Heat dissipation fan optimization method and apparatus, and electronic device and storage medium

Abstract

The present application relates to the technical field of computers. Disclosed are a heat dissipation fan optimization method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring a first optimization parameter sample set of a heat dissipation fan; on the basis of the first optimization parameter sample set, performing simulation calculation on a performance parameter of the heat dissipation fan; on the basis of a simulation result of the performance parameter and a simulation result of a noise parameter, selecting a target optimization index that is positively correlated with the noise parameter; performing heat dissipation performance simulation calculation and target optimization index simulation calculation of the heat dissipation fan, so as to obtain a heat dissipation performance simulation result and a target optimization index simulation result; and on the basis of the heat dissipation performance simulation result and the target optimization index simulation result, determining a target optimization strategy for the heat dissipation fan, so as to optimize the heat dissipation fan according to the target optimization strategy. A target optimization index is selected to replace a noise parameter, and a target optimization strategy for a heat dissipation fan is then determined on the basis of a simulation result of the target optimization index, so that the optimization efficiency of the heat dissipation fan is improved.

Description

Cooling fan optimization method, device, electronic device and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to a Chinese patent application filed with the Patent Office of China on January 30, 2024, with application number 202410125302.5 and application name “A cooling fan optimization method, device, electronic device and storage medium”, all contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of computer technology, and in particular to a cooling fan optimization method, device, electronic device, and storage medium. Background Art

[0004] Axial-flow fans are a key component of air-cooled servers. Their comprehensive performance significantly impacts the server's heat dissipation, energy consumption, and noise characteristics. With the rapid increase in chip and server power consumption, servers are placing increasing demands on air volume and pressure for cooling. Therefore, optimizing server cooling fans has become a key research topic.

[0005] In related technologies, the heat dissipation performance and noise simulation of a fan are usually performed under the fan free-field working condition.

[0006] However, the noise simulation results of the fan in the free field cannot replace its actual noise manifestation in the server system. However, due to the complex server system environment, if the noise simulation calculation is performed in the server system environment, it will take a lot of time, reducing the optimization efficiency of the cooling fan.

[0007] Summary of the Invention

[0008] A first aspect of the present application provides a cooling fan optimization method, comprising:

[0009] Obtaining a first optimized parameter sample set of the cooling fan;

[0010] Performing simulation calculation of at least one performance parameter of the cooling fan based on the first optimized parameter sample set to obtain a simulation result of the at least one performance parameter;

[0011] Based on the first optimized parameter sample set, performing simulation calculation of noise parameters of the cooling fan to obtain noise parameter simulation results;

[0012] Selecting a target optimization indicator that is positively correlated with the noise parameter based on a simulation result of at least one performance parameter and a simulation result of a noise parameter;

[0013] Obtaining a second optimized parameter sample set of the cooling fan;

[0014] Based on the first optimization parameter sample set and the second optimization parameter sample set, performing a heat dissipation performance simulation calculation and a target optimization index simulation calculation of the heat dissipation fan to obtain a heat dissipation performance simulation result and a target optimization index simulation result; and

[0015] According to the simulation results of the heat dissipation performance and the target optimization index, the target optimization strategy of the heat dissipation fan is determined, so as to optimize the heat dissipation fan according to the target optimization strategy.

[0016] In some embodiments, performing simulation calculation of at least one performance parameter of the cooling fan based on the first optimized parameter sample set to obtain a simulation result of the at least one performance parameter includes:

[0017] Get the impedance operating range of the cooling fan;

[0018] screening at least one target operating point within the impedance operating range; and

[0019] Based on the first optimized parameter sample set, a simulation calculation is performed on at least one performance parameter of the heat dissipation fan at at least one target operating point to obtain a simulation result of the at least one performance parameter.

[0020] In some embodiments, based on the first optimized parameter sample set, a simulation calculation is performed on at least one performance parameter of the cooling fan at at least one target operating point to obtain a simulation result of the at least one performance parameter, including:

[0021] Determining a plurality of sample groups to be simulated based on the first optimization parameter sample set; and

[0022] For any sample group to be simulated, based on any sample group to be simulated, at least one performance parameter of the cooling fan at at least one target working point is simulated and calculated to obtain a simulation result of the at least one performance parameter.

[0023] In some embodiments, multiple target operating points and multiple performance parameters are included. Based on the sample group to be simulated, at least one performance parameter of the cooling fan at at least one target operating point is simulated and calculated to obtain a simulation result of the at least one performance parameter, including:

[0024] For any target operating point, based on any sample group to be simulated, simulate and calculate at least one performance parameter of the cooling fan at the target operating point to obtain a performance parameter simulation result corresponding to any target operating point; and

[0025] For any performance parameter, the performance parameter simulation results of any performance parameter at multiple target working points are summarized to obtain the simulation result of any performance parameter.

[0026] In some embodiments, based on the simulation results of at least one performance parameter and the simulation results of the noise parameter, screening a target optimization indicator that is positively correlated with the noise parameter includes:

[0027] Determining a correlation characteristic between the at least one performance parameter and the noise parameter based on a simulation result of the at least one performance parameter and a simulation result of the noise parameter; and

[0028] According to the correlation characteristics between the at least one performance parameter and the noise parameter, a target optimization index that is positively correlated with the noise parameter is screened from the at least one performance parameter.

[0029] In some embodiments, multiple performance parameters are included, and based on the correlation characteristics between at least one performance parameter and the noise parameter, a target optimization indicator that is positively correlated with the noise parameter is screened from the at least one performance parameter, including:

[0030] Determining the degree of positive correlation between the multiple performance parameters and the noise parameter based on correlation characteristics between the multiple performance parameters and the noise parameter; and

[0031] The performance parameter with the highest positive correlation is used as the target optimization indicator.

[0032] In some embodiments, before using the performance parameter with the highest positive correlation as the target optimization indicator, the method further includes:

[0033] Verifying the degree of positive correlation between the performance parameter with the highest degree of positive correlation and the noise parameter, and obtaining a corresponding positive correlation degree verification result; and

[0034] In response to determining that the positive correlation verification result indicates that the positive correlation between the performance parameter with the highest positive correlation and the noise parameter meets a preset requirement, the performance parameter with the highest positive correlation is used as a target optimization indicator.

[0035] In some embodiments, based on the first optimization parameter sample set and the second optimization parameter sample set, a heat dissipation performance simulation calculation and a target optimization index simulation calculation of the heat dissipation fan are performed to obtain a heat dissipation performance simulation result and a target optimization index simulation result, including:

[0036] Get the impedance operating range of the cooling fan;

[0037] screening at least one simulation operating point within the impedance operating range; and

[0038] Based on the first optimization parameter sample set and the second optimization parameter sample set, the heat dissipation performance and the target optimization index of the heat dissipation fan at at least one simulation working point are simulated and calculated to obtain heat dissipation performance simulation results and target optimization index simulation results.

[0039] In some embodiments, determining a target optimization strategy for the cooling fan based on the heat dissipation performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy, includes:

[0040] Determine the comprehensive simulation results of the cooling fan based on the simulation results of the heat dissipation performance and the target optimization index;

[0041] Determine the target optimization strategy for the cooling fan according to the preset optimization constraints and the comprehensive simulation results of the cooling fan; and

[0042] The cooling fan is optimized according to the target optimization parameters represented by the target optimization strategy.

[0043] In some embodiments, determining a comprehensive simulation result of a cooling fan according to the heat dissipation performance simulation result and the target optimization index simulation result includes:

[0044] Determine the coupling relationship between the heat dissipation performance of the cooling fan and the target optimization index based on the heat dissipation performance simulation results and the target optimization index simulation results; and

[0045] According to the coupling relationship between the heat dissipation performance of the cooling fan and the target optimization index, the comprehensive simulation results of the cooling fan are determined.

[0046] In some embodiments, according to preset optimization constraints and based on comprehensive simulation results of the cooling fan, a target optimization strategy for the cooling fan is determined, including:

[0047] According to the preset optimization constraints and the comprehensive simulation results of the cooling fan, the optimal comprehensive simulation point is determined;

[0048] The heat dissipation performance corresponding to the optimal comprehensive simulation point is taken as the optimal heat dissipation performance;

[0049] The target optimization index corresponding to the optimal comprehensive simulation point is used as the optimal target optimization index; and

[0050] The target optimization strategy of the cooling fan is determined based on the optimization parameters corresponding to the optimal heat dissipation performance and the optimal target optimization index.

[0051] In some embodiments, further comprising:

[0052] According to the target optimization parameters represented by the target optimization strategy, the noise parameters of the cooling fan are simulated and calculated to obtain the target noise parameter simulation results;

[0053] Verify the effectiveness of the target optimization strategy based on the target noise parameter simulation results according to the preset optimization requirements; and

[0054] In response to determining that the target optimization strategy is invalid, return to the step of screening the target optimization index that is positively correlated with the noise parameter based on the simulation results of at least one performance parameter and the simulation results of the noise parameter to replace the target optimization index until the target optimization strategy is valid.

[0055] In some implementations, the second optimized parameter sample set has a larger sample size than the first optimized parameter sample set.

[0056] In some implementations, the simulation of the noise parameters of the cooling fan uses unsteady calculations, and the simulation of the target optimization index of the cooling fan uses steady calculations.

[0057] In some embodiments, the at least one performance parameter of the heat dissipation fan includes at least one of power consumption, turbulent kinetic energy, and average static pressure.

[0058] In some implementations, the step of determining a plurality of sample groups to be simulated based on the first optimization parameter sample set includes:

[0059] Obtaining value results corresponding to a plurality of blade design parameters to be optimized in the first optimization parameter sample set; and

[0060] The value results are arranged and combined to obtain multiple sample groups to be simulated.

[0061] In some embodiments, the positive correlation between the performance parameter with the highest positive correlation and the noise parameter is verified to obtain a corresponding positive correlation verification result, including:

[0062] Constructing a relationship curve between the performance parameter and the noise parameter with the highest degree of positive correlation; and

[0063] The positive correlation between the performance parameter with the highest positive correlation and the noise parameter is verified according to the relationship curve to obtain the corresponding positive correlation verification result.

[0064] A second aspect of the present application provides a cooling fan optimization device, comprising:

[0065] A first acquisition module is used to acquire a first optimization parameter sample set of the cooling fan;

[0066] a first simulation module, configured to perform simulation calculation of at least one performance parameter of the cooling fan based on the first optimization parameter sample set, and obtain a simulation result of the at least one performance parameter;

[0067] A second simulation module is used to simulate and calculate the noise parameters of the cooling fan based on the first optimized parameter sample set to obtain a noise parameter simulation result;

[0068] A screening module, configured to screen a target optimization indicator that is positively correlated with the noise parameter based on a simulation result of at least one performance parameter and a simulation result of a noise parameter;

[0069] A second acquisition module is used to acquire a second optimization parameter sample set of the cooling fan; wherein the sample size of the second optimization parameter sample set is greater than that of the first optimization parameter sample set;

[0070] A third simulation module is used to perform a heat dissipation performance simulation calculation and a target optimization index simulation calculation of the heat dissipation fan based on the first optimization parameter sample set and the second optimization parameter sample set, to obtain a heat dissipation performance simulation result and a target optimization index simulation result; and

[0071] The optimization module is used to determine the target optimization strategy of the cooling fan according to the heat dissipation performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy.

[0072] A third aspect of the present application provides an electronic device, including:

[0073] one or more processors; and

[0074] A memory associated with one or more processors is used to store computer-readable instructions. The computer-readable instructions implement the above-mentioned method when read and executed by the one or more processors.

[0075] A fourth aspect of the present application provides a non-transitory computer-readable storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the method described above is implemented.

[0076] The present application provides a cooling fan optimization method, device, electronic device and storage medium, the method comprising: obtaining a first optimization parameter sample set of the cooling fan; based on the first optimization parameter sample set, performing simulation calculation of the performance parameters of the cooling fan to obtain simulation results of each performance parameter; based on the first optimization parameter sample set, performing simulation calculation of the noise parameters of the cooling fan to obtain noise parameter simulation results; based on the simulation results of the performance parameters and the noise parameter simulation results, screening a target optimization index that is positively correlated with the noise parameter; obtaining a second optimization parameter sample set of the cooling fan; based on the first optimization parameter sample set and the second optimization parameter sample set, performing simulation calculation of the cooling performance and the target optimization index of the cooling fan to obtain cooling performance simulation results and target optimization index simulation results; determining a target optimization strategy for the cooling fan based on the cooling performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following is a brief introduction to the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0078] FIG1 is a schematic structural diagram of a cooling fan optimization system according to an embodiment of the present application;

[0079] FIG2 is a schematic diagram of a flow chart of a cooling fan optimization method provided in an embodiment of the present application;

[0080] FIG3 is a schematic structural diagram of a heat dissipation fan provided in an embodiment of the present application;

[0081] FIG4 is a schematic diagram of camber line parameters of a two-dimensional blade profile provided in an embodiment of the present application;

[0082] FIG5 is a curve showing the relationship between total power consumption and noise value provided by an embodiment of the present application;

[0083] FIG6 is a schematic diagram of an exemplary Pareto frontier curve provided in an embodiment of the present application;

[0084] FIG7 is a schematic structural diagram of a heat dissipation fan optimization device provided in an embodiment of the present application;

[0085] FIG8 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0086] FIG9 is a schematic diagram of the structure of a non-transitory computer-readable storage medium provided in an embodiment of the present application.

[0087] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present disclosure in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0088] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0089] In addition, the terms "first," "second," etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. In the description of the following embodiments, "plurality" means more than two, unless otherwise explicitly defined.

[0090] Axial-flow fans, as cooling fans, are one of the key components of air-cooled servers. Their comprehensive performance has a significant impact on the server's heat dissipation, energy consumption, and noise characteristics. With the rapid increase in chip and server power consumption, servers are placing increasingly higher demands on air volume and pressure for heat dissipation. Air-cooling technology has been developed for many years. Due to the limited space and cost constraints of servers, fan performance improvements are increasingly approaching their technical bottlenecks. Fan optimization design methods are already very mature, and the speed is already very high. However, fan iterations still rely on increasing speed to achieve higher air volume and pressure. Therefore, the current fan design faces challenges in balancing high PQ (air volume and pressure) performance (heat dissipation performance) with low noise.

[0091] The fan design optimization process widely used in relevant research at this stage is as follows: comprehensively consider the fan speed required for the longest operation of the server, the fan speed required for the worst heat dissipation environment, and the system impedance operating range, and determine the aerodynamic design point of the fan blade / frame. After completing the preliminary design of the fan, the fan design point is generally used as the research object, and a series of design parameters are selected as optimization variables to perform DOE (Design of Experiment) optimization on the fan aerodynamic performance. After multiple rounds of optimization design, several models are usually obtained, and PQ curves, free-field noise simulations and proofing tests are performed on these models. Generally, based on noise source analysis, some noise reduction technical paths are superimposed or the fan blade design is modified to reduce noise, seeking a balance between PQ, power consumption and noise. Then, the final design plan and sample are confirmed based on the PQ performance of the fan under variable working conditions and the noise results under free field.

[0092] However, the problem with this approach is that as fan design methods mature, the balance between PQ and noise is increasingly reaching its limits. More refined design optimization methods are needed, taking into account the total sound pressure level and PQ. Otherwise, some designs that balance PQ and noise may be lost. Furthermore, noise test results under free-field conditions cannot replace the noise performance within the server system. Even if the noise improves under free-field conditions, the noise reduction effect may not be reflected after entering the system, or even the opposite may occur, rendering the initial noise reduction optimization in vain. To address these two points, during the DOE optimization process, dual-objective optimization can be performed on the noise near the PQ and impedance operating points. However, noise simulation calculations require unsteady calculations based on steady-state calculations at specific PQ operating points. Accurate calculations are very time-consuming, significantly extending the optimization time compared to optimizing only aerodynamic performance, and reducing the optimization efficiency of the cooling fan.

[0093] In response to the above problems, an embodiment of the present application provides a cooling fan optimization method, device, electronic device, and storage medium. The method includes: obtaining a first optimization parameter sample set for the cooling fan; based on the first optimization parameter sample set, performing simulation calculations on multiple performance parameters of the cooling fan to obtain simulation results of each performance parameter; based on the first optimization parameter sample set, performing simulation calculations on the noise parameters of the cooling fan to obtain noise parameter simulation results; based on the simulation results of each performance parameter and the noise parameter simulation results, screening a target optimization index that is positively correlated with the noise parameter; obtaining a second optimization parameter sample set for the cooling fan; wherein the second optimization parameter sample set has a larger sample size than the first optimization parameter sample set; based on the first optimization parameter sample set and the second optimization parameter sample set, performing simulation calculations on the cooling performance and target optimization index of the cooling fan to obtain cooling performance simulation results and target optimization index simulation results; determining a target optimization strategy for the cooling fan based on the cooling performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy. The system provided by the above solution improves the optimization efficiency of the cooling fan by screening the target optimization index instead of the noise parameter, and then determining the target optimization strategy for the cooling fan based on the simulation results of the target optimization index.

[0094] The following embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. The following describes the embodiments of the present application in conjunction with the accompanying drawings.

[0095] First, the structure of the cooling fan optimization system on which this application is based is described:

[0096] The cooling fan optimization method, device, electronic device, and storage medium provided in the embodiments of the present application are suitable for optimizing cooling fans in electronic devices such as servers. As shown in Figure 1, a schematic diagram of the structure of the cooling fan optimization system based on the embodiments of the present application is shown. The system primarily includes a cooling fan, a data acquisition device, and a cooling fan optimization device. The data acquisition device is used to collect a first optimization parameter sample set and a second optimization parameter sample set for the cooling fan, and transmit the collected sample sets to the cooling fan optimization device. The device determines a target optimization strategy for the cooling fan based on the obtained sample sets, and then optimizes the cooling fan according to the target optimization strategy.

[0097] The present invention provides a cooling fan optimization method for optimizing cooling fans in electronic devices such as servers. The embodiments of the present invention are implemented by electronic devices such as servers, desktop computers, laptops, tablet computers, and other electronic devices that can be used to optimize cooling fans in electronic devices such as servers. The methods provided in the embodiments of the present invention can be applied to any type of low-subsonic fan, not just cooling fans in servers.

[0098] FIG2 is a flow chart of a cooling fan optimization method according to an embodiment of the present invention, wherein the method includes:

[0099] Step 201: Obtain a first optimization parameter sample set of a cooling fan.

[0100] The first optimization parameter sample set includes a variety of blade design parameters to be optimized for the cooling fan, such as the two-dimensional cross-sectional coordinates of the blade tip and the two-dimensional cross-sectional coordinates of the blade hub.

[0101] For example, if the cooling fan is a counter-rotating fan as shown in Figure 3, which is a schematic diagram of the structure of the cooling fan provided in an embodiment of the present application, the cooling fan includes a front rotor, a frame, and a rear rotor. To optimize the cooling fan, the camber line is optimized. Figure 4 shows a schematic diagram of the camber line parameters of a two-dimensional blade profile provided in an embodiment of the present application. It can be seen that because the camber line needs to be represented by two coordinates, a round of optimization requires eight parameters for the modified design of the blade root and blade tip sections of the front and rear rotors only. The value ranges corresponding to these eight parameters are shown in Table 1 below:

[0102] Table 1

[0103] Step 202 : performing simulation calculations on the performance parameters of the cooling fan based on the first optimized parameter sample set to obtain simulation results of the performance parameters.

[0104] The performance parameters of the cooling fan include but are not limited to power consumption, turbulent kinetic energy and / or average static pressure, etc. In some embodiments, the performance parameters of the cooling fan include at least one of power consumption, turbulent kinetic energy and average static pressure.

[0105] In some embodiments, a simulation calculation of the performance parameters of the cooling fan may be performed based on a small number of first optimization parameter sample sets to obtain simulation results of the performance parameters.

[0106] Step 203 : performing simulation calculation of noise parameters of the cooling fan based on the first optimized parameter sample set to obtain noise parameter simulation results.

[0107] In some embodiments, the first optimized parameter sample set may be input into a preset noise parameter simulation tool to perform simulation calculations on the noise parameters of the cooling fan based on the noise parameter simulation tool to obtain noise parameter simulation results.

[0108] Step 204 : Screening target optimization indicators that are positively correlated with the noise parameters based on the simulation results of the performance parameters and the noise parameters.

[0109] In some embodiments, the simulation results of each performance parameter and the noise parameter simulation results can be analyzed to determine which performance parameters have a positive correlation with the noise parameter, and then the performance parameter with the highest positive correlation can be selected as the target optimization indicator.

[0110] Step 205: Obtain a second optimized parameter sample set of the cooling fan;

[0111] The second optimization parameter sample set has a larger sample size than the first optimization parameter sample set. The second optimization parameter sample set and the first optimization parameter sample set include the same parameter types, but the second optimization parameter sample set has a much larger sample size than the first optimization parameter sample set.

[0112] In some embodiments, since the simulation of the cooling fan's noise parameters requires lengthy unsteady calculations, a DES unsteady calculation is also required during the transition from steady-state calculations to the noise calculation model. During this process, detailed adjustments to parameters such as the time step length and the number of iterations required within each time step are required, resulting in significant computational complexity. Therefore, a small number of first optimization parameter sample sets are used to simulate various performance parameters and noise parameters. After determining the target optimization indicator that can replace the noise parameter, a larger number of second optimization parameter sample sets can be used to simulate the target optimization indicator, as the simulation process for the target optimization indicator utilizes steady-state calculations, resulting in relatively low computational complexity.

[0113] Step 206 : Based on the first optimization parameter sample set and the second optimization parameter sample set, a heat dissipation performance simulation calculation and a target optimization index simulation calculation of the heat dissipation fan are performed to obtain a heat dissipation performance simulation result and a target optimization index simulation result.

[0114] The heat dissipation performance may refer to a PQ curve of a cooling fan, and the PQ curve is used to describe the performance variation relationship of the cooling fan under different flow and pressure conditions.

[0115] In some embodiments, in order to make full use of the existing sample set, after obtaining the second optimization parameter sample set, the first optimization parameter sample set and the second optimization parameter sample set can be fused, and then based on the first optimization parameter sample set and the second optimization parameter sample set, the heat dissipation performance simulation calculation and the target optimization index simulation calculation of the cooling fan are performed to obtain the heat dissipation performance simulation results and the target optimization index simulation results.

[0116] Step 207 : determining a target optimization strategy for the cooling fan according to the cooling performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy.

[0117] In some embodiments, the balance point between the cooling fan's cooling performance and the target optimization index can be determined based on the cooling performance simulation results and the target optimization index simulation results. Furthermore, a target optimization strategy for the cooling fan can be determined based on the optimization parameter sample corresponding to the balance point. The cooling fan's blade design parameters can then be optimized accordingly based on the target optimization strategy. The target optimization strategy includes the blade design parameters to be optimized.

[0118] Based on the above embodiment, as an implementable approach, in one embodiment, simulation calculations are performed on multiple performance parameters of a cooling fan based on the first optimization parameter sample set to obtain simulation results of each performance parameter, including:

[0119] Step 2021, obtaining the impedance operating range of the cooling fan;

[0120] Step 2022: screening multiple target operating points within the impedance operating range;

[0121] Step 2023 : Based on the first optimized parameter sample set, simulation calculation is performed on the performance parameters of the cooling fan at the target operating point to obtain simulation results of the performance parameters.

[0122] It should be noted that the cooling fan's impedance operating range is the actual impedance operating range of the cooling fan in the server. Each target operating point corresponds to a specific operating condition. The target operating point selection is implemented using the optimized Latin square sampling method. For example, within the impedance operating range, 20 simulation operating points are selected.

[0123] In some embodiments, the performance parameters of the cooling fan under the working conditions corresponding to each target working point may be simulated based on the first optimization parameter sample set to obtain simulation results of each performance parameter.

[0124] In some embodiments, a sample group to be simulated can be determined based on the first optimization parameter sample set; for any sample group to be simulated, based on any sample group to be simulated, the performance parameters of the cooling fan at each target operating point are simulated and calculated to obtain simulation results of the performance parameters.

[0125] It should be noted that the first optimization parameter sample set includes multiple values ​​of blade design parameters to be optimized. By arranging and combining the value results of the blade design parameters, multiple sample groups to be simulated are obtained, and each sample group to be simulated corresponds to a blade design parameter configuration type of the cooling fan.

[0126] In some embodiments, including multiple target operating points and multiple performance parameters, for any target operating point, the performance parameters of the cooling fan at any target operating point can be simulated and calculated based on any sample group to be simulated to obtain the performance parameter simulation results corresponding to the target operating point; for any performance parameter, the performance parameter simulation results of the performance parameter at multiple target operating points are summarized to obtain the simulation result of any performance parameter.

[0127] In some embodiments, for any cooling fan operating condition corresponding to a target operating point, simulation calculations can be performed on the cooling fan under that operating condition for each simulation sample group to obtain performance parameter simulation results corresponding to the target operating point. The performance parameters are then used as a basis for data collation to determine the performance parameter simulation results for each performance parameter at each target operating point, thereby obtaining simulation results for each performance parameter.

[0128] On the basis of the above embodiment, in order to ensure the reliability of the selected target optimization index, as an implementable method, in one embodiment, based on the simulation results of the performance parameters and the simulation results of the noise parameters, the target optimization index positively correlated with the noise parameter is selected, including:

[0129] Step 2041: Determine the correlation characteristics between the performance parameters and the noise parameters based on the simulation results of the performance parameters and the noise parameters;

[0130] Step 2042: Based on the correlation characteristics between the performance parameters and the noise parameters, select a target optimization indicator that is positively correlated with the noise parameters from the performance parameters.

[0131] In some embodiments, after obtaining the simulation results of each performance parameter, for any performance parameter, a corresponding simulation curve can be constructed based on the simulation results of the performance parameter, and the correlation characteristics between the performance parameter and the noise parameter can be determined based on the relationship between the simulation curve of the performance parameter and the simulation curve of the noise parameter.

[0132] In some embodiments, multiple performance parameters are included, and the degree of positive correlation between each performance parameter and the noise parameter is determined based on the correlation characteristics between each performance parameter and the noise parameter; the performance parameter with the highest positive correlation degree is used as the target optimization indicator.

[0133] In some embodiments, based on the correlation characteristics between each performance parameter and the noise parameter, the candidate performance parameter that is positively correlated with the noise parameter can be determined, and then based on the degree of positive correlation between the candidate performance parameter and the noise parameter, the performance parameter with the highest degree of positive correlation can be used as the target optimization indicator, for example, power consumption can be used as the target optimization indicator.

[0134] In some embodiments, in one embodiment, in order to further determine the reliability of the selected target optimization indicator, before using the performance parameter with the highest positive correlation as the target optimization indicator, the positive correlation degree between the performance parameter with the highest positive correlation and the noise parameter can be verified to obtain a corresponding positive correlation degree verification result; in response to the positive correlation degree verification result indicating that the positive correlation degree between the performance parameter with the highest positive correlation and the noise parameter meets the preset requirements, the parameter with the highest positive correlation is used as the target optimization indicator.

[0135] In some embodiments, the degree of positive correlation between the performance parameter with the highest degree of positive correlation and the noise parameter can be verified by constructing a relationship curve between the performance parameter with the highest degree of positive correlation and the noise parameter. When the obtained positive correlation verification result indicates that there is indeed a positive correlation between the two, it is determined that the degree of positive correlation between the performance parameter and the noise parameter meets the preset requirements.

[0136] For example, if the performance parameter with the highest correlation is the total power consumption of the cooling fan, a relationship curve as shown in Figure 5 is constructed. Figure 5 is a relationship curve between the total power consumption and the noise value provided in an embodiment of the present application. According to Figure 5, it can be determined that the degree of positive correlation between the total power consumption and the noise parameter meets the preset requirements.

[0137] On the basis of the above embodiment, in order to improve the simulation efficiency and the accuracy of the simulation calculation results, as an implementable method, in one embodiment, based on the first optimization parameter sample set and the second optimization parameter sample set, the heat dissipation performance simulation calculation and the target optimization index simulation calculation of the heat dissipation fan are performed to obtain the heat dissipation performance simulation results and the target optimization index simulation results, including:

[0138] Step 2061, obtaining the impedance operating range of the cooling fan;

[0139] Step 2062, screening a simulation operating point within the impedance operating range;

[0140] Step 2063 , based on the first optimization parameter sample set and the second optimization parameter sample set, simulate and calculate the heat dissipation performance and target optimization index of the heat dissipation fan at the simulation working point to obtain heat dissipation performance simulation results and target optimization index simulation results.

[0141] The selection of simulation working points is implemented based on the optimized Latin square sampling method. For example, 80 simulation working points are selected within the impedance working range.

[0142] In some embodiments, based on the first optimization parameter sample set and the second optimization parameter sample set, the heat dissipation performance and target optimization index of the cooling fan under the working conditions corresponding to each simulation working point can be simulated to obtain the heat dissipation performance simulation results and the target optimization index simulation results, and simulation calculations can be performed based on a preset simulation tool.

[0143] On the basis of the above embodiment, in order to further improve the optimization efficiency of the cooling fan, as an implementable method, in one embodiment, a target optimization strategy for the cooling fan is determined based on the cooling performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy, including:

[0144] Step 2071 , determining a comprehensive simulation result of the cooling fan according to the cooling performance simulation result and the target optimization index simulation result;

[0145] Step 2072: determining a target optimization strategy for the cooling fan according to preset optimization constraints and comprehensive simulation results of the cooling fan;

[0146] Step 2073 : Optimize the cooling fan according to the target optimization parameters represented by the target optimization strategy.

[0147] In some embodiments, dual-objective optimization (DOE optimization) can be performed based on heat dissipation performance and a target optimization indicator. For example, taking power consumption as the target optimization indicator, the preset optimization constraint is to improve heat dissipation performance while reducing power consumption. The target optimization parameters refer to the values ​​of each optimization parameter in a set of simulation samples corresponding to the target optimization strategy.

[0148] In some embodiments, in one embodiment, the coupling relationship between the heat dissipation performance of the cooling fan and the target optimization index can be determined based on the heat dissipation performance simulation results and the target optimization index simulation results; based on the coupling relationship between the heat dissipation performance of the cooling fan and the target optimization index, the comprehensive simulation results of the cooling fan can be determined.

[0149] In some embodiments, the coupling relationship between the heat dissipation performance of the cooling fan and the target optimization index can be represented based on the Pareto front curve. Among them, when the heat dissipation performance is a PQ curve index and the target optimization index is total power consumption, the Pareto front curve is shown in Figure 6, which is an exemplary Pareto front curve schematic diagram provided in an embodiment of the present application. The aerodynamic performance target of the Pareto front curve ranges from -1.056 to -0.9205, which means that the blade parameter combination can improve the aerodynamic performance by up to 6% without considering the power consumption; the power consumption target ranges from 53.97 watts W to 81.94W, while the total power consumption of the prototype fan is 70.44W, indicating that the blade parameter combination has a large potential for optimizing the fan power consumption. Without considering the aerodynamic performance, it is expected to reduce it by up to 16.47W.

[0150] In some embodiments, in one embodiment, the optimal comprehensive simulation point can be determined according to the preset optimization constraints and the comprehensive simulation results of the cooling fan; the heat dissipation performance corresponding to the optimal comprehensive simulation point is used as the optimal heat dissipation performance; the target optimization index corresponding to the optimal comprehensive simulation point is used as the optimal target optimization index; and the target optimization strategy of the cooling fan is determined according to the optimization parameters corresponding to the optimal heat dissipation performance and the optimal target optimization index.

[0151] In some embodiments, based on a preset genetic algorithm and in accordance with preset optimization constraints, the optimal comprehensive simulation point (for example, point 5 and point 36 in FIG6 ) can be determined according to the comprehensive simulation results of the cooling fan (Pareto front curve), and the optimal heat dissipation performance and the optimal target optimization index corresponding to the optimal comprehensive simulation point are used as the optimization target of the cooling fan, and then the target optimization strategy of the cooling fan is determined according to the optimization parameters corresponding to the optimal heat dissipation performance and the optimal target optimization index.

[0152] Among them, the preset genetic algorithm can determine the optimal comprehensive simulation point based on the following formula:

[0153] in, Indicates the PQ curve index (heat dissipation performance), represents power consumption (target optimization indicator), This is the sample group to be simulated. It represents the optimization objective function of the genetic algorithm. The optimization objective function corresponds to the preset optimization constraints. The optimization goal is high PQ performance and low power consumption.

[0154] On the basis of the above embodiment, in order to further ensure the reliability of the target optimization strategy, as an implementable approach, in one embodiment, before applying the target optimization strategy, the method further includes:

[0155] Step 301, performing simulation calculation of noise parameters of a cooling fan according to target optimization parameters represented by a target optimization strategy to obtain simulation results of target noise parameters;

[0156] Step 302: Verify whether the target optimization strategy is effective according to the preset optimization requirements and the target noise parameter simulation results;

[0157] Step 303, in response to determining that the target optimization strategy is invalid, returns to the step of screening the target optimization index that is positively correlated with the noise parameter based on the simulation results of each performance parameter and the noise parameter simulation results, to replace the target optimization index until the target optimization strategy is valid.

[0158] In some embodiments, the sample group to be simulated (for example, point number 5 and point number 36) corresponding to the optimal comprehensive simulation point in the Pareto front curve can be selected to perform noise parameter simulation calculations on the cooling fan to obtain the target noise parameter simulation results for the optimal comprehensive simulation point. The preset optimization requirement of the embodiment of the present application is to reduce noise. Therefore, according to the simulation results of the target noise parameters, it is verified whether the noise of the cooling fan is reduced. If it is reduced, it indicates that the target optimization strategy is effective. Otherwise, it is invalid. If it is invalid, it indicates that the target optimization index currently used cannot replace the noise. Therefore, in response to determining that the target optimization strategy is invalid, return to the step of screening the target optimization index that is positively correlated with the noise parameter according to the simulation results of each performance parameter and the noise parameter simulation results to replace the target optimization index until the target optimization strategy is effective.

[0159] For example, if the optimal comprehensive simulation points selected in the Pareto front curve include point 5 and point 36, the simulation trend comparison results of the total power consumption and noise value of the cooling fan are shown in Table 2 below:

[0160] Table 2

[0161] Among them, the prototype is the original state of the cooling fan. According to Table 2, it can be determined that the current target optimization strategy not only reduces the total power consumption, but also reduces the noise value, which proves that the target optimization strategy is effective.

[0162] The cooling fan optimization method provided in an embodiment of the present application comprises the following steps: obtaining a first optimization parameter sample set for the cooling fan; simulating and calculating multiple performance parameters of the cooling fan based on the first optimization parameter sample set to obtain simulation results for each performance parameter; simulating and calculating noise parameters of the cooling fan based on the first optimization parameter sample set to obtain noise parameter simulation results; screening a target optimization index that is positively correlated with the noise parameter based on the simulation results for each performance parameter and the noise parameter simulation results; obtaining a second optimization parameter sample set for the cooling fan; simulating and calculating the cooling performance and target optimization index of the cooling fan based on the first optimization parameter sample set and the second optimization parameter sample set to obtain cooling performance simulation results and target optimization index simulation results; and determining a target optimization strategy for the cooling fan based on the cooling performance simulation results and the target optimization index simulation results, thereby optimizing the cooling fan according to the target optimization strategy. The system provided by the above scheme improves the optimization efficiency of the cooling fan by screening the target optimization index instead of the noise parameter and then determining the target optimization strategy for the cooling fan based on the simulation results of the target optimization index. Furthermore, the reliability of the cooling fan optimization results is ensured through multiple verification processes. In addition, it is proposed to replace the noise simulation calculation method with the parameters obtained by steady-state calculation, and it is proposed to verify the positive correlation method with the simulation results of small batch sample points, so that in the DOE optimization process, while ensuring the optimization accuracy, the optimization time can be effectively shortened and the work efficiency can be improved.

[0163] An embodiment of the present application provides a heat dissipation fan optimization device for executing the heat dissipation fan optimization method provided in the above embodiment.

[0164] As shown in Figure 7, a schematic diagram of the structure of a cooling fan optimization device provided in an embodiment of the present application is shown. The cooling fan optimization device 70 includes: a first acquisition module 701, a first simulation module 702, a second simulation module 703, a screening module 704, a second acquisition module 705, a third simulation module 706, and an optimization module 707.

[0165] Among them, the first acquisition module is used to obtain the first optimization parameter sample set of the cooling fan; the first simulation module is used to perform simulation calculation of the performance parameters of the cooling fan based on the first optimization parameter sample set to obtain simulation results of the performance parameters; the second simulation module is used to perform simulation calculation of the noise parameters of the cooling fan based on the first optimization parameter sample set to obtain noise parameter simulation results; the screening module is used to screen the target optimization index that is positively correlated with the noise parameter according to the simulation results of the performance parameters and the simulation results of the noise parameters; the second acquisition module is used to obtain the second optimization parameter sample set of the cooling fan; the third simulation module is used to perform simulation calculation of the cooling performance and the target optimization index of the cooling fan based on the first optimization parameter sample set and the second optimization parameter sample set to obtain cooling performance simulation results and target optimization index simulation results; the optimization module is used to determine the target optimization strategy of the cooling fan according to the cooling performance simulation results and the target optimization index simulation results, so as to optimize the cooling fan according to the target optimization strategy.

[0166] Regarding the heat dissipation fan optimization device in this embodiment, the manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0167] The cooling fan optimization device provided in the embodiment of the present application is used to execute the cooling fan optimization method provided in the above embodiment. Its implementation method and principle are the same and will not be repeated here.

[0168] An embodiment of the present application provides an electronic device for executing the cooling fan optimization method provided in the above embodiment.

[0169] As shown in FIG8 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, the electronic device 80 includes: one or more processors 81 and a memory 82 associated with the processor 81 .

[0170] The memory 82 stores computer-readable instructions; when the computer-readable instructions are read and executed by one or more processors 81 , the cooling fan optimization method provided in the above embodiment is implemented.

[0171] The electronic device provided in the embodiment of the present application is used to execute the cooling fan optimization method provided in the above embodiment. Its implementation method and principle are the same and will not be repeated here.

[0172] An embodiment of the present application provides a non-transitory computer-readable storage medium, as shown in Figure 9. The non-transitory computer-readable storage medium stores computer-readable instructions, which, when executed by one or more processors, implement the cooling fan optimization method provided in any of the above embodiments.

[0173] The storage medium containing computer-readable instructions provided in the embodiment of the present application can be used to store computer-readable instructions of the cooling fan optimization method provided in the aforementioned embodiment. Its implementation method and principle are the same and will not be repeated here.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0175] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0176] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0177] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program code.

[0178] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The working process of the device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A cooling fan optimization method, characterized in that: include: Obtaining a first optimized parameter sample set of the cooling fan; Performing simulation calculation of at least one performance parameter of the cooling fan based on the first optimization parameter sample set to obtain a simulation result of the at least one performance parameter; Based on the first optimized parameter sample set, performing simulation calculation of the noise parameters of the cooling fan to obtain a noise parameter simulation result; Screening a target optimization indicator that is positively correlated with the noise parameter according to the simulation result of the at least one performance parameter and the simulation result of the noise parameter; Obtaining a second optimized parameter sample set for the cooling fan; Based on the first optimization parameter sample set and the second optimization parameter sample set, performing a heat dissipation performance simulation calculation and a target optimization index simulation calculation of the heat dissipation fan to obtain a heat dissipation performance simulation result and a target optimization index simulation result; as well as According to the heat dissipation performance simulation result and the target optimization index simulation result, a target optimization strategy of the heat dissipation fan is determined, so as to optimize the heat dissipation fan according to the target optimization strategy.

2. The method according to claim 1, characterized in that The performing simulation calculation of at least one performance parameter of the cooling fan based on the first optimization parameter sample set to obtain a simulation result of the at least one performance parameter includes: Obtaining the impedance operating range of the cooling fan; within the impedance operating range, screening at least one target operating point; and Based on the first optimization parameter sample set, a simulation calculation is performed on at least one performance parameter of the cooling fan at the at least one target operating point to obtain a simulation result of the at least one performance parameter.

3. The method according to claim 2, characterized in that The step of simulating and calculating at least one performance parameter of the cooling fan at the at least one target operating point based on the first optimization parameter sample set to obtain a simulation result of the at least one performance parameter includes: Determining a plurality of sample groups to be simulated based on the first optimization parameter sample set; and For any sample group to be simulated, based on the sample group to be simulated, a simulation calculation is performed on at least one performance parameter of the cooling fan at the at least one target operating point to obtain a simulation result of the at least one performance parameter.

4. The method according to claim 3, characterized in that The method includes multiple target operating points and multiple performance parameters. Based on any one of the sample groups to be simulated, a simulation calculation is performed on at least one performance parameter of the cooling fan at the at least one target operating point to obtain a simulation result of the at least one performance parameter, including: For any target operating point, based on any sample group to be simulated, simulate and calculate multiple performance parameters of the cooling fan at the target operating point to obtain a performance parameter simulation result corresponding to the target operating point; and For any performance parameter, summarize the performance parameter simulation results of the any performance parameter at the multiple target operating points, A simulation result of any of the performance parameters is obtained.

5. The method according to claim 1, wherein The step of screening a target optimization indicator that is positively correlated with the noise parameter based on the simulation result of the at least one performance parameter and the simulation result of the noise parameter includes: Determining a correlation characteristic between the at least one performance parameter and the noise parameter based on a simulation result of the at least one performance parameter and a simulation result of the noise parameter; and According to the correlation characteristic between the at least one performance parameter and the noise parameter, a target optimization index that is positively correlated with the noise parameter is screened from the at least one performance parameter.

6. The method according to claim 5, characterized in that Including multiple performance parameters, said selecting a target optimization index positively correlated with the noise parameter from the at least one performance parameter according to the correlation characteristics between the at least one performance parameter and the noise parameter, including: Determining, based on correlation characteristics between the multiple performance parameters and the noise parameter, a degree of positive correlation between the multiple performance parameters and the noise parameter; and The performance parameter with the highest positive correlation degree is used as the target optimization indicator.

7. The method according to claim 6, characterized in that Before using the performance parameter with the highest positive correlation as the target optimization indicator, the method further includes: Verifying the degree of positive correlation between the performance parameter with the highest degree of positive correlation and the noise parameter to obtain a corresponding positive correlation verification result; and In response to determining that the positive correlation verification result indicates that the positive correlation between the performance parameter with the highest positive correlation and the noise parameter meets a preset requirement, the performance parameter with the highest positive correlation is used as the target optimization indicator.

8. The method according to claim 1, characterized in that The heat dissipation performance simulation calculation and the target optimization index simulation calculation of the heat dissipation fan are performed based on the first optimization parameter sample set and the second optimization parameter sample set to obtain the heat dissipation performance simulation result and the target optimization index simulation result, including: Obtaining the impedance operating range of the cooling fan; within the impedance operating range, screening at least one simulation operating point; and Based on the first optimization parameter sample set and the second optimization parameter sample set, the heat dissipation performance and the target optimization index of the heat dissipation fan at the at least one simulation working point are simulated and calculated to obtain the heat dissipation performance simulation result and the target optimization index simulation result.

9. The method according to claim 1, characterized in that The step of determining a target optimization strategy for the heat dissipation fan according to the heat dissipation performance simulation result and the target optimization index simulation result, so as to optimize the heat dissipation fan according to the target optimization strategy, includes: Determining a comprehensive simulation result of the cooling fan according to the heat dissipation performance simulation result and the target optimization index simulation result; Determining the target optimization strategy of the cooling fan according to preset optimization constraints and comprehensive simulation results of the cooling fan; and The cooling fan is optimized according to the target optimization parameters represented by the target optimization strategy.

10. The method according to claim 9, characterized in that Determining the comprehensive simulation result of the cooling fan according to the heat dissipation performance simulation result and the target optimization index simulation result includes: Determining a coupling relationship between the heat dissipation performance of the heat dissipation fan and the target optimization index according to the heat dissipation performance simulation result and the target optimization index simulation result; and A comprehensive simulation result of the cooling fan is determined based on a coupling relationship between the cooling performance of the cooling fan and the target optimization index.

11. The method according to claim 9, characterized in that Determining the target optimization strategy of the cooling fan according to the preset optimization constraints and the comprehensive simulation results of the cooling fan includes: According to the preset optimization constraints and the comprehensive simulation results of the cooling fan, determining the optimal comprehensive simulation point; The heat dissipation performance corresponding to the optimal comprehensive simulation point is used as the optimal heat dissipation performance; Using the target optimization index corresponding to the optimal comprehensive simulation point as the optimal target optimization index; and The target optimization strategy of the cooling fan is determined according to the optimal heat dissipation performance and the optimization parameters corresponding to the optimal target optimization index.

12. The method according to claim 9, characterized in that Also includes: Performing simulation calculation of noise parameters of the cooling fan according to the target optimization parameters represented by the target optimization strategy to obtain target noise parameter simulation results; According to the preset optimization requirements, based on the simulation results of the target noise parameters, verify whether the target optimization strategy is effective; as well as In response to determining that the target optimization strategy is invalid, return to the step of screening the target optimization index that is positively correlated with the noise parameter based on the simulation results of the at least one performance parameter and the noise parameter simulation results to replace the target optimization index until the target optimization strategy is valid.

13. The method according to claim 1, wherein The second optimization parameter sample set has a larger sample size than the first optimization parameter sample set.

14. The method according to claim 13, characterized in that The simulation of the noise parameters of the cooling fan adopts unsteady calculation, and the simulation of the target optimization index of the cooling fan adopts steady calculation.

15. The method according to claim 1, wherein The at least one performance parameter of the heat dissipation fan includes at least one of power consumption, turbulent kinetic energy, and static pressure average value.

16. The method according to claim 3, characterized in that The step of determining a plurality of sample groups to be simulated based on the first optimization parameter sample set includes: Obtaining value results corresponding to a plurality of blade design parameters to be optimized in the first optimization parameter sample set; and The value-taking results are arranged and combined to obtain the multiple sample groups to be simulated.

17. The method according to claim 7, characterized in that The verifying the positive correlation between the performance parameter with the highest positive correlation and the noise parameter to obtain a corresponding positive correlation verification result includes: Constructing a relationship curve between the performance parameter with the highest positive correlation and the noise parameter; and The positive correlation between the performance parameter with the highest positive correlation and the noise parameter is verified according to the relationship curve to obtain a corresponding positive correlation verification result.

18. A cooling fan optimization device, characterized in that: include: A first acquisition module is used to acquire a first optimization parameter sample set of the cooling fan; a first simulation module, configured to perform simulation calculation of at least one performance parameter of the cooling fan based on the first optimization parameter sample set, and obtain a simulation result of the at least one performance parameter; a second simulation module, configured to perform simulation calculation of noise parameters of the cooling fan based on the first optimization parameter sample set to obtain noise parameter simulation results; a screening module, configured to screen a target optimization indicator that is positively correlated with the noise parameter based on the simulation result of the at least one performance parameter and the simulation result of the noise parameter; A second acquisition module is used to acquire a second optimization parameter sample set of the cooling fan; a third simulation module, configured to perform a heat dissipation performance simulation calculation and a target optimization index simulation calculation of the heat dissipation fan based on the first optimization parameter sample set and the second optimization parameter sample set, to obtain a heat dissipation performance simulation result and a target optimization index simulation result; as well as The optimization module is used to determine the target optimization strategy of the cooling fan according to the heat dissipation performance simulation result and the target optimization index simulation result, so as to optimize the cooling fan according to the target optimization strategy.

19. An electronic device, characterized in that: include: one or more processors; as well as A memory associated with the one or more processors, the memory being configured to store computer-readable instructions, wherein the computer-readable instructions implement the method according to any one of claims 1 to 17 when read and executed by the one or more processors.

20. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the method according to any one of claims 1 to 17 is implemented.

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