance index and sound field performance index corresponding to each set of design parameter values, the merged design parameter values ​​are sorted in a non-dominated manner. Based on the non-dominated layer priority, crowding degree, and elite strategy, N sets of design parameter values ​​are selected from the merged population as the optimal design parameter values.

5. The ultrasonic gas meter flow channel optimization method based on acoustic-fluid coupling according to claim 2, characterized in that, The constraints include: The flow channel length L satisfies: ,in, , These are the minimum and maximum values ​​of the flow channel length, respectively; Flow channel radius satisfy: ,in, , These are the minimum and maximum values ​​of the flow channel radius, respectively; Flow channel spatial location ( , )satisfy: , ,in, , These are the minimum and maximum x-coordinates of the flow channel center, respectively. , These are the minimum and maximum values ​​of the y-coordinate of the flow channel center, respectively; Flow channel rectifier ring radius satisfy: ,in, The minimum radius of the rectifier ring is given by , and D is the sum of the inner wall thickness and the minimum manufacturable inter-wall distance. Inlet length satisfy: ,in, , These are the minimum and maximum values ​​of the air intake length, respectively; Inlet curvature satisfy: ,in, , These are the minimum and maximum values ​​of the air intake curvature, respectively; Inlet radius satisfy: ,in, , These are the minimum and maximum values ​​of the air intake arc radius, respectively; The number N of air intake grilles satisfies: ,in, , These represent the minimum and maximum values ​​for the number of air intake grilles, respectively. Distance from imported transducer to air inlet satisfy: ,in, , These are the minimum and maximum distances from the inlet transducer to the air inlet, respectively. The radius at the maximum diameter of the inlet transducer is ≤ the inner radius of the flow channel inlet - 2mm; The outer diameter of the flow channel inlet is ≤ the distance from the front and back of the case and the bottom to the center axis of the flow channel -5mm.

6. The ultrasonic gas meter flow channel optimization method based on acoustic-fluid coupling according to claim 4, characterized in that, During the flow field simulation and sound field simulation in step S3, when simulation divergence or stagnation is detected, the mesh generation parameters are automatically adjusted or the solution settings are changed and then restarted until the simulation is completed or the preset number of restarts is reached; if the number of restarts is reached, the flow channel geometry model is assigned an elimination optimization target value, and the model is eliminated through step S4.

7. An ultrasonic gas meter flow channel optimization system based on acoustic-fluid coupling, characterized in that, include: The parametric modeling module is used to establish a parametric geometric model of the ultrasonic gas meter. The parametric geometric model includes a fixed template, flow channel design parameters, constraints, and optimization objectives. The fixed template consists of gas meter components whose structure remains unchanged during the optimization process. The model generation module is used to generate N sets of design parameter values ​​based on the flow channel design parameters and constraints, and to generate the corresponding flow channel geometric model based on the parameterized geometric model. The coupled simulation module is used to perform flow field simulation and acoustic field simulation on the flow channel geometric model to obtain the flow field performance index and acoustic field performance index values ​​of the flow channel geometric model. The flow field performance index includes at least the sum of squared residuals obtained by fitting the flow correction coefficient at each flow rate point, the turbulence intensity at the maximum flow rate point, and the pressure drop at the maximum flow rate point. The acoustic field performance index includes at least the acoustic attenuation of the backflow at the maximum flow rate point. The coupled simulation module includes a flow field simulation submodule and an acoustic field simulation submodule. The flow field simulation submodule uses flow field simulation software to read the flow channel geometric model, completes adaptive mesh generation and adaptive boundary condition setting, and adopts a realizable k-ε turbulence model to obtain the flow correction coefficient at each flow point, the turbulence intensity and pressure drop at the maximum flow point; The sound field simulation submodule uses sound field simulation software to read the flow channel geometry model, establish a finite element simulation model based on the coupling of fluid dynamics and acoustics, complete adaptive mesh generation and adaptive boundary condition setting, set the ultrasonic propagation medium to be in the reverse flow state at the maximum flow point, and obtain the acoustic attenuation of the reverse flow at the maximum flow point. The optimization module is used to optimize the flow channel design parameters with the goal of minimizing the flow field performance index and the sound field performance index, and to generate optimized design parameter values. The output judgment module is used to determine whether the optimized design parameter values ​​meet the convergence condition. If they do, the Pareto optimal solution is output as the optimal flow channel design. If they do not meet the condition, the flow channel geometric model is established based on the optimized design parameter values, and the simulation module is returned to perform a loop.

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