A high-efficiency parallel runner cooling system runner width optimization method

Abstract

The application discloses a kind of efficient parallel flow passage cooling system flow passage width optimization method.The method introduces local flow regulation coefficient to adjust the flow distribution of each parallel flow passage of system, obtains equivalent flow distribution.Subsequently, based on the equivalent flow distribution of parallel flow passage cooling working medium, new flow passage width distribution is obtained using flow passage width distribution optimization strategy.Finally, by width adjustment, the equivalent flow distribution is homogenized.The parallel flow passage width optimization method developed by the application can effectively reduce the heat source temperature difference of parallel flow passage cooling system and achieve better temperature uniformity, thereby guaranteeing the use performance and life of new energy automobile power battery pack.The parallel flow passage width optimization method has the advantages of simple operation, fast optimization speed, good optimization effect and strong universality.

Description

Technical Field

[0001] This invention relates to the field of cooling for power battery packs in new energy vehicles, and specifically to a method for optimizing the flow channel width of a high-efficiency parallel flow channel cooling system. Background Technology

[0002] To address the increasingly severe "thermal barrier" problem in modern high-power-density electronic devices (such as high-performance chips and power batteries), researchers have delved into various advanced cooling technologies to solve the heat dissipation problem of electronic devices. Among them, parallel channel cooling systems are widely used in fields such as high heat flux density electronic device cooling, power battery thermal management, and laser diode heat dissipation due to their advantages of high heat dissipation capacity, high reliability, low energy consumption, compact structure, and lightweight design. A parallel channel cooling system is a compact and efficient heat dissipation device based on a multi-channel parallel design. It achieves efficient and uniform temperature dissipation by distributing the cooling medium to multiple parallel channels to remove heat generated by the heat source. The width of the parallel channels in the system is a key design parameter affecting the flow distribution of the cooling medium and the heat dissipation performance. It controls the flow resistance of each channel to dominate the flow distribution of the cooling medium, thus affecting the system's heat dissipation performance. An unreasonable channel width distribution can cause local overheating of the system, significantly increase the temperature gradient, and seriously threaten the safety and reliability of the equipment. Therefore, it is necessary to optimize the parallel channel width distribution of the parallel channel cooling system. Existing research mainly uses optimization algorithms or empirical adjustments to design the parallel channel width distribution of the parallel channel cooling system. For example, Lyu et al. (Lyu C, Song YK, Wang LX, Ge YM, Xiong R, Lan T. A new structure optimization method for forced air-cooling system based on the simplified multi-physics model [J]. Applied Thermal Engineering, 2021, 198: 117455.) optimized the deflection angle of the guide vane and further optimized the parallel channel width distribution of the system using a genetic algorithm, resulting in a decrease of approximately 30% in the maximum temperature of the battery pack and a decrease of approximately 80% in the temperature difference. Chen et al. (Chen K, Chen YM, Li ZY, Yuan F, Wang S F. Design of the cell spacings of batterypack in parallel air-cooled battery thermal management system [J]. International Journal of Heat and Mass Transfer, 2018, 127: 393-401.) proposed a heuristic method for locally adjusting the channel width of the Z-shaped parallel channel cooling system based on the battery temperature. After optimization, the maximum temperature of the battery pack decreased by 3 K and the temperature difference decreased by more than 60%.However, the methods described above, which employ stochastic combination operators to adjust the parallel channel width distribution of the system, suffer from high computational costs, slow convergence speeds, and complex parameter tuning. While empirical heuristic adjustment strategies have relatively lower computational costs and simpler parameter tuning, the heuristic method based on battery temperature can only adjust two channel widths at a time, i.e., localized channel width adjustment. This results in numerous adjustments and a long time required to obtain an optimized system. Therefore, an efficient optimization method that can globally adjust the parallel channel width distribution of the system is still lacking. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method for optimizing the width of parallel flow channels in a highly efficient parallel flow channel cooling system.

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

[0005] An efficient method for optimizing the flow channel width of a parallel flow channel cooling system includes the following steps: S1, given the cooling fluid flow rate of the multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperature T 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C The width distribution of the parallel flow channels is [ w 1, w 2, ···, w i , ···, w N , w N+1 ],in w i For the first i The width of each parallel flow channel. The initial distribution of parallel flow channel widths is set by the sum of the widths of the parallel flow channels. w 1= w 2= ​​···= w N = w N+1 = W total / ( N +1). Local flow rate adjustment coefficient for the first flow channel. R 1. Local flow rate adjustment coefficient of the last flow channel R N+1 Local flow adjustment coefficients in other flow channels R 2= R 3=···= RN-1 = R N .

[0006] S2. Calculate the temperature field of the current system and the cooling fluid flow distribution in the parallel flow channels using numerical simulation methods, and obtain the corresponding temperature parameters, which are denoted as the optimal temperature parameters. T opt The corresponding parallel channel width distribution at this time is denoted as W opt The cooling fluid flow distribution in the parallel flow channels is denoted as [ Q 1, Q 2, ···, Q i , ···, Q N , Q N+1 ],in Q i For the first i The flow rate of the cooling medium in the parallel flow channels.

[0007] S3. Obtain the equivalent flow distribution of the cooling working fluid in the parallel flow channel based on the local flow rate adjustment coefficient, denoted as [ Q’ 1, Q’ 2,···, Q’ i , ···, Q’ N , Q’ N+1 ],in Q’ i For the first i The equivalent flow rate of the cooling working fluid in the parallel flow channels.

[0008] S4. Based on the equivalent flow rate distribution of the cooling working fluid in the parallel flow channel, a new flow channel width distribution is obtained using a flow channel width distribution optimization strategy, denoted as... W '.

[0009] S5. Calculate the updated [value] using numerical simulation methods. W The temperature field of the system and the flow rate distribution of the cooling medium in the parallel flow channels are distributed. The temperature index of the heat source group is denoted as... T’ opt ,if T’ opt < T opt Then let T opt = T’ opt , W opt = W '.

[0010] S6. Return to step S3 and repeat the above steps. When the width of each parallel channel no longer changes with the above operation steps, stop the optimization. At this time, the distribution of the width of the parallel channels is the final optimization result.

[0011] Furthermore, in step S1, the initial parallel channel width is uniformly distributed. w 1= w 2= ​​···= w N = w N+1 Furthermore, in steps S2 and S5, the optimized temperature index can be the heat source temperature difference (Δ). T ), or it can be the highest temperature of the heat source ( T max ), where the heat source temperature difference Δ T It is the difference between the maximum and minimum average temperatures for each heat source.

[0012] Furthermore, in steps S2 and S5, the numerical simulation method used in the calculation process can be computational fluid dynamics (CFD), or a simplified physical model such as a flow resistance network model and a simplified heat transfer model can be used.

[0013] Furthermore, since the first and last flow channels of the system cool only one heat source, a local flow regulation coefficient is introduced. R i ,in R 1 and R N+1 The values ​​of a and b are denoted as a and b, respectively. The values ​​of a and b can be the same or different.

[0014] Furthermore, the local flow rate adjustment coefficients of the first and last flow channels of the system. R 1 and R N+1 The values ​​of a and b both range from 0.5 to 1. The local flow rate adjustment coefficients for all flow channels except the first and last flow channels. R 2= R 3 = ··· = R N-1 = R N =1.

[0015] Further, in step S3, the formula for calculating the equivalent flow rate distribution of the cooling medium in the parallel flow channel is as follows:

[0016] in Q’ i For the first iThe equivalent flow rate of the cooling working fluid in the parallel flow channels, Q i For the first i Cooling fluid flow rate in parallel flow channels R i For the first i The local flow rate regulation coefficient of a parallel flow channel.

[0017] Furthermore, in step S4, the channel width distribution optimization strategy is as follows:

[0018] in w’ i Represents the updated W 'Flow channel width distribution under the first i The width of each flow channel w i The first step is to update the channel width distribution before the update. i The width of each flow channel Q’ i The first step is to update the channel width distribution before the update. i The equivalent flow rate of the cooling working fluid in the parallel flow channels, This represents the equivalent average flow rate of the cooling medium in all parallel channels under the original channel width distribution. C This is the global flow regulation coefficient.

[0019] Furthermore, in step S6, when the difference between the width of each parallel channel after the update and the width of the corresponding parallel channel before the update in the channel width distribution optimization strategy is less than 0.1 mm, it is considered that the width of the parallel channel no longer changes, and the optimization stops. At this time, the parallel channel width distribution is the final optimization result.

[0020] Furthermore, the global flow regulation coefficient C The value range is 0.01 to 100.

[0021] This invention also provides a high-efficiency parallel flow channel cooling system flow channel width optimization system, comprising: The parameter acquisition module is used to acquire the flow rate of the cooling medium in a multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperature T 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C ; The numerical simulation module is used to calculate the temperature field of the current system and the cooling fluid flow distribution of the parallel flow channel using numerical simulation methods, and obtain the corresponding temperature indicators. The equivalent flow calculation module is used to obtain the equivalent flow distribution of the cooling medium in the parallel flow channel based on the local flow adjustment coefficient. The width optimization module is used to obtain a new channel width distribution based on the equivalent flow distribution of the cooling medium in the parallel channel and by using a channel width distribution optimization strategy. The iterative control module is used to control the repeated optimization process. When the width of each parallel channel does not change with the operation steps, the optimization stops and the final optimization result is output.

[0022] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. Simple Operation. The optimization method provided by this invention includes two key technical steps: First, during the optimization process, the temperature field of the current system and the cooling medium flow distribution of the parallel flow channels are calculated using numerical simulation methods, and then the equivalent flow distribution of the cooling medium in the parallel flow channels is obtained based on the local flow adjustment coefficient; second, based on the equivalent flow distribution of the cooling medium in the parallel flow channels, a new flow channel width distribution is obtained using a flow channel width distribution optimization strategy. The optimization process does not involve complex calculation methods and has the advantages of simple operation and convenient implementation.

[0023] 2. Fast optimization speed. The optimization method provided by this invention requires fewer adjustments to the flow channel width distribution. Only a few steps are needed to quickly obtain the final optimization result. Therefore, this invention can quickly obtain an optimized system, which has the advantages of being fast and efficient.

[0024] 3. Excellent optimization effect. The optimization method provided by this invention adjusts the flow distribution by adjusting the width distribution of parallel flow channels, thereby affecting the temperature distribution of the system heat source. The proposed optimization strategy can effectively improve the uniformity of the system heat source temperature distribution, and has the advantage of excellent optimization effect.

[0025] 4. High versatility. The optimization method provided by this invention only involves the velocity field and temperature field of the system, and is independent of the working fluid properties, working fluid flow rate, ambient temperature, heat source properties, heat source size, number of heat sources, and heat generation rate of the heat source. Therefore, this invention can be extended to the solution of similar problems, and has the advantages of high versatility and scalability. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating an efficient parallel flow channel cooling system flow channel width optimization method according to an embodiment of the present invention; Figure 2 This is a front view of the parallel flow channel cooling system according to an embodiment of the present invention; Figure 3 This is a front view of the Z-type battery thermal management system of Embodiment 1 of the present invention; Figure 4 This is a temperature distribution diagram of the Z-type battery thermal management system in Embodiment 1 of the present invention; Figure 5 This is a front view of the U-shaped battery thermal management system of Embodiment 2 of the present invention; Figure 6 This is a temperature distribution diagram of the U-shaped battery thermal management system in Embodiment 2 of the present invention; Figure 7 This is a front view of the J-type battery thermal management system of Embodiment 3 of the present invention; Figure 8 This is a temperature distribution diagram of the J-type battery thermal management system in Embodiment 3 of the present invention. Detailed Implementation

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the embodiments will be briefly introduced below. The present invention will be further described in detail below with reference to the embodiments, but the implementation of the present invention is not limited thereto.

[0028] Example 1 The embodiments of the present invention consider Figure 2 The given front view of the parallel flow channel cooling system, in which w i For parallel flow channel width, w in and w out These are the widths of the inlet and outlet sections, respectively. l in and l out These represent the lengths of the inlet and outlet sections, and the number of heat sources, respectively. N . Figure 3 The Z-shaped parallel flow channel cooling system shown is illustrated. The heat source in the system is 12 prismatic cells. The cell dimensions are 16 × 65 × 151 mm, and the density is 1542.9 kg / m³. 3 The specific heat capacity is 1337 J / (kg·K), and the thermal conductivity is anisotropic, with values ​​of 1.05, 21.1, and 21.1 W / (m·K) along the x, y, and z axes, respectively. Air is used as the cooling medium, and its density is 1.165 kg / m³. 3 The specific heat capacity is 1005 J / (kg·K), the thermal conductivity is 0.0267 W / (m·K), the inlet temperature is 298.15 K, and the inlet air flow rate is 0.015 m³ / s. 3 / s. The system dimensions are 231 × 130 × 191 mm (excluding the inlet and outlet sections), where the width of the inlet and outlet sections is ( w in , w out Both are 20 mm, and the length is ( l in ,l out All parallel flow channels are 100 mm wide, with a total width of 39 mm. The initial parallel flow channel width distribution is uniform, meaning each channel in the initial system has a width of 3 mm. A global flow coefficient is given. C Given a local flow rate adjustment coefficient of 1 for the first and last flow channels. R 1 and R 13 All are 0.6, representing the local flow rate adjustment coefficients for all flow channels except the first and last ones. R 2= R 3 =··· = R 11 = R 12 =1.

[0029] The heat generation rate of the battery region was calculated using a 12Ah LiFePO4 battery heat generation rate model, as follows:

[0030] in Φ b For battery cell heat generation rate, It is the discharge current of the battery cell. It is the equivalent resistance of the battery cell. It is the temperature of the battery cell. V b d represents the volume of the battery cell. U / d T The voltage temperature coefficient is obtained by electrochemical calorimetry and has a value of -0.22 mV / K.

[0031] The present invention is used to optimize the parallel flow channel width of the system. The flowchart of the method is as follows: Figure 1 As shown, it includes the following steps: S1, given the cooling fluid flow rate of the multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperature T 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C The width distribution of the parallel flow channels is [ w 1, w 2, ···, w i , ···, w N , w N+1 ],in w i For the firsti The width of each parallel flow channel. The initial distribution of parallel flow channel widths is set by the sum of the widths of the parallel flow channels. w 1= w 2= ​​···= w N = w N+1 = W total / ( N +1). Local flow rate adjustment coefficient for the first flow channel. R 1. Local flow rate adjustment coefficient of the last flow channel R N+1 Local flow adjustment coefficients in other flow channels R 2= R 3=···= R N-1 = R N .

[0032] S2. Calculate the temperature field of the current system and the cooling fluid flow distribution in the parallel flow channels using numerical simulation methods, and obtain the corresponding temperature parameters, which are denoted as the optimal temperature parameters. T opt The corresponding parallel channel width distribution at this time is denoted as W opt The cooling fluid flow distribution in the parallel flow channels is denoted as [ Q 1, Q 2, ···, Q i , ···, Q N , Q N+1 ],in Q i For the first i The flow rate of the cooling medium in the parallel flow channels.

[0033] S3. The equivalent flow distribution of the cooling medium in the parallel flow channel is obtained based on the local flow rate adjustment coefficient. The formula for calculating the equivalent flow rate of the cooling medium in the parallel flow channel is as follows:

[0034] Q’ i For the first i The equivalent flow rate of the cooling working fluid in the parallel flow channels, Q i For the first i Cooling fluid flow rate in parallel flow channels R i For the first i The local flow rate regulation coefficient of the parallel flow channels. The equivalent flow rate distribution of the cooling medium in the parallel flow channels is denoted as [Q’ 1, Q’ 2, ···, Q’ i , ···, Q’ N , Q’ N+1 ].

[0035] S4. Based on the equivalent flow rate distribution of the cooling working fluid in the parallel flow channels, an updated flow channel width distribution is obtained using a flow channel width distribution optimization strategy, denoted as... W The optimization strategy for flow channel width distribution is as follows:

[0036] in w’ i Represents the updated W 'Flow channel width distribution under the first i The width of each flow channel w i The first step is to update the channel width distribution before the update. i The width of each flow channel Q’ i The first step is to update the channel width distribution before the update. i The equivalent flow rate of the cooling working fluid in the parallel flow channels, This represents the equivalent average flow rate of the cooling medium in all parallel channels under the original channel width distribution. C This is the global flow regulation coefficient.

[0037] S5. Calculate the updated [value] using numerical simulation methods. W The temperature field of the system and the flow rate distribution of the cooling medium in the parallel flow channels are distributed. The temperature index of the heat source group is denoted as... T’ opt ,if T’ opt < T opt Then let T opt = T’ opt , W opt = W '.

[0038] S6. Return to step S3 and repeat the above steps. When the difference between the width of each parallel channel after the update and the width of the corresponding parallel channel before the update in the channel width distribution optimization strategy is less than 0.1mm, stop the optimization. The parallel channel width distribution at this time is the final optimization result.

[0039] After optimization using the above steps, the parallel channel width distribution of the optimized system is [3.8, 4.3, 3.8, 3.6, 3.3, 3.1, 2.9, 2.7, 2.6, 2.5, 2.4, 2.3, 1.7]. Computational fluid dynamics was used to evaluate the temperature parameters of the Z-type parallel channel cooling system (BTMS-Z-0) with a uniform channel width distribution and the optimized system (BTMS-Z-opt) obtained in this embodiment. The average temperature distribution of each cell in both systems is as follows: Figure 4 As shown, the highest temperature of the BTMS-Z-0 battery pack is 336.4 K, with a temperature difference of 9.7 K; the highest temperature of the BTMS-Z-opt battery pack is 332.5 K, with a temperature difference of 3.2 K. Compared to BTMS-Z-0, the highest temperature of BTMS-Z-opt is reduced by 3.9 K, and the temperature difference is reduced by 6.5 K. This embodiment verifies the effectiveness of the above-described parallel flow channel width optimization method.

[0040] Example 2 This embodiment considers, for example Figure 5 The U-shaped parallel flow channel cooling system shown here has its parallel flow channel width optimized. All system parameters are the same as in Example 1. A global flow coefficient is given. C Given a local flow rate adjustment coefficient of 1 for the first and last flow channels. R 1 and R 13 All are 0.7, representing the local flow rate adjustment coefficients for all flow channels except the first and last ones. R 2= R 3 =··· = R 11 = R 12 =1.

[0041] After optimization, the parallel channel width distribution of the U-shaped optimized system is [2.1, 2.7, 2.8, 2.9, 3.0, 3.1, 3.1, 3.2, 3.3, 3.3, 3.3, 3.4, 2.8]. Computational fluid dynamics was used to evaluate the temperature parameters of the U-shaped parallel channel cooling system (BTMS-U-0) with a uniform channel width distribution and the optimized system (BTMS-U-opt) obtained in this embodiment. The average temperature distribution of each cell in the two systems is as follows: Figure 6As shown, the highest temperature of the BTMS-U-0 battery pack is 332.5 K, with a temperature difference of 5.3 K; the highest temperature of the BTMS-U-opt battery pack is 331.3 K, with a temperature difference of 3.0 K. Compared to BTMS-U-0, the highest temperature of BTMS-U-opt is reduced by 1.2 K, and the temperature difference is reduced by 2.3 K. This embodiment verifies the effectiveness of the above-mentioned parallel flow channel width optimization method in the U-shaped cooling system, which can effectively improve the cooling performance of the U-shaped system.

[0042] Example 3 This embodiment considers, for example Figure 7 The J-type parallel flow channel cooling system shown here has its parallel flow channel width optimized. All system parameters are the same as in Example 1. A global flow coefficient is given. C Given a local flow rate adjustment coefficient of 1 for the first and last flow channels. R 1 and R 13 All are 0.8, representing the local flow rate adjustment coefficients for all flow channels except the first and last ones. R 2= R 3 =··· = R 11 = R 12 =1.

[0043] After optimization, the parallel channel width distribution of the J-type optimized system is [3.0, 3.3, 3.2, 3.2, 3.1, 3.1, 3.1, 3.0, 3.0, 2.9, 2.9, 2.8, 2.4]. Computational fluid dynamics was used to evaluate the temperature parameters of the J-type parallel channel cooling system (BTMS-J-0) with a uniform channel width distribution and the optimized system (BTMS-J-opt) obtained in this embodiment. The average temperature distribution of each cell in both systems is as follows: Figure 8 As shown, the highest temperature of the BTMS-J-0 battery pack is 332.3 K, with a temperature difference of 3.1 K; the highest temperature of the BTMS-J-opt battery pack is 331.3 K, with a temperature difference of 1.2 K. Compared to BTMS-J-0, the highest temperature of BTMS-J-opt decreased by 1.0 K, and the temperature difference decreased by 1.9 K. This embodiment verifies the effectiveness of the above-mentioned parallel flow channel width optimization method in the J-type cooling system, which can effectively improve the cooling performance of the J-type system. This also verifies that the optimization method can be effectively applied to parallel flow channel cooling systems with different flow patterns.

[0044] A high-efficiency parallel flow channel cooling system with optimized flow channel width, characterized by comprising: The parameter acquisition module is used to acquire the flow rate of the cooling medium in a multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperatureT 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C ; The numerical simulation module is used to calculate the temperature field of the current system and the cooling fluid flow distribution of the parallel flow channel using numerical simulation methods, and obtain the corresponding temperature indicators. The equivalent flow calculation module is used to obtain the equivalent flow distribution of the cooling medium in the parallel flow channel based on the local flow adjustment coefficient. The width optimization module is used to obtain a new channel width distribution based on the equivalent flow distribution of the cooling medium in the parallel channel and by using a channel width distribution optimization strategy. The iterative control module is used to control the repeated optimization process. When the width of each parallel channel does not change with the operation steps, the optimization stops and the final optimization result is output.

[0045] This embodiment can also provide an efficient parallel flow channel cooling system flow channel width optimization system for the above method, which includes: The parameter acquisition module is used to acquire the flow rate of the cooling medium in a multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperature T 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C ; The numerical simulation module is used to calculate the temperature field of the current system and the cooling fluid flow distribution of the parallel flow channel using numerical simulation methods, and obtain the corresponding temperature indicators. The equivalent flow calculation module is used to obtain the equivalent flow distribution of the cooling medium in the parallel flow channel based on the local flow adjustment coefficient. The width optimization module is used to obtain a new channel width distribution based on the equivalent flow distribution of the cooling medium in the parallel channel and by using a channel width distribution optimization strategy. The iterative control module is used to control the repeated optimization process. When the width of each parallel channel does not change with the operation steps, the optimization stops and the final optimization result is output.

[0046] The above description is merely a representative embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A method for optimizing the flow channel width of a high-efficiency parallel flow channel cooling system, characterized in that, Includes the following steps: S1. Obtain the cooling fluid flow rate of the multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperature T 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C The width distribution of parallel flow channels is denoted as [ w 1, w 2, , w i , , w N , w N+1 ],in w i For the first i The width of each parallel flow channel; the initial distribution of parallel flow channel widths is set by the sum of the widths of the parallel flow channels. w 1= w 2= = w N = w N+1 = W total / ( N +1); The first and last flow channels of the system cool only one heat source, introducing a local flow regulation coefficient. R i Given the local flow rate adjustment coefficient of the first flow channel R 1. Local flow rate adjustment coefficient of the last flow channel R N+1 Local flow adjustment coefficients in other flow channels R 2= R 3= = R N-1 = R N ;in R 1 and R N+1 The values ​​of a and b are denoted as a and b, respectively, and a and b may have the same or different values. Local flow regulation coefficients of the first and last flow channels of the system R 1 and R N+1 The values ​​of a and b are both in the range of 0.5 to 1; the local flow rate adjustment coefficients for all flow channels except the first and last flow channels. R 2= R 3= = R N-1 = R N =1; S2. Calculate the temperature field of the current system and the cooling fluid flow distribution in the parallel flow channels using numerical simulation methods, and obtain the corresponding temperature parameters, which are denoted as the optimal temperature parameters. T opt The corresponding parallel channel width distribution at this time is denoted as W opt The cooling fluid flow distribution in the parallel flow channels is denoted as [ Q 1, Q 2, , Q i , , Q N , Q N+1 ],in Q i For the first i Cooling fluid flow rate in parallel flow channels N +1 represents the total number of parallel flow channels; S3. Obtain the equivalent flow distribution of the cooling working fluid in the parallel flow channel based on the local flow rate adjustment coefficient, denoted as [ Q’ 1, Q’ 2, , Q’ i , , Q’ N , Q’ N+1 ],in Q’ i For the first i The equivalent flow rate of the cooling medium in each parallel flow channel; the calculation formula for the equivalent flow rate distribution of the cooling medium in the parallel flow channels: Q’ i For the first i The equivalent flow rate of the cooling working fluid in the parallel flow channels, Q i For the first i Cooling fluid flow rate in parallel flow channels R i For the first i The local flow rate regulation coefficient of a parallel flow channel; S4. Based on the equivalent flow rate distribution of the cooling working fluid in the parallel flow channel, a new flow channel width distribution is obtained using a flow channel width distribution optimization strategy, denoted as... W The channel width distribution optimization strategy is as follows: in w’ i Represents the updated W 'Flow channel width distribution under the first i The width of each flow channel w i The first step is to update the channel width distribution before the update. i The width of each flow channel Q’ i The first step is to update the channel width distribution before the update. i The equivalent flow rate of the cooling working fluid in the parallel flow channels, The equivalent average flow rate of the cooling medium in all parallel channels under the original channel width distribution; C This is the global flow regulation coefficient; S5. Calculate the new [item] using numerical simulation methods. W The temperature field of the system and the flow rate distribution of the cooling medium in the parallel flow channels are distributed. The temperature index of the heat source group is denoted as... T’ opt ,if T’ opt < T opt Then let T opt = T’ opt , W opt = W '; S6. Return to step S3 and repeat the above steps. When the width of each parallel channel no longer changes with the above operation steps, stop the optimization. At this time, the distribution of the width of the parallel channels is the final optimization result.

2. The method for optimizing the flow channel width of a high-efficiency parallel flow channel cooling system according to claim 1, characterized in that, In steps S2 and S5, the temperature index is the heat source temperature difference Δ. T or the highest temperature of the heat source T max The temperature difference Δ of the heat source T It is the difference between the maximum and minimum average temperatures for each heat source.

3. The method for optimizing the channel width of a high-efficiency parallel channel cooling system according to claim 1, characterized in that, In steps S2 and S5, the numerical simulation method used in the calculation process is a computational fluid dynamics method or a simplified physical model, wherein the simplified physical model includes a flow resistance network model and a simplified heat transfer model.

4. The method for optimizing the flow channel width of a high-efficiency parallel flow channel cooling system according to claim 1, characterized in that, In step S6, when the difference between the width of each parallel channel after the update and the width of the corresponding parallel channel before the update in the channel width distribution optimization strategy is less than 0.1 mm, it is considered that the width of the parallel channel no longer changes, and the optimization stops. At this time, the parallel channel width distribution is the final optimization result.

5. A system for optimizing the flow channel width of a high-efficiency parallel flow channel cooling system that implements the method of any one of claims 1 to 4, characterized in that... include: The parameter acquisition module is used to acquire the flow rate of the cooling medium in a multi-heat-source parallel flow channel cooling system. Q 0. Inlet temperature T 0. Heat source heat generation rate Φ, heat source size and quantity N Total width of parallel flow channels W total Global flow regulation coefficient C ; The numerical simulation module is used to calculate the temperature field of the current system and the cooling fluid flow distribution of the parallel flow channel using numerical simulation methods, and obtain the corresponding temperature indicators. The equivalent flow calculation module is used to obtain the equivalent flow distribution of the cooling medium in the parallel flow channel based on the local flow adjustment coefficient. The width optimization module is used to obtain a new channel width distribution based on the equivalent flow distribution of the cooling medium in the parallel channel and by using a channel width distribution optimization strategy. The iterative control module is used to control the repeated optimization process. When the width of each parallel channel does not change with the operation steps, the optimization stops and the final optimization result is output.

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