Method for generating a heat sink design

Abstract

A method or system for generating a heat sink design includes receiving, from a user, a characteristic input including either a target physical dimension of the heat sink including the heat sink's constituent materials or a maximum temperature of an electrical component adjacent to the heat sink, along with the electrical power level of the electrical component; calculating a predicted temperature of the electrical component; generating a comparison showing the impact of independently varying each of the characteristic inputs and the constituent materials, and the resulting changes, on the predicted temperature of the electrical component; enabling the user to change the characteristic inputs after the comparison has been generated; selecting a heat sink design; and generating design specifications for manufacturing the heat sink.

Description

Technical Field

[0001] The present disclosure relates to methods and systems for generating a design of a heat sink for dissipating heat from an electrical component, and in non-limiting embodiments, to methods and systems for manufacturing a heat sink.

Background Art

[0002] Heat sinks are used with electrical components such as lights. During operation of an electrical component, heat is generated due to the technology, materials, and power used by the electrical component. Heat is typically an unwanted side effect of the electrical component, but its generation does not always require a heat sink. However, as more electrical components and materials are placed in a smaller area, heat sinks are used and relied upon more often to prevent an increase in heat that could damage the electrical component. Thus, heat sinks are often required when used with such electrical components.

[0003] Existing methods of designing heat sinks can be time-consuming and resource-intensive. First, a usage analysis is performed, and the need for cooling is determined from the expected power output of the device. Computer-aided design (CAD) is known to be used in the design of heat sinks. Then, simulations are performed using computational fluid dynamics (CFD), followed by analysis. This can take several hours to complete. Based on the results of the analysis, it can be determined whether the heat sink design overperforms, resulting in waste or abuse of resources, or underperforms and the heat sink is unacceptable for its intended use. Then, the designer must predict what modifications need to be made to the design and repeat this process, performing the CFD simulation and subsequent analysis again. If the results are again determined to be either overperforming or underperforming, modifications are made to the design, and the CFD simulation, analysis, and modifications to the CAD design are repeated continuously. Once the performance of the heat sink is determined to be acceptable, another step can be taken to optimize the design. For example, aluminum plates can be added to or removed from the design, and the CFD simulation step and analysis step are performed again. When the design is complete, the user must determine how the design can be manufactured.

[0004] In addition, newer techniques and materials are now available that can improve the design of heat sinks. This combination of new techniques and materials results in new methods and systems for designing and manufacturing heat sinks in a more efficient and shorter period of time, and in an integrated manner that performs both the design and manufacturing of the heat sink. SUMMARY OF THE INVENTION

[0005] One embodiment is a method of generating a heat sink design, comprising receiving at least four characteristic inputs from a user via a user interface, the characteristic inputs including: (i) at least two target physical dimensions of the heat sink, (ii) at least one power level of an electrical component adjacent to the heat sink, and (iii) at least one thermal conductivity of a thermally conductive polymer composition constituting the heat sink; calculating a predicted temperature of the electrical component based on the at least four characteristic inputs using at least one processor; generating, using at least one processor, from zero to five first comparisons, each first comparison indicating the impact and resulting change of independently varying each of the at least four characteristic inputs on the predicted temperature of the electrical component; generating, using at least one processor, a second comparison indicating the impact of independently varying each of the at least two physical dimensions on the predicted temperature of the electrical component; generating, using at least one processor, a third comparison indicating the impact of changing the constituent material on the predicted temperature of the electrical component; after each of the comparisons is generated, enabling the user to change at least one of the at least four characteristic inputs; after the predicted temperature of the electrical component is displayed via the user interface, enabling the user to select a design of the heat sink having at least two target physical dimensions and a constituent material; and generating design specifications for manufacturing the heat sink.

[0006] In another embodiment, a method of generating a heat sink design includes, via a user interface, receiving (i) at least one power level of an electrical component adjacent to the heat sink and (ii) a maximum allowable application temperature of the electrical component, and using at least one processor to calculate a modified heat sink design based on the power level and the maximum allowable temperature input by the user and also based on a standard heat sink design having standard physical dimensions including heat sink size, number of fins, fin height, and fin spacing, wherein the modified heat sink design has physical dimensions changed from the standard design to allow the input maximum temperature and power level, given a fixed heat sink size and a thermal conductivity of a thermally conductive polymer composition forming the heat sink; using at least one processor to generate from zero to five first comparisons, each first comparison indicating the impact of independently varying each of at least two physical dimensions, the power level input by the user, and at least one thermal conductivity of a thermally conductive polymer composition forming the heat sink; using at least one processor to generate a second comparison indicating the impact of independently varying at least two physical dimensions on the predicted temperature of the electrical component; using at least one processor to generate a third comparison indicating the impact of changing the constituent material on the predicted temperature of the electrical component; after each of the comparisons is generated, enabling the user to change at least one of the physical dimensions, the power level, or the thermal conductivity; after the predicted temperature of the electrical component is displayed via the user interface, enabling the user to select a design of the heat sink having at least two physical dimensions and a constituent material; and generating design specifications for manufacturing the heat sink.

[0007] In other embodiments, the characteristic input is selected from the group consisting of the number of fins, the height of the fins, the fin spacing, and the thickness of the heat spreader. In yet another embodiment, the characteristic input includes at least three target physical dimensions of the heat sink product, and the user selects a design of a heat sink having at least three target physical dimensions and a constituent material. In another embodiment, the fixed heat sink size includes a fixed heat sink diameter and a fixed heat sink thickness.

[0008] In different embodiments, calculating the predicted temperature of the electrical element uses a neural network model prepared using a training set and a validation set of prior CFD simulations to optimize the proposed heat sink design based on the characteristic input, or to compare possible heat sink designs and material selections with the input design parameters and optimize the proposed heat sink design based on the predicted weight of the heat sink.

[0009] In other embodiments, each of the zero to five first comparisons is selected from the group consisting of a table and a line graph. The method can further include generating three, four, or five first comparisons using at least one processor, each first comparison showing the impact and resulting changes of independently varying each of the at least four characteristic inputs on the predicted temperature of the electrical element. The second comparison can be a heat map, and the predicted temperature of the electrical element can be shown in color in the heat map, with the color changing with the change in the predicted temperature.

[0010] In another embodiment not yet disclosed, the method further includes the user changing at least one characteristic input and recalculating based on the changed input using at least one processor. In another embodiment, the user changes the at least one characteristic input by moving a point in the line graph or heat map of the first comparison or the second comparison to represent the change in the at least one characteristic input. In yet another embodiment, the method further includes regenerating at least one of the first comparison, the second comparison, and the third comparison based on the changed input.

[0011] In a further embodiment, the method further includes transmitting the design specification for manufacturing the heat sink to an injection molding machine, a 3D printer, or an external molder.

[0012] These features and characteristics of the present invention, as well as other features and characteristics, and furthermore the operational methods and functions of the related elements and components of the structure and the manufacturing economy, will become more apparent by considering the following description of the accompanying drawings and the appended claims. The accompanying drawings are all part of this specification, and like reference numerals designate corresponding parts in the various figures. However, it should be clearly understood that the drawings are for illustrative and explanatory purposes only and are not intended to define the scope of the present invention. As used in this specification and the claims, unless the quantity is explicitly stated, and unless otherwise clearly specified in the context, the singular also includes the plural.

[0013] Further advantages and details will be described in more detail below with respect to the exemplary embodiments shown in the accompanying schematic diagrams.

Brief Description of the Drawings

[0014]

Figure 1

Figure 2

Figure 3

Figures 4-6

[0015] As used herein, the term "computing device" can refer to one or more electronic devices configured to communicate directly or indirectly by or through one or more networks. A computing device may be a mobile device. By way of example, mobile devices can include cellular phones (e.g., smartphones or standard cellular phones), portable computers (e.g., laptop computers or tablet computers), wearable devices (e.g., watches, glasses, lenses), personal digital assistants (PDAs), and / or other similar devices. In other non-limiting embodiments, a computing device can be a desktop computer or other non-mobile computer. Further, the term "computer" can refer to any computing device that includes components necessary for receiving, processing, and outputting data, typically including a display, a processor, memory, an input device, and a network interface. An "interface" refers to a generated display, such as one or more graphical user interfaces (GUIs) with which a user can interact either directly or indirectly (e.g., via a keyboard, mouse, etc.). Further, one or more computers that communicate directly or indirectly in a network environment, such as servers or other computerized devices, can constitute a "system".

[0016] This specification describes a method and system for generating a heat sink design. In one embodiment, a user specifies characteristic inputs for a heat sink, including physical dimensions, power levels, and thermal conductivity, and the method or system calculates a predicted temperature and provides the user with an opportunity to review the results of these inputs. The results of the inputs include how changing one or more of the inputs will affect the predicted temperature. In another embodiment, the user specifies power levels and a maximum temperature, and the method or system calculates the physical dimensions of the heat sink using a standard design and also provides the user with an opportunity to review the results of these inputs. The results of the inputs include how changing one or more of the inputs will affect the predicted temperature. In each case, the method or system provides an iterative interaction between the user and the system such that the user can review, approve, or modify the heat sink design. This interactive communication between the user and the system can include an iterative process in which the user and the system communicate with each other until the system or method generates a heat sink design with the desired characteristics, materials, and performance. Thus, the user can maintain control over the design while continuing to receive recommendations from the system. Non-limiting embodiments enable the user to receive a heat sink design and select a method for manufacturing it.

[0017] The heat sink designs described herein have an optional heat spreader plate having a diameter, wall thickness, number of fins, height of each fin, fin spacing (distance between fins), plate thickness, and a material having a thermal conductivity. One or more of these variables can be fixed values and the others can be variable. The heat sink is assumed to be adjacent to an electrical element having an input power level and to be in an environment of ambient air temperature. This ambient air temperature can also be a fixed value. There can be several different materials having different thermal conductivities. These materials are most often aluminum, which has conventionally been used as a heat sink material, and there can also be one or more grades of thermally conductive thermoplastic materials that can be molded using one of the methods described herein. Finally, there is the temperature of the electrical element when the electrical element is operating. This characteristic can also be calculated using the methods or systems described herein, or can be input as the maximum temperature at which the electrical element can operate, and the heat sink design is calculated, inter alia, from that input.

[0018] The methods and systems described in detail with respect to the figures are for heat sinks used in a circular dome-shaped reflector having a fixed diameter. However, it can easily be adapted to another geometric shape. However, this adaptation requires appropriate geometric input parameters for that geometric shape. Additional simulations using CFD are required, including validation using temperature measurements and subsequent generation of a neural network model. The other geometric shapes can be rectangular or square reflectors, or can be a continuous series of circular, rectangular or square reflectors, and these reflectors can be the same size or different sizes to match any desired light design. These additional geometric shapes may be associated with additional physical dimensions such as the height, width, and number of elements of the continuous series, and differences in the height, width, and diameter of the continuous series.

[0019] Referring to FIG. 1, a system 100 for generating a heat sink design is shown. System 100 includes step 10 of inputting characteristics from a computing device via a user interface. These characteristics can include characteristic inputs such as the target physical dimensions of the heat sink, the power level of the electrical elements adjacent to the heat sink, and the thermal conductivity of the material. The target physical dimensions of the heat sink can include the number of fins, the height of the fins, the fin spacing, and the thickness of the heat spreader. The heat spreader is optional and its constituent material can be aluminum. Physical dimensions such as diameter and wall thickness can also be included and they can be fixed values. The system then calculates the predicted temperature of the electrical element based on the characteristic inputs (11). Next, the system generates comparisons of some of the characteristic inputs (12), and in generating these comparisons, the system can generate a graph showing how much the temperature will rise or fall if its input changes. The system can generate 0 to 5 comparisons depending on the power level and the thermal conductivity of the material. Using some combinations of these inputs, there may be no options left for the physical characteristics, and thus the number of comparison graphs may be less than for other combinations of inputs. Then, the system generates a comparison of the physical dimensions with respect to the temperature of the electrical element (13). In this comparison, it is shown how adjusting two physical dimensions simultaneously can potentially affect the temperature of the electrical element. Next, the system generates a comparison of the use of different materials for the heat sink where each material has a different thermal conductivity (14), showing how the choice of material can affect the predicted temperature of the electrical element. In each of these comparisons, the user can change the characteristic inputs (15), and then the system recalculates the predicted temperature (11). If the user is satisfied with the heat sink design and as a result no further changes are made, the system generates design specifications 16, which can be further transmitted to a molding machine 17 for fabricating the heat sink. Other optional things include transmitting the design specifications 16 to a 3D printer 18 or an external molder 19.

[0020] In another embodiment shown in FIG. 2, a system 200 for generating a heat sink design is shown. System 200 includes the steps of inputting the power level 20 of an electrical component adjacent to the heat sink and inputting the maximum operating temperature 21 of the electrical component. Each input is performed from a computing device via a user interface. The system also has as an input the physical dimensions from a standard heat sink design 22. The physical dimensions of a standard heat sink can include the number of fins, the height of the fins, the fin spacing, and the thickness of the heat spreader. The heat spreader is optional and its constituent material can be aluminum. Physical dimensions such as diameter and wall thickness can also be included and they can be fixed values. The system then calculates the predicted heat sink design (23). The predicted heat sink design has physical dimensions modified from the standard heat sink design 22 to allow the maximum operating temperature 21 of the electrical component and the power level 20 of the electrical component based on the characteristic inputs. In this embodiment, the predicted heat sink design 23 also includes the selection of the material that makes up the heat sink. Next, the system generates some comparisons of the characteristic inputs (24), and in generating these comparisons, the system can generate a graph showing how much the temperature will rise or fall if its inputs change. The system can generate from 0 to 5 comparisons depending on the power level and maximum operating temperature of the electrical component. Using some combinations of these inputs, there may be no options left for the physical characteristics, and thus the number of comparison graphs may be less than for other combinations of inputs. Then, the system generates a comparison of the physical dimensions with respect to the temperature of the electrical component (25). In this comparison, it is shown how adjusting two physical dimensions simultaneously can potentially affect the temperature of the electrical component. Next, the system generates a comparison of the use of different materials for the heat sink where each material has a different thermal conductivity (26), showing how the selection of the material can affect the predicted temperature of the electrical component. In each of these comparisons, the user can change the characteristic inputs (27), and then the system recalculates the predicted temperature (23).If the user is satisfied with the heat sink design and as a result no further changes are made, the system generates design specification 28, which can be further transmitted to molding machine 29 for fabricating the heat sink. Other options include transmitting design specification 28 to 3D printer 30 or external molder 31.

[0021] The calculations to reach the predicted temperature of the electrical component or the predicted heat sink design can rely on a neural network. An example of a neural network that can be used in one embodiment of the present invention is shown in FIG. 3. A neural network is a machine learning technique generally based on the network within the human brain. The neural network model is used to predict the heat sink temperature generated from computational fluid dynamics (CFD) simulations. This model was constructed using the input variables of power level 35, number of fins 36, fin length 37, fin spacing 38, thickness of the heat spreader 39, and thermal conductivity 40. The constituent material of the heat spreader was assumed to be aluminum. The neural net consists of a one-layer four-node model having a first node 41, a second node 42, a third node 43, and a fourth node 44. Each of the first node, the second node, the third node, and the fourth node is used in the calculation of the electrical component temperature 45. A dataset consisting of over 600 CFD simulations with changes in the settings of the input variables was used to train and validate the model. For a few configurations, the electrical component temperature was determined using thermocouples and forward-looking infrared (FLIR) imaging. The data was compared with the CFD simulations and how well the CFD matched the experimental results was evaluated. Thereafter, the electrical component temperature was measured using thermocouples and forward-looking infrared imaging, and the accuracy of the model and the CFD simulations was confirmed. It was confirmed by measurement that the CFD simulations had predicted the accurate temperature.

[0022] The following equations show the format of neural network equations used to predict the temperature of an electrical component with respect to a heat sink. There is an equation for each node, and the solution of each node equation functions as an input to Equation 5 that predicts the temperature of the electrical component. (I) H1 = Tanh(0.5 * (B0 + (B1 * Power level) + (B2 * Thermal conductivity) + (B3 * Number of fins) + (B4 * Fin length) + (B5 * Heat spreader thickness) + (B6 * Fin spacing))) (II) H2 = Tanh(0.5 * (B0 + (B1 * Power level) + (B2 * Thermal conductivity) + (B3 * Number of fins) + (B4 * Fin length) + (B5 * Heat spreader thickness) + (B6 * Fin spacing))) (III) H3 = Tanh(0.5 * (B0 + (B1 * Power level) + (B2 * Thermal conductivity) + (B3 * Number of fins) + (B4 * Fin length) + (B5 * Heat spreader thickness) + (B6 * Fin spacing))) (IV) H4 = Tanh(0.5 * (B0 + (B1 * Power level) + (B2 * Thermal conductivity) + (B3 * Number of fins) + (B4 * Fin length) + (B5 * Heat spreader thickness) + (B6 * Fin spacing))) (V) Predicted electrical component temperature = B0 + (B1 * H1) + (B2 * H2) + (B3 * H3) + (B4 * H4)

[0023] Referring back to FIG. 3, nodes 41-44 are functions of input variables 35-40. Tanh is the hyperbolic tangent function. Incorporating this hyperbolic tangent function into these formulas has been found to improve the prediction of the electrical device temperature by providing a good fit to the temperature function for the prediction curve. Each of B0, B1, B2, B3, B4, B5, and B6 is a coefficient in each formula, which may be different from each other and may be different in each of formulas (I)-(V). These coefficients are determined using a neural network platform for the training set. The data generated by the above-described CFD simulation is randomly divided between the training set and the validation set. This approach has been found to avoid overfitting and select a better prediction model. H1, H2, H3, and H4 are the outputs of each of formulas (I)-(IV). Formula V then uses outputs H1-H4 to obtain the predicted value of the electrical device temperature. The coefficients B0-B5 in each formula, and the resulting electrical device temperature, will vary slightly when the model is run again because the training set and the validation set are randomly selected.

[0024] Using a neural network for these calculations provides a significant benefit over the prior art. By using a neural network, it is possible to predict the electrical device temperature, or the heat sink design, much faster than when performing a CFD simulation in the conventional manner.

[0025] Referring to FIGS. 4-6, various non-limiting comparisons and user interfaces are shown that are displayed on a computing device for the steps of the system and method for generating a heat sink design. It will be understood that other arrangements of the user interface can also be used for generating the heat sink design.

[0026] Referring to FIG. 4, a first comparison and user interface 50 is shown. This first comparison and user interface 50 can be generated after a user inputs one or more physical dimensions, the power level of an electrical element, and the thermal conductivity of a heat sink material, and the temperature of the electrical element is calculated using the system described herein. In another embodiment, the first comparison and user interface 50 can be generated after a user inputs the power level of an electrical element and the maximum allowable application temperature of the electrical element, and the predicted heat sink design is calculated using the steps of the system or method described herein. In another embodiment, the first comparison and user interface 50 can be generated after one or more of the inputs are changed as described herein. The user can select the first comparison and user interface 50 by selecting tab 51. The first comparison and user interface 50 shows a sensitivity analysis of the selection of one or more inputs with respect to the electrical element temperature (shown as the LED temperature), or another characteristic. This comparison and user interface can be shown in tabular or graphical form. The various fields can include drop-down boxes, free-form fields, radio buttons, or any type of field commonly used in a user interface. As shown in FIG. 4, the comparison and user interface can be shown as a series of line graphs, such as comparison graph 52, which shows how the electrical element temperature 53 can change when the power level 54 is changed. In this embodiment, the user can slide a point 55 on the curve 56 along the curve to reach different power levels 54 and electrical element temperatures 53. After this input is changed, the system can regenerate each of the comparison graphs, including graphs showing the effect of changes in thermal conductivity 57, heat spreader thickness 58, number of fins 59, fin height 60, and fin spacing 61, as well as each of the comparisons shown in FIGS. 5 and 6.

[0027] Referring to FIG. 5, a second comparison and user interface 65 is shown. The user can select the second comparison and user interface 65 by selecting tab 66. The second comparison and user interface 65 can include one or more heatmaps 67 showing the influence of the first characteristic 68 and the second characteristic 69 on the electrical element temperature 70. The electrical element temperature 70 can be shown in the heatmap as different colors depending on how many times the predicted temperature is at that combination of characteristic values shown at point 71 within the heatmap. In this embodiment, the user can click on a point within the heatmap, drag that point to a different location, simultaneously change the two input characteristics, and then update each of the heatmap 67 and the comparison graphs including the comparisons shown in FIGS. 4 and 6.

[0028] Referring to FIG. 6, a third comparison and user interface 75 is shown. The user can select the third comparison and user interface 75 by selecting tab 76. The third user comparison and interface 43 can include a comparison table 77 and one or more bar graphs 78. Each of these shows a comparison of the effects when different materials are used with respect to the temperature of the electrical element, or a comparison of the effects that the choice of material can have on performance, such as weight reduction compared to when aluminum is used. Table 77 can further include a dropdown having a list of materials that can be selected. The user can select different materials, and then each of table 77 and bar graph 78 can be updated along with the comparisons shown in FIGS. 4 and 5.

[0029] The present invention is described in detail by way of example based on what is currently considered to be the most practical and preferred embodiments. However, such details are for the purpose thereof only, and the present invention is not limited to the disclosed embodiments. On the contrary, it is understood that the present invention is intended to cover modifications and equivalent configurations within the spirit and scope of the appended claims. For example, it will be understood that the present invention assumes that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

[0030] Further embodiments or aspects are defined in the following numbered items.

[0031] Item 1. A system or method for generating a heat sink design, comprising: Receiving at least four characteristic inputs from a user via a user interface, the characteristic inputs including: (i) at least two target physical dimensions of the heat sink, (ii) at least one power level of an electrical component adjacent to the heat sink, and (iii) at least one thermal conductivity of a thermally conductive polymer composition constituting the heat sink; Calculating a predicted temperature of the electrical component based on the at least four characteristic inputs using at least one processor; Generating from zero to five first comparisons using at least one processor, each first comparison showing the impact and resulting changes of independently changing each of the at least four characteristic inputs on the predicted temperature of the electrical component; Generating a second comparison using at least one processor, showing the impact of independently changing each of the at least two physical dimensions on the predicted temperature of the electrical component; Generating a third comparison using at least one processor, showing the impact of changing the constituent material on the predicted temperature of the electrical component; Enabling the user to change at least one of the at least four characteristic inputs after each of the above comparisons has been generated. After the predicted temperature of an electrical component is shown via a user interface, the user selects a design for a heat sink having at least two target physical dimensions and constituent materials, generates design specifications for manufacturing the heat sink, A system or method including:

[0032] Item 2. A system or method for generating a heat sink design, receiving, via a user interface, (i) at least one power level of an electrical component adjacent to the heat sink, and (ii) a maximum allowable application temperature of the electrical component, using at least one processor, calculating a modified heat sink design based on the power level and maximum allowable temperature input by the user and also based on a standard heat sink design having standard physical dimensions including heat sink size, number of fins, fin height, and fin spacing, the modified heat sink design having physical dimensions changed from the standard design to allow the input maximum temperature and power level, given a fixed heat sink size and a thermal conductivity of a thermally conductive polymer composition constituting the heat sink, using at least one processor to generate from 0 to 5 first comparisons, each first comparison showing the impact of independently varying each of at least two physical dimensions, the power level input by the user, and at least one thermal conductivity of a thermally conductive polymer composition constituting the heat sink, using at least one processor to generate a second comparison showing the impact of independently varying at least two physical dimensions on the predicted temperature of the electrical component, using at least one processor to generate a third comparison showing the impact of changing the constituent material on the predicted temperature of the electrical component, after each of the above comparisons is generated, enabling the user to change at least one of the physical dimensions, power level, or thermal conductivity After the predicted temperature of the electrical element is shown via a user interface, the user selects a design of a heat sink having at least two physical dimensions and a constituent material, generates design specifications for manufacturing the heat sink, A system or method comprising:

[0033] Item 3. The system or method according to item 2, wherein the fixed heat sink size includes a fixed heat sink diameter and a fixed heat sink thickness.

[0034] Item 4. The system or method according to any one of the above, wherein the characteristic input is selected from the group consisting of the number of fins, the height of the fins, the fin pitch, and the thickness of the heat spreader.

[0035] Item 5. The system or method according to any one of the above, wherein the characteristic input includes at least three target physical dimensions of the heat sink product, and the user selects a design of a heat sink having at least three target physical dimensions and a constituent material.

[0036] Item 6. Calculating the predicted temperature of the electrical element uses a neural network model prepared using a training set and a validation set of prior CFD simulations to optimize the proposed heat sink design based on the characteristic input, or compares the proposed heat sink design and material selection with the input design parameters and optimizes the proposed heat sink design based on the predicted weight of the heat sink. The system or method according to any one of the above.

[0037] Item 7. Each of the zero to five first comparisons is selected from the group consisting of a table and a line graph. The system or method according to any one of the above.

[0038] Item 8. Using at least one processor to generate three, four, or five first comparisons, each first comparison showing the impact of independently varying each of at least four characteristic inputs on the predicted temperature of an electrical element and the resulting changes, the system or method according to any one of the above.

[0039] Item 9. The second comparison is a heat map, and the predicted temperature of the electrical element is indicated by color in the heat map, the color changing with the change in the predicted temperature, the system or method according to any one of the above.

[0040] Item 10. A user changing at least one characteristic input, recalculating using at least one processor based on the changed input, the system or method according to any one of the above further comprising.

[0041] Item 11. The user changes at least one characteristic input by moving a point in a line graph or heat map of the first comparison or the second comparison to represent the change in the at least one characteristic input, the system or method according to any one of the above.

[0042] Item 12. Regenerating at least one of the first comparison, the second comparison, and the third comparison based on the changed input, the system or method according to any one of the above further comprising.

[0043] Item 13. Further comprising transmitting design specifications for manufacturing a heat sink to an injection molding machine, a 3D printer, or an external molder, the system or method according to any one of the above.

Claims

Claim 1 A method for generating a heat sink design, comprising: Receiving, via a user interface, at least four characteristic inputs from a user, the characteristic inputs including: (i) at least two target physical dimensions of the heat sink; (ii) at least one power level of an electrical component adjacent to the heat sink; and (iii) at least one thermal conductivity of a thermally conductive polymer composition constituting the heat sink; Calculating, using at least one processor, a predicted temperature of the electrical component based on the at least four characteristic inputs; Generating, using at least one processor, zero to five first comparisons, each first comparison indicating the impact and resulting changes of independently changing each of the at least four characteristic inputs on the predicted temperature of the electrical component; Generating, using at least one processor, a second comparison indicating the impact of independently changing each of the at least two physical dimensions on the predicted temperature of the electrical component; Generating, using at least one processor, a third comparison indicating the impact of changing the constituent material on the predicted temperature of the electrical component; After each of the comparisons is generated, enabling the user to change at least one of the at least four characteristic inputs; After the predicted temperature of the electrical component is displayed via the user interface, enabling the user to select a heat sink design having at least two target physical dimensions and a constituent material; Generating design specifications for manufacturing the heat sink; A method comprising the above steps. Claim 2 The method according to claim 1, wherein the characteristic inputs are selected from the group consisting of the number of fins, the height of the fins, the fin pitch, and the thickness of the heat spreader. Claim 3 The method according to claim 1, wherein the characteristic inputs include at least three target physical dimensions of the heat sink product, and the user selects a heat sink design having at least three target physical dimensions and a constituent material. Claim 4 Calculating the predicted temperature of the electrical element using a neural network model prepared using a training set and a validation set of a prior CFD simulation to optimize the proposed heat sink design based on the characteristic inputs, the method according to claim 1.

5. Each of the zero to five first comparisons is selected from the group consisting of a table and a line graph, the method according to claim 1.

6. Including generating three, four, or five first comparisons using at least one processor, each first comparison showing the impact of independently changing each of the at least four characteristic inputs on the predicted temperature of the electrical element and the resulting changes, the method according to claim 1.

7. The second comparison is a heat map, and the predicted temperature of the electrical element is shown in color in the heat map, and the color changes with the change in the predicted temperature, the method according to claim 1.

8. The user changing at least one characteristic input; Recalculating using at least one processor based on the changed input; The method according to claim 1, further comprising.

9. The user changes the at least one characteristic input by moving a point in a line graph or a heat map of the first comparison or the second comparison to represent the change in the at least one characteristic input, the method according to claim 8.

10. Regenerating at least one of the first comparison, the second comparison, and the third comparison based on the changed input; The method according to claim 8, further comprising.

11. The method according to claim 1, further comprising transmitting the design specification for manufacturing the heat sink to an injection molding machine, a 3D printer, or an external molder.

12. A method for generating a heat sink design, comprising: Receiving, via a user interface, (i) at least one power level of an electrical element adjacent to the heat sink, and (ii) the maximum allowable application temperature of the electrical element; Using at least one processor, calculating a modified heat sink design based on the power level and the maximum allowable temperature input by the user, and also based on a standard heat sink design having standard physical dimensions including heat sink size, number of fins, fin height, and fin spacing, wherein the modified heat sink design has physical dimensions changed from the standard design to allow the input maximum temperature and power level, with a given fixed heat sink size and a given thermal conductivity of the thermally conductive polymer composition constituting the heat sink; Using at least one processor to generate from zero to five first comparisons, each first comparison indicating the impact of independently varying each of at least two physical dimensions, the power level input by the user, and at least one thermal conductivity of the thermally conductive polymer composition constituting the heat sink; Using at least one processor to generate a second comparison indicating the impact of independently varying at least two physical dimensions on the predicted temperature of the electrical element; Using at least one processor to generate a third comparison indicating the impact of changing the constituent material on the predicted temperature of the electrical element; After each of the comparisons is generated, enabling the user to change at least one of the physical dimensions, the power level, or the thermal conductivity; After the predicted temperature of the electrical element is shown via the user interface, enabling the user to select a design of the heat sink having at least two physical dimensions and a constituent material; Generating design specifications for manufacturing the heat sink; A method comprising. [

13. ] The method according to claim 12, wherein the fixed heat sink size includes a fixed heat sink diameter and a fixed heat sink thickness. [

14. ] The calculation uses a neural network prepared using a training set and a validation set of a prior CFD simulation to compare the possible heat sink designs and material selections with the input design parameters and optimize the proposed heat sink design based on the predicted weight of the heat sink, the method according to claim 12.

15. Each of the four first comparisons is selected from the group consisting of a table and a line graph, the method according to claim 12.

16. The second comparison is a heat map, and the predicted temperature of the electrical element is shown in color in the heat map, and the color changes with the change in the predicted temperature, the method according to claim 12.

17. the user changing at least one characteristic input; recalculating using at least one processor based on the changed input; The method according to claim 12, further comprising.

18. The user changes the at least one characteristic input by moving a point in the line graph or heat map of the first comparison or the second comparison to represent the change in the at least one characteristic input, the method according to claim 17.

19. regenerating at least one of the first comparison, the second comparison, and the third comparison based on the changed input; The method according to claim 17, further comprising.

20. The method according to claim 12, further comprising transmitting the design specifications for manufacturing the heat sink to an injection molding machine, a 3D printer, or an external molder.

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