An optimization method for the structure of an efficient parallel flow channel cooling system

Abstract

The present invention discloses an optimization method for the structure of an efficient parallel flow channel cooling system. The method assumes that the average temperature of all heat sources is the same, and constructs an optimization model by combining the flow resistance network model and the simplified heat transfer model. By giving the value of a certain structure parameter to be optimized, the average temperature of the heat source and the remaining structure parameters to be optimized of the system are calculated using the optimization model. Among the calculation results obtained under different selected parameter values, the structure parameters of the parallel flow channel cooling system corresponding to the lowest average temperature of the heat source are used as the optimization result. The optimization method of the present invention obtains an optimized system structure based on the optimization model characterized by algebraic equations, can significantly improve the temperature uniformity of the heat source, and has the advantages of being simple and easy to operate, having good optimization effect, strong scalability, being fast and efficient, etc.

Description

Technical Field

[0001] The present invention relates to the field of thermal control of heating elements, and particularly to an optimization method for the structure of an efficient parallel flow channel cooling system. Background Art

[0002] The parallel flow channel cooling system is a compact and efficient heat dissipation device. Heat generated by a heat source is removed through heat exchange between the cooling working fluid diverted to each parallel flow channel and the high-temperature heat source. Currently, it is widely used in the cooling of high heat flux density electronic devices, thermal management of power batteries, heat dissipation of laser diodes, cooling of nuclear reactor systems, and other fields. The structure of the system has a significant impact on the flow rate distribution of the cooling working fluid, thereby affecting the heat dissipation performance of the system. An unreasonable structure form will lead to higher hot spot temperatures and larger temperature differences in the system, making it difficult to meet the thermal control requirements of heat-generating components such as electronic devices and power batteries. Therefore, it is very necessary to design the structure of the parallel flow channel cooling system. Existing research mainly uses optimization algorithms to design the structural parameters of the parallel flow channel cooling system. For example, Chen et al. (Chen K, Chen Y M, Li Z Y, Yuan F, Wang SF. Design of the cell spacings of battery pack in parallel air-cooled battery thermal management system[J]. International Journal of Heat and Mass Transfer, 2018, 127: 393-401.) proposed a heuristic method to adjust the width of the parallel flow channels according to the heat source temperature. When used in the structural design of the Z-type parallel flow channel cooling system, the highest temperature of the heat source decreased by 3K, and the temperature difference decreased by more than 60%; Chen et al. (Chen K, Wang S F, Song M X, Chen L. Structure optimization of parallel air-cooled battery thermal management system[J]. International Journal of Heat and Mass Transfer, 2017, 111: 943-952.) used the nested Newton iteration method to optimize the inclination angle of the flow guiding plate of the Z-type parallel flow channel cooling system, reducing the temperature difference of the heat source by 45% under the same cooling flow rate; Liao et al. (Liao X P, MaC, Peng X B, Garg A, Bao N S. Temperature distribution optimization of an air-cooling lithium-ion battery pack in electric vehicles based on the response surface method[J].(Journal of Electrochemical Energy Conversion and Storage, 2019, 16: 041002.) The genetic algorithm was used to design the distribution channels, confluence channels and the widths of parallel channels of the U-shaped parallel-channel cooling system, which reduced the maximum temperature of the heat source by 2.7 K and decreased the standard deviation of temperature by 0.3 K; Liu et al. (Liu Y Z, Zhang J. Design a J-type air-based battery thermal management system through surrogate-based optimization [J]. Applied Energy, 2019, 252: 113426.) optimized the widths of the parallel channels of the Z-, U- and J-type parallel-channel cooling systems by using the genetic algorithm, effectively improving the heat dissipation performance of the system. However, the above optimization algorithms mainly adjust the system structure through empirical heuristic adjustment strategies or stochastic combination operators, making it difficult to obtain the optimal solution and hindering the further improvement of the system's heat dissipation performance. Therefore, there is still a lack of an efficient method for optimizing the structure of the parallel-channel cooling system. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the present invention provides an efficient method for optimizing the structure of a parallel-channel cooling system.

[0004] The present invention is achieved at least by one of the following technical solutions.

[0005] An efficient method for optimizing the structure of a parallel-channel cooling system includes the following steps:

[0006] S1. Given the cooling medium flow rate Q0, inlet temperature T0, heat generation rate Φ of the heat source, the size and number N of the heat sources s , and the total volume of the system, determine the number N PD of parallel channels and the total width W according to the total volume of the system, the size and number of the heat sources;

[0007] S2. Assume that the temperature distribution inside a single heat source is uniform and the average temperatures of all heat sources are the same. The heat source temperature equation is:

[0008] T s，i = T const , i = 1, 2,..., N s

[0009] where T s，i represents the average temperature of the i-th heat source, T const represents the average temperature value of the heat source to be solved, and N srepresents the number of heat sources; this equation, together with the flow resistance network model and the simplified heat transfer model, constitutes the optimization model;

[0010] S3. Select one of the structural parameters to be optimized in the optimization model as a known parameter, and traverse all values of this parameter according to a certain value range and value step; for each value, solve the optimization model to obtain the values of the remaining structural parameters to be optimized and the corresponding heat source temperature T const , and select the structural parameter result with the lowest heat source temperature as the final optimization result.

[0011] Furthermore, the optimization model includes a heat source temperature equation, a flow resistance network model, and a simplified heat transfer model.

[0012] Furthermore, the optimization model assumes that the heat source temperatures are the same but unknown, and supplements N s heat source temperature equations.

[0013] Furthermore, the flow resistance network model is as follows:

[0014] For the i-th distribution node:

[0015] Q DD，i = Q PD，i + Q DD，i+1

[0016] For the i-th confluence node:

[0017] Q CD，i = Q PD，i + Q CD，i-1

[0018] where, Q DD，i represents the cooling working fluid flow rate of the i-th distribution channel; Q PD，i represents the cooling working fluid flow rate of the i-th parallel channel; Q CD，i represents the cooling working fluid flow rate of the i-th confluence channel;

[0019] For the i-th flow loop:

[0020] ΔP loss，DD，i+1 + ΔP loss，PDi+1 - ΔP loss，CD，i - ΔP loss，PD，i = 0

[0021] ΔP loss = ΔP friction + ΔP local

[0022]

[0023]

[0024]

[0025]

[0026] Among them, ΔP loss，DD，i 、ΔP loss，PD，i and ΔP loss，CD，i respectively represent the total resistance losses of the i-th distribution channel, the i-th parallel channel, and the i-th confluence channel. ΔP friction represents the frictional resistance loss of the channel, and ΔP local represents the local resistance loss of the node. ξ and χ respectively represent the local resistance coefficient and the frictional resistance coefficient, l and D respectively represent the length and equivalent diameter of the channel, and ρ f represents the density of the cooling working fluid, U represents the average flow velocity of the cooling working fluid in the channel, and the subscripts PD, DD, and CD respectively represent the parallel channel, the distribution channel, and the confluence channel;

[0027] Furthermore, the simplified heat transfer model is:

[0028] For the i-th heat source:

[0029] Φ s，i V s，i = h CD，i S up，i ΔT up，i + h DD，i+1 S down，i ΔT down，i + h PD，i S left，i ΔT left，i + h PD，i+1 S right，i ΔT right，i

[0030] For the cooling working fluid in the i-th parallel channel:

[0031] ρ f c p，f U PD，i (T PD，i - T0)A PD，i = h PD，i S right，i-1 ΔT right，i-1 + h PD，i S left，i ΔT left，i

[0032] Among them, Φ s，i and V s，i respectively represent the heat generation rate and volume of the i-th heat source, h DD，i 、h PD，i and h CD，iThey respectively represent the convective heat transfer coefficients of the cooling working fluid in the i-th distribution channel, the i-th parallel channel, and the i-th confluence channel, and are calculated using experimental correlation formulas, S up，i 、S down，i 、S left，i and S right，i respectively represent the heat transfer areas between the i-th heat source and the confluence channel above it, the distribution channel below it, the parallel channel on the left, and the parallel channel on the right of the cooling working fluid, ΔT up，i 、ΔT down，i 、ΔT left，i 、ΔT right，i respectively represent the heat transfer temperature differences between the i-th heat source and the confluence channel above it, the distribution channel below it, the parallel channel on the left, and the parallel channel on the right of the cooling working fluid, and are calculated using the logarithmic mean temperature difference or its approximate formula, ρ f and c p，f respectively represent the density and specific heat at constant pressure of the cooling working fluid, T0 represents the inlet temperature of the cooling medium of the system, T PD，i represents the temperature of the cooling working fluid at the outlet of the i-th parallel channel, U PD，i represents the average flow velocity of the cooling working fluid in the i-th parallel channel, A PD，i represents the cross-sectional area of the i-th parallel channel.

[0033] Furthermore, the convective heat transfer coefficient h in the simplified heat transfer model is calculated using the flow and heat transfer criterion relationship:

[0034] h = (λ f / D)0.036Re 4 / 5 Pr 1 / 3 (D / l) 0.055

[0035] Among them, λ f represents the thermal conductivity of the cooling working fluid, Re and Pr represent the Reynolds number and the Prandtl number, and l and D respectively represent the length and equivalent diameter of the current channel.

[0036] Furthermore, in the step S3, any values can be taken for the selected structural parameters to be traversed.

[0037] Furthermore, the structural parameters to be optimized include the width distribution of the parallel channels, the width distribution of the distribution channels, or the width distribution of the confluence channels.

[0038] Furthermore, if the optimization parameter is the width distribution of the parallel channels in the system, the equation also needs to be supplemented:

[0039]

[0040] Among them, i represents the serial number of the parallel channel, N PD represents the number of parallel channels, w PD，irepresents the width of the i-th parallel flow channel, and W represents the total width of the parallel flow channels.

[0041] Furthermore, an iterative method is used to solve the algebraic equations that make up the optimization model.

[0042] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0043] 1. In the implementation process of the optimization method provided by the present invention, it includes two key technical steps: one is to give the value range and value step size of the structure parameters to be traversed, solve the optimization model at each value to obtain the values of the remaining structure parameters to be optimized and the corresponding heat source temperature; the other is to compare the heat source temperatures obtained at different values, and take the structure parameter result with the lowest heat source temperature as the final optimization result; the optimization process does not contain complex calculation methods and has the advantages of being simple and easy to operate.

[0044] 2. The computational complexity of the optimization method provided by the present invention comes from the solution of the optimization model, which is composed of algebraic equations. The time required for one solution is only a few seconds. Therefore, the present invention can quickly obtain the optimized system structure and has the advantages of being fast and efficient.

[0045] 3. The optimization method provided by the present invention directly obtains the structure parameters of the system by using the assumption of uniform heat source temperature distribution. The implementation idea is novel. It is not necessary to adjust the system structure according to the combination operator or empirical adjustment strategy, and can effectively improve the uniformity of the heat source temperature distribution, having the advantage of good optimization effect.

[0046] 4. The optimization method provided by the present invention only involves the velocity field and temperature field of the system, and has nothing to do with the physical properties of the working medium, the flow rate of the working medium, the environmental temperature, the physical properties of the heat source, the size of the heat source, the number of heat sources, and the heat generation rate of the heat source. Therefore, the present invention can be extended to the solution of similar problems and has the advantage of strong scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a flowchart of an efficient parallel flow channel cooling system structure optimization method according to an embodiment of the present invention;

[0048] Figure 2 is a front view of a parallel flow channel cooling system according to an embodiment of the present invention;

[0049] Figure 3 is a front view of a Z-shaped battery thermal management system according to Embodiments 1 and 2 of the present invention;

[0050] Figure 4 is a temperature distribution diagram of a Z-shaped battery thermal management system according to Embodiment 2 of the present invention;

[0051] Figure 5 is a front view of a U-shaped battery thermal management system according to Embodiment 3 of the present invention;

[0052] Figure 6 This is the temperature distribution diagram of the U-shaped battery thermal management system in Embodiment 3 of the present invention. Detailed implementation manners

[0053] The present invention will be further described in detail below in conjunction with embodiments and the accompanying drawings, but the implementation manners of the present invention are not limited thereto.

[0054] Embodiment 1

[0055] This embodiment considers a Z-shaped parallel flow channel cooling system as shown in Figure 3 and optimizes the width of the parallel flow channels of the system. The heat source cooled by the system is 12 × 2 prismatic batteries. The battery size is 16 × 65 × 151 mm, the density is 1337 kg / m 3 , the specific heat capacity is 1542.9 J / (kg·K), and the thermal conductivity is 1.05 × 21.1 × 21.1 W / (m·K). The system uses air as the cooling working medium. The density of air is 1.165 kg / m 3 , the specific heat capacity is 1006.43 J / (kg·K), the thermal conductivity is 0.027 W / (m·K), the inlet temperature is 298.15 K, and the flow rate is 0.015 m 3 / s. The size of the system is 231 × 130 × 191 mm (excluding the inlet section and the outlet section), where the width of the distribution flow channel (w DD ) and the width of the confluence flow channel (w CD ) are both 20 mm, and the total width of the parallel flow channels is 39 mm. The present invention is used to optimize the width of the parallel flow channels of the system. The flow chart of the method is as shown in Figure 1 and includes the following steps:

[0056] S1. Given the cooling working medium flow rate Q0, inlet temperature T0, heat generation rate Φ, size and quantity N s of the parallel flow channel cooling system, and the total volume of the system, determine the number N PD of the parallel flow channels and the total width W according to the total volume of the system, the size and quantity of the heat sources;

[0057] S2. Assume that the internal temperature distribution of a single heat source is uniform and the average temperature of all heat sources is the same, and give the following heat source temperature equation:

[0058] T s，i = T const , i = 1, 2,..., N s

[0059] where T s represents the average temperature of the heat source, T const represents the value of the average temperature of the heat source to be solved, and N sRepresents the number of heat sources. This equation, together with the flow resistance network model and the simplified heat transfer model, constitutes the optimization model.

[0060] S3. Select one of the structural parameters to be optimized as the known parameter, and set the remaining structural parameters as unknowns; traverse all the values of this parameter according to a certain value range and value step; for each value, use the iterative method to solve the algebraic equations that make up the optimization model, and the values of the remaining structural parameters to be optimized and the corresponding heat source temperature T can be obtained. const . Select the structural parameter result with the lowest heat source temperature as the final optimization result.

[0061] The optimization model includes a heat source temperature equation, a flow resistance network model, and a simplified heat transfer model. The flow resistance network model and the simplified heat transfer model are (taking the Z-type parallel flow channel cooling system as an example):

[0062] Flow resistance network model:

[0063] For the i-th distribution node:

[0064] Q DD，i = Q PD，i + Q DD，i+1

[0065] For the i-th confluence node:

[0066] Q CD，i = Q PD，i + Q CD，i-1

[0067] Among them, Q DD，i represents the cooling working fluid flow rate of the i-th distribution channel; Q PD，i represents the cooling working fluid flow rate of the i-th parallel channel; Q CD，i represents the cooling working fluid flow rate of the i-th confluence channel;

[0068] For the i-th flow loop:

[0069] ΔP lossDD，i+1 + ΔP loss，PDi+1 - ΔP loss，CD，i - ΔP loss，PD，i = 0

[0070] ΔP loss = ΔP friction + ΔP local

[0071]

[0072]

[0073]

[0074]

[0075] Among them, ΔP loss，DD，i 、ΔP loss，PD，i and ΔP loss，CD，i represent the total resistance losses of the i-th distribution channel, the i-th parallel channel, and the i-th confluence channel respectively. ΔP friction represents the frictional resistance loss of the channel, and ΔP local represents the local resistance loss of the node. ξ and χ represent the local resistance coefficient and the frictional resistance coefficient respectively, l and D represent the length and equivalent diameter of the channel, and ρ f represents the density of the cooling working fluid, U represents the average flow velocity of the cooling working fluid in the channel, and the subscripts PD, DD, and CD represent the parallel channel, the distribution channel, and the confluence channel respectively;

[0076] Simplified heat transfer model:

[0077] For the i-th heat source:

[0078] Φ s，i V s，i = h CD，i S up，i ΔT up，i + h DD，i+1 S down，i ΔT down，i + h PD，i S left，i ΔT left，i + h PD，i+1 S right，i ΔT right，i

[0079] For the cooling working fluid in the i-th parallel channel:

[0080] ρ f c p，f U PD，i (T PD，i - T0)A PD，i = h PD，i S right，i-1 ΔT right，i-1 + h PD，i S left，i ΔT left，i

[0081] Among them, Φ s，i and V s，i represent the heat generation rate and volume of the i-th heat source respectively. h DD，i 、h PD，i and h CD，i represent the convective heat transfer coefficients of the cooling working fluid in the i-th distribution channel, the i-th parallel channel, and the i-th confluence channel respectively, and are calculated using experimental correlations. Sup，i and S down，i and S left，i and S right，i respectively represent the heat transfer area between the i-th heat source and the cooling working medium in the upper converging flow channel, the lower distribution flow channel, the left parallel flow channel, and the right parallel flow channel. ΔT up，i and ΔT down，i and ΔT left，i and ΔT right，i respectively represent the heat transfer temperature difference between the i-th heat source and the cooling working medium in the upper converging flow channel, the lower distribution flow channel, the left parallel flow channel, and the right parallel flow channel, and are calculated using the logarithmic heat transfer temperature difference or its approximate formula. ρ f and c p，f respectively represent the density and specific heat at constant pressure of the cooling working medium. T0 represents the inlet temperature of the cooling medium of the system, and T PD，i represents the temperature of the cooling working medium at the outlet of the i-th parallel flow channel. U PD，i represents the average flow velocity of the cooling working medium in the i-th parallel flow channel, and A PD，i represents the cross-sectional area of the i-th parallel flow channel.

[0082] The convective heat transfer coefficient h is calculated using the flow heat transfer criterion relationship:

[0083] h = (λ f / D)0.036Re 4 / 5 Pr 1 / 3 (D / l) 0.055

[0084] where λf represents the thermal conductivity of the cooling working medium, Re and Pr represent the Reynolds number and the Prandtl number, and l and D represent the length and equivalent diameter of the flow channel respectively.

[0085] The structural parameters to be optimized include the width distribution of the parallel flow channels, the width distribution of the distribution flow channels, or the width distribution of the converging flow channels. If the optimization parameter is the width distribution of the system parallel flow channels, the equation needs to be supplemented:

[0086]

[0087] where i represents the serial number of the parallel flow channel, N PD represents the number of parallel flow channels, w PD，i represents the width of the i-th parallel flow channel, and W represents the total width of the parallel flow channels.

[0088] Example 2

[0089] This example considers the Z-shaped parallel flow channel cooling system as shown in Figure 3 and optimizes the width of the parallel flow channels of the system. The parameters of the system are the same as those in Example 1. Select the width w of the first parallel flow channel PD，1As the structural parameter to be traversed, w PD，1 The value range and value step are respectively set to [3 10] mm and 0.1 mm. For each value of w PD，1 the optimization model is solved, and the values of the remaining structural parameters to be optimized and the corresponding heat source temperature T const are obtained. The results show that when w PD，1 is 6.7 mm, T const reaches the minimum value, and the corresponding parallel channel width distribution [6.7, 5.0, 3.4, 3.3, 2.8, 2.8, 2.4, 2.5, 2.2, 2.3, 2.0, 2.1, 1.5] mm at this time is used as the optimization result.

[0090] The computational fluid dynamics method is used to evaluate the performance of the system (BTMS Z-0) with a uniform parallel channel width distribution and the optimized system (BTMS Z-opt) obtained in this embodiment. The average battery temperature distribution is as Figure 4 shown. The highest temperature of the battery pack of BTMS Z-0 is 336.4 K, and the temperature difference is 9.7 K; the highest temperature of the battery pack of BTMS Z-opt is 331.4 K, and the temperature difference is 1.6 K. Compared with BTMS Z-0, BTMS Z-opt reduces the highest temperature of the battery pack by 5.0 K and reduces the temperature difference by 83%. This example verifies the effectiveness of the present invention for optimizing the structure of the parallel channel cooling system.

[0091] Example 3

[0092] This embodiment considers the U-shaped parallel channel cooling system as shown in Figure 5 and optimizes the parallel channel width of the system. The parameters of the system are the same as those in Example 1. The width w of the first parallel channel is selected PD，1 as the structural parameter to be traversed, and the value range and value step of w PD，1 are respectively set to [0.1 3.1] mm and 0.1 mm. For each value of w PD，1 the optimization model is solved, and the values of the remaining structural parameters to be optimized and the corresponding heat source temperature T const are obtained. The results show that when w PD，1 is 1.8 mm, T const reaches the minimum value, and the corresponding parallel channel width distribution [1.8, 2.4, 2.4, 2.6, 2.6, 2.9, 2.9, 3.2, 3.3, 3.7, 3.8, 4.1, 3.3] mm at this time is used as the optimization result.

[0093] The computational fluid dynamics method is used to evaluate the performance of the system (BTMS U-0) with a uniform parallel channel width distribution and the optimized system (BTMS U-opt) obtained in this embodiment. The average battery temperature distribution is asFigure 6 As shown, the maximum temperature of the battery pack of BTMS U-0 is 332.4 K, and the temperature difference is 5.2 K; the maximum temperature of the battery pack of BTMS U-opt is 331.7 K, and the temperature difference is 1.0 K. Compared with BTMS U-0, BTMS U-opt reduces the maximum temperature of the battery pack by 0.7 K and the temperature difference by 81%. This example verifies the effectiveness of the present invention for the structural optimization of the parallel flow channel cooling system.

[0094] As mentioned above, only the preferred embodiments of the present invention are described, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, all belong to the protection scope of the present invention.

Claims

1. An optimization method for the structure of an efficient parallel flow channel cooling system, characterized in that, It includes the following steps: S1. Given the cooling working fluid flow rate Q0, inlet temperature T0, heat generation rate Φ of the heat source, heat source size, and the number of heat sources N of the parallel flow channel cooling system s , the total volume of the system, determine the number of parallel flow channels N and the total width W according to the total volume of the system, heat source size, and the number of heat sources PD ; S2. Assume that the temperature distribution inside a single heat source is uniform and the average temperatures of all heat sources are the same. The heat source temperature equation is: T s,j = T const , j = 1, 2, …, N s where T s,j represents the average temperature of the j-th heat source, and T const represents the average temperature value of the heat source to be solved, and N s represents the number of heat sources; this equation, together with the flow resistance network model and the simplified heat transfer model, constitutes an optimization model; The simplified heat transfer model is: For the j-th heat source: Φ s,j V s,j = h CD,i S up,j ΔT up,j + h DD,i+1 S down,j ΔT down,j + h PD,i S left,j ΔT left,j + h PD,i+1 S right,j ΔT right,j For the cooling working medium in the i-th parallel flow channel: ρ f c p,f U PD,i (T PD,i -T0)A PD,i =h PD,i S right,j-1 ΔT right,j-1 +h PD,i S left,j ΔT left,j Among them, Φ s,j and V s,j represent the heat generation rate and volume of the j-th heat source respectively, h DD,i 、h PD,i and h CD,i represent the convective heat transfer coefficients of the cooling working fluid in the i-th distribution channel, the i-th parallel channel and the i-th confluence channel respectively, and are calculated using experimental correlation formulas. S up,j 、S down,j 、S left,j and S right,j represent the heat transfer areas between the j-th heat source and the confluence channel above it, the distribution channel below it, the parallel channel on the left and the parallel channel on the right respectively. ΔT up,j 、ΔT down,j 、ΔT left,j 、ΔT right,j represent the heat transfer temperature differences between the j-th heat source and the cooling working fluid in the confluence channel above it, the distribution channel below it, the parallel channel on the left and the parallel channel on the right respectively, and are calculated using the logarithmic mean temperature difference or its approximate formula. ρ f and c p,f represent the density and specific heat at constant pressure of the cooling working fluid respectively. T0 represents the inlet temperature of the cooling medium of the system, and T PD,i represents the temperature of the cooling working fluid at the outlet of the i-th parallel channel. U PD,i represents the average flow velocity of the cooling working fluid in the i-th parallel channel, and A PD,i represents the cross-sectional area of the i-th parallel channel; S3. Select one of the structural parameters to be optimized from the optimization model as a known parameter, and traverse all the values of this parameter according to a certain value range and value step; for each value, solve the optimization model to obtain the values of the remaining structural parameters to be optimized and the corresponding heat source temperature T const , and select the structural parameter result with the lowest heat source temperature as the final optimization result.

2. An optimization method for the structure of an efficient parallel flow channel cooling system according to claim 1, characterized in that, The optimization model includes a heat source temperature equation, a flow resistance network model, and a simplified heat transfer model.

3. An optimization method for the structure of an efficient parallel flow channel cooling system according to claim 1, characterized in that, The optimization model assumes that the heat source temperatures are the same but unknown, and supplements N s heat source temperature equations.

4. An optimized method for the structure of an efficient parallel flow channel cooling system according to claim 2, characterized in that, The flow resistance network model is: For the i-th distribution node: Q DD,i = Q PD,i + Q DD,i+1 For the i-th confluence node: Q CD,i = Q PD,i + Q CD,i-1 Among them, Q DD,i represents the cooling working fluid flow rate of the i-th distribution flow channel; Q PD,i represents the cooling working fluid flow rate of the i-th parallel flow channel; Q CD,i represents the cooling working fluid flow rate of the i-th confluence flow channel; For the i-th flow loop: ΔP loss,DD,i+1 +ΔP loss,PD,i+1 -ΔP loss,CD,i -ΔP loss,PD,i =0 ΔP loss = ΔP friction + ΔP local Among them, ΔP loss,DD,i , ΔP loss,PD,i and ΔP loss,CD,i respectively represent the total resistance losses of the i-th distribution channel, the i-th parallel channel, and the i-th confluence channel. ΔP friction represents the frictional resistance loss of the channel, and ΔP local represents the local resistance loss of the node. ξ and χ respectively represent the local resistance coefficient and the frictional resistance coefficient. l and D respectively represent the length and the equivalent diameter of the channel. ρ f represents the density of the cooling working fluid, U represents the average flow velocity of the cooling working fluid in the channel, and the subscripts PD, DD, and CD respectively indicate the parallel channel, the distribution channel, and the confluence channel.

5. An optimization method for the structure of an efficient parallel flow channel cooling system according to claim 1, characterized in that, The convective heat transfer coefficient h in the simplified heat transfer model is calculated using the flow heat transfer criterion relationship: h = (λ f / D) 0.036Re 4 / 5 Pr 1 / 3 (D / l) 0.055 Among them, λ f represents the thermal conductivity of the cooling working fluid, Re and Pr represent the Reynolds number and Prandtl number, and l and D respectively represent the length and equivalent diameter of the current flow channel.

6. An optimization method for the structure of an efficient parallel flow channel cooling system according to claim 1, characterized in that, In step S3, the selected structural parameters to be traversed take arbitrary values.

7. An optimization method for the structure of an efficient parallel flow channel cooling system according to claim 1, characterized in that, The structural parameters to be optimized include the width distribution of parallel flow channels, the width distribution of distribution flow channels, or the width distribution of confluence flow channels.

8. An optimization method for the structure of an efficient parallel flow channel cooling system according to claim 1, characterized in that, If the optimization parameter is the width distribution of the system parallel flow channels, an additional equation needs to be supplemented: Among them, i represents the serial number of the parallel flow channels, and N PD represents the number of parallel flow channels, w PD,i represents the width of the i-th parallel flow channel, and W represents the total width of the parallel flow channels.

9. An optimization method for the structure of an efficient parallel flow channel cooling system according to any one of claims 1 to 8, characterized in that An iterative method is used to solve the algebraic equations that make up the optimization model.

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