Passenger car air filter assembly full working condition acoustic performance positive design and simulation optimization method, system and equipment

Abstract

The application discloses a passenger car air filter assembly full working condition acoustic performance positive design and simulation optimization method, system and equipment, and the method comprises the following steps: obtaining air filter assembly three-dimensional structure data and dividing multiple simulation domains, respectively building a filter core acoustic simulation submodel and pre-processing the remaining simulation domains, and integrating multiple physical field coupling acoustic simulation models; collecting engine full working condition data, extracting corresponding intake characteristic parameters of each working condition to construct a dynamic simulation boundary library; importing the working condition boundary into the simulation model, and correcting the simulation model in combination with bench test data; taking the calibrated simulation model as an evaluation tool, constructing a multi-parameter collaborative optimization model considering noise reduction, intake resistance and assembly volume, solving and screening the optimized structure scheme through a multi-objective genetic algorithm; and establishing a filter performance attenuation model to simulate and verify the full life cycle NVH stability of the optimized structure. The application can improve simulation accuracy, shorten development cycle, and meet air filter acoustic simulation optimization development.

Description

Technical Field

[0001] This invention relates to the technical field of automobile manufacturing, and in particular to a method, system, and equipment for forward design and simulation optimization of the acoustic performance of passenger car air filter assemblies under all operating conditions. Background Technology

[0002] In the development of NVH (Noise, Vibration, and Harshness) for passenger vehicles, intake system noise is one of the core noise sources of the entire vehicle, and the air filter assembly (including filter element, housing, intake and exhaust pipes, and muffler unit) is the core component for noise reduction in the intake system. In the early stages of vehicle development, acoustic simulation is needed to predict and optimize the noise reduction performance of the air filter assembly, and the accuracy and efficiency of the simulation directly determine the success or failure of the intake system NVH development.

[0003] Currently, the existing technology still has the following limitations: 1. It can only realize the static acoustic simulation of the filter element, without considering the strong coupling effect of flow field and sound field between the filter element and the air filter housing, air inlet and outlet pipes, and integrated silencing unit. In actual vehicles, the silencing performance of the filter element is directly related to the flow field distribution and structural resonance characteristics of the assembly. The results of simulating the filter element alone deviate greatly from the actual performance of the assembly in the vehicle, and cannot directly support the development of NVH of the whole vehicle.

[0004] 2. The simulation only considers static steady-state conditions and does not take into account the dynamic intake boundary changes under all operating conditions such as engine idling, acceleration, start-stop, and intermittent hybrid operation. In particular, the frequent start-stop and intermittent operation of the engine in PHEV hybrid models result in severe intake airflow pulsation, which the static simulation cannot cover at all, leading to a disconnect between the simulation results and the actual operating conditions.

[0005] 3. It can only realize the simulation verification of the acoustic performance of the filter element, but cannot realize the forward design optimization of the air filter assembly structure. The existing technology is a reverse mode of "designing the structure first and then simulating and verifying", which cannot directly output the optimal structural solution through simulation in the early stage of development, resulting in a long development cycle and high iteration cost.

[0006] 4. The simulation only considers the filter element in its new state and does not take into account the impact of changes in flow resistance and porosity caused by dust accumulation and aging on the acoustic performance throughout the filter element's entire life cycle. Therefore, it cannot guarantee the stability of NVH performance throughout the vehicle's entire life cycle.

[0007] Therefore, the industry urgently needs a method for positive design and simulation optimization of acoustic performance at the air filter assembly level across all operating conditions and the entire life cycle, thereby filling the gap in existing technology. Summary of the Invention

[0008] The present invention aims to solve the technical problems existing in the above-mentioned related technologies, and proposes a method for forward design and simulation optimization of acoustic performance of passenger car air filter assembly under all working conditions, which can realize forward design and simulation optimization of acoustic performance of air filter assembly under all working conditions and throughout the entire life cycle.

[0009] A method for forward design and simulation optimization of the acoustic performance of a passenger vehicle air filter assembly under all operating conditions, according to a first aspect of the present invention, includes: The three-dimensional structural data of the target air filter assembly is obtained and divided into multiple simulation domains including the filter element domain. An acoustic simulation sub-model of the filter element is constructed for the filter element domain. Pre-simulation processing is performed on the other simulation domains. The simulation content of all simulation domains is integrated to obtain the multi-physics field coupled acoustic simulation model of the air filter assembly. Collect engine operating data under all operating conditions of the target vehicle model, divide it into multiple core operating condition clusters and extract the intake characteristic parameters corresponding to each operating condition, and establish a dynamic simulation boundary library that corresponds one-to-one with each operating condition. The boundary conditions of each working condition in the dynamic boundary library are imported into the multiphysics coupled acoustic simulation model to calculate the acoustic performance index of the air filter assembly under all working conditions and form an assembly acoustic performance database; the measured acoustic data of the whole vehicle bench are collected, and the coupling parameters of the multiphysics coupled acoustic simulation model are corrected based on the measured acoustic data until the performance deviation between simulation and measurement is less than a preset threshold. The calibrated multi-physics coupled acoustic simulation model is used as a performance evaluation tool. The optimization objectives are to maximize the average noise reduction under all working conditions, minimize the intake resistance under rated working conditions, and minimize the assembly volume. The structural parameters of the air filter assembly are used as optimization variables, and the vehicle layout and intake performance requirements are used as constraints. A multi-parameter collaborative optimization model is constructed together. The optimal solution set is obtained by iteratively solving the problem using a multi-objective genetic algorithm. The preferred structural scheme of the air filter assembly is then selected and output. A filter element performance degradation model was established, and the acoustic performance changes of the preferred structural scheme under different usage stages of the filter element were simulated to verify the NVH performance stability of the preferred structural scheme throughout its entire life cycle.

[0010] The method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all operating conditions according to embodiments of the present invention has at least the following beneficial effects: 1. This invention constructs a complete multi-physics coupled acoustic simulation model of the air filter assembly, divides multiple simulation domains including the filter element domain, and models and integrates them separately to achieve assembly-level flow field-sound field coupled joint simulation. This overcomes the shortcomings of existing technologies that can only conduct static simulation of individual filter elements, and fully incorporates the coupling effect between the filter element and the housing, pipeline, and silencing unit. This significantly improves the accuracy of acoustic simulation in conforming to the actual vehicle installation state, and the simulation results can be directly used for the NVH development of the vehicle intake system. 2. This invention builds a dynamic simulation boundary library based on engine full-condition operating data, which solves the problems that traditional static simulation cannot adapt to the airflow pulsation conditions of hybrid vehicles and the simulation results are out of sync with the actual vehicle conditions. It can be adapted to the NVH simulation development of intake systems of both fuel vehicles and hybrid vehicles, and has a wider range of applicable scenarios. 3. This invention uses a calibrated multi-physics coupled acoustic simulation model as an evaluation tool to build a multi-parameter collaborative optimization model, and uses a multi-objective genetic algorithm to complete the iterative solution. The optimal structural scheme of the air filter assembly is directly screened by simulation, which changes the reverse development mode of the existing technology of processing and prototyping first and then verifying by simulation. It realizes the forward design of the acoustic performance of the air filter assembly, and can output a structural scheme that meets multiple requirements such as noise reduction, intake resistance and assembly volume in the early stage of project development. This reduces multiple rounds of physical iteration, shortens the development cycle and reduces the cost of R&D iteration. 4. This invention establishes a filter element performance degradation model to simulate the acoustic performance changes of the filter element at different stages of use. It conducts full life cycle NVH stability verification on the optimized preferred structural scheme, which makes up for the shortcomings of the existing technology that only simulates new filter elements and ignores the acoustic degradation caused by dust accumulation and aging. It can predict the noise performance changes of the filter element during long-term use, ensure the NVH performance stability of the vehicle throughout its entire life cycle, and form a complete and closed-loop acoustic forward simulation optimization development system for air filter assembly.

[0011] According to some embodiments of the present invention, constructing a filter cartridge acoustic simulation sub-model for the filter cartridge domain includes: The characteristic coefficients of the porous media of the filter paper were calibrated by the impedance tube method. The flow resistance and porosity of the filter element were calculated by combining the structural parameters of the filter element itself. The acoustic simulation sub-model of the filter element was built by using the porous media acoustic model.

[0012] According to some embodiments of the present invention, the step of acquiring the three-dimensional structural data of the target air filter assembly and dividing it into multiple simulation domains including the filter element domain includes: The simulation domains also include an air inlet domain, an air outlet domain, a shell chamber domain, an integrated silencing unit domain, and an air inlet / outlet connection pipeline domain.

[0013] According to some embodiments of the present invention, the pre-simulation processing for the remaining simulation domain includes: Establish the flow field-sound field coupled control equations, divide the fluid domain and structural domain into grids respectively, and set the fully sound-absorbing boundary of the air inlet, the non-reflective boundary of the air outlet, and the rigid boundary of the shell wall.

[0014] According to some embodiments of the present invention, the step of dividing into multiple core operating condition clusters and extracting the intake characteristic parameters corresponding to each operating condition includes: The operating condition clusters include idling operating condition clusters, rated speed operating condition clusters, acceleration transient operating condition clusters, and start-stop cycle operating condition clusters; the extracted intake characteristic parameters include intake flow rate, intake pressure, airflow pulsation frequency, and intake temperature.

[0015] According to some embodiments of the present invention, the step of correcting the coupling parameters of the multiphysics coupled acoustic simulation model based on the measured acoustic data until the performance deviation between the simulation and the measured data is less than a preset threshold includes: By comparing simulation and measured data, the turbulence model parameters and shell wall damping coefficient of the multiphysics coupled acoustic simulation model are adjusted until the acoustic transmission loss amplitude deviation and the intake resistance deviation are less than their respective preset allowable thresholds.

[0016] According to some embodiments of the present invention, establishing the filter element performance degradation model includes: Multiple dust holding capacity tests were conducted on filter cartridges with different dust holding capacities. Data on the changes in flow resistance and porosity of filter cartridges corresponding to each dust holding capacity were collected. Based on the collected data, a filter cartridge performance degradation model was obtained.

[0017] According to some embodiments of the present invention, after verifying the NVH performance stability of the preferred structural scheme throughout its entire life cycle, the method further includes: The robustness of the preferred structural scheme is verified by combining extreme working conditions. If the robustness does not meet the requirements, the performance constraints are fed back to the multi-parameter collaborative optimization model for iterative solution until the robustness meets the requirements.

[0018] A forward design and simulation optimization system for the acoustic performance of a passenger vehicle air filter assembly under all operating conditions, according to a second aspect of the present invention, comprises: The coupled model construction module is configured to acquire the three-dimensional structural data of the air filter assembly and construct the filter element acoustic simulation sub-model and the air filter assembly flow field-sound field coupled acoustic simulation model. The operating condition boundary library module is configured to collect engine operating data under all operating conditions and establish a dynamic simulation boundary library that corresponds one-to-one with the operating conditions. The dynamic simulation verification module is configured to perform acoustic performance simulation calculations of the assembly under all operating conditions, and to complete the benchmark verification and correction of the simulation model by combining bench test data. The multi-objective optimization module is configured to construct a multi-parameter collaborative optimization model and output the preferred structural scheme of the air filter assembly through iterative solution; The full life cycle verification module is configured to establish a filter element performance degradation model and complete the full life cycle NVH performance stability verification of the preferred structural scheme; the full life cycle verification module can also be extended to perform robust performance verification under extreme working conditions.

[0019] According to a third aspect of the present invention, an apparatus includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for forward design and simulation optimization of the acoustic performance of a passenger vehicle air filter assembly under all operating conditions.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all working conditions, as provided in this embodiment of the invention. Figure 2 This is a flowchart illustrating the construction process of the multi-physics coupled acoustic simulation model of the air filter assembly provided in this embodiment of the invention. Figure 3 This is a flowchart illustrating the dynamic simulation and benchmark verification of acoustic performance under all operating conditions provided in this embodiment of the invention. Figure 4 This is an acoustic transmission loss curve of the air filter assembly corresponding to the three usage stages of the filter element provided in the embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the interaction between the various modules of the passenger vehicle air filter assembly full-condition acoustic performance forward design and simulation optimization system provided in this embodiment of the invention. Detailed Implementation

[0022] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0023] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0024] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0025] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0026] This invention provides a method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assemblies under all operating conditions. It is primarily used for NVH development of passenger vehicle intake system air filter assemblies, and is particularly suitable for the early-stage forward simulation design of intake muffler structures in traditional gasoline passenger vehicles and PHEV plug-in hybrid passenger vehicles. It can fully realize multi-physics coupled acoustic modeling at the assembly level, full dynamic intake condition simulation calibration, multi-objective structural forward optimization, and acoustic attenuation prediction throughout the filter element's life cycle. It can also be extended to complete robustness verification under extreme operating conditions and closed-loop iterative optimization. In practical engineering applications, the entire method relies on a computer simulation host computer, acoustic test bench equipment, impedance tube testing equipment, and filter element dust holding durability test bench for collaborative completion.

[0027] like Figure 1 As shown, a forward design and simulation optimization method for the acoustic performance of a passenger vehicle air filter assembly under all operating conditions is presented. This method includes: S1. Construction of multi-physics coupling acoustic simulation model for air filter assembly; S2. Establishment of dynamic boundary library for all engine operating conditions; S3. Dynamic simulation and benchmark verification of acoustic performance under all working conditions; S4. Forward optimization of the assembly structure through multi-objective collaboration; S5. Robustness verification of acoustic performance throughout the entire life cycle.

[0028] like Figure 2 As shown, specifically, the detailed process of step S1 is as follows: S11. Obtain the three-dimensional structural data of the target air filter assembly, including the full-size three-dimensional model of the upper and lower cavities of the housing, the inlet and outlet pipes, the paper filter element, the integrated resonant cavity, and the mounting bracket. Perform geometric cleanup on the model, removing small features such as rounded corners and bolt holes that do not affect the simulation results, and avoiding distortion of subsequent mesh generation caused by small features, which would unnecessarily increase the amount of simulation calculation.

[0029] S12. Divide six simulation domains, which are the air inlet domain, air outlet domain, housing chamber domain, filter element domain, integrated silencing unit domain, and inlet-outlet connection pipeline domain. The geometric matching relationship of structural contact and fluid communication is retained between each simulation domain, ensuring that the gas flow path and acoustic transmission path inside the air filter assembly can be truly restored after multi-domain integration. It should be noted that since most filter elements of air filter assemblies are paper filter elements, the filter element domain can also be referred to as the paper filter element domain.

[0030] S13. Independently build a filter element acoustic simulation sub-model for the divided filter element domain. First, conduct a porous medium acoustic performance calibration test on the filter paper sample matched with the target vehicle through the impedance tube method, test and record the porous medium characteristic coefficients corresponding to the filter paper. The porous medium characteristic coefficients tested in this application are: tortuosity 1.42, viscous characteristic length 0.048mm, and thermal characteristic length 0.096mm. Simultaneously read the structural parameters of the filter element itself in the three-dimensional model. The filter element structural parameters selected in this embodiment are: height 45mm, effective filtration area of the filter element 0.35m 2 , number of filter element pleats 86, height of a single pleat 10mm. Import the above-mentioned filter element geometric parameters into the CFD intake resistance simulation program to carry out steady-state airflow simulation, calculate the overall flow resistance of the filter element under rated working conditions, and further convert to obtain the flow resistivity of the filter element material of 8.2×10 7 Pa s / m². Combined with the volume of the filter element entity and the volume of the filter paper solid material, the porosity of the filter paper is calculated to be 0.78. Enter all calibration coefficients, filter element structural parameters, flow resistivity and porosity values into the Johnson-Champoux-Allard porous medium acoustic model uniformly, complete parameter assignment and model construction of the filter element acoustic simulation sub-model, and this sub-model can accurately characterize the absorption and attenuation effect of the filter element porous medium on airflow noise.

[0031] S14. After completing the construction of the filter element simulation sub-model, uniformly establish flow field-acoustic field coupling governing equations for the other five types of simulation domains, namely the air inlet domain, air outlet domain, housing chamber domain, integrated silencing unit domain, and inlet-outlet connection pipeline domain. The governing equations include the fluid motion N-S equation, linear Euler perturbation equation and acoustic wave transmission equation, so as to realize the two-way coupling calculation of fluid airflow pulsation and acoustic noise propagation. Perform meshing operation on all fluid simulation domains and housing structural domains: regular hexahedral structured grids are adopted for fluid flow areas, and tetrahedral unstructured grids are adopted for housing solid structural areas. The minimum grid size is uniformly limited to 0.5mm and the maximum grid size is 2mm. The total number of grids of the entire simulation model of the air filter assembly is controlled at 8 million, which balances simulation calculation accuracy and calculation efficiency.

[0032] S15. After mesh generation, conditional values ​​are assigned to the boundaries of the remaining five simulation domains. The inlet end face is uniformly set as a fully sound-absorbing boundary, the outlet end face as a non-reflective boundary, and all outer walls of the air filter housing are set as rigid boundaries to prevent sound absorption loss and structural vibration radiation noise. Following this, the filter element acoustic simulation sub-model is integrated with the simulation content of the other five simulation domains, ensuring real-time transmission of fluid and acoustic interface data from each simulation domain. This integration ultimately forms a multi-physics coupled acoustic simulation model of the air filter assembly. This model can simultaneously calculate the internal airflow characteristics and full-frequency acoustic silencing performance of the air filter assembly, no longer limited to individual filter element simulation, and fully reflects the flow field and sound field coupling interaction effects between the filter element, housing, piping, and integrated silencing unit.

[0033] After completing the construction of the multiphysics coupled acoustic simulation model, the detailed process of step S2 is as follows: Taking a PHEV model with a 1.5T engine as an example, we collected full-condition operating data of the 1.5T engine in the target PHEV model and divided it into five core operating condition clusters according to the engine operating status and intake airflow fluctuation characteristics: 1. Idle operating conditions: Engine speed 800±50rpm, intake air flow 35-45m³ / h 3 / h, intake pressure -8~-5kPa.

[0034] 2. Rated speed operating conditions: Engine speed 5500 rpm, intake air flow 320-350 m³ / h 3 / h, intake pressure -25~-20kPa.

[0035] 3. Acceleration transient operating condition cluster: engine speed sweep frequency of 1000-5000rpm, linear change of intake air flow, and airflow pulsation frequency of 20-200Hz.

[0036] 4. Start-stop cycle operating condition cluster: engine start-stop transient, intake pressure fluctuation range -15~0kPa, airflow pulsation peak frequency 50Hz.

[0037] 5. Hybrid Intermittent Operating Conditions Cluster: This cluster includes three sub-conditions: switching between pure electric drive and hybrid drive, engine cold start, and intermittent hybrid operation. It separately collects exclusive intake parameters such as the alternating amplitude of intake pressure and the time-domain characteristics of airflow pulsation in hybrid mode, forming hybrid-specific boundary conditions. This cluster is exclusive to hybrid vehicles and can be ignored by traditional gasoline vehicles.

[0038] All intake characteristic parameters corresponding to each of the above-mentioned operating condition clusters are standardized and organized. A one-to-one mapping relationship between parameters is established using the operating condition number as an index, forming a dynamic simulation boundary library corresponding to each operating condition. The complete boundary library is stored in the database of the simulation calculation system. When conducting subsequent simulation calculations, the complete set of boundary parameters for the corresponding operating condition can be directly retrieved without repeatedly entering the operating condition data. This can fully cover the stable operating conditions of traditional fuel vehicles and the severe airflow pulsation conditions caused by frequent start-stop and intermittent operation of hybrid vehicles. It solves the problem that traditional static single-condition simulation cannot match the dynamic intake boundary of the real vehicle and the simulation results are out of sync with the actual performance of the vehicle.

[0039] After the dynamic simulation boundary library is built, the detailed process of step S3 is as follows: S31. Sequentially retrieve the various working condition boundary conditions stored in the dynamic simulation boundary library, import them one by one into the previously built multi-physics field coupled acoustic simulation model of the air filter assembly, and perform coupled simulation calculations separately for each type of working condition to obtain three core acoustic performance indicators: the full-frequency acoustic transmission loss curve of the air filter assembly under the corresponding working condition, the rated working condition intake resistance value, and the amplitude of the radiation noise from the outer wall of the housing. Record the simulation calculation results corresponding to all working conditions into the database to form an assembly acoustic performance database, which fully records the noise reduction and intake resistance performance of the air filter assembly under different dynamic intake boundaries.

[0040] S32. Subsequently, a dedicated acoustic test bench for the intake system was built inside the semi-anechoic laboratory for the whole vehicle. The actual air filter assembly to be tested was assembled onto the intake pipe of the bench. Acoustic sensors and pressure sensors were placed at the corresponding acoustic monitoring points inside the simulation model to simulate the intake flow and airflow pulsation under various engine operating conditions. Measured acoustic data of the air filter assembly's acoustic transmission loss and intake resistance were collected under each operating condition. The bench measured data was compared one by one with the simulation calculation results of the same operating conditions in the simulation database. The coupling parameters of the multiphysics coupled acoustic simulation model were adjusted, such as turbulence model parameters and shell wall damping coefficient. After each round of parameter adjustment, the full-condition simulation calculation was re-executed, and the simulation output data was compared with the bench measured data again. The model coupling parameters were continuously iterated and corrected until the acoustic transmission loss amplitude deviation and intake resistance deviation were less than their respective preset allowable thresholds. This completed the benchmark calibration of the multiphysics coupled acoustic simulation model. In this embodiment, the preset allowable threshold for acoustic transmission loss amplitude deviation is ≤2dB, and the preset allowable threshold for intake resistance deviation is ≤5%.

[0041] The calculation accuracy of the multi-physics coupled acoustic simulation model after bench testing and calibration has been greatly improved. It can be used as a reliable performance evaluation tool for subsequent multi-objective optimization. Structural optimization calculations based on the calibrated model can ensure that the optimization results are consistent with the actual vehicle performance.

[0042] A calibrated multiphysics coupled acoustic simulation model was used as the performance evaluation tool for the entire optimization process to carry out multi-parameter collaborative forward structural optimization of the air filter assembly. For example... Figure 3 As shown, the detailed process of step S4 is as follows: S41. The three core optimization objectives are maximizing the average noise reduction under all operating conditions, minimizing the intake resistance under rated operating conditions, and minimizing the assembly volume. These three optimization objectives are mutually restrictive and conflicting, and cannot be simultaneously achieved at their extreme optimum.

[0043] S42. Various adjustable structural dimensions of the air filter assembly are used as optimization variables, including the length, width and height of the housing chamber, the installation angle of the air inlet and outlet, the height of the filter element pleats, the number of filter element pleats, the volume of the integrated resonant cavity, and the diameter and length of the resonant cavity neck tube.

[0044] S43. The vehicle engine compartment layout boundary and the rigid performance indicators of the intake system are used as constraints. In this embodiment, the constraints include the upper limit of the intake resistance under rated operating conditions of 1.5kPa (≤1.5kPa), the lower limit of the average noise reduction of the full frequency band from 50Hz to 4000Hz of 15dB (≥15dB), the outer contour dimensions of the air filter assembly not exceeding the reserved installation space in the engine compartment, and the lower limit of the effective filtration area of ​​the filter element of 0.3㎡ (≥0.3㎡).

[0045] S44. Integrate the above optimization objectives, optimization variables, and constraints into the calculation program to jointly construct a multi-parameter collaborative optimization model. After the model is constructed, a multi-objective genetic algorithm is used to carry out iterative solution work. In this embodiment, the NSGA-II multi-objective genetic algorithm is selected to complete the population iterative calculation. The basic running parameters of the algorithm are pre-configured, with the population size set to 80 groups, the maximum number of iterations set to 100 rounds, the crossover probability set to 0.8, and the mutation probability set to 0.05.

[0046] S45. After initializing the population, the program iteratively executes individual fitness calculations, population crossover and mutation updates, and iteration termination condition judgments. In each iteration, the calibrated multiphysics coupled acoustic simulation model is called to calculate the noise reduction, drag, and volume performance indicators corresponding to each structural scheme within the population, which are used as the basis for judging individual fitness. When the number of iterations reaches the preset maximum number of iterations, the iteration calculation is terminated, and the program outputs a Pareto optimal solution set that satisfies all constraints. There is no absolutely optimal solution in the Pareto optimal solution set that has a single performance that is superior to all other schemes. It can only achieve a trade-off between multiple objectives. Subsequently, in combination with the actual needs of the vehicle engine compartment layout engineering, the feasibility of component processing and manufacturing, and cost control requirements, all schemes in the Pareto optimal solution set are manually screened, and finally, the preferred structural scheme of the air filter assembly adapted to the target vehicle model is output.

[0047] Compared to the initial structural design before optimization, the selected optimal structural design has an average noise reduction of 8dB under all operating conditions, a 12% reduction in intake resistance under rated operating conditions, and a 10% reduction in the overall volume occupied by the assembly. This transforms the air filter assembly from the traditional reverse development model of design first and then simulation verification to a simulation-driven forward structural design in the early stage, which significantly reduces the number of iterations of physical prototype prototyping, shortens the development cycle of vehicle intake NVH, and reduces the cost of structural iteration R&D.

[0048] After obtaining the preferred structural scheme, the detailed process of step S5 is as follows: S51. Establish a filter element performance degradation model. The establishment process is completed using a filter element dust holding durability test bench. Design multiple sets of filter element dust accumulation tests corresponding to gradient dust holding capacities. Gradually introduce standard test dust into the filter paper surface and collect real-time change data of internal flow resistance and filter paper porosity under different cumulative dust holding capacities. Import the dust holding capacity, flow resistance, and porosity data collected from multiple sets of tests into the fitting program to establish a correlation function between dust holding capacity and filter element porous media performance parameters. Complete the establishment of a filter element dust holding blockage-acoustic performance degradation correlation model. This model can accurately characterize the filter element porosity decrease and flow resistance increase as dust accumulation increases.

[0049] S52. Embed the completed filter performance degradation model into the previously calibrated multi-physics coupled acoustic simulation model, and substitute it into the geometric model of the optimized structural scheme output in the previous step. Simulate and calculate the acoustic performance of the air filter assembly in three typical usage stages. The three usage stages are: a brand new, dust-free filter, a filter with dust accumulation at half its life cycle after 10,000 km of vehicle driving, and a filter with heavy dust accumulation at the end of its full life cycle after 20,000 km of vehicle driving. Output the acoustic transmission loss curves of the air filter assembly corresponding to the three usage stages and create a simulation as shown below. Figure 4 The chart shown.

[0050] By comparing the changes in acoustic curves at different usage stages, the stability of the NVH performance of the optimized structural scheme throughout its entire lifecycle was verified. Simulation calculations show that the overall attenuation rate of the optimized structural scheme in this embodiment does not exceed 10% under all operating conditions throughout its entire lifecycle. The attenuation of intake noise is controllable during long-term use. This overcomes the shortcomings of existing technologies that only simulate brand-new filters while ignoring the continuous acoustic performance degradation caused by filter dust accumulation and aging. It can predict the trend of intake noise changes throughout the vehicle's entire lifecycle, ensuring stable NVH performance of the entire vehicle during long-term use.

[0051] S53. After completing the full life cycle NVH stability verification of the optimized structural scheme, extend the robustness verification process under extreme working conditions. Retrieve the boundary conditions corresponding to four types of extreme vehicle use conditions: high cold, high temperature, high altitude, and dusty conditions. Import the boundary parameters of extreme working conditions temperature (-40℃ for high cold and 45℃ for high temperature), atmospheric pressure at 4500m in high altitude (approximately 58kPa), and extreme intake dust concentration of 2g / m³ into the coupled acoustic simulation model. With the filter performance degradation model, conduct coupled simulation of multiple extreme working conditions on the optimized structural scheme again. Statistically analyze the fluctuation range of the noise reduction performance and intake resistance of the air filter assembly under extreme working conditions, and evaluate the working condition robustness performance of the optimized structural scheme.

[0052] If the simulation results show that the acoustic performance and intake drag under extreme conditions exceed the vehicle's preset allowable fluctuation range, it is determined that the robustness of the preferred structural scheme does not meet the vehicle development requirements. Therefore, performance indicators exceeding the threshold under extreme conditions are extracted, and the newly added performance fluctuation constraints are synchronously fed back into the previously built multi-parameter collaborative optimization model. After updating the model constraints, the multi-objective genetic algorithm iterative solution process is restarted to generate a new Pareto optimal solution set and select a new structural scheme. The full lifecycle NVH verification and extreme condition robustness verification processes are repeatedly executed, continuously iterating until the performance fluctuation of the preferred structural scheme meets the vehicle design threshold under all lifecycle conditions and various extreme conditions. After robustness is achieved, the finalized air filter assembly structural scheme and a complete set of simulation optimization reports are output. The report fully stores the modeling files, full-condition simulation data, multi-objective optimization solution set, full lifecycle acoustic attenuation simulation curves, and extreme condition robustness verification results, providing complete data support for component drawing development, physical prototype manufacturing, and vehicle NVH matching.

[0053] like Figure 5 As shown, this embodiment of the invention also provides a forward design and simulation optimization system for the acoustic performance of passenger vehicle air filter assembly under all operating conditions. This system is used to execute the above-mentioned forward design and simulation optimization method for the acoustic performance of passenger vehicle air filter assembly under all operating conditions. The system includes five core functional modules: a coupled model construction module, an operating condition boundary library module, a dynamic simulation verification module, a multi-objective optimization module, and a full life cycle verification module. The simulation parameters, structural models, and performance data are exchanged in real time between the modules through an internal data interaction channel.

[0054] The coupled model construction module is used to read the three-dimensional structural data of the air filter assembly imported from outside, complete the simulation domain division, build the filter element acoustic simulation sub-model, preprocess the other simulation domains, configure boundary conditions, and integrate to generate the air filter assembly flow field-sound field coupled acoustic simulation model.

[0055] The operating condition boundary library module is used to collect engine bench operation data under all operating conditions, divide various core operating condition clusters and extract intake characteristic parameters, and build a dynamic simulation boundary library that corresponds one-to-one with operating conditions and intake parameters, supporting the simulation module to call the operating condition boundary in real time.

[0056] The dynamic simulation verification module is responsible for retrieving operating condition parameters from the dynamic boundary library and importing them into the coupled acoustic simulation model. It then performs batch calculations of acoustic performance indicators under all operating conditions and generates an assembly acoustic performance database. Simultaneously, it imports measured acoustic data from the vehicle bench, iteratively corrects the coupling parameters of the simulation model, and completes the benchmark calibration of the simulation model.

[0057] The multi-objective optimization module retrieves the calibrated coupled acoustic simulation model as a performance evaluation tool, inputs the optimization objectives, optimization variables, and vehicle performance constraints to construct a multi-parameter collaborative optimization model, and runs a multi-objective genetic algorithm to iteratively solve and output the optimal structural scheme of the air filter assembly.

[0058] The full lifecycle verification module retrieves the 3D model of the optimized structural scheme, imports data collected from filter element dust-holding tests to build a filter element performance degradation model, and simulates the acoustic performance changes of the filter element after dust accumulation and aging in stages, completing the full lifecycle NVH performance stability verification of the optimized structural scheme. Simultaneously, this module extends its support to import extreme operating condition boundaries, enabling robust performance verification under extreme conditions. If robustness fails to meet the standards, new performance constraint parameters can be sent back to the multi-objective optimization module, triggering a new round of optimization iterations.

[0059] In addition, the passenger vehicle air filter assembly full-condition acoustic performance forward design and simulation optimization system is also equipped with a report output module. After the robustness verification is passed, the report output module automatically integrates all simulation, optimization and verification data to generate standardized air filter assembly design scheme documents and complete simulation optimization reports, which support local storage and external export for viewing.

[0060] This invention also provides a device that integrates a memory and a processor. The memory locally stores executable computer program code, and the processor establishes a data communication connection with the memory. When the processor retrieves and executes the computer program stored in the memory, it fully implements all the steps of the aforementioned method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all working conditions.

[0061] The computer processing equipment can be any one of the following: vehicle controller, host computer, server, or cloud computing platform. It only needs to meet the computing power requirements for 3D model processing, multi-physics field coupled simulation iterative calculation, and multi-objective genetic algorithm solution. The entire simulation optimization method can be digitized and automated through computer program carrier, reducing the workload of manual simulation operation and improving the standardization of NVH forward development of air filter assembly.

[0062] Taking the processor and memory in an onboard controller as an example, the memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the control device via a network.

[0063] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions for executing the aforementioned method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assemblies under all operating conditions.

[0064] It is worth noting that, since the computer-readable storage medium of the present invention is capable of executing the forward design and simulation optimization method for the acoustic performance of the passenger vehicle air filter assembly under all operating conditions in any of the above embodiments, the specific implementation method and technical effects of the computer-readable storage medium of the present invention can be referred to the specific implementation method and technical effects of the forward design and simulation optimization method for the acoustic performance of the passenger vehicle air filter assembly under all operating conditions in any of the above embodiments.

[0065] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0066] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0067] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assemblies under all operating conditions, characterized in that, include: The three-dimensional structural data of the target air filter assembly is obtained and divided into multiple simulation domains including the filter element domain. An acoustic simulation sub-model of the filter element is constructed for the filter element domain. Pre-simulation processing is performed on the other simulation domains. The simulation content of all simulation domains is integrated to obtain the multi-physics field coupled acoustic simulation model of the air filter assembly. Collect engine operating data of the target vehicle under all operating conditions, divide it into multiple core operating condition clusters and extract the intake characteristic parameters corresponding to each operating condition, and establish a dynamic simulation boundary library that corresponds one-to-one with each operating condition. The boundary conditions of each working condition in the dynamic boundary library are imported into the multiphysics coupled acoustic simulation model to calculate the acoustic performance index of the air filter assembly under all working conditions and form an assembly acoustic performance database; the measured acoustic data of the whole vehicle bench are collected, and the coupling parameters of the multiphysics coupled acoustic simulation model are corrected based on the measured acoustic data until the performance deviation between simulation and measurement is less than a preset threshold. The calibrated multi-physics coupled acoustic simulation model is used as a performance evaluation tool. The optimization objectives are to maximize the average noise reduction under all working conditions, minimize the intake resistance under rated working conditions, and minimize the assembly volume. The structural parameters of the air filter assembly are used as optimization variables, and the vehicle layout and intake performance requirements are used as constraints. A multi-parameter collaborative optimization model is constructed together. The optimal solution set is obtained by iteratively solving the problem using a multi-objective genetic algorithm. The preferred structural scheme of the air filter assembly is then selected and output. A filter element performance degradation model was established, and the acoustic performance changes of the preferred structural scheme under different usage stages of the filter element were simulated to verify the NVH performance stability of the preferred structural scheme throughout its entire life cycle.

2. The passenger car air filter assembly full operating condition acoustic performance positive design and simulation optimization method according to claim 1, characterized in that, The construction of the filter cartridge acoustic simulation sub-model for the filter cartridge domain includes: The characteristic coefficients of the porous media of the filter paper were calibrated by the impedance tube method. The flow resistance and porosity of the filter element were calculated by combining the structural parameters of the filter element itself. The acoustic simulation sub-model of the filter element was built by using the porous media acoustic model.

3. The passenger car air filter assembly full operating condition acoustic performance positive design and simulation optimization method of claim 1, wherein, The process of acquiring the three-dimensional structural data of the target air filter assembly and dividing it into multiple simulation domains, including the filter element domain, includes: The simulation domains also include an air inlet domain, an air outlet domain, a shell chamber domain, an integrated silencing unit domain, and an air inlet / outlet connection pipeline domain.

4. The method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all operating conditions according to claim 3, characterized in that, The pre-simulation processing for the remaining simulation domains includes: Establish the flow field-sound field coupled control equations, divide the fluid domain and structural domain into grids respectively, and set the fully sound-absorbing boundary of the air inlet, the non-reflective boundary of the air outlet, and the rigid boundary of the shell wall.

5. The method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all operating conditions according to claim 1, characterized in that, The process of dividing the system into multiple core operating condition clusters and extracting the intake characteristic parameters corresponding to each operating condition includes: The operating condition clusters include idling operating condition clusters, rated speed operating condition clusters, acceleration transient operating condition clusters, and start-stop cycle operating condition clusters; the extracted intake characteristic parameters include intake flow rate, intake pressure, airflow pulsation frequency, and intake temperature.

6. The passenger car air filter assembly full operating condition acoustic performance positive design and simulation optimization method of claim 1, wherein, The step of correcting the coupling parameters of the multiphysics coupled acoustic simulation model based on the measured acoustic data until the performance deviation between the simulation and the measured data is less than a preset threshold includes: By comparing simulation and measured data, the turbulence model parameters and shell wall damping coefficient of the multiphysics coupled acoustic simulation model are adjusted until the acoustic transmission loss amplitude deviation and the intake resistance deviation are less than their respective preset allowable thresholds.

7. The method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all operating conditions according to claim 1, characterized in that, The establishment of the filter element performance degradation model includes: Multiple dust holding capacity tests were conducted on filter cartridges with different dust holding capacities. Data on the changes in flow resistance and porosity of filter cartridges corresponding to each dust holding capacity were collected. Based on the collected data, a filter cartridge performance degradation model was obtained.

8. The method for forward design and simulation optimization of the acoustic performance of passenger vehicle air filter assembly under all operating conditions according to claim 1, characterized in that, After verifying the NVH performance stability of the preferred structural scheme throughout its entire lifecycle, the following steps are also included: The robustness of the preferred structural scheme is verified by combining extreme working conditions. If the robustness does not meet the requirements, the performance constraints are fed back to the multi-parameter collaborative optimization model for iterative solution until the robustness meets the requirements.

9. A forward design and simulation optimization system for the acoustic performance of a passenger vehicle air filter assembly under all operating conditions, characterized in that, include: The coupled model construction module is configured to acquire the three-dimensional structural data of the air filter assembly and construct the filter element acoustic simulation sub-model and the air filter assembly flow field-sound field coupled acoustic simulation model. The operating condition boundary library module is configured to collect engine operating data under all operating conditions and establish a dynamic simulation boundary library that corresponds one-to-one with the operating conditions. The dynamic simulation verification module is configured to perform acoustic performance simulation calculations of the assembly under all operating conditions, and to complete the benchmark verification and correction of the simulation model by combining bench test data. The multi-objective optimization module is configured to construct a multi-parameter collaborative optimization model and output the preferred structural scheme of the air filter assembly through iterative solution; The full life cycle verification module is configured to establish a filter element performance degradation model and complete the full life cycle NVH performance stability verification of the preferred structural scheme; the full life cycle verification module can also be extended to perform robust performance verification under extreme working conditions.

10. A device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the forward design and simulation optimization method for the acoustic performance of the passenger vehicle air filter assembly under all operating conditions as described in any one of claims 1 to 8.

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