A parallel channel cooling system optimization method based on field synergy equation

Abstract

The present invention discloses a method for optimizing a parallel flow channel cooling system based on a field synergy equation. The present invention provides an initial parallel flow channel cooling system with a given flow channel width distribution, solves the momentum equation and the energy equation to obtain the system velocity field and temperature field, and records the average boundary temperature (T s ); Set each heat source boundary as an isothermal boundary, and the temperature value is the average temperature T recorded for each heat source boundary s , calculate the synergistic equation of the convection heat transfer field to obtain the velocity field of the parallel channel cooling system; use the obtained velocity field to calculate the energy equation again to obtain the average temperature T of each heat source boundary s ', T s 'Recalculate the field synergy equation as a heat source boundary and repeat the above process until T s 'With T s The component residual is less than the set convergence residual; then based on the flow channel velocity distribution of the system at this time, the flow resistance network reverse calculation model is solved to obtain the optimized structural parameters of the system. The present invention has the advantages of no experience and randomness, short optimization time, good optimization effect, strong applicability, etc.

Description

Technical Field

[0001] The invention relates to the field of cooling system optimization, and in particular to a parallel flow channel cooling system optimization method based on a field synergy equation. Background Art

[0002] With the continuous development of science and technology, the size of various electronic devices is getting smaller and smaller, but the heat generated by electronic devices during work is not easy to discharge. The heat accumulation causes its temperature to rise significantly, which will seriously affect the safety and life of the electronic equipment.

[0003] Therefore, it is necessary to design an efficient cooling system for electronic equipment to ensure that the heat generated by it is removed in time, thereby extending the life of the electronic equipment.

[0004] The cooling system is generally divided into serial flow channel cooling system and parallel flow channel cooling system.

[0005] Compared with the serial flow channel cooling system, the parallel flow channel cooling system has the advantages of compact structure and small pressure drop, so it is widely used in the field of electronic heat dissipation.

[0006] However, in the parallel flow channel cooling system, it is easy to cause large flow differences between the flow channels, resulting in large temperature differences in the system. Therefore, it is necessary to optimize the design of the parallel flow channel cooling system.

[0007] Traditional system optimization mainly uses combinatorial optimization algorithms or heuristic adjustment criteria to design cooling systems.

[0008] The combinatorial optimization algorithm mainly relies on parameter matching attempts, which requires a large amount of calculation. In addition, random operators are introduced in the optimization process, and the optimization results are random. The heuristic adjustment criteria mainly rely on the researcher's experience and cannot guarantee the optimal results. Summary of the invention

[0009] The purpose of the present invention is to overcome the shortcomings and deficiencies of the above-mentioned prior art and provide a parallel flow channel cooling system optimization method based on the field synergy equation. The present invention obtains the optimal velocity field corresponding to the optimization target through the convection heat transfer field synergy equation, without introducing experience and randomness, and significantly improves the uniformity of the flow of each parallel flow channel.

[0010] The present invention is achieved through the following technical solutions:

[0011] A parallel flow channel cooling system optimization method based on field synergy equation includes the following steps:

[0012] S-1, given the total parallel channel width Dcc and iterative convergence residual ε of the parallel channel cooling system, the parallel channel width of the initial parallel channel cooling system is set to a uniform width, and the widths of the separation channel and the converging channel are both set to a uniform width;

[0013] S-2, solve the computational fluid dynamics equation to obtain the velocity field of the parallel channel cooling system, further solve the energy equation to obtain the system temperature field, and record the average temperature of each heat source boundary, denoted as T s =[T s1 ,T s2 ,…,T sN ], where T si is the average boundary temperature of the ith heat source, and N is the number of heat sources;

[0014] S-3, set each heat source wall as an isothermal boundary, and the temperature value is the average temperature T of the heat source boundary recorded si , the velocity field of the parallel channel cooling system is calculated using the synergistic equation of the convection heat transfer field, and the velocity distribution of the separation channel, the confluence channel and each parallel channel is recorded;

[0015] S-4, based on the velocity field obtained in step S-3, the energy equation is solved again to obtain the average temperature distribution T of the heat source boundary. s '=[T s1 ',T s2 ',……,T sN '];when When , the velocity distribution of the separation channel, the converging channel and each parallel channel corresponding to the parallel channel cooling system at this time is recorded as the optimal channel velocity distribution; otherwise, let T s =T s ', return to step S-3;

[0016] Based on the optimal flow channel velocity distribution obtained by S-4, S-5 uses the flow resistance network reverse calculation model to calculate the structural parameters of the parallel flow channel cooling system, which is the final optimization result and the optimization is completed.

[0017] In the above step S-3, each heat source wall is set as an isothermal boundary, and the temperature value is the average temperature T of the heat source boundary recorded. si Specifically, in each iteration, the field synergy equation is used to solve the temperature field corresponding to the optimal velocity field and the energy equation, and then the average temperature of the heat source boundary obtained by the energy equation is used as the isothermal boundary of the heat source in the field synergy equation, and the obtained isothermal boundary of the heat source is used as the boundary condition of the field synergy equation in the next iteration step.

[0018] In the above step S-3, the velocity field of the parallel flow channel cooling system is calculated using the convective heat transfer field synergy equation. Specifically, the convective heat transfer field synergy equation is derived by the variational principle, and its form is as follows:

[0019]

[0020] Where F is the additional volume force term of the momentum equation, corresponding to the optimization objective. For example, when the optimization objective is cumulative dissipation,

[0021] In the above step S-5, the optimized structural parameters include the parallel flow channel width distribution, the separation flow channel width distribution and the converging flow channel width distribution.

[0022] In the above step S-5, the formula of the flow resistance network reverse calculation model is as follows:

[0023] △P loss,DP,i+1 +△P loss,CC,i+1 -△P loss,CP,i -△P loss,CC,i =0

[0024] △P loss =△P local +△P friction

[0025]

[0026]

[0027]

[0028]

[0029] in,

[0030] The subscripts “DP” and “CP” indicate the separation flow channel and the confluence flow channel, respectively;

[0031] The subscript “CC” indicates parallel flow channels;

[0032] ΔP local and ΔP friction They are local resistance loss and along-the-way resistance loss;

[0033] ξ and λ represent the local resistance coefficient and the along-the-line resistance coefficient, respectively, and are calculated using the empirical formula for pipeline flow;

[0034] l and D represent the pipe length and equivalent diameter respectively; when the optimization parameter is the parallel flow channel width distribution, the following equation needs to be added:

[0035]

[0036] Where N is the number of heat sources.

[0037] Compared with the prior art, the present invention has the following advantages and effects:

[0038] (1) No empiricism and randomness: Compared with the traditional optimization method, the present invention obtains the optimal velocity field corresponding to the optimization target through the synergistic equation of the convection heat transfer field, without introducing empiricism and randomness.

[0039] (2) Short optimization time: The present invention only needs to solve the convective heat transfer field synergy equation and the energy equation several times, and solve the flow resistance network reverse calculation model once, to obtain the optimized system; compared with the traditional optimization method, the present invention is simple to operate and has a short optimization time.

[0040] (3) Good optimization effect: The present invention takes into account the inherent mechanism of convective heat transfer optimization through the field synergy equation, which can significantly improve the non-uniformity of the flow rate of each parallel flow channel, thereby significantly improving the heat transfer performance of the parallel flow channel cooling system.

[0041] (4) Strong applicability: The optimization process of the present invention is independent of the operating parameters, structural parameters, and material properties of the parallel flow channel cooling system, and therefore the present invention is applicable to the optimization of other similar problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of optimization of parallel flow channel cooling system based on field synergy equation of the present invention;

[0043] Figure 2 is a schematic diagram of a single heat source in a parallel flow channel cooling system according to an embodiment of the present invention;

[0044] Figure 3 It is a side view of the calculation area of ​​the parallel flow channel cooling system of an embodiment of the present invention; wherein the figure marks are: separation flow channel 1, parallel flow channel 2, and converging flow channel 3. DETAILED DESCRIPTION

[0045] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0046] As attached Figure 3 As shown, the inlet and outlet (W) of the parallel flow channel cooling system of the embodiment of this invention in、 W out ) width is 15mm. There are 10 heat sources, and the size of the heat sources is 65mm long × 16mm wide × 151mm high; 11 cooling channels are formed between the 10 heat sources, and the total width of the channels is 5mm; the inlet cooling air temperature is 298.15K, and the wind speed is 0.8m / s; the field synergy equation with the optimization objective as the product is adopted, given C Φ =-5×10 -4 The physical properties of the battery and cooling air are shown in Table 1.

[0047] Table 1 Heat source and air physical parameters

[0048]

[0049] The present invention is used to optimize the above parallel flow channel cooling system, the flow chart is as follows Figure 1 As shown, the specific steps are as follows:

[0050] S-1 Given the total parallel channel width Dcc and iterative convergence residual ε of the parallel channel cooling system, the initial parallel channel cooling system, the parallel channel width is set to a uniform width, and the separation channel and converging channel width are both set to a uniform width;

[0051] S-2 solves the computational fluid dynamics equation to obtain the velocity field of the parallel channel cooling system, further solves the energy equation to obtain the system temperature field, and records the average temperature of each heat source boundary, denoted as T s =[T s1 ,T s2 ,…,T sN ], where T si is the average boundary temperature of the ith heat source, and N is the number of heat sources;

[0052] S-3 sets each heat source wall as an isothermal boundary, and the temperature value is the average temperature T recorded at the heat source boundary. si , the velocity field of the parallel channel cooling system is calculated using the synergistic equation of the convection heat transfer field, and the velocity distribution of the separation channel, the confluence channel and each parallel channel is recorded;

[0053] S-4 Based on the velocity field obtained in S-3, the energy equation is solved again to obtain the average temperature distribution T of the heat source boundary. s '=[T s1 ',T s2 ',……,T sN '];when When , the velocity distribution of the separation channel, the converging channel and each parallel channel corresponding to the parallel channel cooling system at this time is recorded as the optimal channel velocity distribution; otherwise, let T s =T s ', return to step S-3;

[0054] S-5 obtains the optimal flow channel velocity distribution based on S-4, and uses the flow resistance network reverse calculation model to calculate the structural parameters of the parallel flow channel cooling system, which is the final optimization result, and the optimization is completed.

[0055] When the optimization method is implemented, the parallel flow channel width of the initial parallel flow channel cooling system is a uniform width, and the inlet and outlet flow channel widths are respectively set to a uniform width;

[0056] The optimization of this embodiment only needs to calculate the third-order field synergy equation and the fourth-order energy equation to obtain the optimized parallel flow channel cooling system, and the optimization time is short. The optimal parallel flow channel width distribution is shown in Table 2.

[0057] Table 2 The width of each channel after optimization

[0058]

[0059] Before optimization, the maximum temperature and temperature difference of the heat source in the parallel flow channel cooling system were 328.1K and 5.4K respectively. After optimization, they were 327.0K and 1.9K respectively, while the system pressure drop remained basically unchanged.

[0060] After optimization by the present invention, the maximum temperature and temperature difference of the heat source in the parallel flow channel cooling system are reduced by 1.1K and 3.5K respectively, and the temperature difference is reduced by 65%.

[0061] In summary, the present invention achieves better performance optimization in a shorter optimization time. This example effectively verifies the effectiveness of the present invention in optimizing the structure of a parallel flow channel cooling system.

[0062] The present invention provides an initial parallel flow channel cooling system with a given flow channel width distribution, solves the momentum equation and the energy equation to obtain the system velocity field and temperature field, and records the average boundary temperature (T s ); Set each heat source boundary as an isothermal boundary, and the temperature value is the average temperature T recorded for each heat source boundary s , calculate the synergistic equation of the convection heat transfer field to obtain the velocity field of the parallel channel cooling system; use the obtained velocity field to calculate the energy equation again to obtain the average temperature T of each heat source boundary s ', T s 'Recalculate the field synergy equation as a heat source boundary and repeat the above process until T s 'With T s The component residual is less than the set convergence residual; then based on the flow channel velocity distribution of the system at this time, the flow resistance network reverse calculation model is solved to obtain the optimized structural parameters of the system. The present invention can obtain the final optimization result by solving the field synergy equation and the energy equation several times, and has the advantages of no experience and randomness, short optimization time, good optimization effect, strong applicability, etc.

[0063] As described above, the present invention can be better implemented.

[0064] The implementation methods of the present invention are not limited to the above-mentioned embodiments, and any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principles of the present invention shall be equivalent replacement methods and shall be included in the protection scope of the present invention.

Claims

1. A parallel flow channel cooling system optimization method based on field synergy equation, characterized in that: The steps include: S-1, given the total parallel channel width Dcc and iterative convergence residual ε of the parallel channel cooling system, the parallel channel width of the initial parallel channel cooling system is set to a uniform width, and the widths of the separation channel and the converging channel are both set to a uniform width; S-2, solve the computational fluid dynamics equation to obtain the velocity field of the parallel channel cooling system, further solve the energy equation to obtain the system temperature field, and record the average temperature of each heat source boundary, denoted as T s =[T s1 ,T s2 ,…,T sN ], where T si is the average boundary temperature of the ith heat source, and N is the number of heat sources; S-3, set each heat source wall as an isothermal boundary, and the temperature value is the average temperature T of the heat source boundary recorded si , the velocity field of the parallel channel cooling system is calculated using the synergistic equation of the convection heat transfer field, and the velocity distribution of the separation channel, the confluence channel and each parallel channel is recorded; S-4, based on the velocity field obtained in step S-3, the energy equation is solved again to obtain the average temperature distribution T of the heat source boundary. s '=[T s1 ',T s2 ',……,T sN '];when When , the velocity distribution of the separation channel, the converging channel and each parallel channel corresponding to the parallel channel cooling system at this time is recorded as the optimal channel velocity distribution; otherwise, let T s =T s ', return to step S-3; S-5, based on the optimal flow channel velocity distribution obtained in S-4, the flow resistance network reverse calculation model is used to calculate the structural parameters of the parallel flow channel cooling system, which is the final optimization result, and the optimization is completed; In step S-3, each heat source wall is set as an isothermal boundary, and the temperature value is the average temperature of the heat source boundary recorded T si Specifically, the field synergy equation is used to solve the temperature field corresponding to the optimal velocity field and the energy equation in each iteration, and then the average temperature of the heat source boundary obtained by the energy equation is used as the heat source isothermal boundary of the field synergy equation, and the obtained heat source isothermal boundary is used as the boundary condition of the field synergy equation in the next iteration step; In step S-3, the velocity field of the parallel flow channel cooling system is calculated using the convective heat transfer field synergy equation. Specifically, the convective heat transfer field synergy equation is derived by the variational principle, and its form is as follows: Where U represents the velocity vector of each point in the parallel channel cooling system, represents the velocity gradient; P represents the pressure at each point in the parallel channel cooling system, represents the pressure gradient, μ and ρ represent the dynamic viscosity and density, and F is the additional volume force term of the momentum equation, corresponding to the optimization objective; In step S-5, the optimized structural parameters include parallel flow channel width distribution, separation flow channel width distribution and confluence flow channel width distribution; In step S-5, the formula of the flow resistance network reverse calculation model is as follows: ΔP loss,DP,i+1 +ΔP loss,CC,i+1 -ΔP loss,CP,i -ΔP loss,CC,i =0 ΔP loss =ΔP local +ΔP friction in, The subscripts "DP" and "CP" indicate the separation flow channel and the confluence flow channel, respectively; The subscript "CC" indicates parallel flow channels; ΔP loss,DP,i+1 and ΔP loss,CC,i+1 represents the resistance loss of the i+1th separation channel and the i+1th parallel channel; ΔP loss,CC,i and ΔP loss,CP,i represent the resistance loss of the i-th parallel flow channel and the i-th converging flow channel respectively; ΔP local and ΔP friction Represent the local resistance loss and the resistance loss along the way respectively; ΔP local,DP,i+1 and ΔP local,CC,i+1 represents the local resistance loss of the i+1th separated flow channel and the i+1th parallel flow channel; ΔP local,CC,i and ΔP local,CP,i Represent the local resistance loss of the i-th parallel flow channel and the i-th converging flow channel respectively; ξ DP,i+1 represents the local resistance coefficient of the i+1th separation channel, which is calculated by the empirical formula for pipe flow; ξ CP,i represents the local resistance coefficient of the i-th confluence channel, which is calculated by the empirical formula of pipe flow; ξ DP→CC,i and DP→CC,i+1 They represent the local resistance coefficient of the fluid from the separation channel entering the i-th parallel channel and the local resistance coefficient of the fluid from the separation channel entering the i+1-th parallel channel, respectively, and are calculated using the empirical formula for pipeline flow; ξ CC→CP,i and CC→CP,i+1 They represent the local resistance coefficient of the fluid from the ith parallel flow channel entering the confluent flow channel and the local resistance coefficient of the fluid from the i+1th parallel flow channel entering the confluent flow channel, respectively, and are calculated using the empirical formula for pipeline flow; λ represents the resistance coefficient along the way, which is calculated by the empirical formula of pipeline flow; U DP,i and U DP,i+1 represent the flow rates of the i-th and i+1-th separation channels respectively; U CP,i and U CP,i+1 represent the flow velocities of the i-th and i+1-th confluent channels respectively; l and D represent the pipe length and equivalent diameter respectively; when the optimization parameter is the parallel flow channel width distribution, the following equation needs to be added: Where N is the number of heat sources, D CC,i represents the width of the ith parallel flow channel, D CC Represents the sum of the widths of parallel flow channels.

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