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

Abstract

The application discloses a kind of bucket type filter shell multidirectional drainage passage design method, it is related to filter structure design technical field, the specific steps of this method are as follows: the invention first digitizes and compiles drainage passage parameter, and constraint such as assembly, process is converted into rule simultaneous solution legal boundary, invalid combination is obtained by global traversal and is removed, and conformity sample is obtained again; Uniformity, etc. are constructed to contain airflow loss, multiple sub-item evaluation functions and are fitted as single optimization target, and is combined with flow field vortex, strain rate and streamline quantitative evaluation, and the optimal design parameter is output by genetic algorithm in conformity sample and is iterated and optimized, and is output optimal design parameter;The application is verified by digitizing and compiling constraint parameter and global, and conformity sample is formed by removing invalid design, and multiple sub-item evaluation functions are constructed, and optimal parameter is screened by combining flow field analysis and iterative optimization, this method considers multiple constraints such as assembly, process, balances airflow performance and structure index, and improves conformity, filtration uniformity and operating stability of filter drainage passage design.

Description

Technical Field

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

[0002] The cylindrical air filter is a core component of the powertrain's intake filtration system. The structure and layout of its housing and airflow channels directly determine the airflow efficiency, internal flow field distribution, and overall filtration performance, serving as a crucial foundation for ensuring stable powertrain operation. As powertrain performance demands continue to rise, the market places increasingly stringent requirements on airflow resistance control, filtration uniformity, lightweight design, and vehicle assembly compatibility for filters. Multi-directional optimization design of the airflow channels has become a core direction for industry technological upgrades. Current research and development of filter housing airflow channels needs to simultaneously consider multiple constraints, including assembly space limitations, intake performance standards, structural weight control, and manufacturing process feasibility. A systematic and numerical design methodology is urgently needed to integrate multi-dimensional design elements and performance indicators to meet the evolving needs of powertrain upgrades and drive a steady improvement in filter product performance and adaptability.

[0003] Traditional barrel-type filter housing airflow channel designs largely rely on experience-based design models, lacking parametric and standardized design processes. This makes it impossible to systematically organize and precisely control various design elements. Constraint verification often relies on manual judgment, making it difficult to achieve simultaneous calculations of multi-dimensional constraints and accurately define legal value boundaries. Design schemes are prone to deviations from actual assembly, process, and performance requirements. Traditional designs lack a multi-index collaborative optimization system, failing to balance and coordinate core objectives such as airflow performance, structural weight, and space occupancy. This often results in the deterioration of other indicators after optimizing a single index. Flow field evaluation relies solely on qualitative judgment, lacking quantitative analysis and iterative optimization mechanisms, making it impossible to accurately identify the optimal design scheme. This easily leads to problems such as turbulent flow field in the airflow channel, excessive airflow loss, and uneven utilization of filtration efficiency. Overall design efficiency and scheme quality cannot meet the requirements of high-end power systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a design method for multi-directional flow channels in barrel-shaped filter housings. Through a complete process of parameter digitization, constraint digitization, parameter compliance verification, optimization function construction, and flow field evaluation and optimization, this method achieves systematic and numerical design of the flow channels. It transforms multi-dimensional constraints into unified judgment rules, accurately defines the legal boundaries of design variables and screens compliant samples. Relying on multi-objective optimization models and quantitative flow field analysis, combined with iterative optimization algorithms, it locks in the optimal design parameters. This effectively solves the problems of traditional design relying on experience, insufficient constraint adaptation, difficulty in coordinating the optimization of multiple indicators, and qualitative flow field evaluation. It significantly improves design efficiency and scheme reliability, optimizes airflow distribution and filtration efficiency, and takes into account structural, process, and assembly requirements, promoting the digital and intelligent upgrading of filter housing design.

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

[0006] S1. Parameter Digital Compilation: Extract parameters such as structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement target resistance, filtration efficiency target improvement degree, and structural weight change degree of the diversion channel. After classifying and digitally assigning values, generate a normalized design variable carrier through the design variable vector construction algorithm.

[0007] S2. Digitalization of Constraints: Collect constraints on vehicle assembly space, engine intake performance, shell structure weight, manufacturing process and installation dimensional tolerances, convert them into constraint judgment rules, and solve the legal value boundaries of the design variable carrier through multi-dimensional constraint simultaneous numerical calculation.

[0008] S3. Parameter compliance verification: Using the legal value boundary as a constraint, all combinations of design variables are compared one by one according to the preset step size. The design variable combinations whose comparison results exceed the legal value boundary are eliminated to obtain a set of compliant design samples.

[0009] The preset step size includes the channel cross-section width step size, the channel cross-section depth step size, the axial arrangement group number step size, and the circumferential arrangement group number step size. It iterates through and compares all possible combinations of design variables, eliminating those that exceed structural dimension boundaries, performance threshold boundaries, weight ratio boundaries, and dimensional deviation boundaries. The remaining design variable combinations are then organized to form a compliant design sample set.

[0010] S4. Construction of Optimization Function: Based on the set of compliant design samples, a multi-item evaluation function is constructed with airflow pressure loss, airflow distribution uniformity, filtration capacity utilization uniformity, structural weight increase rate, and space occupation increase. A single optimization objective is formed through a multi-objective linear combination algorithm. The numerical correspondence between design variables and the single optimization objective is established through numerical fitting to obtain the optimization model.

[0011] S5. Flow Field Evaluation and Optimization: For the flow field of the flow channel of each sample in the compliant design sample set, the vortex structure morphology is solved by calculating the difference between the flow field vorticity and strain rate parameters, and the streamline distribution state value is solved by the streamline morphology classification and quantification assignment method. The vortex structure morphology and streamline distribution state value are used as fitness, and a genetic algorithm is used to iteratively optimize within the compliant design sample set to output the final design parameter combination.

[0012] Furthermore, in S1, parameter digitization and compilation, the structural dimensions of the drainage channel include the channel cross-sectional width, channel cross-sectional depth, and channel axial extension length. The spatial distribution of the drainage channel includes the number of axial arrangement groups, the number of circumferential arrangement groups, the initial arrangement angle, the center-to-center distance between axially adjacent channels, and the center-to-center distance between circumferentially adjacent channels.

[0013] Furthermore, in S1, during the parameter digitization and organization, the mathematical expression for the normalized design variable carrier generated by the design variable vector construction algorithm is as follows: ,in: This indicates the width of the drainage channel's cross-section. This indicates the depth of the drainage channel's cross-section. This indicates the axial extension length of the drainage channel. Indicates the number of axial arrangement groups of drainage channels. This indicates the number of circumferential arrangement groups of the drainage channels. This indicates the initial angle of the drainage channel.

[0014] Furthermore, in S2, during the digitization of constraints, the vehicle assembly space constraints are converted into numerical range determination rules for the outer contour dimensions of the housing, the engine intake performance constraints are converted into numerical threshold determination rules for airflow resistance, the housing structure weight constraints are converted into numerical ratio determination rules for weight change, the manufacturing process constraints are converted into determination rules for the forming dimension limit of the airflow channel, and the installation dimension tolerance constraints are converted into determination rules for the deviation range of the housing mating dimensions.

[0015] Furthermore, in S2, the process of solving the legal value boundary of the design variable carrier through multi-dimensional constraint simultaneous numerical operation in the digital constraint conditions is as follows: All constraint judgment rules and corresponding numerical limits corresponding to the vehicle assembly space, engine intake performance, shell structure weight, manufacturing process, and installation dimensional tolerances are summarized; the dimensions, calculation accuracy, and numerical measurement units of all constraint parameters are unified; each design parameter contained in the design variable carrier is matched with the corresponding single constraint judgment condition and independently verified; the effective value intervals of all single constraints are collected and organized; the intersection and simultaneous operation of the collected multi-dimensional value intervals is performed; the common value intervals that simultaneously satisfy all constraint judgment rules are selected; and all common value intervals are combined to form a complete and closed legal value boundary of the design variable carrier.

[0016] Furthermore, in S4, during the construction of the optimization function, the mathematical expression for the multi-objective linear combination algorithm to form a single optimization objective is: , , , , , Indicates the weighting coefficient. Indicates airflow pressure loss. Indicates the uniformity of airflow distribution. The uniformity of filtration capacity is used to indicate filtration efficiency. Indicates the rate of increase in structural weight. This indicates the increase in space usage.

[0017] Furthermore, in S4, during the construction of the optimization function, based on multiple sets of design variable data within the compliant design sample set and the single optimization target data corresponding to the design variable data, a continuous numerical fitting method is selected to perform global data point fitting calculations on the design variable data and the single optimization target data, generating a fixed numerical correspondence between the input of the design variable data and the output of the single optimization target data.

[0018] Furthermore, in S5, during flow field evaluation and optimization, the calculation method for the difference between the flow field vorticity and strain rate parameters is as follows: collect vorticity tensor data and strain rate tensor data of the entire flow field of the diversion channel, unify the spatial grid dimension and calculation accuracy of the two types of tensor data, calculate the square of the vorticity tensor modulus and the square of the strain rate tensor modulus respectively, and then calculate the difference between the square of the strain rate tensor modulus and the square of the vorticity tensor modulus to obtain the Q criterion value. Based on the Q criterion value of the entire grid points, calibrate the vortex structure morphology at each point to complete the solution of the vortex structure morphology of the entire flow field region of the diversion channel.

[0019] Furthermore, in S5, during flow field evaluation and optimization, the streamline morphology classification and quantification assignment method is as follows: all air streamlines inside the flow channel are collected in full coverage, and the collected streamlines are divided into three fixed morphologies: regular and smooth streamlines, locally bent and turning streamlines, and globally disordered and intersecting streamlines. A unique fixed quantification value is assigned to each of the three streamline morphologies. The spatial proportion parameters of each type of streamline in a single sample flow field are statistically analyzed, and weighted calculations are performed in combination with the corresponding quantification values ​​to obtain a unique corresponding streamline distribution state value.

[0020] Furthermore, in S5, during flow field evaluation and optimization, the calculated values ​​corresponding to the vortex structure morphology and the streamline distribution state values ​​are integrated to construct a complete fitness parameter. All design parameter samples within the compliant design sample set are used as the data source for the iterative population. The iterative operation process of population selection, gene crossover, and gene mutation is completed sequentially. The design parameter samples within the population are continuously updated. After the preset iteration termination condition is met, the optimal parameter sample within the iterative population is locked, and a fixed final design parameter combination is output.

[0021] Compared with existing technologies, the multi-directional flow channel design method for the barrel-shaped filter housing has the following advantages:

[0022] I. This invention digitizes and organizes relevant parameters of the drainage channel, transforming various constraints into standardized numerical judgment rules. Through multi-dimensional constraint simultaneous calculations, the legal value boundaries of design variables are determined. Then, a full-domain traversal comparison is used to complete parameter compliance verification, forming a well-organized set of compliant design samples. This method can accurately adapt to multiple practical constraints such as vehicle assembly, engine intake, structural weight, manufacturing processes, and installation dimensional tolerances. It completely eliminates design variable combinations that do not meet constraint requirements, eliminates invalid design steps, and significantly improves the compliance and feasibility of design solutions. The standardized design variable carrier and unified constraint judgment logic simplify the complex process of multi-parameter collaborative design, making the selection and verification of design variables more efficient and orderly, laying a solid foundation for subsequent design optimization, and ensuring the rigor and stability of the overall design process.

[0023] II. This invention constructs a multi-item evaluation function and integrates it into a single optimization objective, establishing a numerical correspondence between design variables and the optimization objective. It combines flow field vorticity, strain rate analysis, and streamline morphology quantification to complete flow field evaluation. Then, an iterative optimization algorithm is used to select the optimal design parameters from compliant samples. This method can balance and coordinate core indicators such as airflow performance, airflow distribution uniformity, filtration capacity utilization, structural weight, and space occupancy, accurately optimizing the flow field structure of the drainage channel, improving airflow state and vortex distribution morphology, and enhancing the uniformity of filtration efficiency. The entire optimization process relies on compliant samples, taking into account both performance improvement and the actual adaptation requirements of structure and process. This makes the filter housing drainage channel design more suitable for actual usage scenarios, improving overall product efficiency and operational stability, and extending product service life.

[0024] 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

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0026] Figure 1 Flowchart of the design method for multi-directional drainage channels in barrel-shaped filter housing;

[0027] Figure 2 Flow field diagram of a barrel-shaped filter device without a flow channel;

[0028] Figure 3 Flow field changes after designing an axial flow channel;

[0029] Figure 4 Optimized flow field distribution diagram after design of axial flow channel;

[0030] Figure 5 Flow field changes after designing axial and circumferential multi-directional diversion channels;

[0031] Figure 6 Flow field improvement effect diagram under different design parameter matching;

[0032] Figure 7 This is a schematic diagram of the global and local coordinate systems of the filter housing. Detailed Implementation

[0033] 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.

[0034] Reference Figure 1 One embodiment of the present invention proposes a design method for a multi-directional flow channel in a barrel-shaped filter housing. It adopts a complete design process that integrates parameter digital compilation, constraint digitalization, parameter compliance verification, optimization function construction, and flow field evaluation optimization. Under multiple engineering conditions such as fitting the vehicle assembly space, matching engine intake performance, controlling housing structure weight, adapting to manufacturing process requirements, and meeting installation dimensional tolerances, it can accurately optimize the airflow characteristics, flow field distribution, and filtration efficiency of the flow channel. At the same time, it strictly controls the variation of structural weight and space occupation, and finally outputs a stable, feasible, and performance-compliant combination of flow channel design parameters.

[0035] The method described in this embodiment specifically includes:

[0036] S1. Parameter Digital Compilation: Extract parameters such as structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement target resistance, filtration efficiency target improvement degree, and structural weight change degree of the diversion channel. After classifying and digitally assigning values, generate a normalized design variable carrier through the design variable vector construction algorithm.

[0037] S2. Digitalization of Constraints: Collect constraints on vehicle assembly space, engine intake performance, shell structure weight, manufacturing process and installation dimensional tolerances, convert them into constraint judgment rules, and solve the legal value boundaries of the design variable carrier through multi-dimensional constraint simultaneous numerical calculation.

[0038] S3. Parameter compliance verification: Using the legal value boundary as a constraint, all combinations of design variables are compared one by one according to the preset step size. The design variable combinations whose comparison results exceed the legal value boundary are eliminated to obtain a set of compliant design samples.

[0039] S4. Construction of Optimization Function: Based on the set of compliant design samples, a multi-item evaluation function is constructed with airflow pressure loss, airflow distribution uniformity, filtration capacity utilization uniformity, structural weight increase rate, and space occupation increase. A single optimization objective is formed through a multi-objective linear combination algorithm. The numerical correspondence between design variables and the single optimization objective is established through numerical fitting to obtain the optimization model.

[0040] S5. Flow Field Evaluation and Optimization: For the flow field of the flow channel of each sample in the compliant design sample set, the vortex structure morphology is solved by calculating the difference between the flow field vorticity and strain rate parameters, and the streamline distribution state value is solved by the streamline morphology classification and quantification assignment method. The vortex structure morphology and streamline distribution state value are used as fitness, and a genetic algorithm is used to iteratively optimize within the compliant design sample set to output the final design parameter combination.

[0041] The key to the technical principle of this invention lies in establishing a design system that standardizes parameters, closes loops constraints, ensures sample compliance, integrates objectives, and enables intelligent optimization. First, discrete parameters related to the flow channel, such as structure, performance, and weight, are transformed into digital design variables in a unified format. Then, multi-dimensional constraints related to the vehicle, performance, process, and tolerances are converted into quantifiable numerical judgment rules. Through simultaneous operations, the legal value range of all parameters is locked, and design combinations that do not meet engineering requirements are eliminated. Subsequently, multiple objectives, such as airflow characteristics, filtration efficiency, structural weight, and space occupancy, are transformed into linearly combinable evaluation functions, forming a unified optimization objective and establishing a numerical mapping relationship between parameters and objectives. Finally, a fitness index is constructed by combining the vortex morphology and streamline distribution characteristics of the flow field. Iterative screening is completed through a genetic algorithm, realizing the transformation of flow channel design from qualitative judgment to quantitative calculation, and from single optimization to multi-objective collaborative optimization, comprehensively ensuring the feasibility and excellence of the design scheme.

[0042] Optionally, in S1, the specific execution process of parameter digitization and compilation, including parameter extraction, digitization assignment, and design variable carrier generation for the drainage channel, includes: firstly, extracting the structural dimension parameters and spatial distribution parameters of the drainage channel from the design requirements of the multi-directional drainage channel of the barrel-type filter housing. The structural dimensions include the channel cross-sectional width, channel cross-sectional depth, and channel axial extension length; the spatial distribution includes the number of axial arrangement groups, the number of circumferential arrangement groups, the initial arrangement angle, the center-to-center distance between axially adjacent channels, and the center-to-center distance between circumferentially adjacent channels. Simultaneously, extracting four types of performance and weight-related parameters: airflow drainage target resistance, flow field improvement target resistance, filtration efficiency target improvement degree, and structural weight change degree. All extracted parameters are categorized and organized according to structural, spatial, performance, and weight categories. Then, each type of parameter is standardized and digitized to eliminate differences in parameter representation. After completing the parameter digitization assignment, a design variable vector construction algorithm is used to generate a normalized design variable carrier, the mathematical expression of which is: ,in: This indicates the width of the drainage channel's cross-section. This indicates the depth of the drainage channel's cross-section. This indicates the axial extension length of the drainage channel. Indicates the number of axial arrangement groups of drainage channels. This indicates the number of circumferential arrangement groups of the drainage channels. This indicates the initial angle of the drainage channel.

[0043] For example, during the parameter digitization process, three types of structural dimension parameters—channel cross-sectional width, channel cross-sectional depth, and channel axial extension length—are extracted according to design requirements. Five types of spatial distribution parameters are also extracted: the number of axial arrangement groups, the number of circumferential arrangement groups, the initial arrangement angle, the center-to-center distance between axially adjacent channels, and the center-to-center distance between circumferentially adjacent channels. Simultaneously, four types of related parameters are extracted: airflow diversion target resistance, flow field improvement target resistance, filtration efficiency target improvement degree, and structural weight change degree. All these parameters are then categorized according to structural dimensions, spatial distribution, performance targets, and weight changes. Each parameter is then uniformly digitized to ensure that all parameters are standardized values ​​that can be directly used in numerical calculations. Subsequently, according to a preset design variable vector construction algorithm, the six design variables—channel cross-sectional width, channel cross-sectional depth, channel axial extension length, number of axial arrangement groups, number of circumferential arrangement groups, and initial arrangement angle—are integrated into a regularized design variable carrier in column vector form, completing the parameter digitization and preparing for subsequent constraint matching and numerical calculations.

[0044] Optionally, in S2, the specific execution process of constraint rule transformation and legal value boundary determination in constraint digitization includes: firstly, converting the five types of collected constraints one by one into quantifiable constraint judgment rules, whereby the vehicle assembly space constraint is converted into a numerical range judgment rule for the outer contour dimensions of the housing, the engine intake performance constraint is converted into a numerical threshold judgment rule for airflow resistance, the housing structure weight constraint is converted into a numerical ratio judgment rule for weight change, the manufacturing process constraint is converted into a limit judgment rule for the forming dimensions of the drainage channel, and the installation dimension tolerance constraint is converted into a judgment rule for the range of deviations in the housing fit dimensions; the numerical range judgment rule for the outer contour dimensions of the housing corresponding to the vehicle assembly space constraint is to limit the numerical range of the maximum outer diameter and the maximum axial length of the filter housing, with the maximum outer diameter and the maximum axial length of the housing both within their respective numerical ranges; the numerical threshold judgment rule for the airflow resistance corresponding to the engine intake performance constraint is to preset a fixed threshold for the maximum allowable airflow resistance of the engine intake, with the airflow resistance values ​​corresponding to all design variables not exceeding this fixed threshold; the numerical ratio judgment rule for the weight change corresponding to the housing structure weight constraint is to preset a maximum weight change ratio limit for the housing relative to the original structure, with the airflow resistance values ​​corresponding to all design variables not exceeding this fixed threshold. The weight change ratio of the shell does not exceed the specified limit; the manufacturing process constraint-related limit for the forming dimension of the drainage channel is determined by setting the minimum and maximum forming dimensions of the drainage channel width, depth, and axial length, and ensuring that the structural dimension parameters of the drainage channel are within the corresponding dimension range; the installation dimension tolerance constraint-related limit for the shell mating dimension deviation range is determined by setting the positive and negative extreme values ​​of the shell assembly mating dimensions, and ensuring that the deviation values ​​of the shell installation mating dimensions are within the range formed by the bidirectional extreme values; after completing the constraint rule conversion, all constraint determination rules and their corresponding limits are summarized. The corresponding numerical limits are set, and the dimensions, calculation precision, and numerical units of all constraint parameters are unified to avoid calculation errors caused by inconsistent dimensions. For each design parameter contained in the design variable carrier, the corresponding single constraint judgment conditions are matched one by one and independent verification is completed to obtain the effective value range of each parameter. The effective value ranges of all single constraints are collected and organized, and the intersection and joint operation of the collected multi-dimensional value ranges are performed to screen out the common value ranges that can simultaneously satisfy all constraint judgment rules. Finally, all common value ranges are combined to form a complete and closed legal value boundary of the design variable carrier.

[0045] For example, when performing the constraint digitization operation, the vehicle assembly space constraint is first converted into a numerical range judgment rule that limits the maximum outer diameter and maximum axial length of the filter housing; the engine intake performance constraint is converted into a numerical threshold judgment rule that limits the upper limit of airflow resistance; the housing structure weight constraint is converted into a numerical ratio judgment rule that limits the upper limit of weight change ratio; the manufacturing process constraint is converted into a dimensional limit judgment rule that limits the upper and lower limits of the forming size of the airflow channel; and the installation dimensional tolerance constraint is converted into a deviation extreme value judgment rule that limits the range of mating dimensional deviation. The numerical limits are then determined according to the judgment rules corresponding to each type of constraint. The above constraint judgment rules and numerical limits are set and summarized, and the dimensions, calculation accuracy and units of measurement of the parameters are unified. Then, for each parameter in the design variable carrier, such as the channel cross-section width, channel cross-section depth, and number of axial arrangement groups, the corresponding single constraint is matched and independently checked to obtain the single valid value range of each parameter. The intersection and simultaneous operation of all single valid value ranges are performed to screen out the common value range that simultaneously meets all requirements of vehicle assembly, air intake performance, structural weight, manufacturing process and installation tolerance. Finally, they are integrated to form a closed legal value boundary of design variables to ensure that all subsequent design parameters are within the compliance range.

[0046] Optionally, in S3, the specific execution process of design variable combination traversal, invalid combination elimination, and compliant sample set generation in parameter compliance verification includes: pre-setting four types of traversal step lengths: channel cross-section width step length, channel cross-section depth step length, axial arrangement group number step length, and circumferential arrangement group number step length. The step length values ​​are determined according to the design accuracy requirements to ensure that the traversal process can cover all possible design variable combinations; using the legal value boundaries obtained in S2 as the judgment basis, all combinations of design variables are traversed and compared one by one according to the preset step length, and each group of design variable combinations is judged to see if it exceeds the structural size boundary, performance threshold boundary, weight ratio boundary, and size deviation boundary; design variable combinations that exceed any type of legal boundary in the comparison results are judged as invalid combinations and directly eliminated, and all design variable combinations that do not exceed the legal boundary are judged as valid combinations; all remaining valid design variable combinations are systematically organized and classified to form a complete compliant design sample set. All samples in this set meet all constraints and can be directly used for subsequent optimization calculations and optimization operations.

[0047] For example, when performing parameter compliance verification, the step size for the channel cross-section width, channel cross-section depth, axial arrangement group number, and circumferential arrangement group number are preset according to the design accuracy requirements to ensure that the traversal process can completely cover all combinations of design variables. Using the legal value boundary obtained from constraint calculation as the sole criterion, all combinations of design variables such as channel cross-section width, channel cross-section depth, axial arrangement group number, and circumferential arrangement group number are traversed one by one according to the preset step size. Each combination is checked to see if it exceeds the legal boundaries of structural dimensions, performance thresholds, weight ratios, and dimensional deviations. All combinations that exceed any type of boundary are eliminated, and only the design variable combinations that simultaneously meet all boundary requirements are retained. The retained valid combinations are sorted and classified to form a complete set of compliant design samples, providing a reliable sample basis for subsequent optimization function construction and flow field optimization.

[0048] Optionally, in step S4, the specific execution process of establishing multi-sub-item evaluation functions, generating a single optimization objective, and fitting the optimization model in the construction of the optimization function includes: constructing five multi-sub-item evaluation functions based on the compliant design sample set. Each function term corresponds to airflow pressure loss, airflow distribution uniformity, filtration capacity utilization uniformity, structural weight increase rate, and space occupancy increase rate, and each function term has a clear mathematical expression:

[0049] Airflow pressure loss function: ;

[0050] Airflow distribution uniformity function: ;

[0051] Filtering capacity utilizes a uniformity function: ;

[0052] Structural weight increase rate function: ;

[0053] Space occupancy increase function: ;

[0054] A multi-objective linear combination algorithm is adopted to integrate five multi-sub-item evaluation functions into a single optimization objective. By assigning weight coefficients to each evaluation function, the transformation from multi-objective optimization to single-objective optimization is achieved. The mathematical expression of the single optimization objective is as follows:

[0055]

[0056] in: , , , , This represents the weighting coefficient, which is calculated using a combined weighting method combining the analytic hierarchy process (AHP) and the entropy weighting method. Its mathematical expression is: , ,in The final weight coefficient corresponding to the i-th evaluation function is... These are the subjective weight coefficients of the i-th evaluation function determined by the analytic hierarchy process. Let be the objective weight coefficients of the i-th evaluation function determined by the entropy weight method. This is the sum of the products of the subjective and objective weights of the five evaluation functions, used to normalize the weight coefficients to ensure that the sum of all weight coefficients is 1. Let be a single optimization objective function to be minimized. Let be the airflow pressure loss function. The total pressure at the inlet of the drainage channel. The total pressure at the outlet of the drainage channel. Let be the airflow distribution uniformity function. The cross-sectional area of ​​the filter outlet. Let V be the airflow velocity at any coordinate point on the outlet cross-section. The average airflow velocity at the outlet section. Apply a uniformity function to the filtering capability. This represents the filtration efficiency value at any coordinate point within the effective filtration space of the housing. V represents the average filtration efficiency within the effective filtration space of the filter housing, where V is the effective filtration space of the filter housing. Let the structural weight increase rate function be . The added drainage channel will increase the weight of the casing. This is the original shell weight. Add an amplitude function to the space occupancy. To maximize the outer volume of the rear shell after adding the drainage channel, This represents the maximum outer volume of the original shell. To normalize the design of variable carriers, The width of the drainage channel cross-section. The depth of the drainage channel cross-section. This refers to the axial extension length of the drainage channel. The number of groups arranged axially in the drainage channel. The number of groups arranged circumferentially in the diversion channel. The initial angle for the drainage channel;

[0057] Based on multiple sets of design variable data and the single optimization target data corresponding to the design variable data in the compliant design sample set, a continuous numerical fitting method is selected to perform global data point fitting calculation on the design variable data and the single optimization target data, generating a fixed numerical correspondence between the input of the design variable data and the output of the single optimization target data, thus obtaining the optimization model.

[0058] For example, when performing the optimization function construction operation, firstly, based on the compliant design sample set, five multi-sub-item evaluation functions are established sequentially: airflow pressure loss, airflow distribution uniformity, filtration capacity utilization uniformity, structural weight increase rate, and space occupancy increase. The function definition and variable assignment rules are strictly set according to the corresponding mathematical expressions. Then, through a multi-objective linear combination algorithm, corresponding weight coefficients are assigned to the five evaluation functions, integrating multiple objectives into a single optimization objective that can be directly solved. The objective fusion calculation is completed according to the preset linear combination formula. Subsequently, the design variable data and single optimization objective data of all samples in the compliant design sample set are retrieved, and the data points of the entire domain are fitted and calculated using a continuous numerical fitting method. Through numerical fitting, a fixed numerical correspondence between the design variables and the single optimization objective is established, and finally, an optimization model that can be directly used for flow field optimization is generated, realizing the rapid mapping calculation from design parameters to optimization objectives.

[0059] Optionally, in S5, the specific execution process of solving the vortex structure morphology and streamline distribution state values ​​in the flow field evaluation and optimization includes: for the flow field of the drainage channel of each sample in the compliant design sample set, firstly, collecting the vortex tensor data and strain rate tensor data of the entire flow field of the drainage channel, unifying the spatial grid dimension and calculation accuracy of the two types of tensor data to ensure the consistency of data calculation; calculating the square of the vortex tensor modulus and the square of the strain rate tensor modulus respectively, and then calculating the difference between the square of the strain rate tensor modulus and the square of the vortex tensor modulus to obtain the Q criterion value, based on... The Q-criterion value of the whole-domain grid points is used to mark the vortex structure morphology at each point, and the vortex structure morphology of the flow field region of the entire diversion channel is solved. All air streamlines inside the diversion channel are collected in full coverage. The collected streamlines are divided into three fixed morphologies: regular smooth streamlines, locally bent and turning streamlines, and globally turbulent and intersecting streamlines. A unique fixed quantitative value is assigned to each of the three streamline morphologies. The spatial proportion parameters of each type of streamline in a single sample flow field are statistically analyzed. The corresponding quantitative values ​​are combined and weighted to calculate the unique streamline distribution state value.

[0060] For example, when performing the operation of solving the vortex structure morphology and streamline distribution state value, firstly, the vorticity tensor and strain rate tensor data of the entire flow field of the guide channel in the compliant sample are collected. The spatial grid dimension and calculation accuracy of the two types of data are unified. The Q criterion value of each grid point is obtained according to the difference between the square of the vorticity tensor modulus and the square of the strain rate tensor modulus. Then, the vortex structure morphology of each region of the flow field is calibrated point by point according to the Q criterion value to complete the quantitative solution of the entire vortex structure morphology. At the same time, all air streamlines inside the guide channel are collected in full coverage. The streamlines are divided into three types: regular and smooth, locally bent and turning, and globally turbulent and intertwined, and assigned fixed quantitative values. The spatial proportion of the three types of streamlines in the sample flow field is statistically analyzed. The weighted calculation is completed by combining the quantitative values ​​of each type of streamline, and finally the streamline distribution state value corresponding to the sample is obtained, thus completing the quantitative characterization of the flow field characteristics.

[0061] Optionally, in S5, the specific execution process of fitness construction, genetic algorithm iterative optimization, and final parameter output in flow field evaluation and optimization includes: integrating the calculated values ​​corresponding to the vortex structure morphology and the streamline distribution state values ​​to construct complete fitness parameters; using all design parameter samples within the compliant design sample set as the data source for the iterative population; sequentially completing the iterative operation process of population selection, gene crossover, and gene mutation; continuously updating the design parameter samples within the population; continuously optimizing the fitness index of the samples within the population during the iteration process until the preset iteration termination condition is met; locking the optimal parameter sample within the iterative population; and outputting a fixed final design parameter combination.

[0062] For example, when performing fitness construction and genetic algorithm iterative optimization, the calculated values ​​of vortex structure morphology and streamline distribution state values ​​obtained from the solution are first integrated to form fitness parameters that can evaluate the quality of the flow field. Using all parameter samples from the compliant design sample set as the initial iterative population, the genetic algorithm sequentially performs population selection, gene crossover, and gene mutation operations, continuously updating the design parameter samples within the population and gradually improving the overall fitness of the population. When the iterative operation reaches the preset termination condition, the iteration process stops, and the parameter sample with the highest fitness is selected from the current population. The parameters of this sample, such as channel cross-sectional width, channel cross-sectional depth, axial extension length, number of axial arrangement groups, number of circumferential arrangement groups, and initial arrangement angle, are locked as the final design parameter combination output, resulting in a flow channel design scheme that satisfies all constraints and optimization objectives, such as... Figure 2-7 As shown.

[0063] 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 multi-directional flow channel for a barrel-shaped filter housing, characterized in that, The specific steps of this method are as follows: S1. Parameter Digital Compilation: Extract parameters such as structural dimensions, spatial distribution, airflow diversion target resistance, flow field improvement target resistance, filtration efficiency target improvement degree, and structural weight change degree of the diversion channel. After classifying and digitally assigning values, generate a normalized design variable carrier through the design variable vector construction algorithm. S2. Digitalization of Constraints: Collect constraints on vehicle assembly space, engine intake performance, shell structure weight, manufacturing process and installation dimensional tolerances, convert them into constraint judgment rules, and solve the legal value boundaries of the design variable carrier through multi-dimensional constraint simultaneous numerical calculation. S3. Parameter compliance verification: Using the legal value boundary as a constraint, all combinations of design variables are compared one by one according to the preset step size. The design variable combinations whose comparison results exceed the legal value boundary are eliminated to obtain a set of compliant design samples. S4. Construction of Optimization Function: Based on the set of compliant design samples, a multi-item evaluation function is constructed with airflow pressure loss, airflow distribution uniformity, filtration capacity utilization uniformity, structural weight increase rate, and space occupation increase. A single optimization objective is formed through a multi-objective linear combination algorithm. The numerical correspondence between design variables and the single optimization objective is established through numerical fitting to obtain the optimization model. S5. Flow Field Evaluation and Optimization: For the flow field of the flow channel of each sample in the compliant design sample set, the vortex structure morphology is solved by calculating the difference between the flow field vorticity and strain rate parameters, and the streamline distribution state value is solved by the streamline morphology classification and quantification assignment method. The vortex structure morphology and streamline distribution state value are used as fitness, and a genetic algorithm is used to iteratively optimize within the compliant design sample set to output the final design parameter combination.

2. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In the S1 parameter digitization and compilation, the structural dimensions of the drainage channel include the channel cross-sectional width, channel cross-sectional depth, and channel axial extension length. The spatial distribution of the drainage channel includes the number of axial arrangement groups, the number of circumferential arrangement groups, the initial arrangement angle, the center-to-center distance between axially adjacent channels, and the center-to-center distance between circumferentially adjacent channels.

3. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In S1, during parameter digitization and organization, the mathematical expression for the normalized design variable carrier generated by the design variable vector construction algorithm is as follows: ,in: This indicates the width of the drainage channel's cross-section. This indicates the depth of the drainage channel's cross-section. This indicates the axial extension length of the drainage channel. Indicates the number of axial arrangement groups of drainage channels. This indicates the number of circumferential arrangement groups of the drainage channels. This indicates the initial angle of the drainage channel.

4. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In S2, during the digitization of constraints, the vehicle assembly space constraint is converted into a numerical range determination rule for the outer contour dimensions of the housing, the engine intake performance constraint is converted into a numerical threshold determination rule for airflow resistance, the housing structure weight constraint is converted into a numerical ratio determination rule for weight change, the manufacturing process constraint is converted into a limit determination rule for the forming dimensions of the drainage channel, and the installation dimension tolerance constraint is converted into a determination rule for the range of deviations in the housing mating dimensions.

5. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In S2, the process of solving the legal value boundary of the design variable carrier through multi-dimensional constraint simultaneous numerical operation in the digital constraint conditions is as follows: All constraint judgment rules and corresponding numerical limits corresponding to the vehicle assembly space, engine intake performance, shell structure weight, manufacturing process, and installation dimensional tolerances are summarized; the dimensions, calculation accuracy, and numerical measurement units of all constraint parameters are unified; each design parameter included in the design variable carrier is matched with the corresponding single constraint judgment condition and independently verified; the effective value intervals of all single constraints are collected and organized; the intersection and simultaneous operation of the collected multi-dimensional value intervals is performed; the common value intervals that simultaneously satisfy all constraint judgment rules are selected; and all common value intervals are combined to form a complete and closed legal value boundary of the design variable carrier.

6. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In step S4, during the construction of the optimization function, the mathematical expression for the formation of a single optimization objective by the multi-objective linear combination algorithm is: , , , , , Indicates the weighting coefficient. Indicates airflow pressure loss. Indicates the uniformity of airflow distribution. The uniformity of filtration capacity is used to indicate filtration efficiency. Indicates the rate of increase in structural weight. This indicates the increase in space usage.

7. The design method for a multi-directional drainage channel of a barrel-shaped filter housing according to claim 1, characterized in that, In step S4, during the construction of the optimization function, based on multiple sets of design variable data within the compliant design sample set and the single optimization target data corresponding to the design variable data, a continuous numerical fitting method is selected to perform global data point fitting calculations on the design variable data and the single optimization target data, generating a fixed numerical correspondence between the input of the design variable data and the output of the single optimization target data.

8. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In S5, during flow field evaluation and optimization, the calculation method for the difference between the flow field vorticity and strain rate parameters is as follows: collect vorticity tensor data and strain rate tensor data of the entire flow field of the diversion channel, unify the spatial grid dimension and calculation accuracy of the two types of tensor data, calculate the square of the vorticity tensor modulus and the square of the strain rate tensor modulus respectively, and then calculate the difference between the square of the strain rate tensor modulus and the square of the vorticity tensor modulus to obtain the Q criterion value. Based on the Q criterion value of the entire grid points, mark the vortex structure morphology at each point to complete the solution of the vortex structure morphology of the entire flow field region of the diversion channel.

9. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In S5, during flow field evaluation and optimization, the streamline morphology classification and quantification assignment method is as follows: all air streamlines inside the flow channel are collected in full coverage, and the collected streamlines are divided into three fixed morphologies: regular and smooth streamlines, locally bent and turning streamlines, and globally disordered and intersecting streamlines. A unique fixed quantification value is assigned to each of the three streamline morphologies. The spatial proportion parameters of each type of streamline in a single sample flow field are statistically analyzed, and weighted calculations are performed in combination with the corresponding quantification values ​​to obtain a unique corresponding streamline distribution state value.

10. The design method for a multi-directional flow channel in a barrel-shaped filter housing according to claim 1, characterized in that, In S5, during flow field evaluation and optimization, the calculated values ​​corresponding to the vortex structure morphology and the streamline distribution state values ​​are integrated to construct a complete fitness parameter. All design parameter samples within the compliant design sample set are used as the data source for the iterative population. The iterative operation process of population selection, gene crossover, and gene mutation is completed sequentially. The design parameter samples within the population are continuously updated. After the preset iteration termination condition is met, the optimal parameter sample within the iterative population is locked, and a fixed final design parameter combination is output.

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