A design method of circumferential drainage channel of a barrel-type filter housing

Abstract

The application discloses a kind of circumferential drainage channel design methods of barrel type filter shell, it is related to filter structure design technical field, the specific steps of this method are as follows: first, import benchmark model, and the parameter of drainage channel is coded as standardization vector to construct basic design space;Manufacturing process and material constraint are extracted again, and compliant design space is generated and non-compliant parameters are removed;Then, a multi-objective optimization model containing five sub-targets such as filtering efficiency and flow resistance is constructed;Intelligent optimization is carried out using parameter population iterative optimization technology, and the optimal variable combination is locked;Finally, after passing through constraint check and multi-dimensional simulation verification, the digital design scheme is output;The application standardizes the parameter of filter drainage channel and codes it, generates a compliant design interval in combination with process constraints, constructs an optimization model relying on multiple performance indicators, locks the optimal variable combination through intelligent iterative optimization, outputs the design results after simulation verification, and realizes full-process intelligentization.

Description

Technical Field

[0001] This invention relates to the field of filter structure design technology, specifically to a design method for a circumferential flow channel in a barrel-shaped filter housing. Background Technology

[0002] As a core component of the engine intake system, the cylindrical air filter's circumferential flow channel structure design directly determines the intake airflow distribution, fluid transmission efficiency, and filtration effect. It is a key factor affecting engine power output, fuel consumption, and overall engine lifespan. As the vehicle manufacturing industry continues to develop towards lightweighting, high efficiency, and intelligence, the market is placing more stringent demands on the fluid adaptability, structural stability, and production adaptability of filters. The design of the circumferential flow channel needs to simultaneously consider multiple dimensions such as fluid dynamics, structural mechanical strength, manufacturing process limitations, and vehicle assembly space requirements. Traditional design models in the industry can no longer meet the development needs of multi-performance synergistic optimization. There is an urgent need to build a standardized and intelligent design system to solve the industry pain points of current non-standard design processes and insufficient performance matching.

[0003] The traditional design of the circumferential flow channel in barrel-type filter housings relies excessively on the practical experience of designers. Parameter adjustments and scheme formulation are completed through manual trial and error. The design parameters lack unified coding standards and systematic management, and parameter values ​​are prone to deviating from actual manufacturing processes, material performance boundaries, and assembly space limitations. This makes it difficult to ensure the compliance of the design scheme. The design process only optimizes a single performance index, failing to achieve a synergistic balance among multiple core performance aspects. This easily leads to problems such as uneven flow field distribution, excessive fluid resistance, insufficient structural strength, or weight redundancy. Parameter iterative optimization relies on manual operation, resulting in low design efficiency, lengthy R&D cycles, and a lack of intelligent optimization and precise convergence judgment mechanisms. It is impossible to quickly identify the optimal design scheme. At the same time, the lack of a full-process performance simulation verification process means that the actual application effect of the design results cannot be verified in advance. After production, problems such as compatibility failures and substandard performance are likely to occur, significantly increasing the overall cost of R&D and production. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a design method for the circumferential flow channel of a barrel-shaped filter housing. Through a complete process of parameter vector construction, constraint boundary generation, multi-objective optimization model building, intelligent parameter optimization, and scheme review and output, the method achieves standardization and intelligentization of flow channel design. This method unifies the coding and management of design parameters, defines a compliant design space based on engineering constraints, constructs a multi-dimensional performance collaborative optimization model, accurately locks the optimal parameter combination using intelligent iteration technology, and outputs a qualified scheme after simulation verification. This effectively solves problems such as reliance on experience, performance imbalance, and low efficiency in traditional design, significantly improving design accuracy and compliance, optimizing filter fluid performance, structural strength, and service life, adapting to industrial production needs, and reducing R&D and manufacturing costs.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a design method for a circumferential drainage channel in a barrel-shaped filter housing, the specific steps of which are as follows:

[0006] S1. Parameter Vector Construction: Import the reference 3D model of the barrel-shaped filter housing into the computer-aided design platform, collect all design parameters of the circumferential drainage channel, and use the variable mapping algorithm to encode all design parameters into a standardized design variable vector to form the basic design space.

[0007] S2. Constraint Boundary Generation: Based on the basic design space, engineering constraints consisting of manufacturing process limitations and material performance boundaries are extracted. An interval generation algorithm is used to calculate the numerical constraint interval for each design variable according to the engineering constraints, generating a compliant design space and eliminating parameter combinations that exceed the constraint interval.

[0008] S3. Optimization Model Construction: Based on the compliant design space, five sub-objective functions are constructed: filtration efficiency, flow resistance, structural strength, weight, and service life. These are combined into a total optimization objective function using a linear weighted fusion algorithm, forming a multi-objective optimization mathematical model.

[0009] S4. Intelligent parameter optimization: With compliance design space as constraint and minimization of the overall optimization objective function as objective, it uses parameter population iterative optimization technology to iterate. The change of objective function value in each iteration step is calculated by the convergence judgment algorithm. The iteration terminates when the change is less than the threshold. The combination of design variables that minimizes the objective function is output as the optimal parameters.

[0010] S5. Scheme review and output: Perform constraint verification and multi-dimensional performance simulation of fluid structure on the optimal parameter scheme. If the simulation results meet the preset engineering performance threshold, update to the benchmark model and output digital design scheme.

[0011] Furthermore, in the construction of S1 and the parameter vector, the mathematical expression of the variable mapping algorithm is: ,in To standardize the design variable vector, For circumferential structural dimensions, For spatial distribution, To guide the airflow to the target resistance, To improve the flow field and reduce drag, To improve the target value of filtration efficiency, This represents the range of change in the shell weight.

[0012] Furthermore, in the S1 parameter vector construction, all the design parameters collected are, in order, circumferential structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement and drag reduction target value, filtration efficiency improvement target value, and shell weight variation range.

[0013] Furthermore, in S1, the standardized design variable vector generated by encoding is a six-dimensional vector, and the vector data is stored in the structured parameter database of the computer-aided design system, supporting parameter retrieval, parameter modification, and batch parameter editing operations.

[0014] Furthermore, in S2, the mathematical expression for the interval generation algorithm in constraint boundary generation is:

[0015] ,

[0016] in Design the space for compliance. For the first The lower limit value of the design variable, For the first The upper limit of the design variable. This refers to the index of the design variable.

[0017] Furthermore, in S2, during the generation of constraint boundaries, the extracted engineering constraints include engine compartment assembly space limitations, filter intake total resistance threshold, housing injection molding process requirements, vehicle weight control standards, and engine intake filtration efficiency threshold.

[0018] Furthermore, in S2, during the generation of constraint boundaries, when eliminating parameter combinations that exceed the constraint range, the independent numerical constraint ranges corresponding to all design variables are integrated, and the variable value range of the basic design space is fully limited to complete the construction of the compliant design space. All parameter combinations within the basic design space are traversed, and the values ​​of each dimension of the design variables contained in the parameter combination are matched and compared with the corresponding numerical constraint range one by one. Parameter combinations in which any dimension of the design variable values ​​exceeds the corresponding numerical constraint range are selected, and the selected parameter combinations are eliminated.

[0019] Furthermore, in the S3 optimization model construction, the mathematical expression of the linear weighted fusion algorithm is:

[0020]

[0021] in Head , The overall optimization objective function is... The weighting coefficient for the filtering efficiency sub-objective. The weighting coefficient for the flow resistance sub-objective. The weighting coefficients for the structural strength sub-objective are... The weighting coefficients for the weight sub-objectives. Weighting coefficients for the sub-target of service life The objective function is the filtering efficiency. Let the flow resistance sub-objective function be... The structural strength sub-objective function is... For the weight sub-objective function, The lifespan sub-objective function.

[0022] Furthermore, in the S4 intelligent parameter optimization process, when using the parameter population iterative optimization technique, an initial parameter population is determined based on the compliant design space. The parameter individuals within the initial parameter population are screened, and the screened parameter individuals are selected as parent parameter individuals. The parent parameter individuals are subjected to parameter fragment cross-recombination to form intermediate parameter individuals. The intermediate parameter individuals are subjected to parameter value fine-tuning to generate a child parameter population. The child parameter population is used as the processing object for the next round of iteration. The operations of screening, cross-recombination, and value fine-tuning are repeatedly executed to complete multiple rounds of parameter population iterative updates.

[0023] Furthermore, in the S4 intelligent parameter optimization process, the output optimal parameter is a combination of six standardized design variables within the compliant design space. This combination of design variables fully corresponds to six design parameters: circumferential structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement and drag reduction target value, filtration efficiency improvement target value, and shell weight change range. The values ​​of each dimension of the design variable are the variable values ​​corresponding to the minimum value of the overall optimization objective function after the iteration terminates.

[0024] Compared with existing technologies, the design method of the circumferential flow channel of the barrel-shaped filter housing has the following advantages:

[0025] I. This invention encodes the design parameters of the circumferential flow channel of the barrel-type filter housing into a standardized vector, establishing a standardized basic design space. It generates a compliant design range by combining manufacturing processes, material properties, and assembly constraints, automatically eliminating parameter combinations that do not meet engineering requirements. This avoids the problems of parameter exceeding limits and non-compliant solutions in manual design. Based on multiple core performance indicators, it constructs a comprehensive optimization model, incorporating fluid characteristics, filtration effect, structural strength, weight control, and durability into a unified evaluation system. This breaks the limitations of traditional single-indicator design, allowing the flow channel design to comprehensively consider multiple core usage requirements. The entire process relies on a computer-aided design platform to complete parameter processing and space constraints, simplifying the design operation process, reducing manual trial and error, significantly improving the compliance and generation efficiency of the design scheme, and ensuring that the design results meet the actual requirements of vehicle assembly and mass production.

[0026] Second, this invention utilizes parameter population iterative optimization technology to conduct intelligent parameter optimization, combined with a convergence judgment algorithm to accurately lock the optimal combination of design variables. This ensures optimal matching of the overall optimization goal within the compliant design space, allowing the fluid distribution, structural load-bearing capacity, and weight control of the drainage channel to reach a stable equilibrium. After constraint verification and multi-dimensional fluid structure simulation review, the engineering performance of the optimal solution is verified to meet the standards before outputting digital design results, ensuring the reliability of the final solution in practical applications. This design method realizes intelligentization of the entire process from parameter modeling, constraint screening, optimization to simulation verification, improving the accuracy and stability of circumferential drainage channel design, optimizing the overall working efficiency of the filter, extending the product service life, and adapting to the process requirements of large-scale manufacturing, thereby reducing the overall design and production costs.

[0027] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 Flowchart of the design method for the circumferential drainage channel of a barrel-shaped filter housing;

[0030] Figure 2 A schematic diagram of the structure and flow field effect of a barrel-type filter without circumferential drainage grooves;

[0031] Figure 3 This is a schematic diagram showing the structure and performance optimization of a barrel-shaped filter with a circumferential drainage channel. Detailed Implementation

[0032] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0033] Reference Figure 1 One embodiment of the present invention proposes a design method for the circumferential flow channel of a barrel-shaped filter housing. It adopts an integrated digital design mechanism that combines parameter vector standardized coding, full-domain limitation of engineering constraint intervals, linear weighted fusion of multiple performance sub-objectives, intelligent iterative optimization of parameter populations, and multi-dimensional simulation verification of fluid structure. Under strict engineering constraints that meet the requirements of engine compartment assembly, injection molding manufacturing process, material mechanical properties, vehicle weight control, and intake performance, it can efficiently optimize the parameters of the circumferential flow channel, simultaneously improve the filter filtration efficiency, reduce intake flow resistance, ensure the structural strength of the housing, control the weight of the housing, and extend the service life of the filter element. The final output is a compliant digital design solution that can be directly used for production and manufacturing.

[0034] The method described in this embodiment specifically includes: importing a reference 3D model of a barrel-shaped filter housing into a computer-aided design platform; collecting all design parameters of the circumferential flow channel; encoding all design parameters into a standardized design variable vector using a variable mapping algorithm to form a basic design space; based on the basic design space, extracting engineering constraints constituted by manufacturing process limitations and material performance boundaries; using an interval generation algorithm to calculate the numerical constraint interval for each design variable according to the engineering constraints to generate a compliant design space; and eliminating parameter combinations that exceed the constraint interval; based on the compliant design space, constructing five sub-objective functions: filtration efficiency, flow resistance, structural strength, weight, and service life; combining these into a total optimization objective function using a linear weighted fusion algorithm to form a multi-objective optimization mathematical model; using the compliant design space as constraints and minimizing the total optimization objective function as the objective, iterating using parameter population iterative optimization technology; calculating the change in the objective function value at each iteration step using a convergence judgment algorithm; terminating the iteration when the change is less than a threshold; and outputting the design variable combination that minimizes the objective function as the optimal parameters; performing constraint verification and multi-dimensional performance simulation of the fluid structure on the optimal parameter scheme; if the simulation results meet the preset engineering performance threshold, updating the optimal parameters to the reference model and outputting a complete digital design scheme.

[0035] Specifically, the key to the technical principle of this invention lies in constructing a closed-loop design system that encompasses parameter standardization coding, constraint space screening, multi-objective optimization modeling, intelligent iterative optimization, and multi-dimensional simulation verification. This system first transforms various discrete design parameters of the circumferential flow channel into a unified standardized vector form, establishing a basic design space adaptable to computer-aided calculations, thus solving the problems of scattered design parameters and the inability to manage them in batches and perform iterative calculations in traditional systems. Then, it defines parameter value boundaries through engineering constraints such as vehicle assembly specifications, manufacturing process requirements, and performance index thresholds, automatically traversing and eliminating non-compliant parameter combinations, significantly narrowing the scope of ineffective design optimization and improving overall optimization efficiency. Subsequently, it transforms five key performance indicators—filtration efficiency, flow resistance, structural strength, shell weight, and service life—into quantifiable sub-objective functions, using a linear weighted algorithm to integrate multiple indicators into a unified overall optimization objective, achieving synergistic optimization of multiple performance indicators and avoiding overall performance imbalance caused by over-optimization of a single indicator. Next, it conducts efficient searches within the compliant space using a population iterative optimization algorithm, with a convergence judgment algorithm ensuring the stability and accuracy of the optimization results, avoiding over- or under-iteration problems. Finally, it verifies the actual engineering feasibility of the scheme through engineering constraint verification and joint fluid and structural simulation, achieving standardized control of design parameters, intelligent execution of the optimization process, and engineering verification of design results throughout the entire process, balancing design efficiency and product performance compliance rate.

[0036] Optionally, the parameter vector construction employs a variable mapping algorithm to encode design parameters and construct the basic design space. This includes: importing the complete three-dimensional model of the barrel-shaped filter housing into the computer-aided design platform; accurately collecting all design parameters of the circumferential flow channel using a model parameter extraction tool; and collecting the parameters in the following order: circumferential structural dimensions, spatial distribution, target airflow resistance, target value for flow field improvement and drag reduction, target value for filtration efficiency improvement, and range of housing weight variation. The variable mapping algorithm is then used to encode the above six design parameters into a six-dimensional standardized design variable vector. The vector data is stored in a structured form in the parameter database of the computer-aided design system, supporting designers to perform parameter retrieval, real-time modification, and batch editing operations.

[0037] Specifically, the mathematical expression for the variable mapping algorithm is:

[0038] ,in To standardize the design variable vector, For circumferential structural dimensions, For spatial distribution, To guide the airflow to the target resistance, To improve the flow field and reduce drag, To improve the target value of filtration efficiency, This represents the range of change in the shell weight.

[0039] For example, when performing the parameter vector construction operation, the reference 3D model of the barrel-shaped filter housing with the preliminary structural design completed is first imported into the computer-aided design platform. Through the platform's built-in parameter extraction function, all design parameters corresponding to the circumferential drainage channel are completely collected to ensure that the parameter collection is complete and without deviation. Then, the collected discrete design parameters are substituted into the variable mapping algorithm to complete the encoding and generation of the six-dimensional standardized design variable vector. The encoded vector data is stored in the system parameter database in a structured format and can be directly called by the subsequent constraint generation and optimization modeling modules without repeating the parameter collection work, thus ensuring the continuity and efficiency of the entire design process.

[0040] Optionally, the constraint boundary generation process employs an interval generation algorithm to construct a compliant design space and eliminate parameter combinations exceeding limits. This includes: extracting engineering constraints from vehicle assembly specifications, filter performance indicators, injection molding manufacturing processes, and vehicle weight control requirements. Specifically, the extracted engineering constraints include engine compartment assembly space limitations, filter intake total resistance thresholds, housing injection molding process requirements, vehicle weight control standards, and engine intake filtration efficiency thresholds; using an interval generation algorithm, calculating the corresponding lower and upper limits of numerical constraints for each design variable to form an independent constraint interval for each design variable; integrating the independent numerical constraint intervals of all design variables to impose a global value limit on the basic design space; traversing all parameter combinations within the basic design space, matching and comparing the values ​​of each dimension of the design variables in each parameter combination with the corresponding constraint intervals, filtering and eliminating parameter combinations where any dimension variable value exceeds the constraint interval, ultimately forming a complete compliant design space.

[0041] Specifically, the mathematical expression for the interval generation algorithm is:

[0042] ,

[0043] in Design the space for compliance. For the first The lower limit value of the design variable, For the first The upper limit of the design variable. This refers to the index of the design variable.

[0044] For example, when performing the constraint boundary generation operation, all engineering constraints are first extracted from the vehicle development specifications, filter performance standards, and injection molding process requirements to clarify the engineering limitations of each design parameter. Then, the engineering constraints are substituted into the interval generation algorithm to calculate the upper and lower limits of each design variable, forming an independent constraint interval. Next, all independent constraint intervals are integrated to complete the full-domain limitation of the basic design space. Then, a traversal algorithm is used to verify all parameter combinations in the basic design space one by one, and all parameter combinations with variable values ​​exceeding the limits are eliminated. The remaining parameter combinations together constitute a compliant design space, ensuring from the source that the parameters used in subsequent optimization calculations meet the actual engineering requirements.

[0045] Optionally, the optimization model construction includes building an airflow distribution uniformity evaluation function, five sub-objective functions, and a total optimization objective function, including: constructing an airflow distribution uniformity evaluation function to quantify the uniformity of airflow distribution on the filter element surface and provide a quantitative evaluation basis for flow field optimization; based on the compliant design space, constructing five sub-objective functions for filtration efficiency, flow resistance, structural strength, weight, and service life respectively to achieve independent quantitative calculation of each key performance indicator; and using a linear weighted fusion algorithm to combine the five sub-objective functions according to preset weight coefficients into a total optimization objective function, forming a multi-objective optimization mathematical model that can be directly used for iterative optimization.

[0046] Specifically, the mathematical expression for the airflow distribution uniformity evaluation function is as follows:

[0047]

[0048] in This is the function for evaluating the uniformity of airflow distribution. For the filter element surface Airflow velocity at each sampling point The average airflow velocity at all sampling points on the filter element surface. The total number of sampling points on the filter element surface is used to uniformly characterize the overall airflow velocity level on the filter element surface. The average airflow velocity is calculated by arithmetically summing the airflow velocities of all sampling points on the filter element surface and dividing by the total number of sampling points.

[0049] The mathematical expression for the linear weighted fusion algorithm is:

[0050]

[0051] in Head , The overall optimization objective function is... The weighting coefficient for the filtering efficiency sub-objective. The weighting coefficient for the flow resistance sub-objective. The weighting coefficients for the structural strength sub-objective are... The weighting coefficients for the weight sub-objectives. Weighting coefficients for the sub-target of service life The objective function is the filtering efficiency. Let the flow resistance sub-objective function be... The structural strength sub-objective function is... For the weight sub-objective function, The weight coefficients of the service life sub-objective function are calculated by dividing the importance score of each sub-objective by the sum of the importance scores of all sub-objectives, ensuring that the weight allocation fits the actual needs of the project and meets the normalization constraints.

[0052] The mathematical expressions for the five sub-objective functions are as follows:

[0053] The mathematical expression for the filtering efficiency sub-function is: ;

[0054] The mathematical expression for the flow resistance sub-function is: ;

[0055] The mathematical expression for the structural strength subfunction is: ;

[0056] The mathematical expression for the weight subfunction is: ;

[0057] The mathematical expression for the lifetime subfunction is: ;

[0058] in: This refers to the total filtration area of ​​the filter element. For the cylindrical coordinates of the filter element Local filtration efficiency at the location, The area of ​​a micro-element on the surface of the filter element. For a given design variable vector Below, the average pressure at the inlet section of the circumferential drainage channel, This represents the average pressure at the outlet section of the circumferential drainage channel under the same design variable vector X. For the cylindrical coordinates of the shell Local stress at the location, To optimize the overall weight of the rear shell, The total weight of the reference housing, This refers to the maximum local scale capacity of the filter element. The average local fouling capacity of the filter element is calculated by multiplying the density of the shell material by the design volume of the shell, which is used to accurately quantify the variation range of the shell weight.

[0059] For example, when performing the optimization model building operation, firstly, the number of sampling points on the filter surface is determined according to the flow field evaluation requirements, and an airflow distribution uniformity evaluation function is constructed; then, the weight coefficients of each sub-objective are set in combination with the product performance requirements, ensuring that the weight coefficients meet the requirement that the summation is 1 and non-negative; subsequently, the parameter combinations in the compliant design space are substituted into each sub-objective function to complete the independent quantitative calculation of each performance index; finally, the values ​​of each sub-objective function are substituted into the linear weighted fusion algorithm to obtain the total optimization objective function value, providing a clear quantitative optimization direction for subsequent intelligent parameter optimization.

[0060] Optionally, the intelligent parameter optimization process involves performing parameter population iterative optimization and convergence determination to output the optimal design parameter combination. This includes: randomly generating an initial parameter population based on the value range of the compliant design space, with the population containing multiple independent six-dimensional parameter individuals; performing performance screening on the individuals within the initial parameter population, selecting the individuals with better overall objective function values ​​as parent parameter individuals; performing parameter fragment cross-recombination on the parent parameter individuals to form intermediate parameter individuals; performing small-range parameter value fine-tuning on the intermediate parameter individuals to generate a child parameter population; using the child parameter population as the processing object for the next iteration, repeatedly performing screening, cross-recombination, and value fine-tuning operations to complete multiple rounds of population iterative updates; using a convergence determination algorithm to calculate the rate of change of the overall optimization objective function value between adjacent iterations, terminating the iteration process when the rate of change is less than a preset convergence threshold; and outputting the six-dimensional standardized design variable combination that minimizes the overall optimization objective function value after iteration termination as the optimal design parameters for the circumferential flow channel.

[0061] Specifically, the mathematical expression for the convergence determination algorithm is:

[0062]

[0063] in For the first The combination of design variables obtained through iteration, For the first The combination of design variables obtained through iteration, For the first The total objective function value corresponding to each iteration. For the first The total objective function value corresponding to each iteration. This is the preset convergence threshold.

[0064] For example, when performing intelligent parameter optimization, an initial parameter population is first generated based on the scope of the compliant design space, ensuring that individuals within the population cover the main value range of the compliant space. Subsequently, the population is iterated through a process of screening, cross-recombination, and numerical fine-tuning. In each iteration, the parameter individuals with better performance are retained, gradually approaching the optimal solution. During the iteration process, the rate of change of the objective function between adjacent generations is continuously calculated using a convergence judgment algorithm to determine in real time whether the iteration has reached a stable state. When the rate of change meets the convergence condition, the iteration is terminated immediately to avoid unnecessary iterations consuming computational resources. The final output optimal parameter combination is the parameter scheme with the minimum total optimization objective function value within the compliant design space.

[0065] Optionally, the scheme review output includes constraint verification, multi-dimensional simulation, and digital scheme output, including: performing full-dimensional engineering constraint verification on the optimal parameter scheme, checking whether the values ​​of the six design variables are within the corresponding constraint range, and confirming that no variables exceed the limits; conducting joint simulation of fluid dynamics and structural mechanics, verifying filtration efficiency, flow resistance, and airflow uniformity through fluid simulation, and verifying shell stress, deformation, and weight through structural simulation; if all simulation results meet the preset engineering performance thresholds, the optimal parameters are synchronously updated to the shell reference three-dimensional model to generate a complete digital design scheme including the three-dimensional model, parameter list, and process requirements; if any indicators fail to meet the standards, the process returns to the intelligent parameter optimization stage to re-perform iterative calculations.

[0066] For example, when executing the scheme review output operation, firstly, a full-dimensional constraint verification is carried out on the optimal parameter combination, checking each design variable's value to ensure compliance with engineering constraints; then, fluid and structure co-simulation is initiated to comprehensively verify the performance indicators of the optimal parameter scheme and determine whether they meet the preset engineering performance standards; when all indicators meet the standards, the optimal parameters are updated to the baseline 3D model, integrating model files, parameter data, and production process requirements to form a digital design scheme that can be directly delivered to the production stage; if the simulation shows that the indicators do not meet the standards, the problem is fed back to the optimization stage, the iteration conditions are adjusted, and the calculation is repeated until a design scheme that meets the requirements is obtained, such as... Figure 2 , Figure 3 As shown.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for designing a circumferential flow channel for a barrel-shaped filter housing, characterized in that, The specific steps of this method are as follows: S1. Parameter Vector Construction: Import the reference 3D model of the barrel-shaped filter housing into the computer-aided design platform, collect all design parameters of the circumferential drainage channel, and use the variable mapping algorithm to encode all design parameters into a standardized design variable vector to form the basic design space. S2. Constraint Boundary Generation: Based on the basic design space, engineering constraints consisting of manufacturing process limitations and material performance boundaries are extracted. An interval generation algorithm is used to calculate the numerical constraint interval for each design variable according to the engineering constraints, generating a compliant design space and eliminating parameter combinations that exceed the constraint interval. S3. Optimization Model Construction: Based on the compliant design space, five sub-objective functions are constructed: filtration efficiency, flow resistance, structural strength, weight, and service life. These are combined into a total optimization objective function using a linear weighted fusion algorithm, forming a multi-objective optimization mathematical model. S4. Intelligent parameter optimization: With compliance design space as constraint and minimization of the overall optimization objective function as objective, it uses parameter population iterative optimization technology to iterate. The change of objective function value in each iteration step is calculated by the convergence judgment algorithm. The iteration terminates when the change is less than the threshold. The combination of design variables that minimizes the objective function is output as the optimal parameters. S5. Scheme review and output: Perform constraint verification and multi-dimensional performance simulation of fluid structure on the optimal parameter scheme. If the simulation results meet the preset engineering performance threshold, update to the benchmark model and output digital design scheme.

2. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the construction of S1 and the parameter vector, the mathematical expression of the variable mapping algorithm is: ,in To standardize the design variable vector, For circumferential structural dimensions, For spatial distribution, To guide the airflow to the target resistance, To improve the flow field and reduce drag, To improve the target value of filtration efficiency, This represents the range of change in the shell weight.

3. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S1 parameter vector construction, all the design parameters collected are, in order, circumferential structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement and drag reduction target value, filtration efficiency improvement target value, and shell weight variation range.

4. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S1 parameter vector construction, the standardized design variable vector generated by encoding is a six-dimensional vector. The vector data is stored in the structured parameter database of the computer-aided design system, which supports parameter retrieval, parameter modification and batch parameter editing operations.

5. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the generation of S2 and constraint boundaries, the mathematical expression of the interval generation algorithm is: ， in Design the space for compliance. For the first The lower limit value of the design variable, For the first The upper limit of the design variable. This refers to the index of the design variable.

6. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S2 constraint boundary generation process, the extracted engineering constraints include engine compartment assembly space limitations, filter intake total resistance threshold, housing injection molding process requirements, vehicle weight control standards, and engine intake filtration efficiency threshold.

7. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S2 constraint boundary generation process, when eliminating parameter combinations that exceed the constraint range, the independent numerical constraint ranges corresponding to all design variables are integrated to fully limit the variable value range of the basic design space, thereby completing the construction of the compliant design space. All parameter combinations within the basic design space are traversed, and the values ​​of each dimension of the design variables contained in the parameter combination are matched and compared with the corresponding numerical constraint ranges one by one. Parameter combinations in which any dimension of the design variable values ​​exceed the corresponding numerical constraint ranges are selected, and the selected parameter combinations are eliminated.

8. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S3 optimization model construction, the mathematical expression of the linear weighted fusion algorithm is: in Head , The overall optimization objective function is... The weighting coefficient for the filtering efficiency sub-objective. The weighting coefficient for the flow resistance sub-objective. The weighting coefficients for the structural strength sub-objective are... The weighting coefficients for the weight sub-objectives. Weighting coefficients for the sub-target of service life The objective function is the filtering efficiency. Let the flow resistance sub-objective function be... The structural strength sub-objective function is... For the weight sub-objective function, The lifespan sub-objective function.

9. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S4 intelligent parameter optimization process, when using the parameter population iterative optimization technique, an initial parameter population is determined based on the compliant design space. The parameter individuals within the initial parameter population are screened, and the screened parameter individuals are selected as parent parameter individuals. The parent parameter individuals are subjected to parameter fragment cross-recombination to form intermediate parameter individuals. The intermediate parameter individuals are subjected to parameter value fine-tuning to generate a child parameter population. The child parameter population is used as the processing object for the next round of iteration. The operations of screening, cross-recombination, and value fine-tuning are repeatedly executed to complete multiple rounds of iterative updates of the parameter population.

10. The design method for the circumferential flow channel of a barrel-shaped filter housing according to claim 1, characterized in that, In the S4 intelligent parameter optimization process, the output optimal parameters are a combination of six standardized design variables within the compliant design space. This combination of design variables fully corresponds to six design parameters: circumferential structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement and drag reduction target value, filtration efficiency improvement target value, and shell weight change range. The values ​​of each dimension of the design variables are the variable values ​​corresponding to the minimum value of the overall optimization objective function after the iteration terminates.

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