Methods and devices for optimizing the diameter of multiple oil outlets, electric drive axle lubrication systems and vehicles

Abstract

This disclosure relates to a method and apparatus for optimizing the diameter of multiple oil outlets, an electric drive axle lubrication system, and a vehicle. The method includes: determining a flow model for multiple oil outlets of the electric drive axle lubrication system, wherein the flow model includes a correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets; and performing multi-objective optimization on the flow rates of the multiple oil outlets in the flow model based on the target flow rate of each oil outlet, thereby determining the target diameter of all oil outlets. This disclosure achieves an order-of-magnitude improvement in design efficiency, compressing the traditional experience-dependent, weeks-long manual trial-and-error process of related technologies into a few days of automated completion.

Description

Technical Field

[0001] This disclosure relates to the field of multi-outlet diameter optimization technology, and particularly to a method and apparatus for multi-outlet diameter optimization, an electric drive axle lubrication system, and a vehicle. Background Technology

[0002] In high-speed, heavy-load transmission systems such as electric drive axles, centralized lubrication systems require a main oil pipe and dozens of branch ports to precisely deliver lubricating oil to each gear meshing point and bearing. Whether the flow distribution at each lubrication point meets the design requirements directly affects the heat dissipation, wear, and service life of critical components. Summary of the Invention

[0003] Research revealed that traditional design methods for related technologies heavily rely on engineers' experience, employing a trial-and-error process of "manually adjusting the diameter of a single oil outlet - performing CFD (Computational Fluid Dynamics) simulation - evaluating the results." When the number of oil outlets is large (up to dozens), manual traversal optimization becomes impractical in terms of time and cost, and is highly prone to getting trapped in local optima, leading to uneven lubrication at various points within the system and the risk of localized overheating or insufficient lubrication.

[0004] In view of at least one of the above technical problems, this disclosure provides a method and apparatus for optimizing the diameter of multiple oil outlets, an electric drive axle lubrication system and a vehicle, which achieves an order-of-magnitude improvement in design efficiency and can compress the experience-dependent manual trial-and-error process of related technologies, which typically takes weeks, into an automated process that can be completed in a few days.

[0005] According to one aspect of this disclosure, a method for optimizing the diameter of multiple oil outlets is provided, comprising:

[0006] Determine the flow rate model of multiple oil outlets of the electric drive axle lubrication system, wherein the flow rate model includes the correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets of the electric drive axle lubrication system;

[0007] Based on the target flow rate of each oil outlet in the flow model, multi-objective optimization is performed on the flow rate of multiple oil outlets to determine the target diameter of all oil outlets.

[0008] In some embodiments of this disclosure, the step of performing multi-objective optimization on the flow rates of multiple oil outlets in the flow rate model based on the target flow rate of each oil outlet in all oil outlets, and determining the target diameter of all oil outlets, includes:

[0009] Determine the standard deviation of the difference between the flow rate of each outlet and the target flow rate of that outlet;

[0010] Based on the flow volume extraction of each oil outlet in all oil outlets under the condition of minimum standard deviation and the flow model, the target diameter of all oil outlets is determined.

[0011] In some embodiments of this disclosure, the flow rate model for determining the multiple oil outlets of the electric drive axle lubrication system includes:

[0012] Acquire multiple sample point data, wherein each sample point data includes the diameter data of all oil outlets of the electric drive axle lubrication system;

[0013] Simulation is performed on the multiple sample point data to obtain the flow data corresponding to each sample point data, wherein the flow data includes the flow rate of all oil outlets;

[0014] Based on the multiple sample point data and the flow data corresponding to each sample point data, the flow model of multiple oil outlets of the electric drive axle lubrication system is determined.

[0015] In some embodiments of this disclosure, acquiring multiple sample point data includes:

[0016] The lubrication pipeline of the electric drive axle lubrication system is modeled using geometric parametric methods, wherein the diameter of all oil outlets is a parametric feature.

[0017] In some embodiments of this disclosure, acquiring multiple sample point data further includes:

[0018] The design space for the diameter of each oil outlet is set as a set of discrete values ​​corresponding to the machining tool;

[0019] Multiple sample point data are generated within the design space.

[0020] In some embodiments of this disclosure, simulating the plurality of sample point data to obtain the traffic data corresponding to each sample point data includes:

[0021] Choose a flow model;

[0022] The flow model is used to simulate the data of the multiple sample points to determine the flow data corresponding to each sample point.

[0023] In some embodiments of this disclosure, determining the flow rate model of multiple oil outlets of the electric drive axle lubrication system based on the plurality of sample point data and the flow rate data corresponding to each sample point data includes:

[0024] Based on the multiple sample point data and the flow data corresponding to each sample point data, a second-order polynomial model is used for fitting to determine the flow model of multiple oil outlets of the electric drive axle lubrication system.

[0025] In some embodiments of this disclosure, the method for optimizing the diameter of multiple oil outlets further includes:

[0026] Simulate the target diameter of all the oil outlets and obtain the flow verification results of multiple oil outlets corresponding to the target diameter;

[0027] Based on the flow rate model and the target diameter of all oil outlets, determine the flow rate prediction result corresponding to the target diameter;

[0028] Based on the traffic verification results and the traffic prediction results, determine the standard deviation of traffic allocation;

[0029] If the standard deviation of the flow distribution meets the predetermined requirements, the target diameter is taken as the final optimization result.

[0030] In some embodiments of this disclosure, determining the standard deviation of traffic allocation based on the traffic verification result and the traffic prediction result includes:

[0031] The standard deviation of the difference between the flow rate verification result and the flow rate prediction result for each of the various oil outlets is used as the standard deviation of the flow rate allocation.

[0032] According to another aspect of this disclosure, a multi-outlet diameter optimization device is provided, comprising:

[0033] The flow model determination module is configured to determine the flow model of multiple oil outlets of the electric drive axle lubrication system, wherein the flow model includes the correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets of the electric drive axle lubrication system.

[0034] The target diameter determination module is configured to perform multi-objective optimization on the flow rates of multiple oil outlets in the flow rate model based on the target flow rate of each oil outlet in all oil outlets, and determine the target diameter of all oil outlets.

[0035] According to another aspect of this disclosure, a multi-outlet diameter optimization device is provided, comprising:

[0036] The memory is configured to store instructions; and

[0037] A processor coupled to the memory is configured to execute the multi-outlet diameter optimization method as described in any of the above embodiments, based on instructions stored in the memory.

[0038] According to another aspect of this disclosure, an electric drive axle lubrication system is provided, including a multi-outlet diameter optimization device as described in any of the above embodiments.

[0039] According to another aspect of this disclosure, a vehicle is provided, including an electric drive axle lubrication system as described in any of the above embodiments.

[0040] According to another aspect of this disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the multi-outlet diameter optimization method as described in any of the above embodiments.

[0041] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, it implements the multi-outlet diameter optimization method as described in any of the above embodiments.

[0042] This disclosure achieves an order-of-magnitude improvement in design efficiency, compressing the traditional experience-dependent, weeks-long manual trial-and-error process of related technologies into a few days of automated completion. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of some embodiments of the method for optimizing the diameter of multiple oil outlets disclosed herein.

[0045] Figure 2 This is a schematic diagram of some other embodiments of the method for optimizing the diameter of multiple oil outlets disclosed herein.

[0046] Figure 3 This is a schematic diagram of some embodiments of the method for optimizing the diameter of multiple oil outlets disclosed herein.

[0047] Figure 4 This is a schematic diagram of the lubrication pipeline in some embodiments of this disclosure.

[0048] Figure 5 This is a schematic diagram showing the final optimized results of the lubrication pipeline in some embodiments of this disclosure.

[0049] Figure 6 This is a schematic diagram of some embodiments of the multi-outlet diameter optimization device disclosed herein.

[0050] Figure 7This is a schematic diagram of the structure of some other embodiments of the multi-outlet diameter optimization device disclosed herein. Detailed Implementation

[0051] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0052] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0053] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0054] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0055] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0056] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0057] The study also revealed that although parametric design, DOE (Design of Experiments), and response surface methodology have been applied in related technology optimization fields, a deep integration and application to solve the specific engineering problem of automated, high-dimensional, discrete optimization of dozens of oil outlet diameters in an electric drive axle lubrication system still lacks an effective solution. These technologies typically do not consider the discretization constraints of manufacturing processes on dimensions, nor do they provide a complete closed-loop workflow from parametric modeling to automated verification.

[0058] In view of at least one of the above technical problems, this disclosure provides a method and apparatus for optimizing the diameter of multiple oil outlets, an electric drive axle lubrication system, and a vehicle. The disclosure will be described below through specific embodiments.

[0059] Figure 1 This diagram illustrates some embodiments of the multi-outlet diameter optimization method of this disclosure. Preferably, this embodiment can be performed by the multi-outlet diameter optimization device of this disclosure, the electric drive axle lubrication system of this disclosure, or the vehicle of this disclosure. Figure 1 As shown, Figure 1 The method of the embodiment may include at least one of steps 100 to 200.

[0060] In step 100, a flow model for multiple oil outlets of the electric drive axle lubrication system is determined, wherein the flow model includes the correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets of the electric drive axle lubrication system.

[0061] In some embodiments of this disclosure, the flow rate may be mass flow rate or mass flow rate.

[0062] In some embodiments of this disclosure, step 100 may include at least one of steps 110 to 130.

[0063] In step 110, multiple sample point data are acquired, wherein each sample point data includes the diameter data of all oil outlets of the electric drive axle lubrication system.

[0064] In some embodiments of this disclosure, step 110 may include at least one of steps 111 to 113.

[0065] In step 111, the lubrication pipeline of the electric drive axle lubrication system is geometrically parametrically modeled, wherein the diameter of all oil outlets is a parametric feature.

[0066] In step 112, the design space for the diameter of each oil outlet is set as a set of discrete values ​​corresponding to the machining tool.

[0067] In step 113, multiple sample point data are generated within the design space.

[0068] In step 120, the multiple sample point data are simulated to obtain the flow data corresponding to each sample point data, wherein the flow data includes the flow rate of all oil outlets.

[0069] In some embodiments of this disclosure, step 120 may include at least one of steps 121 to 122.

[0070] In step 121, a flow model is selected.

[0071] In step 122, the flow model is used to simulate the data of the multiple sample points to determine the flow data corresponding to each sample point.

[0072] In step 130, the flow model of multiple oil outlets of the electric drive axle lubrication system is determined based on the multiple sample point data and the flow data corresponding to each sample point data.

[0073] In some embodiments of this disclosure, step 130 may include: using a second-order polynomial model to fit the plurality of sample point data and the flow data corresponding to each sample point data in the plurality of sample point data, to determine the flow model of the plurality of oil outlets of the electric drive axle lubrication system.

[0074] In step 200, based on the target flow rate of each oil outlet in the flow model, multi-objective optimization is performed on the flow rate of multiple oil outlets to determine the target diameter of all oil outlets.

[0075] In some embodiments of this disclosure, step 200 may include at least one of steps 210 to 220.

[0076] In step 210, the standard deviation of the difference between the flow rate of each of all oil outlets and the target flow rate of that oil outlet is determined.

[0077] In step 220, the target diameter of all oil outlets is determined based on the flow rate of each oil outlet in the case of minimum standard deviation and the flow rate model.

[0078] The optimization objective of the above embodiments of this disclosure is to minimize the standard deviation of the difference between the flow rate of each oil outlet and the target flow rate of that oil outlet.

[0079] The embodiments disclosed herein are the first to deeply integrate geometric parametric modeling, automated CFD simulation processes, and discretized experimental design and surrogate model optimization that take into account actual processing constraints into a unified solution for manufacturing.

[0080] The above embodiments of this disclosure can determine the target diameter based on the target flow rate and the flow rate model.

[0081] The above embodiments of this disclosure creatively drive the outlet diameter directly through parametric stretching and lock it into a fully automatic mesh generation, solution setting, and result monitoring process; at the same time, the above embodiments of this disclosure strictly limit the value range of the optimization variables to the discrete values ​​corresponding to the standard tool size, thereby systematically solving the engineering bottleneck that it is difficult to manually optimize the diameter of dozens of oil outlets in the high-dimensional discrete space in the lubrication system of electric drive axle.

[0082] Figure 2This is a schematic diagram of some other embodiments of the multi-outlet diameter optimization method of this disclosure. Preferably, this embodiment can be performed by the multi-outlet diameter optimization device of this disclosure, the electric drive axle lubrication system of this disclosure, or the vehicle of this disclosure. Figure 2 As shown, Figure 2 The method of the embodiment may include at least one of steps 100 to 300. Figure 2 Steps 100 to 200 of the embodiment are respectively with Figure 1 Steps 100 to 200 in the embodiment are the same or similar.

[0083] In step 300, the target diameter of all the oil outlets is verified.

[0084] In some embodiments of this disclosure, step 300 may include at least one of steps 310 to 340.

[0085] In step 310, the target diameter of all oil outlets is simulated (i.e., step 120 is executed) to obtain the flow verification results of multiple oil outlets corresponding to the target diameter.

[0086] In step 320, the flow prediction result corresponding to the target diameter is determined based on the flow model and the target diameter of all oil outlets.

[0087] In step 330, the standard deviation of traffic allocation is determined based on the traffic verification results and the traffic prediction results.

[0088] In some embodiments of this disclosure, step 330 may include: using the standard deviation of the difference between the flow verification result and the flow prediction result of each of the all oil outlets as the flow allocation standard deviation.

[0089] In step 340, if the standard deviation of the flow distribution meets the predetermined requirements, the target diameter is taken as the final optimization result.

[0090] The above embodiments of this disclosure provide an automated optimization method for the diameter of multiple oil outlets in the lubrication pipeline of an electric drive axle for manufacturing purposes. This method automates the entire process from parametric modeling to optimization verification, and accurately solves the problem of uniform lubrication distribution in a high-dimensional discrete design space.

[0091] Figure 3 This is a schematic diagram of some embodiments of the multi-outlet diameter optimization method of this disclosure. Preferably, this embodiment can be performed by the multi-outlet diameter optimization device of this disclosure, the electric drive axle lubrication system of this disclosure, or the vehicle of this disclosure. Figure 3 As shown, Figure 3 The method of the embodiment may include at least one of steps 1 to 5.

[0092] In step 1, geometric parametric modeling is performed.

[0093] In some embodiments of this disclosure, Figure 3 Step 1 of the embodiment can be implemented as follows: Figure 1 or Figure 2 Step 111 of the embodiment.

[0094] In some embodiments of this disclosure, such as Figure 3 As shown, step 1 may include at least one of steps 11 to 15.

[0095] In step 11, import the geometric model.

[0096] In some embodiments of this disclosure, step 11 may include: importing the STP (Standard for the Exchange of Product Data) geometric model of the lubrication pipeline.

[0097] In some embodiments of this disclosure, step 11 may include: importing the STP format geometric model of the lubrication pipeline into three-dimensional CAD modeling software.

[0098] In step 12, the fluid domain is extracted.

[0099] In some embodiments of this disclosure, step 12 may include: segmenting the fluid domain and combining the entire fluid domain into a whole.

[0100] In step 13, the sealing surface is created.

[0101] In some embodiments of this disclosure, step 13 may include: closing all openings in a parametric CAD environment.

[0102] In some embodiments of this disclosure, step 13 may include: sealing all existing inlets and outlets to ensure a completely closed fluid domain entity.

[0103] In step 14, the oil outlet and oil inlet are named.

[0104] In some embodiments of this disclosure, step 14 may include: creating a name (outlet1...outletN) for each oil outlet and creating a name (inlet) for each inlet.

[0105] In some embodiments of this disclosure, the number of oil outlets N is equal to 32.

[0106] Figure 4 This is a schematic diagram of lubrication lines in some embodiments of this disclosure. For example... Figure 4As shown, step 14 may include: creating names at the end face locations that need to be defined as inlets and 32 outlets, selecting groups, and naming them Inlet, Outlet 1, Outlet 2... Outlet 32, etc. Figure 4 As shown.

[0107] In step 15, the oil outlet branch pipe and the sealing surface are parameterized.

[0108] In some embodiments of this disclosure, step 15 may include: at each oil outlet location, generating independent control parameters d1…dN by dimensioning the diameter and creating a parametric feature using the stretching function, where N is the total number of oil outlets.

[0109] In some embodiments of this disclosure, the step of creating parameterized features using the stretching function may include: selecting the branch pipe and sealing surface of the entire oil outlet, selecting the radial direction of the stretching, and through stretching, the marker size becomes a parameterizable variable, thus creating parameter dN.

[0110] In some embodiments of this disclosure, step 15 may include steps 151 and 152.

[0111] In step 151, the diameter d of the circular end face is marked.

[0112] In step 152, select the branch pipe and sealing surface of the entire oil outlet, and perform a stretching operation along the radial direction of the oil pipe. By stretching, the mark size becomes a parameterizable variable. Create the mark size as a parameter and name it d1, d2...d32.

[0113] In step 2, the model is divided and the solution is set up.

[0114] In some embodiments of this disclosure, step 2 may include: parametric mesh generation and solver settings.

[0115] In some embodiments of this disclosure, step 2 can be implemented as follows: Figure 1 or Figure 2 Step 120 of the embodiment.

[0116] In some embodiments of this disclosure, such as Figure 3 As shown, step 2 may include at least one of steps 21 to 25.

[0117] In step 21, the surface network.

[0118] In some embodiments of this disclosure, step 21 may include: after importing the geometry, adding local size control and setting the maximum and minimum face mesh sizes; identifying and creating the fluid domain; and designating the pre-named inlet and outlets 1-32 as "mass flow inlet" and "pressure outlet," respectively.

[0119] In step 22, a boundary layer is added.

[0120] In some embodiments of this disclosure, step 22 may include: adding a boundary layer, setting the number of layers to 3, the transition ratio to 0.272, and the growth rate to 1.2.

[0121] In the embodiments described above, the boundary layer is located near the solid surface and is used to describe the velocity and pressure distribution of the fluid near the solid surface. The presence of the boundary layer makes the flow more closely resemble reality and also helps to reduce calculation errors.

[0122] In step 23, the volume mesh is generated and its quality is improved.

[0123] In some embodiments of this disclosure, step 23 may include: selecting to generate a polyhedral volume mesh; and finally, setting the element quality limit to 0.2, the number of iterations to 5, and performing mesh smoothing.

[0124] The polyhedral volume mesh of the above embodiments of this disclosure has 30%-50% fewer cells than the tetrahedral mesh at the same geometric resolution, which reduces numerical diffusion caused by elongated or distorted cells and can significantly reduce memory usage and computational cost.

[0125] In some embodiments of this disclosure, the element mass limit of 0.2 means that the minimum orthogonal mass of the element is greater than 0.2.

[0126] In some embodiments of this disclosure, mesh quality improvement refers to partial mesh adjustment, which involves re-dividing meshes that do not meet the minimum orthogonal quality requirement.

[0127] In some embodiments of this disclosure, steps 21 to 23 may include: mesh generation; in the workflow, configuring the mesh generation module to generate a volume mesh using a polyhedral mesh combined with boundary layer technology, and performing mesh quality improvement.

[0128] In some embodiments of this disclosure, the step of generating a volume mesh by combining the polyhedral mesh with boundary layer technology may include: setting the maximum / minimum size of the surface mesh, adding a boundary layer with a specified number of layers, transition ratio, and growth rate in the near-wall region, generating a polyhedral volume mesh for the fluid domain, and performing at least 5 iterations of smoothing improvement with a unit mass not less than 0.2 as a constraint.

[0129] In step 24, boundary conditions are set, and the solution scheme is configured.

[0130] In some embodiments of this disclosure, step 24 may include: in the solver module, selecting a flow model based on the Reynolds number, creating a fluid material with specified properties and assigning it a fluid domain, and setting the inlet as a mass flow rate inlet and the outlet as a pressure outlet.

[0131] In some embodiments of this disclosure, step 24 may include: the fluid material is filled with the corresponding density and kinetic viscosity according to the properties of the lubricating oil, and the inlet flow rate is filled according to the experimental design.

[0132] In some embodiments of this disclosure, step 24 may include: entering Fluent (solver); selecting a laminar flow model based on a Reynolds number of 246; creating a new material with a density of 814.1 kg / m³ and a viscosity of 0.015020145 Pa·s, and assigning it a fluid domain; setting the inlet boundary condition to a mass flow rate inlet with a value of 0.040705 kg / s; setting all outlets to pressure outlets with a gauge pressure of 0 Pa; setting the solution method and monitoring the residuals up to 1e-5.

[0133] In some embodiments of this disclosure, the solver's algorithm can be the SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithm. This algorithm first assumes an initial pressure field, then solves the momentum equation to obtain the velocity field, and then corrects the pressure field using the continuity equation to ensure that the velocity field satisfies the mass conservation condition. This process is repeated iteratively until a convergent solution is obtained.

[0134] In some embodiments of this disclosure, the parameters set in step 24 may include the aforementioned basic physical properties, density, viscosity, and inlet flow rate, as well as the solution method.

[0135] In some embodiments of this disclosure, step 24 may include: calculating the flow rate of each outlet using the parameters set.

[0136] In some embodiments of this disclosure, step 24 may include: setting a convergence criterion in the solver, wherein the calculation is considered convergent when the residual values ​​of all variables except the energy residual decrease to below 1e-3, and the convergence criterion for the energy residual is below 1e-6. During the calculation, iterations are continuously performed until the convergence criterion is reached. At this point, the calculation result of the last iteration step is the final result.

[0137] In step 25, the mass flow rate output of the oil outlet is created.

[0138] In some embodiments of this disclosure, step 25 may include: creating a mass flow rate report definition output parameter for each outlet (outlet 1 to outlet N), and setting the solver residual monitoring criteria and iteration steps.

[0139] In some embodiments of this disclosure, step 25 may include: creating a "mass flow rate" report for each face from outlet 1 to outlet N, thereby creating 32 output parameters. The iteration step number is set to 500 and initialized to complete the solution setup.

[0140] In some embodiments of this disclosure, the quality flow rate report actually monitors the flow rate of the current monitored outlet at each iteration step. The purpose of creating this report is for subsequent experimental design. During the experimental design phase, by changing the aforementioned geometric parameters, the final converged outlet flow rate value can be directly obtained through background calculation.

[0141] In some embodiments of this disclosure, the output parameters are the flow rates of the aforementioned N (e.g., 32) oil outlets.

[0142] In step 3, experimental design is performed.

[0143] In some embodiments of this disclosure, step 3 may include: designing a discretized experiment based on the parameters determined in steps 1 and 2.

[0144] In some embodiments of this disclosure, step 3 may include: experimental design and sampling based on manufacturing constraints.

[0145] In some embodiments of this disclosure, step 3 may include Figure 1 or Figure 2 At least one of steps 112, 113 and 120 in the embodiment.

[0146] In some embodiments of this disclosure, step 3 may include at least one of steps 31 to 33.

[0147] In step 31, in the optimization module, a set of discrete values ​​corresponding to the machining tool is set for each input parameter d1…dN as the design space.

[0148] In some embodiments of this disclosure, the set of discrete values ​​may be limited according to the requirements of the machining and manufacturing cutting edge.

[0149] In some embodiments of this disclosure, step 31 can be implemented as follows: Figure 1 or Figure 2 Step 112 of the embodiment.

[0150] In some embodiments of this disclosure, step 3 may include: setting upper and lower limits (1.0 mm, 2.0 mm) for the input parameters d1-d32; and strictly limiting them to a discrete set in the list of allowed values: {1.0, 1.2, 1.4, 1.5, 1.6, 1.8, 2.0}.

[0151] In step 32, multiple sets of sample points are generated in the design space using Latin hypercube or sparse grid experimental design methods.

[0152] In some embodiments of this disclosure, step 32 can be implemented as follows: Figure 1 or Figure 2 Step 113 of the embodiment.

[0153] In some embodiments of this disclosure, step 32 may include: selecting the “optimal Latin hypercube” sampling method and setting the number of samples to 150.

[0154] In some embodiments of this disclosure, step 32 may include: in a 32-dimensional design space, selecting a value from a preset discrete value set {1.0, 1.2, 1.4, 1.5, 1.6, 1.8, 2.0} for each dimension (i.e., d1-d32) to form 150 sample points.

[0155] In some embodiments of this disclosure, the logic of the "optimal Latin hypercube" sampling method can be summarized as follows: uniformly scattering points in the abstract space and finding the optimal distribution, and then converting the coordinates of these points into specified discrete physical values.

[0156] In step 33, the automated workflow is driven to complete the batch simulation according to steps 1 and 2, and collect the mass flow rate data of all oil outlets corresponding to each sample point to form a sample database.

[0157] In some embodiments of this disclosure, the "driving" refers to inputting a set of sample points to obtain mass flow rate data for all oil outlets. The "driving" also refers to automatically executing the above workflow in the background for each of the generated multiple sets of sample points, ultimately presenting the outlet flow rate corresponding to the sample point to the user interface.

[0158] In some embodiments of this disclosure, step 32 may include: the system automatically drives the complete process established in steps 1 and 2, completes the calculation of all sample points, and collects the flow data of each oil outlet.

[0159] In some embodiments of this disclosure, a set of sample points is a modified geometric model, and in the computer background, through the above-described automated process, a set of outflow rates will eventually be calculated for each sample point.

[0160] In step 4, response surface fitting is performed.

[0161] In some embodiments of this disclosure, step 4 can be implemented as follows: Figure 1 or Figure 2 Step 130 of the embodiment.

[0162] In some embodiments of this disclosure, step 4 may include: constructing a response surface proxy model based on the sample database of step 3, to map the flow rate of each oil outlet to the diameter of all oil outlets.

[0163] In some embodiments of this disclosure, the response surface surrogate model is a Kriging model or a multinomial regression model.

[0164] In some embodiments of this disclosure, step 4 may include: obtaining the calculation results of multiple sample points through the aforementioned experimental design in step 3, fitting the data results with a second-order polynomial, and obtaining the relationship curve between any outlet flow rate and any outlet diameter, i.e., a high-precision proxy model.

[0165] In some embodiments of this disclosure, step 4 may include: based on the sample data from step 3, using a second-order polynomial model for fitting to generate 32*32 high-precision proxy models for outflow flow.

[0166] In some embodiments of this disclosure, step 4 may include: obtaining the calculation results of 150 sample points through the experimental design in step 3, and fitting the data results with a second-order polynomial. In this case, for any outlet flow rate, the relationship curve between the outlet flow rate and 32 outlet diameters can be obtained, which is a high-precision proxy model.

[0167] In step 5, optimization is performed.

[0168] In some embodiments of this disclosure, step 5 can be implemented as follows: Figure 1 or Figure 2 Step 200 of the embodiment.

[0169] In some embodiments of this disclosure, step 5 may include: taking the minimization of the standard deviation of the difference between the actual mass flow rate of all oil outlets and their respective target values ​​as the optimization objective, performing global optimization on the surrogate model to obtain the optimal solution set.

[0170] In some embodiments of this disclosure, the global optimization is a multi-objective global optimization.

[0171] In some embodiments of this disclosure, the multi-objective global optimization employs a genetic algorithm or a nonlinear programming algorithm.

[0172] In some embodiments of this disclosure, step 5 may include: defining the optimization objective as: minimizing the formula STD((M1-Target1), (M2-Target2)... (M32-Target32)), where Mx is the flow predicted by the surrogate model and Targetx is the target flow of each outlet; using a genetic algorithm to perform optimization, and finally obtaining a set of solutions with optimal flow uniformity under manufacturing constraints.

[0173] In some embodiments of this disclosure, the flow rate Mx predicted by the surrogate model refers to the correspondence between the outlet diameter dx and the outlet flow rate Mx obtained by fitting the surrogate model through experimental design (step 3) and second-order polynomial fitting (step 4), thus realizing the flow rate prediction by the surrogate model.

[0174] In some embodiments of this disclosure, the manufacturing constraint refers to the exit diameter, which requires different cutting tools for different diameters. Typically, several cutting tools are selected and optimized among these diameters.

[0175] In some embodiments of this disclosure, step 5 may include: minimizing the formula STD((M1-Target1), (M2-Target2)...(Mx-Targetx)), transforming the multi-objective optimization problem into a single-objective optimization, with Mx as the variable. The surrogate model, i.e., the correspondence between the outlet diameter dx and the outlet flow rate Mx, was obtained through experimental design (step 3) and second-order polynomial fitting (step 4). By adjusting the value of Mx, STD((M1-Target1), (M2-Target2)...(Mx-Targetx)) is minimized, thus completing the optimization, where STD is the standard deviation.

[0176] Figure 5 This is a schematic diagram showing the final optimized results of the lubrication piping in some embodiments of this disclosure. For example... Figure 5 As shown, the horizontal axis represents the oil outlet number, and the vertical axis represents the flow rate. For each oil outlet number, the left bar chart represents the final optimized flow rate at 70 degrees Celsius, and the right bar chart represents the target flow rate (flow requirement) for each oil outlet.

[0177] In step 6, verification is performed.

[0178] In some embodiments of this disclosure, step 6 can be implemented as follows: Figure 2 Step 300 of the embodiment.

[0179] In some embodiments of this disclosure, step 6 may include: verifying the optimal solution determined in step 5.

[0180] In some embodiments of this disclosure, step 6 may include: selecting the optimal solution set determined in step 5, substituting it into the parameterized model in step 1, performing a complete high-fidelity CFD simulation as described in step 2, and verifying whether the traffic allocation meets the design requirements.

[0181] In some embodiments of this disclosure, step 6 may include: selecting a recommended diameter combination, inputting it into the parametric model of step 1, and automatically updating the geometry; subsequently, performing a high-precision CFD simulation (step 2) separately; comparing the verification results with the surrogate model prediction values, confirming that the flow distribution standard deviation meets the requirements (less than 1%), and completing the entire optimization process.

[0182] The embodiments disclosed above enable the integration of automated CFD simulation workflows. In the engineering simulation platform, the parameterized geometry module, mesh generation module, fluid dynamics solver module, and optimization module are sequentially connected to form an automated workflow.

[0183] The embodiments described above enable automated workflow integration within the Workbench (simulation integration platform). In the ANSYS Workbench project, the following modules are sequentially dragged in and connected: "Geometry" (import the model saved in step 1) → "Fluent Meshing" → "Fluent" → "Design of Experiments" & "Response Surface Optimization". This chain establishes a complete workflow from geometric changes to automated optimization analysis.

[0184] The embodiments disclosed above can seamlessly integrate parametric design, CAE (Computer Aided Engineering) simulation and optimization technology to form a standardized and automated solution for optimizing the size of multi-outlet pipelines, thereby significantly improving design efficiency and quality.

[0185] The embodiments disclosed above achieve an order-of-magnitude improvement in design efficiency, compressing the traditional experience-dependent, weeks-long manual trial-and-error process into an automated process completed within a few days.

[0186] The method described in the above embodiments of this disclosure, through scientific experimental design and global optimization algorithm, can find a better traffic allocation scheme in high-dimensional space, which greatly improves the scientific nature and reliability of the design.

[0187] The optimization results of the above embodiments of this disclosure directly correspond to the available drill bit sizes, realizing "design as manufacturing", avoiding secondary rounding errors, significantly shortening the cycle from design to production, and reducing the cost of prototype trial production.

[0188] Figure 6 These are schematic diagrams of some embodiments of the multi-outlet diameter optimization device disclosed herein. For example... Figure 6 As shown, the multi-outlet diameter optimization device of this disclosure may include a flow model determination module 61 and a target diameter determination module 62.

[0189] The flow model determination module 61 is configured to determine the flow model of multiple oil outlets of the electric drive axle lubrication system, wherein the flow model includes the correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets of the electric drive axle lubrication system.

[0190] In some embodiments of this disclosure, the flow model determination module 61 can be configured to acquire multiple sample point data, wherein each sample point data includes the diameter data of all oil outlets of the electric drive axle lubrication system; simulate the multiple sample point data to acquire flow data corresponding to each sample point data, wherein the flow data includes the flow of all oil outlets; and determine the flow model of multiple oil outlets of the electric drive axle lubrication system based on the multiple sample point data and the flow data corresponding to each sample point data.

[0191] In some embodiments of this disclosure, the flow model determination module 61, when acquiring multiple sample point data, can be configured to perform geometric parametric modeling of the lubrication pipeline of the electric drive axle lubrication system, wherein the diameter of all oil outlets is a parametric feature.

[0192] In some embodiments of this disclosure, when the flow model determination module 61 acquires multiple sample point data, it can also be configured to set the design space of the diameter of each oil outlet to a set of discrete values ​​corresponding to the machining tool; and generate multiple sample point data within the design space.

[0193] In some embodiments of this disclosure, when the flow model determination module 61 simulates the multiple sample point data to obtain the flow data corresponding to each sample point data, it can be configured to select a flow model; use the flow model to simulate the multiple sample point data to determine the flow data corresponding to each sample point data.

[0194] In some embodiments of this disclosure, when the flow model determination module 61 determines the flow model of multiple oil outlets of the electric drive axle lubrication system based on the multiple sample point data and the flow data corresponding to each sample point data, it can be configured to use a second-order polynomial model to fit the multiple sample point data and the flow data corresponding to each sample point data to determine the flow model of multiple oil outlets of the electric drive axle lubrication system.

[0195] The target diameter determination module 62 is configured to perform multi-objective optimization on the flow rate of multiple oil outlets in the flow rate model based on the target flow rate of each oil outlet in all oil outlets, and determine the target diameter of all oil outlets.

[0196] In some embodiments of this disclosure, the target diameter determination module 62 may be configured to determine the standard deviation of the difference between the flow rate of each oil outlet and the target flow rate of that oil outlet; and determine the target diameter of all oil outlets based on the flow rate of each oil outlet in the case of the minimum standard deviation and the flow rate model.

[0197] In some embodiments of this disclosure, the multi-outlet diameter optimization device may also be configured to simulate the target diameter of all the oil outlets to obtain the flow verification results of the multiple oil outlets corresponding to the target diameter; determine the flow prediction result corresponding to the target diameter based on the flow model and the target diameter of all the oil outlets; determine the flow distribution standard deviation based on the flow verification result and the flow prediction result; and take the target diameter as the final optimization result if the flow distribution standard deviation meets the predetermined requirements.

[0198] In some embodiments of this disclosure, the multi-outlet diameter optimization device, when determining the flow distribution standard deviation based on the flow verification result and the flow prediction result, can be configured to use the standard deviation of the difference between the flow verification result and the flow prediction result of each of the all oil outlets as the flow distribution standard deviation.

[0199] In some embodiments of this disclosure, the multi-outlet diameter optimization device of this disclosure may also be configured to perform the multi-outlet diameter optimization method described in any of the above embodiments of this disclosure.

[0200] Figure 7 This is a schematic diagram of the structure of some other embodiments of the multi-outlet diameter optimization device disclosed herein. For example... Figure 7 As shown, the multi-outlet diameter optimization device includes a memory 71 and a processor 72.

[0201] The memory 71 is used to store instructions, and the processor 72 is coupled to the memory 71. The processor 72 is configured to implement the multi-outlet diameter optimization method of any of the above embodiments of this disclosure based on the instructions stored in the memory.

[0202] like Figure 7 As shown, the multi-outlet diameter optimization device also includes a communication interface 73 for information exchange with other devices. Additionally, the device includes a bus 74, through which the processor 72, communication interface 73, and memory 71 communicate with each other.

[0203] The memory 71 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive. The memory 71 may also be a memory array. The memory 71 may also be divided into blocks, and these blocks may be combined into virtual volumes according to certain rules.

[0204] Furthermore, processor 72 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure.

[0205] According to another aspect of this disclosure, an electric drive axle lubrication system is provided, including a multi-outlet diameter optimization device as described in any of the above embodiments.

[0206] According to another aspect of this disclosure, a vehicle is provided, including an electric drive axle lubrication system as described in any of the above embodiments.

[0207] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, it implements the multi-outlet diameter optimization method as described in any of the above embodiments.

[0208] According to another aspect of this disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the multi-outlet diameter optimization method as described in any of the above embodiments.

[0209] In some embodiments of this disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0210] The embodiments disclosed herein relate to the fields of computer-aided engineering and fluid dynamics optimization design technology, and in particular to an automated optimization method, apparatus, storage medium, and program product for the outlet diameter of complex multi-branch lubrication systems.

[0211] The above embodiments of this disclosure provide an automated optimization method, apparatus, storage medium, and program product for the diameter of multiple oil outlets in the lubrication pipeline of an electric drive axle for manufacturing. The method includes: based on an imported geometric model, sealing and naming all inlet and outlet ports in a parametric CAD (Computer Aided Design) environment, and parameterizing the outlet diameter through dimension marking and stretching operations; integrating the parameterized model with an automated CFD simulation process, which includes automatic mesh generation based on polyhedral meshes and boundary layer technology, solver settings considering laminar / turbulent flow models, and automatic creation of mass flow rate monitoring parameters for each outlet; constructing a sample library through batch simulation driven by experimental design, using discretized manufacturing dimensions as constraints; constructing a response surface surrogate model of flow rate with respect to outlet diameter using sample data; performing global optimization on the surrogate model with the objective of minimizing the standard deviation of the flow rate at each outlet from the target value to obtain a Pareto optimal solution set; and finally, performing high-fidelity CFD verification of the optimal solution. The embodiments disclosed above achieve full automation from parametric modeling to optimization verification, and accurately solve the problem of precise lubrication in high-dimensional discrete design space.

[0212] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, apparatus, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0213] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0214] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0215] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0216] The multi-outlet diameter optimization device, flow model determination module, and target diameter determination module described above can be implemented as a general-purpose processor, programmable logic controller (PLC), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or any suitable combination thereof for performing the functions described in this application.

[0217] This concludes the detailed description of the present disclosure. To avoid obscuring the concept of the disclosure, some details known in the art have not been described. Those skilled in the art will fully understand how to implement the technical solutions disclosed herein based on the above description.

[0218] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing the relevant hardware to implement them. The program can be stored in a non-transitory computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0219] The description in this disclosure is provided for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the disclosure to its forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of this disclosure and to enable those skilled in the art to understand this disclosure and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A method for optimizing the diameter of multiple oil outlets, comprising: Determine the flow rate model of multiple oil outlets of the electric drive axle lubrication system, wherein the flow rate model includes the correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets of the electric drive axle lubrication system; Based on the target flow rate of each oil outlet in all oil outlets, multi-objective optimization is performed on the flow rate of multiple oil outlets in the flow rate model to determine the target diameter of all oil outlets. The flow rate model for determining the multiple oil outlets of the electric drive axle lubrication system includes: Acquire multiple sample point data, wherein each sample point data includes the diameter data of all oil outlets of the electric drive axle lubrication system; Simulation is performed on the multiple sample point data to obtain the flow data corresponding to each sample point data, wherein the flow data includes the flow rate of all oil outlets; Based on the multiple sample point data and the flow data corresponding to each sample point data, the flow model of multiple oil outlets of the electric drive axle lubrication system is determined. This determination includes: using a second-order polynomial model to fit the multiple sample point data and the flow data corresponding to each sample point data to determine the flow model of the multiple oil outlets of the electric drive axle lubrication system. The method for optimizing the diameter of multiple oil outlets further includes: Simulate the target diameter of all the oil outlets and obtain the flow verification results of multiple oil outlets corresponding to the target diameter; Based on the flow rate model and the target diameter of all oil outlets, determine the flow rate prediction result corresponding to the target diameter; Based on the traffic verification results and the traffic prediction results, determine the standard deviation of traffic allocation; If the standard deviation of the flow distribution meets the predetermined requirements, the target diameter is taken as the final optimization result.

2. The method for optimizing the diameter of multiple oil outlets according to claim 1, wherein, The step of performing multi-objective optimization on the flow rate of multiple oil outlets in the flow rate model based on the target flow rate of each oil outlet in all oil outlets, and determining the target diameter of all oil outlets, includes: Determine the standard deviation of the difference between the flow rate of each outlet and the target flow rate of that outlet; Based on the flow rate of each oil outlet in all oil outlets under the condition of minimum standard deviation, and the flow rate model, the target diameter of all oil outlets is determined.

3. The method for optimizing the diameter of multiple oil outlets according to claim 1 or 2, wherein, The acquisition of multiple sample point data includes: The lubrication pipeline of the electric drive axle lubrication system is modeled using geometric parametric methods, wherein the diameter of all oil outlets is a parametric feature.

4. The method for optimizing the diameter of multiple oil outlets according to claim 3, wherein, The acquisition of multiple sample point data also includes: The design space for the diameter of each oil outlet is set as a set of discrete values ​​corresponding to the machining tool; Multiple sample point data are generated within the design space.

5. The method for optimizing the diameter of multiple oil outlets according to claim 1 or 2, wherein, The step of simulating the multiple sample point data to obtain the traffic data corresponding to each sample point data includes: Choose a flow model; The flow model is used to simulate the data of the multiple sample points to determine the flow data corresponding to each sample point.

6. The method for optimizing the diameter of multiple oil outlets according to claim 1 or 2, wherein, The step of determining the standard deviation of traffic allocation based on the traffic verification results and the traffic prediction results includes: The standard deviation of the difference between the flow rate verification result and the flow rate prediction result for each of the various oil outlets is used as the standard deviation of the flow rate allocation.

7. A device for optimizing the diameter of multiple oil outlets, comprising: The flow model determination module is configured to determine the flow model of multiple oil outlets of the electric drive axle lubrication system, wherein the flow model includes the correspondence between the flow rate of each oil outlet of the electric drive axle lubrication system and the diameter of all oil outlets of the electric drive axle lubrication system. The target diameter determination module is configured to perform multi-objective optimization on the flow rate of multiple oil outlets in the flow rate model based on the target flow rate of each oil outlet in all oil outlets, and determine the target diameter of all oil outlets. The flow model determination module is configured to acquire multiple sample point data, wherein each sample point data includes the diameter data of all oil outlets of the electric drive axle lubrication system; simulate the multiple sample point data to acquire the flow data corresponding to each sample point data, wherein the flow data includes the flow rate of all oil outlets; and determine the flow model of multiple oil outlets of the electric drive axle lubrication system based on the multiple sample point data and the flow data corresponding to each sample point data. When the flow model determination module determines the flow model of multiple oil outlets of the electric drive axle lubrication system based on the multiple sample point data and the flow data corresponding to each sample point data, it is configured to use a second-order polynomial model to fit the multiple sample point data and the flow data corresponding to each sample point data to determine the flow model of multiple oil outlets of the electric drive axle lubrication system. The multi-outlet diameter optimization device is further configured to simulate the target diameter of all the oil outlets to obtain the flow verification results of the multiple oil outlets corresponding to the target diameter; determine the flow prediction result corresponding to the target diameter based on the flow model and the target diameter of all the oil outlets; determine the flow distribution standard deviation based on the flow verification result and the flow prediction result; and take the target diameter as the final optimization result if the flow distribution standard deviation meets the predetermined requirements.

8. A device for optimizing the diameter of multiple oil outlets, comprising: The memory is configured to store instructions; as well as A processor coupled to the memory, the processor being configured to execute the multi-outlet diameter optimization method as described in any one of claims 1 to 6 based on instructions stored in the memory.

9. An electric drive axle lubrication system, comprising the multi-outlet diameter optimization device as described in claim 7 or 8.

10. A vehicle comprising the electric drive axle lubrication system as claimed in claim 9.

11. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for optimizing the diameter of multiple oil outlets as described in any one of claims 1 to 6.

12. A computer program product comprising a computer program, wherein, When the computer program is executed by the processor, it implements the method for optimizing the diameter of multiple oil outlets as described in any one of claims 1 to 6.

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