A method for optimizing the flow resistance of a toothed cold plate and a low-flow-resistance toothed cold plate.

Abstract

This application relates to a method for optimizing the flow resistance of a toothed cold plate and a low-flow-resistance toothed cold plate. The method includes: acquiring first parameters of a first toothed cold plate and inputting them into a finite element simulation system, and outputting a first calculation result; determining optimization constraints based on the first calculation result, and obtaining a second calculation result according to Murray's law, a precise heat dissipation strategy, and the optimization constraints; calculating a third calculation result based on the second calculation result and the first calculation result, and filtering the third calculation result according to a filtering rule; and generating an optimized low-flow-resistance toothed cold plate based on a target optimization parameter combination in response to detecting that the optimized parameter combination corresponding to the filtered result conforms to a preset rule. This application combines Murray's law and a precise heat dissipation strategy to optimize the flow resistance of the toothed cold plate, effectively reducing flow resistance while ensuring efficient heat dissipation. Compared with other low-flow-resistance methods, this application has the advantages of convenient processing and low cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cold plate with shoveling teeth, in particular to a flow resistance optimization method of cold plate with shoveling teeth and low flow resistance cold plate with shoveling teeth. BACKGROUND

[0002] With the continuous improvement of chip power density, the traditional air cooling cooling method has reached the limit of its heat dissipation capacity, and cannot solve the problem of chip cooling in higher heat flux density scenarios, so liquid cooling has become the trend of server chip cooling. The current mainstream liquid cooling methods are cold plate liquid cooling and immersion liquid cooling, but the cooling liquid cost, compatibility and other problems of immersion liquid cooling to some extent limit the large-scale popularization and application of this technology, and the cold plate liquid cooling becomes the choice of many liquid cooling manufacturers due to its controllable cost, relatively mature technology and other advantages, and has realized large-scale commercial application.

[0003] In the current cold plate liquid cooling solution, it is mainly composed of a system cold source, a CDU (cold liquid distribution device), an end cold plate liquid cooling cabinet, and a primary side and a secondary side loop. With the batch application of cold plate liquid cooling, the problem of cold plate liquid leakage occurs from time to time, resulting in chip failure or even burning. The negative pressure cold plate liquid cooling technology can well solve the above problems, and has become one of the hotspots of liquid cooling technology research in recent years. For the negative pressure cold plate liquid cooling scheme, the pressure regulating range should be as low as possible. The current mainstream cold plate form is a cold plate with shoveling teeth and straight channels. This type of cold plate has the characteristics of high heat exchange efficiency, but due to the dense arrangement of the channels, the flow resistance is high. The current conventional cold plate has too high flow resistance, which cannot meet the application of the negative pressure cold plate liquid cooling scheme. Therefore, the structure should be designed reasonably to further reduce the flow resistance.

[0004] The prior art usually uses some bionics theory to design the flow channel to reduce the flow resistance, but the cold plate with complex process has high cost, and the heat exchange effect of the cold plate is poorer than that of the cold plate with shoveling teeth. Although the existing conventional cold plate with shoveling teeth has mature manufacturing technology and good heat exchange effect, the problem of high flow resistance has not been effectively solved. Therefore, it is necessary to establish a flow resistance optimization method of cold plate with shoveling teeth to optimize the flow resistance of the cold plate. SUMMARY

[0005] Therefore, it is necessary to provide a flow resistance optimization method of cold plate with shoveling teeth and low flow resistance cold plate with shoveling teeth, which can simultaneously reduce the resistance and improve the heat exchange efficiency.

[0006] In one aspect, a flow resistance optimization method of cold plate with shoveling teeth is provided, and the method comprises:

[0007] Step A: obtaining the first parameters of the first cold plate with shoveling teeth and inputting them into a finite element simulation system to output the first calculation results;

[0008] Step B: determining optimization constraints based on the first calculation result, obtaining a second calculation result according to Murray's law, a precise heat dissipation strategy and the optimization constraints;

[0009] Step C: calculating a third calculation result based on the second calculation result and the first calculation result, and screening the third calculation result according to a screening rule;

[0010] Step D: in response to detecting that an optimization parameter combination corresponding to a screening result meets a preset rule, generating an optimized low-flow-resistance cold board of a shovel tooth according to a target optimization parameter combination.

[0011] In one of the embodiments, the first parameters of the first cold board of the shovel tooth include structure parameters, given heat source parameters and cooling medium parameters, the structure parameters include three-dimensional dimensions of an outer contour of the cold board of the shovel tooth, thickness of the tooth, tooth spacing and tooth length, the given heat source parameters include heat source size and heat source heat dissipation, and the cooling medium parameters include medium type, medium physical parameters, medium flow and temperature parameters, the first parameters of the first cold board of the shovel tooth are input into a finite element simulation system, and the first calculation result is obtained by calculation using the finite element simulation system, the first calculation result includes fluid resistance R f and temperature Tc of a surface of the given heat source.

[0012] In one of the embodiments, the optimization of the tooth channel size of the first cold board of the shovel tooth based on the Murray's law includes: dividing a straight channel of a target heat exchange unit into dense channels and sparse channels, defining an equivalent diameter of the dense channels as D1, defining an equivalent diameter of the sparse channels as D2, and defining a spacing of a shared channel generated by two sparse channels of the target heat exchange unit and an adjacent heat exchange unit as 2D2, the relationship between D1 and D2 satisfies the Murray's law: D1 3 = 2D2 3 , and the relative positions between the sparse channels and the dense channels are optimized based on the relationship that D1 and D2 need to satisfy.

[0013] In one of the embodiments, the precise heat dissipation strategy includes: the center of the dense channel is coincided with the center of the given heat source, and the sparse channel is arranged on one side or both sides of the dense channel.

[0014] In one of the embodiments, further comprising: the obtaining the second calculation result according to the Murray law, the precise heat dissipation strategy and the optimization constraint condition comprises: obtaining the lengths of the dense channel and the sparse channel respectively, calculating the dense-sparse channel length ratio λ, and the calculation formula is λ=L1 / L2, wherein L1 represents the length of the dense channel, and L2 represents the length of the sparse channel; obtaining the channel spacing δ; combining the parameter values corresponding to the dense-sparse channel length ratio λ and the channel spacing δ two by two to generate a plurality of parameter combinations; performing finite element simulation calculation on the target parameter combination to obtain the fluid resistance R of the second toothed cold plate f ′ and the temperature T c ′ of the given heat source surface; and screening out the target parameter combination that meets the optimization constraint condition as the second calculation result.

[0015] In one of the embodiments, further comprising: the determining the optimization constraint condition based on the first calculation result comprises: defining that the fluid resistance R f ′ of the optimized second toothed cold plate is less than the fluid resistance R f of the first toothed cold plate; and defining that the temperature T c ′ of the given heat source surface of the optimized second toothed cold plate is less than Tc+m, wherein m represents the allowable temperature rise.

[0016] In one of the embodiments, further comprising: the calculating the third calculation result based on the second calculation result and the first calculation result, and screening the third calculation result according to the screening rule comprises: calculating the drag reduction capability coefficient η based on the fluid resistance R f of the first toothed cold plate and the temperature Tc of the given heat source surface, the fluid resistance R f ′ of the second toothed cold plate and the temperature T c ′ of the given heat source surface, and the calculation formula is:

[0017] η=|R f ′-R f | / |T c ′-T c |

[0018] Screening out the parameter combination corresponding to the maximum drag reduction capability coefficient η and outputting.

[0019] In one of the embodiments, further comprising: in response to detecting that the optimization parameter combination corresponding to the screening result does not meet the preset rule, modifying the structure parameters of the toothed cold plate, and repeatedly performing the flow resistance optimization step until the obtained optimization parameter combination meets the preset rule.

[0020] On the other hand, a low-flow-resistance toothed cold plate is provided, and the low-flow-resistance toothed cold plate comprises a dense channel, a sparse channel and a dense channel.

[0021] The sparse channel and the dense channel are staggered in the shunt channel, and the sparse channel is arranged on one side or both sides of the dense channel;

[0022] The center of the dense channel and the center of a given heat source are arranged in coincidence, and an interface material is arranged between the given heat source and the low-flow-resistance cold plate.

[0023] In one of the embodiments, the dense channel section adopts a Y-shaped tooth sheet, the Y-shaped tooth sheet is divided into a first-stage tooth sheet and a second-stage tooth sheet, and the cross-sectional diameter relationship of the first-stage tooth sheet and the second-stage tooth sheet satisfies Murray's law, that is, S1 3 = 2S2 3 , wherein S1 represents the cross-sectional diameter of the first-stage tooth sheet, and S2 represents the cross-sectional diameter of the second-stage tooth sheet.

[0024] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0025] Step A: obtaining first parameters of a first cold plate and inputting the first parameters into a finite element simulation system, and outputting first calculation results;

[0026] Step B: determining optimization constraints based on the first calculation results, and obtaining second calculation results according to Murray's law, a precise heat dissipation strategy, and the optimization constraints;

[0027] Step C: obtaining third calculation results based on the second calculation results and the first calculation results, and screening the third calculation results according to a screening rule;

[0028] Step D: in response to detecting that an optimization parameter combination corresponding to a screening result meets a preset rule, generating an optimized low-flow-resistance cold plate according to a target optimization parameter combination.

[0029] In another aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0030] Step A: obtaining first parameters of a first cold plate and inputting the first parameters into a finite element simulation system, and outputting first calculation results;

[0031] Step B: determining optimization constraints based on the first calculation results, and obtaining second calculation results according to Murray's law, a precise heat dissipation strategy, and the optimization constraints;

[0032] Step C: obtaining third calculation results based on the second calculation results and the first calculation results, and screening the third calculation results according to a screening rule;

[0033] Step D: in response to detecting that the optimization parameter combination corresponding to the screening result meets the preset rule, generating an optimized low-flow-resistance cold board of the cutting tooth according to the target optimization parameter combination.

[0034] The flow resistance optimization method of the cold board of the cutting tooth, the low-flow-resistance cold board of the cutting tooth, the computer device and the storage medium, the method comprises: obtaining a first parameter of a first cold board of a cutting tooth and inputting the first parameter into a finite element simulation system, outputting a first calculation result; determining an optimization constraint condition based on the first calculation result, obtaining a second calculation result according to the Murray law, a precise heat dissipation strategy and the optimization constraint condition; obtaining a third calculation result based on the second calculation result and the first calculation result, and screening the third calculation result according to a screening rule; in response to detecting that the optimization parameter combination corresponding to the screening result meets the preset rule, generating an optimized low-flow-resistance cold board of the cutting tooth according to the target optimization parameter combination. The present application combines the Murray law and the precise heat dissipation strategy to realize the flow resistance optimization of the cold board of the cutting tooth, effectively reduces the flow resistance under the condition of ensuring efficient heat dissipation, and has the advantages of convenient processing and low cost compared with other low-flow-resistance methods. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is an application environment diagram of the flow resistance optimization method of the cold board of the cutting tooth in an embodiment;

[0036] Figure 2 It is a flowchart of the flow resistance optimization method of the cold board of the cutting tooth in an embodiment;

[0037] Figure 3 It is a channel size calculation diagram of the flow resistance optimization step of the cold board of the cutting tooth in an embodiment;

[0038] Figure 4 It is a structure diagram of the low-flow-resistance cold board of the cutting tooth in an embodiment;

[0039] Figure 5 It is an internal structure diagram of the computer device in an embodiment.

[0040] In the figure: 1, shunt channel; 2, sparse channel; 3, dense channel; 4, given heat source; 5, interface material; 6, secondary tooth piece; 7, primary tooth piece. DETAILED DESCRIPTION

[0041] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0042] It should be understood that in the description of the present application, unless the context clearly requires otherwise, the terms "comprise", "comprising", and the like in the specification and claims should be interpreted as including the meaning of "including but not limited to".

[0043] It should also be understood that the terms "first", "second" and the like are used only for descriptive purposes and should not be construed as indicating or implying relative importance. In addition, in the description of the present application, the meaning of "multiple" is two or more, unless otherwise stated.

[0044] It should be noted that the terms "S1", "S2" and the like are only used for the purpose of describing the steps and do not specifically refer to the order or sequence, nor are they used to limit the present application. They are only used to facilitate the description of the method of the present application and should not be construed as indicating the order of the steps. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it. When the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist and is not within the scope of protection claimed by the present application.

[0045] The flow resistance optimization method of the cold board provided by the present application can be applied to the application environment as shown in Figure 1 . Among them, the terminal 102 communicates with the data processing platform set on the server 104 through the network, wherein the terminal 102 can be but is not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server 104 can be realized by an independent server or a server cluster composed of multiple servers.

[0046] Embodiment 1

[0047] In one embodiment, as shown in Figures 2-3 , a flow resistance optimization method of a cold board is provided. Taking the terminal in Figure 1 as an example, the method includes the following steps:

[0048] S1: Obtain the first parameter of the first cold board and input it into the finite element simulation system, and output the first calculation result.

[0049] It should be noted that the finite element simulation system is a computer and its supporting equipment used in the field of mechanics, physics, material science and computer science technology, and in the present application, the system is used for simulating and calculating the related parameters of the cold plate of the gullet, wherein the first parameters of the first cold plate of the gullet include structural parameters, given heat source parameters and cooling medium parameters, the structural parameters include the three-dimensional size of the outer contour of the cold plate of the gullet, the thickness of the gullet, the gullet spacing and the gullet length, the given heat source parameters include the heat source size and the heat source heat dissipation, and the cooling medium parameters include the medium type, the medium physical parameters (such as thermal conductivity, density, heat capacity, viscosity, etc.), the medium flow, and the temperature parameter. The above first cold plate of the gullet is the original cold plate of the gullet without optimization, and in the subsequent optimization process, only the structural parameters (gullet thickness, spacing and length) of the original cold plate of the gullet are optimized and adjusted, and other parameters remain unchanged. Further, the first parameters of the first cold plate of the gullet are input into the finite element simulation system, and the first calculation result is obtained by simulating and calculating by using the finite element simulation system, wherein the first calculation result includes the fluid resistance R f of the first cold plate of the gullet and the temperature Tc of the surface of the given heat source.

[0050] S2: determining the optimization constraint condition based on the first calculation result, and obtaining the second calculation result according to the Murray law, the accurate heat dissipation strategy and the optimization constraint condition.

[0051] It should be noted that the fluid resistance R f of the first cold plate of the gullet and the temperature Tc of the surface of the given heat source calculated according to step S1 determine the constraint condition that needs to be met in the subsequent optimization process:

[0052] (1) defining that the fluid resistance R f ′ of the second cold plate of the gullet after optimization is less than the fluid resistance R f of the first cold plate of the gullet;

[0053] (2) defining that the temperature T c ′ of the surface of the given heat source of the second cold plate of the gullet after optimization is less than Tc+m, wherein m represents the allowable temperature rise, which can be limited according to actual requirements.

[0054] Further, the gullet channel size of the first cold plate of the gullet is optimized according to the Murray law, and for example, as shown in Figure 3 , the straight channel of the target heat exchange unit is divided into dense channels and sparse channels, the equivalent diameter of the dense channel is defined as D1, the equivalent diameter of the sparse channel is defined as D2, and the spacing of the common channel generated by the two sparse channels of the target heat exchange unit and the adjacent heat exchange unit is 2D2, and the relationship between D1 and D2 satisfies the Murray law: D1 3 =2D2 3The relative positions between the sparse channels and the dense channels are optimized based on the relationship that D1 and D2 need to satisfy.

[0055] Furthermore, the precise heat dissipation strategy includes:

[0056] Based on the location of the given heat source, the center of the dense channel is aligned with the center of the given heat source, and the sparse channel is set on one or both sides of the dense channel. If the sparse channel is set on both sides of the dense channel, the sparse channels on both sides can be symmetrically or asymmetrically set, and can be reasonably adjusted according to the given heat source.

[0057] The second calculation result obtained based on Murray's law, the precise heat dissipation strategy, and the aforementioned optimization constraints includes:

[0058] Obtain the lengths of the dense channel and the sparse channel after size optimization, respectively, and calculate the ratio of the dense channel lengths to the sparse channel lengths λ. The calculation formula is λ = L1 / L2, where L1 represents the length of the dense channel and L2 represents the length of the sparse channel.

[0059] Obtain the channel spacing δ;

[0060] The parameter values ​​corresponding to the density-sparse channel length ratio λ and the channel spacing δ are combined in pairs to generate multiple parameter combinations, such as {λ1,δ1}, {λ1,δ2}, {λ2,δ1}, etc.

[0061] The fluid resistance R of the second shovel-tooth cold plate was obtained by performing finite element simulation calculations on the combination of target parameters. f ′ and the given temperature T of the heat source surface c ′;

[0062] The combination of target parameters that meets the optimization constraints is the second calculation result.

[0063] S3: Calculate and obtain a third calculation result based on the second calculation result and the first calculation result, and filter the third calculation result according to the filtering rules.

[0064] It should be noted that this step specifically involves: based on the fluid resistance R of the first shovel-tooth cold plate f Given the temperature Tc of the heat source surface and the fluid resistance R of the second shovel-toothed cold plate f ′ and the given temperature T of the heat source surface c The drag reduction coefficient η is calculated using the following formula:

[0065] η=|R f ′-R f | / |T c ′-T c |

[0066] The parameter combination corresponding to the maximum drag reduction coefficient η is screened out and output.

[0067] S4: In response to detecting that the optimization parameter combination corresponding to the screening result meets the preset rule, generating an optimized low-flow-resistance cold board of the cutting tooth according to the target optimization parameter combination.

[0068] It should be noted that this step is performed after the flow resistance optimization step, and checks whether the tooth thickness and tooth spacing output meet the manufacturing requirements. If they meet the requirements, the optimized second cold board of the cutting tooth is output, and if they do not meet the requirements, the structure parameters of the cold board of the cutting tooth are modified, and the flow resistance optimization step is repeatedly performed until the optimization parameter combination meets the preset rule.

[0069] In the flow resistance optimization method of the cold board of the cutting tooth, the method comprises: obtaining first parameters of a first cold board of a cutting tooth and inputting the first parameters into a finite element simulation system to output first calculation results; determining optimization constraints based on the first calculation results, obtaining second calculation results according to the Murray law, the accurate heat dissipation strategy and the optimization constraints; obtaining third calculation results based on the second calculation results and the first calculation results, and screening the third calculation results according to a screening rule; and in response to detecting that an optimization parameter combination corresponding to a screening result meets a preset rule, generating an optimized low-flow-resistance cold board of the cutting tooth according to the target optimization parameter combination. The present application combines the Murray law and the accurate heat dissipation strategy, and can simultaneously consider drag reduction and efficient heat exchange. The drag reduction coefficient is used to represent the optimization effect, so as to ensure that the optimized cold board has high drag reduction efficiency, realize flow resistance optimization of the cold board of the cutting tooth, effectively reduce the flow resistance under the condition of ensuring efficient heat dissipation, and has the advantages of convenient processing and low cost compared with other low-flow-resistance methods (such as bionic channels).

[0070] It should be understood that, although Figure 2 The steps in the flowchart of the method are displayed in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps in the method can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0071] Example 2

[0072] In one embodiment, as Figure 4As shown, a low-flow-resistance cold board of a shovel tooth is provided, wherein:

[0073] The low-flow-resistance cold board of the shovel tooth comprises a shunt channel 1, a sparse channel 2 and a dense channel 3; the sparse channel 2 and the dense channel 3 are arranged in the shunt channel 1 in a staggered manner, and the sparse channel 2 is arranged on one side or both sides of the dense channel 3.

[0074] The center of the dense channel 3 and the center of a given heat source 4 are arranged in coincidence, and an interface material 5 is arranged between the low-flow-resistance cold board of the shovel tooth and the given heat source 4 to reduce the contact thermal resistance.

[0075] The length ratio of the dense channel and the sparse channel and the spacing between the dense channel and the sparse channel in the embodiment are determined by the flow resistance optimization process in Embodiment 1, and will not be repeated here. During normal operation, the working medium enters the sparse-dense-sparse channel through the shunt channel 1 in sequence, and the sparse-dense channel is arranged in a staggered manner, which can play a role in flow disturbance to enhance heat exchange.

[0076] Further, the dense channel section adopts a Y-shaped tooth design to reduce the conduction thermal resistance, and the Y-shaped tooth is divided into a primary tooth 7 and a secondary tooth 6, and the cross-sectional diameter relationship of the primary tooth 7 and the secondary tooth 6 satisfies the Murray law, that is, S1 3 =2S2 3 Wherein, S1 represents the cross-sectional diameter of the primary tooth, and S2 represents the cross-sectional diameter of the secondary tooth, so as to ensure that the heat transfer resistance is small.

[0077] The present application designs a low-flow-resistance cold board of a shovel tooth based on an optimization method, and further designs a two-stage bifurcated tooth based on the Murray law to reduce the heat transfer resistance and further improve the heat exchange efficiency.

[0078] Embodiment 3

[0079] In one embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 5As shown in the figure. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The network interface of the computer device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to implement a flow resistance optimization method of a cold plate of a tooth. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0080] Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0081] In one embodiment, a computer device is provided, including a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the computer program:

[0082] S1: Obtain the first parameter of the first cold plate of the tooth and input it into the finite element simulation system, and output the first calculation result;

[0083] S2: Determine the optimization constraint condition based on the first calculation result, and obtain the second calculation result according to Murray's law, the accurate heat dissipation strategy and the optimization constraint condition;

[0084] S3: Calculate the third calculation result based on the second calculation result and the first calculation result, and screen the third calculation result according to the screening rule;

[0085] S4: In response to detecting that the optimization parameter combination corresponding to the screening result meets the preset rule, generating an optimized low-flow-resistance cold plate of tooth according to the target optimization parameter combination.

[0086] In one embodiment, the processor further implements the following steps when executing the computer program:

[0087] The first parameters of the first gullet cold plate include structure parameters, given heat source parameters and cooling medium parameters, the structure parameters include gullet cold plate outer contour three-dimensional size, tooth thickness, tooth spacing, tooth length, the given heat source parameters include heat source size, heat source heat dissipation, and the cooling medium parameters include: medium type, medium physical parameters, medium flow, temperature parameters, the first parameters of the first gullet cold plate are input into a finite element simulation system, and a first calculation result is obtained by calculation of the finite element simulation system, the first calculation result includes fluid resistance R f of the first gullet cold plate and temperature Tc of the surface of the given heat source.

[0088] In one embodiment, the processor also implements the following steps when executing the computer program:

[0089] The straight channels of the target heat exchange unit are divided into dense channels and sparse channels, the equivalent diameter of the dense channels is defined as D1, the equivalent diameter of the sparse channels is defined as D2, and the spacing of the common channels generated by the two sparse channels of the target heat exchange unit and the adjacent heat exchange unit is 2D2, the relationship between D1 and D2 satisfies the Murray law: D1 3 = 2D2 3 , and the relative positions between the sparse channels and the dense channels are optimized based on the relationship that D1 and D2 need to satisfy.

[0090] In one embodiment, the processor also implements the following steps when executing the computer program:

[0091] The center of the dense channel is coincided with the center of the given heat source, and the sparse channel is arranged on one side or both sides of the dense channel.

[0092] In one embodiment, the processor also implements the following steps when executing the computer program:

[0093] The lengths of the dense channel and the sparse channel are respectively obtained, and the dense-sparse channel length ratio λ is calculated, and the calculation formula is λ = L1 / L2, wherein L1 represents the length of the dense channel, and L2 represents the length of the sparse channel.

[0094] The channel spacing δ is obtained.

[0095] The parameter values corresponding to the dense-sparse channel length ratio λ and the channel spacing δ are combined two by two to generate a plurality of parameter combinations.

[0096] The target parameter combination is subjected to finite element simulation calculation to obtain the fluid resistance R f ′ of the second gullet cold plate and the temperature T c ′ of the surface of the given heat source.

[0097] The target parameter combination meeting the optimization constraint condition is screened out, that is, the second calculation result.

[0098] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0099] Defining the fluid resistance R of the optimized second gullet cold plate f ′ is less than the fluid resistance R of the first gullet cold plate f ;

[0100] Defining the temperature T of the given heat source surface of the optimized second gullet cold plate c ′ is less than Tc+m, where m represents the allowable temperature rise.

[0101] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0102] Based on the fluid resistance R of the first gullet cold plate f and the temperature Tc of the given heat source surface, the fluid resistance R f ′ of the second gullet cold plate and the temperature T c ′ of the given heat source surface, the drag reduction capability coefficient η is calculated, and the calculation formula is:

[0103] η = | R f ′-R f | / |T c ′-T c |

[0104] The parameter combination corresponding to the maximum drag reduction capability coefficient η is screened out and output.

[0105] In one embodiment, the processor, when executing the computer program, also implements the following steps:

[0106] In response to detecting that the optimized parameter combination corresponding to the screening result does not meet the preset rule, the structure parameters of the gullet cold plate are modified, and the flow resistance optimization step is repeatedly executed until the optimized parameter combination meets the preset rule.

[0107] Embodiment 4

[0108] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0109] S1: Obtain the first parameters of the first gullet cold plate and input them into the finite element simulation system, and output the first calculation result;

[0110] S2: Determine the optimization constraint condition based on the first calculation result, and obtain the second calculation result according to the Murray law, the accurate heat dissipation strategy and the optimization constraint condition;

[0111] S3: calculating a third calculation result based on the second calculation result and the first calculation result, and screening the third calculation result according to a screening rule;

[0112] S4: in response to detecting that the optimization parameter combination corresponding to the screening result meets a preset rule, generating an optimized low-flow-resistance cold board according to the target optimization parameter combination.

[0113] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0114] The first parameters of the first cold board include structure parameters, given heat source parameters and cooling medium parameters. The structure parameters include the three-dimensional size of the outer contour of the cold board, the thickness of the tooth, the tooth spacing, and the tooth length. The given heat source parameters include the heat source size and the heat source thermal power consumption. The cooling medium parameters include the medium type, the medium physical parameters, the medium flow, and the temperature parameters. The first parameters of the first cold board are input into the finite element simulation system, and the first calculation result is obtained by calculation using the finite element simulation system. The first calculation result includes the fluid resistance R f and the temperature Tc of the surface of the given heat source.

[0115] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0116] The straight channels of the target heat exchange unit are divided into dense channels and sparse channels. The equivalent diameter of the dense channels is defined as D1, and the equivalent diameter of the sparse channels is defined as D2. The spacing of the common channel generated by the two sparse channels of the target heat exchange unit and the adjacent heat exchange unit is 2D2. The relationship between D1 and D2 satisfies the Murray law: D1 3 = 2D2 3 The relative positions between the sparse channels and the dense channels are optimized based on the relationship that D1 and D2 need to satisfy.

[0117] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0118] The center of the dense channel is coincident with the center of the given heat source, and the sparse channel is arranged on one side or both sides of the dense channel.

[0119] In one embodiment, the computer program, when executed by the processor, further implements the following steps:

[0120] The lengths of the dense channels and the sparse channels are obtained respectively, and the dense-sparse channel length ratio λ is calculated. The calculation formula is λ = L1 / L2, where L1 represents the length of the dense channel, and L2 represents the length of the sparse channel.

[0121] acquiring a channel spacing δ;

[0122] combining the parameter values corresponding to the length ratio λ of the dense channel and the sparse channel and the channel spacing δ in pairs to generate a plurality of parameter combinations;

[0123] performing finite element simulation calculation on the target parameter combination to obtain the fluid resistance R f ′ of the second toothed cold plate c ′;

[0124] screening the target parameter combination that meets the optimization constraint condition as the second calculation result.

[0125] In one embodiment, the computer program is further implemented when executed by the processor to perform the following steps:

[0126] defining the fluid resistance R f ′ of the second toothed cold plate after optimization to be less than the fluid resistance R f of the first toothed cold plate.

[0127] defining the temperature T c ′ of the given heat source surface of the second toothed cold plate after optimization to be less than Tc+m, wherein m represents the allowable temperature rise.

[0128] In one embodiment, the computer program is further implemented when executed by the processor to perform the following steps:

[0129] calculating a drag reduction capability coefficient η based on the fluid resistance R f of the first toothed cold plate and the temperature Tc of the given heat source surface, the fluid resistance R f ′ of the second toothed cold plate and the temperature T c ′ of the given heat source surface, and the formula is:

[0130] η = |R f ′-R f | / |T c ′-T c |

[0131] screening the parameter combination corresponding to the maximum drag reduction capability coefficient η and outputting.

[0132] In one embodiment, the computer program is further implemented when executed by the processor to perform the following steps:

[0133] In response to detecting that the optimization parameter combination corresponding to the screening result does not meet the preset rule, modifying the structure parameters of the toothed cold plate, and repeating the flow resistance optimization step until the optimization parameter combination meets the preset rule.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0135] The technical features of the above embodiments can be combined in any way. In order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0136] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method of flow resistance optimization of a chipped cold plate, characterized by, The method comprises: obtaining a first parameter of the first cold board and inputting into a finite element simulation system, outputting a first calculation result, the first calculation result including fluid resistance through the first cold board R f and the temperature of the surface of the given heat source Tc ; Determine optimization constraints based on the first calculation result, obtain a second calculation result according to Murray's law, a precise heat dissipation strategy and the optimization constraints, the second calculation result is a target parameter combination that meets the optimization constraints, the target parameter combination is determined by two-by-two combination of parameter values corresponding to the dense channel length ratio λ and the channel spacing δ, the dense channel length ratio λ is determined by the calculation formula λ=L1 / L2, wherein L1 represents the dense channel length, and L2 represents the sparse channel length; Based on the second calculation result and the first calculation result, a third calculation result is calculated and obtained, and the third calculation result is screened according to a screening rule; In response to detecting that the optimization parameter combination corresponding to the screening result meets the preset rule, an optimized low-flow-resistance cold board is generated according to the target optimization parameter combination. The method comprises: Defining fluid resistance of an optimized second cold board R f ´ Less than fluid resistance of the first cold board R f ; defining a temperature of a given heat source surface of the optimized second cold shoveller panel T c ´ less than Tc+m wherein m represents the allowable temperature rise, Tc T1 represents a temperature of a given heat source surface of the first cold shoveller panel; The method comprises: fluid resistance of the first gullet cold plate R f temperature of a given heat source surface Tc fluid resistance of the second gullet cold plate R f ´ temperature of a given heat source surface T c ´ calculating the drag reduction capability coefficient η whose formula is η=|R f ´-R f | / |T c ´-T c | The maximum drag reduction capability coefficient is screened out η The corresponding parameter combination is output.

2. The method of flow resistance optimization of a cold board of a chipped tooth according to claim 1, characterized in that, The first parameters of the first cold board include structure parameters, given heat source parameters and cooling medium parameters, the structure parameters include cold board outer contour three-dimensional size, tooth thickness, tooth spacing and tooth length, the given heat source parameters include heat source size and heat source heat dissipation, and the cooling medium parameters include medium type, medium physical parameters, medium flow and temperature parameters.

3. The method of flow resistance optimization of a cold board of a chipped tooth according to claim 1, characterized in that, Based on the Murray's law, the size of the tooth channel of the first cold board is optimized, which comprises: The straight channels of a target heat exchange unit are divided into dense channels and sparse channels, the equivalent diameter of the dense channels is defined as D1, the equivalent diameter of the sparse channels is defined as D2, the distance between the common channels generated by the two sparse channels of the target heat exchange unit and adjacent heat exchange units is defined as 2D2, the relationship between D1 and D2 satisfies the Murray law: D1 3 =2D2 3 , and the relative position between the sparse channels and the dense channels is optimized based on the relationship required to be satisfied by D1 and D2.

4. The method of flow resistance optimization of a cold board of a chipped tooth according to claim 3, characterized in that, The precise heat dissipation strategy comprises: The center of the dense channel is coincided with the center of the given heat source, and the sparse channel is arranged on one side or both sides of the dense channel.

5. The method of flow resistance optimization of a cold board of a chipped tooth according to claim 4, characterized in that, The method comprises: The length of the dense channel and the length of the sparse channel are obtained respectively, the dense channel length ratio λ is calculated, and the calculation formula is λ=L1 / L2, wherein L1 represents the dense channel length, and L2 represents the sparse channel length; The channel spacing δ is obtained; The parameter values corresponding to the dense channel length ratio λ and the channel spacing δ are two-by-two combined to generate a plurality of parameter combinations; The target parameter combination is calculated by finite element simulation to obtain fluid resistance of the second cold board R f ´ and the temperature of the surface of the given heat source T c ´ ; The target parameter combination that meets the optimization constraints is screened out, that is, the second calculation result.

6. The method of flow resistance optimization of a cold board of a chipped tooth according to claim 1, characterized in that, The method further comprises: In response to detecting that the optimization parameter combination corresponding to the screening result does not meet the preset rule, the structure parameters of the cold board are modified, and the flow resistance optimization step is repeatedly executed until the obtained optimization parameter combination meets the preset rule.

7. A low flow resistance cold board of a pick tooth according to the flow resistance optimization method of claim 1-6, characterized in that, The low-flow-resistance cold board comprises a shunt channel, a sparse channel and a dense channel; The sparse channel and the dense channel are arranged in the shunt channel in a staggered manner, and the sparse channel is arranged on one side or both sides of the dense channel; The center of the dense channel is coincided with the center of the given heat source, and an interface material is arranged between the given heat source and the low-flow-resistance cold board.

8. The low-drag cold board of claim 7, wherein, The closed channel section adopts Y-shaped tooth blades, the Y-shaped tooth blades are divided into first-stage tooth blades and second-stage tooth blades, and the cross-sectional diameter relationship of the first-stage tooth blades and the second-stage tooth blades satisfies Murray's law, that is, S1 3 =2S2 3 , wherein S1 represents the cross-sectional diameter of the first-stage tooth blades, and S2 represents the cross-sectional diameter of the second-stage tooth blades.

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