Processing device and method for optimizing heat dissipation from a heat sink

Abstract

A processing device and method for heat dissipation optimization of a heat sink, the processing device comprising a storage and a processor. The processor runs a neural network model and performs the following actions: generating a plurality of tag temperatures on each virtual sample model according to simulation parameters; training the neural network model using the simulation parameters, the plurality of virtual sample models, all tag temperatures, and all tag regions; and generating a plurality of predicted temperatures and a predicted region on a virtual to-be-tested model using the neural network model according to component parameters and the virtual to-be-tested model, wherein the plurality of predicted temperatures indicate respective temperatures of a plurality of positions on a to-be-tested circuit board, and the predicted region indicates an optimal heat dissipation region of a physical heat sink to be applied or arranged on the to-be-tested circuit board.

Description

Technical Field

[0001] This disclosure relates to a heat conduction technology, and more particularly to a processing device and method for optimizing heat dissipation of a heat sink. Background Technology

[0002] As the performance of electronic devices improves, the design and manufacturing technology of printed circuit boards (PCBs) has become increasingly complex. These PCBs typically contain a large number of electronic components, which generate significant heat during operation. If heat dissipation is not handled effectively, the electronic components may overheat, thereby affecting the stability, reliability, and lifespan of the overall system.

[0003] Heat sinks (such as thermal paste) are a common type of heat dissipation material, widely used for heat transfer between printed circuit boards (PCBs) and other heat sources (i.e., electronic components that may generate heat). However, users often find it difficult to directly know the temperature of different areas on the PCB, and also to determine which area of ​​the PCB to apply the thermal paste to, and how much of it to apply, will result in the best heat dissipation effect. Therefore, how to quickly predict the temperature of different areas on the PCB and determine the area and amount of thermal paste to apply is a problem that those skilled in the art urgently need to solve. Summary of the Invention

[0004] The purpose of this disclosure is to provide a processing device and method for optimizing heat dissipation of a heat sink, solving the problem in previous technologies where it was difficult to determine which area of ​​the heat sink should be coated or placed, and how much coating / placement should be applied, resulting in the best heat dissipation effect.

[0005] To achieve the above objectives, this disclosure proposes a processing apparatus for optimizing heat dissipation of a heat sink, comprising:

[0006] A storage device configured to store multiple instructions, multiple virtual sample models corresponding to multiple sample circuit boards, simulation parameters of the multiple sample circuit boards, a virtual model of a circuit board under test (TBD) corresponding to a TBD circuit board, and component parameters of the TBD circuit board, wherein a label area on each virtual sample model has a virtual heat sink, and the label area indicates an optimal heat dissipation area of ​​a physical heat sink coated or disposed on the corresponding sample circuit board; and

[0007] A processor, connected to the memory, is configured to run a type of neural network model and accesses a plurality of instructions to perform the following actions:

[0008] Action a) Generate multiple label temperatures on each virtual sample model based on the simulation parameters;

[0009] Action b) Using the simulation parameters, the multiple virtual sample models, all the label temperatures, and all the label regions, train the neural network model of this type; and

[0010] Action c) Based on the component parameters and the virtual model under test, generate multiple predicted temperatures and a prediction region on the virtual model under test using the trained neural network model, wherein the multiple predicted temperatures indicate the temperature of multiple locations on the circuit board under test, and the prediction region indicates the optimal heat dissipation area of ​​the physical heat sink that should be coated or set on the circuit board under test.

[0011] To achieve the above objectives, this disclosure proposes a method for optimizing heat dissipation of a heat sink, comprising:

[0012] Step a) Using a processor, multiple label temperatures are generated on each of multiple virtual sample models based on a simulation parameter of multiple sample circuit boards, wherein the multiple virtual sample models correspond to the multiple sample circuit boards respectively, a label area on each virtual sample model has a virtual heat sink, and the label area indicates an optimal heat dissipation area of ​​a physical heat sink coated or set on the corresponding sample circuit board.

[0013] Step b) Using the processor, a neural network model is trained using the simulation parameters, the multiple virtual sample models, all the label temperatures, and all the label regions; and

[0014] Step c) Using the processor, based on a component parameter of a circuit board under test and a virtual model under test corresponding to the circuit board under test, multiple predicted temperatures and a predicted region on the virtual model under test are generated using the trained neural network model. The multiple predicted temperatures indicate the temperature of multiple locations on the circuit board under test, and the predicted region indicates the optimal heat dissipation area of ​​the physical heat sink that should be coated or set on the circuit board under test.

[0015] Compared to existing technologies, this disclosure allows for temperature simulation of the circuit board, and the use of a virtual model of the circuit board and simulation parameters to train a neural network model. This model is then trained to predict the temperature at various locations on the circuit board and the optimal range for coating or setting up a physical heat sink. Therefore, this disclosure can quickly predict the temperature of various areas on a printed circuit board and predict the optimal areas and optimal coating / setting amounts for coating or setting up heat sinks. Attached Figure Description

[0016] Figure 1 A block diagram of a processing apparatus for heat dissipation optimization of a heat sink is shown in some embodiments of the present disclosure.

[0017] Figure 2AA top view schematic diagram of a sample circuit board in some embodiments of this disclosure is shown.

[0018] Figure 2B This diagram shows a slanted view of a virtual sample model in some embodiments of the present disclosure.

[0019] Figure 2C A schematic diagram of a grid coordinate system in some embodiments of this disclosure is shown.

[0020] Figure 3 A flowchart illustrating a method for optimizing heat dissipation of a heat sink in some embodiments of this disclosure is shown.

[0021] Figure 4A A schematic diagram of a first initial point cloud map is shown in some embodiments of the present disclosure.

[0022] Figure 4B A schematic diagram of a first temperature point cloud map is shown in some embodiments of the present disclosure. Detailed Implementation

[0023] Reference Figure 1 , Figure 1 This is a block diagram showing a processing apparatus 100 for heat dissipation optimization of a heat sink in some embodiments of the present disclosure.

[0024] like Figure 1 As shown, in this embodiment, the processing device 100 (hereinafter referred to as processing device 100) for heat dissipation optimization of heat sink includes a storage device 110 and a processor 120, and the processor 120 is connected to the storage device 110.

[0025] In some embodiments, the processing device 100 may be implemented by any data processing device (e.g., desktop computer, laptop computer, or tablet computer) or server (e.g., cloud server, virtual server, or rack server).

[0026] In this embodiment, the storage device 110 stores multiple computer-executable instructions, multiple virtual sample models corresponding to multiple sample circuit boards, simulation parameters of the multiple sample circuit boards, a virtual model to be tested corresponding to the circuit board under test, and component parameters of the circuit board under test. Specifically, the processing device 100 of this disclosure uses multiple sample circuit boards to train a neural network-like model, and uses the trained neural network-like model to predict relevant information of the circuit board under test.

[0027] In some embodiments, the storage 110 may be implemented by flash memory, read-only memory, hard disk or any equivalent storage component.

[0028] In some embodiments, each computer-executable instruction can be implemented by any software or firmware. When the processor 120 reads the memory 110 and executes each computer-executable instruction, the heat dissipation optimization processing method of this disclosure for the heat sink can be implemented (described in detail later).

[0029] The sample circuit board is a physical circuit board used for training. In some embodiments, a physical heat sink is coated or disposed on the optimal heat dissipation area of ​​each sample circuit board. The optimal heat dissipation area is the area with the best heat dissipation and heat conduction effect obtained by the user after conducting a large number of experiments in advance (that is, as long as the physical heat sink is coated or disposed on the optimal heat dissipation area of ​​each sample circuit board, each sample circuit board can have the best heat dissipation and heat conduction effect).

[0030] In some embodiments, each sample circuit board can be implemented by any type of printed circuit board (e.g., rigid printed circuit board or flexible printed circuit board). In some embodiments, the physical heat sink can be implemented by any type of heat sink with heat dissipation and heat conduction capabilities (e.g., thermal adhesive (such as thermal conductive adhesive) or heat sink).

[0031] In this embodiment, each virtual sample model has a label area, and the label area contains a virtual heat sink. In this embodiment, the label area is used to indicate the optimal heat dissipation area of ​​the physical heat sink that is coated or installed on the corresponding sample circuit board. In other words, the label area is a virtualized area on each virtual sample model used to simulate the optimal heat dissipation area on the corresponding sample circuit board.

[0032] In some embodiments, each virtual sample model can be implemented by any type of three-dimensional solid model. In some embodiments, the virtual heat sink can be virtual information (e.g., virtual colloid information or virtual heat sink information) used in three-dimensional virtual space to simulate the heat dissipation and thermal conduction state of a physical heat sink.

[0033] In some embodiments, each virtual sample model may have multiple virtual components, which are virtual electronic components in a three-dimensional virtual space used to simulate the operation of multiple physical components on the corresponding sample circuit board.

[0034] In some embodiments, the multiple physical components can be implemented from components of any type of entity. For example, physical components can be inductors, capacitors, bridges, housings, shielding, or wooden support frames, etc.

[0035] In some embodiments, simulation parameters may include, but are not limited to, mesh size, virtual regions corresponding to the various physical components on each sample circuit board, the position of the label region on each virtual sample model, power loss of each physical component, material properties of each physical component, temperature limit of each physical component, and heat transfer equations of each physical component and physical heat sink.

[0036] In some embodiments, the grid size may indicate the dimensions of each grid in a grid coordinate system in three-dimensional virtual space (e.g., the length, width, and height of each grid correspond to 1 cm in real space). The grid coordinate system is a virtual coordinate system commonly used in the art and will not be further explained here.

[0037] In some embodiments, the virtual region corresponding to each physical component on each sample circuit board is used to indicate the three-dimensional virtual region of the virtual component that can be used to simulate each physical component in the above-mentioned grid coordinate system.

[0038] In some embodiments, the processor 120 may pre-use any coordinate system algorithm to convert the three-dimensional real area covered by each entity component in the real coordinate system (e.g., the world coordinate system) into the three-dimensional virtual area covered by the corresponding virtual component in the grid coordinate system, so as to obtain the virtual area corresponding to each entity component.

[0039] In some embodiments, the position of the label area on each virtual sample model is used to indicate the three-dimensional virtual region of the virtual heat sink in the aforementioned grid coordinate system.

[0040] In some embodiments, the power loss of each physical component is used to indicate the power lost by each physical component in converting electrical energy into useless heat energy.

[0041] In some embodiments, the material properties of each physical component are used to indicate the thermal conductivity or thermal conductivity coefficient of each physical component.

[0042] In some embodiments, the upper temperature limit of each physical component is used to indicate the highest operating temperature that each physical component can withstand (i.e., when the upper temperature limit is exceeded, the physical component cannot function properly).

[0043] In some embodiments, the heat transfer equations for each physical component are used to indicate how the temperature on each physical component changes over time.

[0044] In some embodiments, the heat transfer equation of a physical heat sink is used to indicate how the temperature on the physical heat sink changes over time.

[0045] In some embodiments, component parameters may include virtual regions corresponding to each of a plurality of other physical components on the circuit board under test, power losses of all other physical components, material properties (e.g., thermal conductivity) of each of the other physical components, upper temperature limits of each of the other physical components, and heat transfer equations of each of the other physical components. The plurality of other physical components on the circuit board under test may be the same as or different from the plurality of physical components on the sample circuit board.

[0046] It is worth noting that the definitions of virtual regions, power losses, material properties (such as thermal conductivity), upper temperature limits, and heat transfer equations of other physical components included in the component parameters are basically similar to the definitions of virtual regions, power losses, material properties, upper temperature limits, and heat transfer equations of the physical components mentioned above. The only difference is that the former is for the circuit board under test, while the latter is for the sample circuit board. Therefore, it will not be elaborated further here.

[0047] In some embodiments, the processor 120 may use the grid size as a reference to establish a corresponding virtual test model based on the virtual regions corresponding to each physical component on each sample circuit board and the position of the label regions on each virtual sample model, using computational fluid dynamics (CFD) model.

[0048] In some embodiments, the computational fluid dynamics model can be implemented by any type of simulation software with heat conduction simulation capabilities (e.g., ANSYS Fluent or OpenFOAM).

[0049] The following example illustrates how to generate a virtual test model. (See also...) Figures 2A-2C , Figure 2A This diagram shows a top view of a sample circuit board 210 in some embodiments of the present disclosure. Figure 2B This diagram shows a slanted view of the virtual sample model 220 in some embodiments of the present disclosure. Figure 2C A schematic diagram of the grid coordinate system {G} in some embodiments of this disclosure is shown.

[0050] like Figure 2A As shown, the sample circuit board 210 in the real coordinate system {R} has a solid substrate b1, on which multiple solid components r1 to r8 are disposed, and a solid heat sink rg is coated or disposed on the solid components r. In addition, the solid substrate b1 itself can also be regarded as a solid component.

[0051] In other words, the sample circuit board 210 has a real area covered by the physical substrate b1, physical components r1 to r8 and physical heat sink rg in the real coordinate system {R}.

[0052] like Figures 2B-2C As shown, the processor 120 establishes a grid coordinate system {G} in three-dimensional space based on the grid size, and establishes a corresponding virtual test model 220 for the sample circuit board 210 based on the virtual regions corresponding to the physical substrate b1 and the physical components r1 to r8 (i.e., the virtual regions covered by the virtual substrate b2 and the virtual components v1 to v8 in the grid coordinate system {G}) and the position of the label region (i.e., the virtual region covered by the virtual heat sink vg in the grid coordinate system {G}).

[0053] In some embodiments, the storage 110 stores a virtual test model corresponding to the circuit board under test. This virtual test model can be generated in a three-dimensional virtual space by simulating the real circuit board under test using the method described above. Since the simulation method has been described in detail above, it will not be repeated here.

[0054] In some embodiments, the circuit board under test (TBT) may be a circuit board different from the sample circuit board, and the TTB has not yet been coated or fitted with a physical heat sink. In some embodiments, the TTB may also be implemented using any type of printed circuit board. Specifically, the TTB referred to in this disclosure is a circuit board to which a physical heat sink is coated or fitted onto the area indicated by the inference result after inference is performed using a trained neural network model. This achieves better heat dissipation. In some embodiments, the physical heat sink is a thermal colloid, and the coating amount is the total amount of colloid coated onto the area indicated by the inference result. In some embodiments, the physical heat sink is a heat sink plate, and the fitting amount is the size of the heat sink plate fitted onto the area indicated by the inference result.

[0055] In this embodiment, the processor 120 runs a neural network model 121 and accesses the aforementioned multiple instructions to execute the heat dissipation optimization processing method for the heat sink described in the following paragraphs (details to follow).

[0056] In some embodiments, the neural network model 121 may be implemented by any neural network model for image processing (e.g., convolutional neural network model, deep neural network model, YOLO model, or transformer model, etc.).

[0057] In some embodiments, the processor 120 may be implemented by a central processing unit (CPU), a micro control unit (MCU), a programmable logic controller (PLC), a system on chip (SoC), or a field programmable gate array (FPGA).

[0058] Refer to together Figure 3 , Figure 3 This diagram illustrates a flowchart of a method for optimizing heat dissipation of a heat sink according to some embodiments of the present disclosure. This method is applicable to... Figure 1 The processing device 100 shown.

[0059] As mentioned above, the storage 110 of the processing device 100 stores multiple virtual sample models and simulation parameters of multiple sample circuit boards, and also stores a virtual model of the circuit board under test and component parameters. Figure 3 As shown, firstly, in step S310, the processor 120 generates multiple label temperatures on each virtual sample model based on simulation parameters.

[0060] In some embodiments, the processor 120 may use a computational fluid dynamics model to generate the temperatures of each virtual sample model in the grid coordinate system {G} as label temperatures, based on simulation parameters.

[0061] Specifically, the processor 120 can set the coordinates, power loss, material properties (e.g., thermal conductivity), temperature limit, and heat transfer equations for each virtual sample model within multiple grids covered by the grid coordinate system {G}, based on simulation parameters, to generate multiple simulation result files (e.g., VTK or CSV files) corresponding to each virtual sample model. In some embodiments, the multiple simulation result files respectively contain the power loss, material properties, temperature limit, and heat transfer equations for each point in the corresponding virtual sample model. Furthermore, after the user imports the coordinate system (e.g., the aforementioned grid coordinate system) into the simulation software, the multiple simulation result files can be converted into a first initial point cloud map (with a one-to-one correspondence between the grid and the point cloud). The difference from a pure point cloud distribution map is that the first initial point cloud map includes the simulation parameters; therefore, each point cloud in the point cloud map carries corresponding information such as temperature, pressure, and velocity. Furthermore, the processor 120 then uses a computational fluid dynamics model to generate the temperature of each point cloud in each first initial point cloud map as its respective label temperature.

[0062] In some embodiments, the first initial point cloud is a simulation result file with coordinate system information, and each of the multiple point clouds in the first initial point cloud has corresponding coordinates, power loss, material properties (e.g., thermal conductivity), upper temperature limit, and heat transfer equation.

[0063] The following example illustrates the initial point cloud plot. (See also...) Figure 4A , Figure 4A A schematic diagram of a first initial point cloud 410 in some embodiments of the present disclosure is shown.

[0064] like Figure 4A As shown, one of the virtual sample models has multiple point clouds on multiple grids covered by the grid coordinate system {G}. Each point cloud in the first initial point cloud diagram 410 has corresponding coordinates, power loss, material properties, upper temperature limit and heat transfer equation.

[0065] In some embodiments, the processor 120 may set different colors on each first initial point cloud map for different label temperature values ​​to generate a corresponding first temperature point cloud map. Each of the multiple point clouds in each first temperature point cloud map has a corresponding color, coordinates, power loss, material properties, upper temperature limit, and heat transfer equation.

[0066] The following example illustrates the point cloud map of the first temperature. (See also...) Figure 4B , Figure 4B A schematic diagram of a first temperature point cloud 420 in some embodiments of the present disclosure is shown.

[0067] like Figure 4B As shown, one of the virtual sample models has multiple point clouds on multiple grids covered by the grid coordinate system {G}. Each point cloud in the first temperature point cloud map 420 has a label temperature, corresponding color, coordinates, power loss, material properties, upper temperature limit, and heat transfer equation. In this embodiment, when the temperature value of the label temperature of the point cloud in the first temperature point cloud map 420 is higher, the color corresponding to the label temperature is more biased towards the first color (e.g., red). Conversely, when the temperature value of the label temperature of the point cloud in the first temperature point cloud map 420 is lower, the color corresponding to the label temperature is more biased towards the second color (e.g., blue).

[0068] In some embodiments, the processor 120 may control a display (not shown) in the processing device 100 or an external display (not shown) to display each of the first initial point cloud maps or the first temperature point cloud maps for user viewing.

[0069] Back Figure 3In step S320, the processor 120 trains a neural network model 121 using simulation parameters, multiple virtual sample models, all label temperatures, and all label regions.

[0070] In some embodiments, the processor 120 may use multiple simulation result files generated by simulation parameters and each virtual sample model as training samples. More specifically, the processor 120 may use the aforementioned data (i.e., coordinates, power loss, material properties, upper temperature limit, and heat transfer equation) of multiple point clouds corresponding to the first initial point cloud map of each virtual sample model, generated according to the simulation parameters, as training samples. Then, the processor 120 may use the label regions and multiple label temperatures on each virtual sample model as multiple training labels corresponding to the multiple training samples. Next, the processor 120 may update the neural network model 121 using the multiple training samples corresponding to each simulation result file (or the first initial point cloud map) and the multiple training labels corresponding to each training sample.

[0071] In some embodiments, the processor 120 may input each training sample into the neural network model 121 to generate a corresponding result region and the result temperature of each point cloud as multiple result labels, and calculate the multiple result labels corresponding to each training sample and the loss value between the multiple training labels (i.e., the loss value between the result temperature of the point cloud and the label temperature at the same coordinates and the loss value between the coordinates of the result region and the coordinates of the label region in the grid coordinate system {G}).

[0072] Next, the processor 120 uses the calculated loss value to perform a backpropagation algorithm on the neural network model 121 to update the parameters in the neural network model 121 (i.e., the weight values ​​of each of the multiple neural network layers in the neural network model 121). In this way, the processor 120 can repeat the same process to complete the update of the neural network model 121 (i.e., complete the training phase) more than a certain number of recursions (i.e., by using this and a pre-set number of training iterations).

[0073] In step S330, the processor 120 generates multiple predicted temperatures and predicted regions on the virtual model under test using a neural network-like model based on the component parameters and the virtual model under test.

[0074] In other words, whenever it is necessary to infer from a new circuit board under test, the user generates a new virtual test model for the new circuit board under test using the simulation method described above. Then, the processor 120 can input the new virtual test model and the corresponding component parameters into the trained neural network model 121, so that the processing of the neural network model 121 can convert the virtual test model and the corresponding component parameters into multiple predicted temperatures and predicted regions on the virtual test model (i.e., the usage stage of using the trained neural network model 121 for inference).

[0075] In this embodiment, multiple predicted temperatures indicate the temperature at multiple locations on the circuit board under test, and the predicted region indicates the optimal heat dissipation area of ​​a physical heat sink coated or disposed on the circuit board under test.

[0076] In some embodiments, the processor 120 can, according to component parameters, set the coordinates, power loss, material properties, temperature upper limit, and heat transfer equations of each grid within the grid coordinate system {G} for the virtual model under test, thereby generating a second result file (e.g., a VTK file or a CSV file) corresponding to the virtual model under test. After importing the coordinate system (e.g., the aforementioned grid coordinate system) into the second result file, the second result file can be converted into a second initial point cloud plot (similar to...). Figure 4A The first initial point cloud map 410). Furthermore, the processor 120 inputs the second result file (or the second initial point cloud map) into the trained neural network model 121, so as to obtain the temperature of each point in the second result file (or each point cloud in the second initial point cloud map) and the predicted region on the second initial point cloud map from the output of the neural network model 121.

[0077] In some embodiments, the multiple point clouds of the second initial point cloud map each have corresponding coordinates, power loss, material properties, upper temperature limit, and heat transfer equation.

[0078] Next, the processor 120 can, according to the positional correspondence between the grid and the point cloud (i.e., find the grid and point cloud with the same coordinates in the grid coordinate system {G}), use the temperature of each point cloud in the second initial point cloud map and the predicted area on the second initial point cloud map as the predicted temperature of each of the multiple grids covered by the virtual model under test in the grid coordinate system {G} and the predicted area of ​​the virtual model under test.

[0079] In some embodiments, the processor 120 may set different colors on each second initial point cloud map for different predicted temperature values ​​to generate corresponding second temperature point cloud maps. Each second temperature point cloud map also has corresponding color, coordinates, power loss, material properties, upper temperature limit, and heat transfer equation (similar to...). Figure 4B First temperature point cloud map 420).

[0080] In some embodiments, the processor 120 may control a display (not shown) in the processing device 100 or an external display (not shown) to display a second initial point cloud map or a second temperature point cloud map for user viewing.

[0081] In some embodiments, the user can process the physical circuit board under test (e.g., coat or install a physical heat sink) based on the predicted temperature and predicted region output by the neural network to obtain the finished circuit board. Furthermore, the processor 120 can calculate the difference between the actual detected temperature and the predicted temperature at each location on the finished circuit board and determine whether the average of these differences is greater than a user-preset difference threshold. When the average of these differences is greater than the difference threshold, it indicates that the inference result of the neural network model 121 is inaccurate. In this case, the processor 120 can retrain the neural network model 121 using other sample circuit boards and corresponding simulation parameters in the same training manner as described above.

[0082] Through the above steps, this disclosure can automatically generate temperatures at various locations on the circuit board under test using a trained neural network model 121, and automatically generate the optimal range for coating or setting up a physical heat sink on the circuit board under test. Based on this range, the coating / setting amount of the physical heat sink can be determined. In this way, this disclosure eliminates the need for extensive and complex testing and inspection work, and can easily obtain the temperatures at various locations on the circuit board under test and the optimal range for coating or setting up a physical heat sink.

[0083] In summary, this disclosure combines pre-temperature simulation of the circuit board with a neural network-like algorithm to train a method capable of predicting the temperature at various locations on the circuit board and the optimal range for coating or setting up physical heat sinks. Therefore, this disclosure can quickly deduce the temperature of various areas on the printed circuit board to be processed, as well as the area and amount of heat sink coating or setting up. Furthermore, this disclosure also employs a method of converting the virtual model with simulated temperatures into a visualized point cloud map, allowing users to quickly determine the temperature at each location.

[0084] [Symbol Explanation]

[0085] 100: A processing device for optimizing heat dissipation of heat sinks.

[0086] 110: Storage

[0087] 120: Processor

[0088] 121: Neural Network Model

[0089] 210: Sample Circuit Board

[0090] {R}: Real coordinate system

[0091] r1~r8: Solid components

[0092] b1: Solid substrate

[0093] rg: Physical heat sink; 220: Virtual model under test; v1~v8: Virtual component; b2: Virtual substrate

[0094] vg: Virtual heat sink {G}: Mesh coordinate system S310~S330: Step 410: First initial point cloud map 420: First temperature point cloud map.

Claims

1. A processing device for optimizing heat dissipation of a heat sink, characterized in that, include: A storage device configured to store multiple instructions, multiple virtual sample models corresponding to multiple sample circuit boards, simulation parameters of the multiple sample circuit boards, a virtual model under test corresponding to a circuit board under test, and component parameters of the circuit board under test, wherein a label area on each of the virtual sample models has a virtual heat sink, the label area indicating an optimal heat dissipation area of ​​a physical heat sink coated or disposed on the corresponding sample circuit board; and A processor, connected to the storage, configured to run a type of neural network model, and accessing the plurality of instructions to perform the following actions: Action a) Generate multiple label temperatures on each of the virtual sample models based on the simulation parameters; Action b) Using the simulation parameters, the plurality of virtual sample models, all the label temperatures, and all the label regions, train the neural network-like model; as well as Action c) Based on the component parameters and the virtual test model, generate multiple predicted temperatures and a prediction region on the virtual test model using the trained neural network model, wherein the multiple predicted temperatures indicate the temperature of multiple locations on the test circuit board, and the prediction region indicates the optimal heat dissipation area of ​​the physical heat sink that should be coated or set on the test circuit board.

2. The processing apparatus for optimizing heat dissipation of a heat sink according to claim 1, characterized in that, The simulation parameters include the grid size in a grid coordinate system in a three-dimensional virtual space, the virtual regions corresponding to the multiple physical components on each of the sample circuit boards, the position of the label region on each of the virtual sample models, the power loss of all the physical components, the material properties of each of the physical components, the upper temperature limit of each of the physical components, and the heat transfer equations of each of the physical components and the physical heat sink. The component parameters include virtual regions corresponding to each of the plurality of other physical components on the circuit board under test, the power loss of all the other physical components, the material properties of each of the other physical components, the upper temperature limit of each of the other physical components, and the heat transfer equation of each of the other physical components.

3. The processing apparatus for optimizing heat dissipation of a heat sink according to claim 2, characterized in that, The processor is also configured to perform the following actions: Based on the grid size, and according to the position of the virtual region corresponding to each physical component on each sample circuit board and the label region on each virtual sample model, a computational fluid dynamics model is used to establish the corresponding virtual test model.

4. The processing apparatus for optimizing heat dissipation of a heat sink according to claim 1, characterized in that, In action a), the processor is configured to perform the following action: Based on the simulation parameters, a computational fluid dynamics model is used to generate the temperatures of each of the virtual sample models within a grid coordinate system, and these temperatures are used as the label temperatures.

5. The processing apparatus for optimizing heat dissipation of a heat sink according to claim 1, characterized in that, In action b), the processor is configured to perform the following actions: Based on the simulation parameters, generate multiple simulation result files corresponding to the multiple virtual sample models respectively; The multiple simulation result files are used as multiple training samples; The label regions and multiple label temperatures on each virtual sample model are used as multiple training labels corresponding to the multiple training samples; as well as The neural network model is updated using the plurality of training samples and the plurality of training labels corresponding to the plurality of training samples.

6. A method for optimizing heat dissipation of a heat sink, characterized in that, include: Step a) Using a processor, generate multiple label temperatures on each of multiple virtual sample models based on a simulation parameter of multiple sample circuit boards, wherein the multiple virtual sample models correspond to the multiple sample circuit boards respectively, a label area on each of the virtual sample models has a virtual heat sink, and the label area indicates an optimal heat dissipation area of ​​a physical heat sink coated or disposed on the corresponding sample circuit board. Step b) Using the processor, a neural network model is trained using the simulation parameters, the plurality of virtual sample models, all the label temperatures, and all the label regions; as well as Step c) The processor generates multiple predicted temperatures and a predicted region on the virtual test model using the trained neural network model, based on a component parameter of a circuit board under test and a virtual test model corresponding to the circuit board under test. The multiple predicted temperatures indicate the temperature of multiple locations on the circuit board under test, and the predicted region indicates the optimal heat dissipation area of ​​the physical heat sink that should be coated or set on the circuit board under test.

7. The processing method for optimizing heat dissipation of a heat sink according to claim 6, characterized in that, The simulation parameters include the mesh size in a network coordinate system in a three-dimensional virtual space, the virtual regions corresponding to the multiple physical components on each of the sample circuit boards, the positions of the label regions on each of the virtual sample models, the power loss of all the physical components, the material properties of each of the physical components, the upper temperature limit of each of the physical components, and the heat transfer equations between each of the physical components and the physical heat sink. The component parameters include virtual regions corresponding to each of the plurality of other physical components on the circuit board under test, the power loss of all the other physical components, the material properties of each of the other physical components, the upper temperature limit of each of the other physical components, and the heat transfer equation of each of the other physical components.

8. The processing method for optimizing heat dissipation of a heat sink according to claim 7, characterized in that, Also includes: Using the processor and based on the grid size, a corresponding virtual test model is established using a computational fluid dynamics model according to the position of the virtual region corresponding to each physical component on each sample circuit board and the label region on each virtual sample model.

9. The processing method for optimizing heat dissipation of a heat sink according to claim 6, characterized in that, Step a) includes: The processor, based on the simulation parameters, uses a computational fluid dynamics model to generate the temperatures of each of the virtual sample models within a grid coordinate system, which are then used as the label temperatures.

10. The processing method for optimizing heat dissipation of a heat sink according to claim 6, characterized in that, Step b) includes: The processor generates multiple simulation result files corresponding to the multiple virtual sample models according to the simulation parameters. The processor uses the multiple simulation result files as multiple training samples. The processor uses the label regions and multiple label temperatures on each of the virtual sample models as multiple training labels corresponding to the multiple training samples; and The processor updates the neural network-like model using the plurality of training samples and the plurality of training labels corresponding to the plurality of training samples.

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