A parameter optimization method and system for lightweight high-thermal-conductivity components

Abstract

The application provides a parameter optimization method and system for a light high-thermal-conductivity component, wherein the optimization method comprises the following steps: obtaining use data of the light high-thermal-conductivity component in an application scenario; determining an optimization direction based on the use data; constructing multiple implementation schemes from the optimization direction; and verifying the implementation schemes to determine a final optimization result. The parameter optimization method and system for the light high-thermal-conductivity component can ensure that the light high-thermal-conductivity component can adapt to different application scenarios after parameter optimization.

Description

Technical Field

[0001] This invention relates to the field of high thermal conductivity components, and in particular to a parameter optimization method and system for lightweight high thermal conductivity components. Background Technology

[0002] Currently, with the increasing speed of electronic products, the demand for heat dissipation is becoming more and more stringent. Thermally conductive graphite films are widely used in electronic products due to their ultra-high thermal conductivity. Due to the limitation of material thickness, a single layer cannot meet the heat dissipation requirements, so multi-layer stacking is often used to improve its overall thermal conductivity.

[0003] To enable lightweight, high thermal conductivity components to adapt to different application scenarios, optimizing their parameters is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] One of the objectives of this invention is to provide a parameter optimization method and system for lightweight, high thermal conductivity components, so as to ensure that the components can be adapted to different application scenarios after parameter optimization.

[0005] This invention provides a parameter optimization method for lightweight, high thermal conductivity components, comprising:

[0006] Data on the use of lightweight, high thermal conductivity components in application scenarios;

[0007] Determine the optimization direction based on the data used;

[0008] Starting from the optimization direction, multiple implementation schemes were constructed;

[0009] The implementation plan was verified to determine the final optimization result.

[0010] Preferably, the data acquisition methods include:

[0011] The first IoT data acquisition terminal collects the operational data of the target object corresponding to the application scenario, and the second IoT data acquisition terminal collects the temperature data of the heat loss area corresponding to the application scenario.

[0012] The preferred steps for determining the optimization direction are as follows:

[0013] Build an application scenario simulation model and perform feasibility simulation based on a pre-configured feasibility library;

[0014] The simulation results are evaluated based on a pre-configured simulation evaluation library, and the optimization direction is determined based on the evaluation results.

[0015] Preferably, the implementation scheme is constructed by analyzing the application scenario simulation model based on a pre-configured optimization scheme parameter library corresponding to the optimization direction.

[0016] Preferably, the verification of the implementation scheme includes: manufacturing corresponding lightweight high thermal conductivity components according to each optimization scheme; configuring each lightweight high thermal conductivity component in the actual scenario corresponding to the application scenario; obtaining corresponding verification data; and filtering the implementation scheme based on the difference between the verification data and the usage data to obtain the final optimization result.

[0017] This invention also provides a parameter optimization system for lightweight, high thermal conductivity components, comprising: a data acquisition module, an optimization direction determination module, an implementation scheme construction module, and a verification optimization module; wherein, the data acquisition module acquires usage data of the lightweight, high thermal conductivity components in the application scenario; the optimization direction determination module determines the optimization direction based on the usage data; the implementation scheme construction module constructs multiple implementation schemes starting from the optimization direction; and the verification optimization module verifies the implementation schemes and determines the final optimization result.

[0018] Preferably, the data acquisition methods include:

[0019] The first IoT data acquisition terminal collects the operational data of the target object corresponding to the application scenario, and the second IoT data acquisition terminal collects the temperature data of the heat loss area corresponding to the application scenario.

[0020] The preferred steps for determining the optimization direction are as follows:

[0021] Build an application scenario simulation model and perform feasibility simulation based on a pre-configured feasibility library;

[0022] The simulation results are evaluated based on a pre-configured simulation evaluation library, and the optimization direction is determined based on the evaluation results.

[0023] Preferably, the implementation scheme is constructed by analyzing the application scenario simulation model based on a pre-configured optimization scheme parameter library corresponding to the optimization direction.

[0024] Preferably, the verification of the implementation scheme includes: manufacturing corresponding lightweight high thermal conductivity components according to each optimization scheme; configuring each lightweight high thermal conductivity component in the actual scenario corresponding to the application scenario; obtaining corresponding verification data; and filtering the implementation scheme based on the difference between the verification data and the usage data to obtain the final optimization result.

[0025] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0026] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0027] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0028] Figure 1 This is a schematic diagram of a parameter optimization method for a lightweight, high thermal conductivity component according to an embodiment of the present invention;

[0029] Figure 2 This is a schematic diagram of the lightweight, high thermal conductivity component of the present invention;

[0030] Figure 3 This is a schematic diagram of the bottom of the second cover of another lightweight, high thermal conductivity component of the present invention;

[0031] Figure 4 This is a schematic diagram of the surface of the first coating of another lightweight, high thermal conductivity component of the present invention;

[0032] Figure 5 This is a schematic diagram of the side of a step in another lightweight, high thermal conductivity component of the present invention;

[0033] Figure 6 This is a schematic diagram of the bottom of the second cover of another lightweight, high thermal conductivity component of the present invention;

[0034] Figure 7 This is a schematic diagram of a parameter optimization system for lightweight, high thermal conductivity components according to an embodiment of the present invention. Detailed Implementation

[0035] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0036] This invention provides a parameter optimization method for lightweight, high thermal conductivity components, such as... Figure 1 As shown, it includes:

[0037] Step 1: Analyze the usage data of the acquired lightweight, high thermal conductivity components in the application scenario;

[0038] The lightweight, high thermal conductivity component includes: a first coating, a graphite film stack, and a second coating; wherein the first coating and the second coating are located on both sides of the graphite film stack; a step is provided on the outer periphery of the second coating, and the first coating and the second coating form a cavity to accommodate the graphite film stack.

[0039] For parameter optimization of lightweight, high thermal conductivity components, it is first necessary to obtain usage data for specific application scenarios. Based on this data, analysis and optimization are then performed to ensure the effectiveness and accuracy of the optimization. To obtain accurate usage data, the methods for acquiring this data include: collecting operational data of the target object corresponding to the application scenario through a first IoT data acquisition terminal, and collecting temperature data of the heat dissipation area corresponding to the application scenario through a second IoT data acquisition terminal. The operational data of the target object can be analyzed to determine its heat generation; the analysis of the temperature data of the heat dissipation area (the target area to which the heat energy of the target object is transferred) can determine how well the component removes the heat energy from the target object. Both methods provide a direct understanding of the specific usage. The first IoT data acquisition terminal includes: a data acquisition module connected to the controller of the target object, a processor, and a communication module connected to the platform; the second IoT data acquisition terminal includes: at least one temperature sensor, a processor, and a communication module connected to the platform.

[0040] Step 2: Determine the optimization direction based on the usage data;

[0041] With the foundation of data analysis established, the next step is to analyze the data. The first step is to determine the direction of optimization. The steps for determining the optimization direction are as follows: Build an application scenario simulation model; conduct feasibility simulations based on a pre-configured feasibility library; evaluate the simulation results according to a pre-configured simulation evaluation library; and determine the optimization direction based on the evaluation results. Specific optimization directions include: increasing the contact area with the target object, increasing the contact area with areas of heat dissipation, and changing the edging material, etc.

[0042] Step 3: Starting from the optimization direction, construct multiple implementation schemes;

[0043] The implementation plan is constructed by analyzing the application scenario simulation model based on a pre-configured parameter library of optimization schemes corresponding to the optimization direction.

[0044] Step 4: Verify the implementation plan and determine the final optimization result.

[0045] The verification of the implementation plan includes: manufacturing corresponding lightweight and high thermal conductivity components based on each optimization plan; configuring each lightweight and high thermal conductivity component in the actual application scenario; obtaining corresponding verification data; and filtering the implementation plan based on the difference between the verification data and the usage data to obtain the final optimization result.

[0046] The following are several design approaches for lightweight, high thermal conductivity components:

[0047] The first type: lightweight, high thermal conductivity components, such as Figure 2As shown, it includes: a first coating body 1, a graphite film stack 2, and a second coating body 3; wherein, the first coating body 1 and the second coating body 3 are located on both sides of the graphite film stack 2; a step is provided on the outer periphery of the second coating body 3, and the first coating body 1 and the second coating body 3 form a cavity to accommodate the graphite film stack 2.

[0048] The thickness of the first coating 1 is any between 9 μm and 1 mm.

[0049] The thickness of the second coating 3 is any between 9 μm and 1 mm.

[0050] The thickness of the graphite film stack 2 is any between 0.15 mm and 2 mm.

[0051] The first coating body and the graphite film stack, and the second coating body and the graphite film stack are bonded together; double-sided adhesive can be used for bonding, and the positions of the double-sided adhesive for bonding the first coating body and the graphite, and the second coating body and the graphite film stack are staggered;

[0052] The material of the first coating includes any one or a combination of copper, stainless steel and ceramic; for example, copper foil, rigid stainless steel sheet and low-density ceramic sheet can be used, and the material can be silicon carbide, aluminum nitride, silicon nitride, etc., or a combination of these materials;

[0053] The second type: a lightweight, high thermal conductivity component, comprising: a first coating body, a graphite film stack, and a second coating body; wherein the first coating body and the second coating body are located on both sides of the graphite film stack body; a step is provided on the outer periphery of the second coating body, and the first coating body and the second coating body form a cavity to accommodate the graphite film stack body.

[0054] like Figure 3 As shown, a recessed platform 11 is provided in the middle of the side of the second covering body 3 away from the graphite stack 2; the stacking direction of the graphite stack 2 at the position corresponding to the recessed platform 11 is perpendicular to the second covering body 3; the stacking direction of the graphite stack 2 on the outer periphery of the corresponding recessed platform 11 is parallel to the second covering body 3.

[0055] The third type is a lightweight, high thermal conductivity component, comprising: a first coating body, a graphite film stack, and a second coating body; wherein the first coating body and the second coating body are located on both sides of the graphite film stack body; a step is provided on the outer periphery of the second coating body, and the first coating body and the second coating body form a cavity to accommodate the graphite film stack body.

[0056] Multiple protrusions are provided on the step, and a recess that matches the protrusions is provided at the edge of the first coating body on the side away from the graphite film stack.

[0057] After the first and second coatings cover the graphite film stack, the protrusions fold over and are placed into the depressions, becoming flush with the surface of the first coating. For example... Figure 4 The image shows the effect after folding. The protrusion 15 can also serve to fix the first covering body 1.

[0058] The fourth type is a lightweight, high thermal conductivity component, comprising: a first coating body, a graphite film stack, and a second coating body; wherein the first coating body and the second coating body are located on both sides of the graphite film stack body; a step is provided on the outer periphery of the second coating body, and the first coating body and the second coating body form a cavity to accommodate the graphite film stack body.

[0059] like Figure 5 As shown, a protrusion 14 is provided on the side of the step 10 away from the graphite film stack 2.

[0060] This embodiment uses protrusions on the outside of the steps to provide positioning during installation in special environments and to facilitate personnel gripping the edges during installation.

[0061] The fifth type is a lightweight, high thermal conductivity component, comprising: a first coating body, a graphite film stack, and a second coating body; wherein the first coating body and the second coating body are located on both sides of the graphite film stack body; a step is provided on the outer periphery of the second coating body, and the first coating body and the second coating body form a cavity to accommodate the graphite film stack body.

[0062] like Figure 6 As shown, multiple L-shaped protrusions 12 are provided on the side of the second covering body 3 away from the graphite film stack 2, and a positioning and mounting area is formed between the multiple L-shaped protrusions 12. The positioning and mounting area is in contact with the component that needs to be heat-conducted, and the L-shaped protrusions 12 function to restrict the position of the component.

[0063] When the length of the edge of the component that needs to be heat-conducted is large, multiple straight protrusions 13 can be provided between the L-shaped protrusions 12 and the straight protrusions 13 are located at the edge of the positioning and mounting area.

[0064] During module production, adhesive is applied between the graphite stack and the first and second coatings to bond them together. The adhesive has a slight impact on the thermal conductivity of the finished module. To minimize this impact, the adhesive application control parameters need to be optimized based on the actual usage of lightweight, high thermal conductivity modules. The optimization method is as follows:

[0065] The contact surfaces between the heat source and the component in the application scenario are analyzed to determine the dispensing location and amount. Based on the determined dispensing location and amount, a dispensing control set is constructed. The dispensing operation during production is controlled based on the dispensing control set.

[0066] The specific analysis steps include: determining the minimum outer frame based on the planar area formed inside the component after the minimum dispensing amount through the production process; determining the number of units to be dispensed based on the parameters (size, thickness, mass, etc.) of the first encapsulation body, the second encapsulation body, and the graphite stack, the area of ​​the minimum outer frame, and a preset unit number comparison table; using the size of the minimum outer frame as the minimum unit for finite element segmentation, performing finite element segmentation on one side of the corresponding contact surface on the component to form a first finite element unit, and correspondingly segmenting one side of the corresponding contact surface on the heat source to obtain a second finite element unit; and using the first finite element unit, which does not have a corresponding second finite element unit, as the first reference unit.

[0067] When the number of first reference units is greater than or equal to the number of units requiring dispensing, the units requiring dispensing are determined from the first reference units; when the number of first reference units is less than the number of units requiring dispensing, the heating parameters (temperature, temperature change rate, etc.) corresponding to each second finite element unit are obtained, and each second finite element unit is screened based on the heating parameters (the number of screened second finite element units is the difference between the number of units requiring dispensing and the number of first finite element units), and the first finite element units corresponding to the screened second finite element units are used as second reference units; the first reference units and the second reference units are used as units requiring dispensing.

[0068] The units requiring adhesive application, as analyzed above, are all locations within the assembly that require adhesive application, mapped onto the contact surface. For this application, the adhesive application involves two surfaces, which are the two surfaces of the graphite stack. During lamination, the adhesive application positions on the two surfaces should be staggered as much as possible. This ensures that, from the perspective of heat transfer direction, at most only one layer of adhesive is passed, maximizing heat conduction and minimizing the impact of the adhesive on heat conduction. Therefore, the adhesive application location and amount are determined based on the analysis results, specifically as follows:

[0069] When the units that need to be glued are distributed in the same closed area, the units that need to be glued are divided into pre-defined equal parts; the divided areas are staggered and correspond to two faces of the graphite stack (i.e., one divided area is on one face of the graphite stack, and its adjacent wind-cut area is on the other face of the graphite stack). The center of each divided area is used as the position of the graphite stack, and the glue dispensing amount is determined according to the number of units that need to be glued in the divided area.

[0070] When the distribution of units requiring dispensing forms multiple closed regions, the closed regions are numbered and grouped to obtain multiple group sets. The optimal group set is obtained by filtering based on the difference in the number of units between two groups and their relative distance. The two groups in the optimal group set are then assigned to two faces of a graphite stack, with the center of each closed region corresponding to the position on the graphite stack as the dispensing location. The dispensing amount is determined based on the number of units requiring dispensing in each closed region. The filtering is based on the evaluation value of the group sets, with the group set having the highest evaluation value being the optimal group set. The evaluation steps are as follows: Based on the difference in the number of units and a preset first evaluation value determination table, determine the first evaluation value (in the first evaluation value determination table, the smaller the difference, the larger the evaluation value); based on the extracted closed regions in any one of the two groups, calculate the minimum value of the shortest distance between the extracted closed region and the closed region in the other group; then, based on the average and variance of the calculated minimum value, consult the preset second evaluation value determination table (in the second evaluation value determination criteria, the larger the average value, the larger the second evaluation value; the smaller the variance, the larger the second evaluation value), and determine the second evaluation value; the sum of the first evaluation value and the second evaluation value is used as the final evaluation value.

[0071] During analysis, the heating parameters corresponding to each second finite element unit are obtained. Specifically, this can be achieved by configuring a sensor array that is adapted to the arrangement of the finite element units to detect multiple heat sources. The detected data is then statistically analyzed to obtain multiple sets of heating parameters, with each set of heating parameters corresponding to a dispensing control set. After the components are manufactured according to this dispensing control set, the components are identified to distinguish components obtained from different dispensing control sets. During component installation, the heating elements are detected based on the sensor array, and the corresponding component is selected based on the detection results. The detection is to obtain data from the start of the heating source operation to a preset time threshold, and then the heating parameters are calculated.

[0072] The statistical analysis group arranges the parameter data according to the sensor array to obtain the analysis dataset; then the similarity between the analysis datasets is calculated, and those with similarity greater than or equal to the preset similarity threshold are grouped together. Based on this, when the amount of data is sufficient, multiple groups can be obtained, and the heating parameter corresponding to the data with the largest sum of similarity value is used as the heating parameter corresponding to the group.

[0073] This invention also provides a parameter optimization system for lightweight, high thermal conductivity components, such as... Figure 7As shown, the system includes: a data acquisition module 21, an optimization direction determination module 22, an implementation scheme construction module 23, and a verification and optimization module 24. The data acquisition module 21 acquires usage data of the lightweight, high thermal conductivity component in the application scenario; the optimization direction determination module 22 determines the optimization direction based on the usage data; the implementation scheme construction module 23 constructs multiple implementation schemes based on the optimization direction; and the verification and optimization module 24 verifies the implementation schemes to determine the final optimization result. The lightweight, high thermal conductivity component includes: a first coating, a graphite film stack, and a second coating. The first and second coatings are located on opposite sides of the graphite film stack. A step is provided on the outer periphery of the second coating, and the first and second coatings form a cavity to accommodate the graphite film stack.

[0074] The methods for acquiring data include:

[0075] The system collects operational data of the target object corresponding to the application scenario through a first IoT data acquisition terminal and temperature data of the heat dissipation area corresponding to the application scenario through a second IoT data acquisition terminal. The operational data of the target object can be analyzed to determine its heat generation; the analysis of the temperature data of the heat dissipation area (the target area to which the target object's heat energy is transferred) can determine how well the component removes the target object's heat energy. Both methods provide a clear understanding of the specific usage. The first IoT data acquisition terminal includes: a data acquisition module connected to the target object's controller, a processor, and a communication module connected to the platform; the second IoT data acquisition terminal includes: at least one temperature sensor, a processor, and a communication module connected to the platform.

[0076] The steps for determining the optimization direction are as follows:

[0077] Build an application scenario simulation model and perform feasibility simulation based on a pre-configured feasibility library;

[0078] Based on a pre-configured simulation evaluation library, the simulation results are evaluated, and optimization directions are determined according to the evaluation results. Specific optimization directions include: increasing the contact area with the target object, increasing the contact area with the heat dissipation area, and changing the edge covering material, etc.

[0079] The implementation plan is constructed by analyzing the application scenario simulation model based on a pre-configured parameter library of optimization schemes corresponding to the optimization direction.

[0080] The verification of the implementation plan includes: manufacturing corresponding lightweight and high thermal conductivity components based on each optimization plan; configuring each lightweight and high thermal conductivity component in the actual application scenario; obtaining corresponding verification data; and filtering the implementation plan based on the difference between the verification data and the usage data to obtain the final optimization result.

[0081] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for parameter optimization of a lightweight high thermal conductivity assembly, characterized in that, Comprise: The use data of the obtained light high-thermal-conductivity assembly in the application scenario; Determine the optimization direction based on the use data; From the optimization direction, construct multiple implementation schemes; Verify the implementation schemes to determine the final optimization result; Analyze the contact surface of the heat source and the assembly in the application scenario to determine the dispensing position and the dispensing amount based on the analysis result; and then construct a dispensing control set according to the determined dispensing position and the dispensing amount; Control the dispensing operation during production according to the dispensing control set; The specific analysis steps include: Determine the minimum circumscribed frame according to the plane area formed inside the assembly after the minimum dispensing amount passes through the production process; Determine the number of units that need to be dispensed according to the parameters of the first cladding body, the second cladding body and the graphite stack, the area of the minimum circumscribed frame and a preset unit number table; Divide the side of the corresponding contact surface on the assembly into first finite element units by taking the size of the minimum circumscribed frame as the minimum unit of finite element division, and obtain second finite element units by dividing the side of the corresponding contact surface on the heat source; Take the first finite element units that do not have corresponding second finite element units as first reference units; When the number of first reference units is greater than or equal to the number of units that need to be dispensed, determine the units that need to be dispensed from the first reference units; When the number of first reference units is less than the number of units that need to be dispensed, obtain the heat generation parameters corresponding to each second finite element unit, screen each second finite element unit based on the heat generation parameters, and take the first finite element units corresponding to the screened second finite element units as second reference units; take the first reference units and the second reference units as the units that need to be dispensed.

2. The method for parameter optimization of light weight high thermal conductivity assembly as claimed in claim 1 wherein, The acquisition method of the use data includes: Collecting the running data of the target object corresponding to the application scenario through a first Internet of Things data collection terminal and collecting the temperature data of the heat dissipation area corresponding to the application scenario through a second Internet of Things data collection terminal.

3. The method for parameter optimization of light weight high thermal conductivity assembly as claimed in claim 1 wherein, The determination steps of the optimization direction are as follows: Construct an application scenario simulation model, and perform feasibility simulation based on a preconfigured feasibility library; Evaluate the simulation result according to a preconfigured simulation evaluation library, and determine the optimization direction according to the evaluation result.

4. The method for parameter optimization of light weight high thermal conductivity assembly as claimed in claim 1 wherein, The implementation scheme is constructed by analyzing the application scenario simulation model according to a preconfigured optimization scheme parameter library corresponding to the optimization direction.

5. The method for parameter optimization of light weight high thermal conductivity assembly as claimed in claim 1 wherein, The verification of the implementation scheme includes: manufacturing corresponding light high-thermal-conductivity assemblies according to each optimization scheme; configuring each light high-thermal-conductivity assembly in an actual scene corresponding to the application scenario; obtaining corresponding verification data; screening the implementation schemes based on the difference between the verification data and the use data to obtain the final optimization result.

6. A parameter optimization system for lightweight high thermal conductivity assembly, applied to the parameter optimization method for lightweight high thermal conductivity assembly according to claim 1, characterized in that, Comprise: A use data acquisition module, an optimization direction determination module, an implementation scheme construction module and a verification optimization module; wherein the use data acquisition module acquires the use data of the light high-thermal-conductivity assembly in the application scenario; the optimization direction determination module determines the optimization direction based on the use data; the implementation scheme construction module constructs multiple implementation schemes from the optimization direction; and the verification optimization module verifies the implementation schemes to determine the final optimization result.

7. The parameter optimization system for lightweight high thermal conductivity assembly as claimed in claim 6 wherein, The acquisition method of the use data includes: The running data of the target object corresponding to the application scenario is collected by the first Internet of Things data collection terminal, and the temperature data of the heat dissipation area corresponding to the application scenario is collected by the second Internet of Things data collection terminal.

8. The parameter optimization system for lightweight high thermal conductivity assembly as claimed in claim 6 wherein, The determination of the optimization direction is as follows: A simulation model of the application scenario is constructed, and feasibility simulation is performed based on a preconfigured feasibility library; The simulation results are evaluated according to a preconfigured simulation evaluation library, and the optimization direction is determined according to the evaluation results.

9. The parametric optimization system for lightweight high thermal conductivity assembly of claim 6, wherein, The implementation scheme is constructed after the simulation model of the application scenario is analyzed according to a preconfigured optimization scheme parameter library corresponding to the optimization direction.

10. The parametric optimization system for lightweight high thermal conductivity assembly as claimed in claim 6 wherein, The verification of the implementation scheme includes: corresponding light high-thermal-conductivity components are manufactured according to each optimization scheme; each light high-thermal-conductivity component is configured in an actual scene corresponding to the application scenario; corresponding verification data are obtained; and the implementation scheme is screened based on the difference between the verification data and the use data, so as to obtain the final optimization result.

Images