Flow field optimization method and device of multi-branch flow channel system and storage medium

Abstract

The embodiment of the invention provides a flow field optimization method and optimization device of a multi-branch flow channel system and a storage medium, and belongs to the technical field of flow channel flow field optimization. Comprising the following steps: establishing a simulation model of a multi-branch flow channel system; performing flow field uniformity analysis on the simulation model to determine the distribution flow of each branch flow channel of the multi-branch flow channel system; according to the number of branches of the multi-branch flow channel system and the distribution flow and the sectional area of each branch flow channel, the target water inlet area of each branch flow channel is determined; and optimizing and adjusting the distribution flow of each branch flow channel according to the target water inlet area. According to the method, the design process is converted into a systematized and data-driven scientific process, the flow channel design can be quickly and accurately evaluated and optimized by combining model simulation, data processing and an optimization algorithm, homogenization of a cooling liquid flow field in a system is achieved, and then the consistency of a temperature field is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of flow field optimization technology, and more specifically to a flow field optimization method, optimization device, and storage medium for a multi-branch flow channel system. Background Technology

[0002] Energy storage systems, fuel cell stacks, servers, and other equipment all require multi-channel flow systems to dissipate heat in a timely and uniform manner. Taking large-scale energy storage systems as an example, they typically consist of thousands or even tens of thousands of battery cells connected in series and parallel. During charging and discharging, these cells generate a significant amount of heat. If this heat cannot be dissipated in a timely and uniform manner, it will lead to uneven temperature distribution within the system, causing a series of problems. Given these challenges, traditional air-cooling solutions are no longer sufficient to meet the requirements of large-scale energy storage systems in terms of heat dissipation efficiency and space compactness. In contrast, liquid cooling technology, due to its higher specific heat capacity and heat transfer efficiency, enables more precise temperature control of battery modules and has therefore become the mainstream cooling technology for current large-scale energy storage systems.

[0003] The core design goal of a liquid cooling system lies in its flow channel network. An ideal flow channel design should ensure a highly uniform flow distribution of coolant as it flows through each parallel pipe and each battery pack. Ideally, all parallel branches should have the same flow resistance, thereby ensuring uniform coolant distribution and ultimately guaranteeing that all cells operate within a mild and consistent temperature range.

[0004] However, achieving this ideal goal faces significant bottlenecks in actual engineering design and optimization. First, the scale and complexity of the system make experience-based trial-and-error design processes not only inefficient but also difficult to guarantee the reliability of the final solution. Second, design solutions largely stem from imitation of existing successful cases or adjustments based on the limited experience of individual engineers. The entire design and optimization process lacks a standardized and systematic workflow. It typically requires multiple rounds of experience-based "design-simulation-modification" trial-and-error cycles, greatly extending the product development cycle and significantly increasing human and time costs. Summary of the Invention

[0005] The purpose of this invention is to provide a flow field optimization method, optimization device, and storage medium for a multi-branch flow channel system. The aim is to overcome the shortcomings of the prior art, enable rapid and accurate evaluation and optimization of flow channel design, achieve uniformity of the coolant flow field within the system, and thus ensure the consistency of the temperature field.

[0006] To achieve the above objectives, the present invention provides a flow field optimization method for a multi-branch channel system. The optimization method includes: establishing a simulation model of the multi-branch channel system; performing flow field uniformity analysis on the simulation model to determine the allocated flow rate of each branch channel of the multi-branch channel system; determining the target inlet area of ​​each branch channel based on the number of branches of the multi-branch channel system, the allocated flow rate of each branch channel, and the cross-sectional area; and optimizing and adjusting the allocated flow rate of each branch channel based on the target inlet area.

[0007] On the other hand, embodiments of the present invention provide a flow field optimization device for a multi-branch channel system. The optimization device includes: a model building module for establishing a simulation model of the multi-branch channel system; a flow analysis module for performing flow field uniformity analysis on the simulation model to determine the allocated flow rate of each branch channel of the multi-branch channel system; a data processing module for determining the target inlet area of ​​each branch channel based on the number of branches of the multi-branch channel system, the allocated flow rate of each branch channel, and the cross-sectional area; and an optimization adjustment module for optimizing and adjusting the allocated flow rate of each branch channel based on the target inlet area.

[0008] Another aspect of the present invention provides a multi-branch flow channel system applicable to any of the following: energy storage system, fuel cell stack, server, wherein the multi-branch flow channel system includes: a flow field optimization device for the multi-branch flow channel system.

[0009] In another aspect, the present invention provides a machine-readable storage medium storing instructions for causing a machine to execute: the flow field optimization method for a multi-branch flow channel system as described above.

[0010] In another aspect, the present invention provides a processor for running a program, wherein the program is executed to perform: a flow field optimization method for a multi-branch flow channel system as described above.

[0011] Through the above technical solutions, this invention provides a flow field optimization method, optimization device, and storage medium for multi-branch flow channel systems. It can change the traditional "trial and error" model of liquid cooling system design, which relies on engineers' experience, and transform the design process into a systematic, data-driven scientific workflow. By combining model simulation, data processing, and optimization algorithms, the flow channel design can be quickly and accurately evaluated and optimized, achieving uniformity of the coolant flow field within the system, thereby ensuring the consistency of the temperature field.

[0012] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the flow field optimization method for a multi-branch flow channel system provided in this embodiment of the invention.

[0014] Figure 2 This is a schematic diagram of the CFD model for flow field analysis of a large liquid storage cooling system provided in an embodiment of the present invention.

[0015] Figure 3 This is a flowchart illustrating the overall technical route of the optimization method provided in this embodiment of the invention.

[0016] Figure 4 This is a schematic diagram of the CFD model for flow field analysis of the battery cluster liquid cooling system provided in an embodiment of the present invention.

[0017] Figure 5 This is a schematic diagram of the water inflow data for each battery cluster provided in an embodiment of the present invention.

[0018] Figure 6 This is a schematic diagram of the optimized water inflow data for each battery cluster provided in an embodiment of the present invention.

[0019] Figure 7 This is a schematic diagram of the flow field optimization device for a multi-branch flow channel system provided in an embodiment of the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0022] Before introducing the various embodiments of the present invention, a brief description of the prior art is given. In the prior art, uneven temperature in large liquid cooling systems can cause a series of problems, including but not limited to: accelerated cell life decay, as high temperature and temperature difference can significantly shorten the cell cycle life; inconsistent performance, as temperature differences lead to inconsistent cell internal resistance and capacity, affecting the overall output efficiency and safety of the system; and the risk of thermal runaway, as local overheating may become the trigger point for thermal runaway, causing safety accidents.

[0023] The applicant found that existing liquid cooling system designs largely rely on experience and simple trial and error. These systems are structurally simple, with uniform and non-adjustable inlet areas in all branch pipes. Simulation analysis is often performed after design completion, but simulation only serves a verification function and does not demonstrate optimization capabilities. Furthermore, the simulation results for complex systems are numerous, often requiring significant time for statistical analysis. Specific technical problems are summarized as follows: 1. Uneven flow distribution and poor battery temperature uniformity: Traditional designs suffer from uneven flow resistance distribution, leading to significant differences in flow rates in branches near the inlet, ultimately resulting in severely uneven flow and temperature fields; 2. High reliance on subjective experience and lack of scientific theoretical guidance: The design process begins with imitation of similar products or modifications based on the engineer's personal experience. Design quality is related to the engineer's individual skill level, leading to vastly different design solutions from different engineers. The lack of unified and objective evaluation standards results in unstable product performance; 3. Long design cycle and reliance on experience: The design process relies heavily on the engineer's experience and trial-and-error methods, lacking a systematic optimization process. 4. Inefficient processing of simulation results: After analyzing the system, it is often necessary to extract the flow allocated to each battery cluster and battery pack, and then perform statistics and data sorting one by one, which wastes a lot of development time.

[0024] In response, this invention first discloses a flow field optimization method 100 for a multi-branch flow channel system, such as... Figure 1 As shown, the optimization method of the present invention may include the following steps S110-S140.

[0025] Step S110: Establish a simulation model of the multi-branch flow channel system.

[0026] In step S110, establishing a simulation model of the multi-branch flow channel system is the primary foundation for flow field analysis and optimization. The multi-branch flow channel system described in this invention can be widely applied to various scenarios requiring efficient thermal management, including but not limited to liquid cooling systems for energy storage systems, fuel cell stacks, and server cooling systems. Among these, energy storage systems (such as electrochemical energy storage power stations) have been deployed on a large scale in recent years as a key infrastructure for new energy consumption and stable grid operation. Large-scale energy storage systems typically consist of thousands or even tens of thousands of battery cells connected in series and parallel. These cells generate significant heat during charging and discharging. If this heat cannot be dissipated in a timely and uniform manner, it will lead to uneven temperature distribution within the system, severely impacting battery life and system safety. Therefore, establishing an accurate simulation model of the liquid cooling system is crucial for optimizing flow field distribution and improving heat dissipation efficiency.

[0027] Specifically, the liquid cooling system of an energy storage system has a distinct hierarchical structure: the energy storage system comprises multiple battery clusters, and each battery cluster comprises multiple battery packs. Correspondingly, the liquid cooling system may include a first pipeline (main pipeline) corresponding to each battery cluster and a second pipeline (branch pipeline) corresponding to each battery pack. Based on this hierarchical structure, step S110 can be further divided into the following two sub-steps: Step S111: Establish a first simulation model of multiple battery clusters and the first pipeline flow channel in the liquid cooling system of the energy storage system; and Step S112: Establish a second simulation model of multiple battery packs and the second pipeline flow channel in the battery cluster.

[0028] Specifically, step S111 aims to establish a first simulation model of multiple battery clusters and their corresponding first pipeline flow channels in the overall liquid cooling system of the energy storage system; step S112, on the other hand, establishes a second simulation model of multiple battery packs and their corresponding second pipeline flow channels within a single battery cluster. This hierarchical modeling method can effectively reflect the flow and heat transfer behavior of the system in actual operation, providing an accurate numerical analysis platform for subsequent flow field parameter optimization and structural improvement.

[0029] This invention uses a large-scale containerized energy storage system (hereinafter referred to as "large storage") as an example to illustrate the specific modeling process. First, based on the three-dimensional digital model of the large storage liquid cooling system, a flow field analysis model of the overall liquid cooling system can be constructed using computational fluid dynamics (CFD) methods (such as...). Figure 2 (As shown). This model needs to fully depict the complex flow channel structure of all main pipelines and the internal liquid cooling plates of each battery pack, ensuring that geometric details (such as bends, branches, connectors, etc.) are accurately reproduced. Then, refer to... Figure 3 The technology roadmap, based on the completion of the system-level model, also requires further extraction of liquid cooling system sub-models for individual battery clusters from the overall CFD model (such as...). Figure 4As shown in the diagram, this forms a three-level simulation system of "system-battery cluster-battery pack," ultimately adjusting the flow uniformity of each battery cluster and battery pack to be consistent. The process is clear and highly practical. It is important to note that this stage requires careful attention to the setting of model boundaries and data transfer. For example, the flow and pressure conditions at the battery cluster inlet should be obtained in the system-level model and used as input for the cluster-level simulation.

[0030] The aforementioned hierarchical modeling strategy not only allows for a systematic evaluation of the flow distribution uniformity, pressure loss, and heat transfer performance within each flow channel, but also lays a solid foundation for subsequent steps such as flow field simulation calculations, battery pack inlet area statistics, throttle valve configuration, and model verification and optimization. The advantage of this method lies in decomposing the complex multi-branch system into multiple relatively independent simulation modules, progressively refining the analysis, making the optimization process more targeted and operable, ultimately achieving consistent flow control between each battery cluster and battery pack, and improving the overall thermal management efficiency of the system.

[0031] Step S120: Perform flow field uniformity analysis on the simulation model to determine the distributed flow rate of each branch of the multi-branch flow channel system.

[0032] In step S120, analyzing the flow field uniformity of the simulation model is a core step in optimizing a multi-branch flow channel system. By quantitatively evaluating the distributed flow rate of each branch channel, regions of non-uniform flow within the system can be accurately identified, providing data support for subsequent structural adjustments and parameter optimization. The simulation model in this step can be a CFD model, which can reveal the flow characteristics in complex flow channel networks through numerical simulation, and is particularly suitable for analyzing the distribution behavior of cooling media in liquid cooling systems. For energy storage systems, flow field uniformity is directly related to the consistency of heat dissipation effects of each battery cell, thus affecting the overall system performance and lifespan. In specific implementation, step S120 can be further refined into three sub-steps S121-S123.

[0033] Step S121: Set the boundary conditions of the CFD model and draw the mesh.

[0034] Boundary conditions include setting inlet and outlet conditions, as well as setting internal surfaces. Meshing involves setting minimum and maximum mesh sizes, identifying and refining key areas, setting multiple mesh layers at specific boundary levels, and controlling mesh quality.

[0035] Specifically, the process of establishing a CFD model based on a 3D digital model in this step mainly involves steps such as 3D digital model simplification, mesh generation, boundary condition setting, monitor setting, and solution parameter setting. First, the 3D geometric model is simplified as necessary, removing fine structures that do not affect the flow field characteristics. Then, mesh generation is performed, adhering to strict mesh quality standards. Regarding mesh size settings, the minimum size should be controlled to approximately 1 / 3 of the model's minimum geometric feature, and the maximum size should not exceed 1 / 2 of the maximum pipe diameter to ensure resolution in critical areas. In areas with drastic flow changes, such as valves, elbows, and constriction / expansion sections of flow channels, local mesh refinement is required. Considering that the coolant is usually in a turbulent state, 3-5 boundary layer meshes should be arranged in the near-wall region to accurately capture boundary layer effects. Mesh quality must meet the following requirements: surface mesh skewness less than 0.7, and volume mesh orthogonality quality greater than 0.2, to ensure computational accuracy and convergence.

[0036] To better perform subsequent flow field uniformity analysis of the large-scale storage system, the boundary condition settings are also crucial. After completing the CFD model of the battery pack, the inlet and outlet conditions need to be set appropriately: the inlet is usually a mass flow rate inlet or a velocity inlet, and the total inlet flow rate is determined according to the liquid cooler unit selection (480 L / min in this example), while the total outlet is set as a pressure outlet with a pressure value of 0. To facilitate monitoring of the flow rate of each branch, the cross-sections at the inlet positions of each battery cluster and battery pack should be set as shared internal surfaces. This allows the mass flow rate through these cross-sections to be directly extracted during the simulation, providing raw data for uniformity assessment.

[0037] Step S122: Perform simulation analysis on the CFD model and output the distributed flow rate of each branch channel; After the CFD model is established, CFD analysis software can be used to solve and analyze it, thereby outputting the distributed flow rates of each branch channel. During the solution process, it is necessary to set appropriate turbulence models (such as k-ε or k-ω models), convergence criteria, and monitors to ensure that stable flow field solutions are obtained. After the calculation is completed, the specific distributed flow rates of each branch channel are extracted through the software's post-processing function. These data directly reflect the current flow distribution state of the system.

[0038] Step S123: Based on the allocated flow rate of each branch channel, perform automated data post-processing to output statistical tables and / or visualization curves of multiple flow parameters.

[0039] This step can generate statistical tables and visualization curves of multi-dimensional flow parameters through automated data post-processing. CFD calculations produce result files containing large amounts of data, which are traditionally inefficient and error-prone to manual processing. In this patent, a dedicated data processing script can be written to batch process multiple result files within seconds, generating a series of visualization curves and tables, thus achieving automated analysis of the flow rate of each battery cluster. The flow parameters can include at least one of the following: average flow rate, maximum flow rate, minimum flow rate, and flow rate range for each branch channel. This allows for the automatic generation of statistical tables containing key parameters such as average flow rate, maximum flow rate, minimum flow rate, and flow rate range for each branch channel. Simultaneously, the script can also generate intuitive visualization curves, such as... Figure 5 As shown, the flow distribution and uniformity indicators of each battery cluster are clearly displayed. This automated processing method not only significantly improves analysis efficiency and reduces human error, but also quickly identifies problem areas, providing engineers with clear direction for design optimization and significantly saving development time and costs.

[0040] Step S130: Determine the target inlet area of ​​each branch channel based on the number of branches in the multi-branch channel system, the allocated flow rate of each branch channel, and the cross-sectional area.

[0041] This step is a key decision-making step in the flow field optimization process. Based on the flow rate of each battery cluster obtained in step S120, and combined with the cross-sectional area of ​​the inlet and outlet water pipes of a single battery cluster, the target inlet water area of ​​each battery cluster is calculated. The purpose is to optimize the inlet water area of ​​each battery cluster to make the flow rate of each battery cluster uniform.

[0042] In other words, after obtaining the initial flow distribution of each branch channel through CFD simulation in step S120, the core purpose of this step is to transform these data into specific, executable structural parameters—the target inlet area. This area directly determines the subsequent adjustment of the throttle valve opening or the structural modification of the channel inlet, and is a direct control variable for achieving uniform flow distribution in the system. The basic logic is: by quantitatively adjusting the flow resistance of each branch (reflected in changes in the inlet area), the natural flow deviation caused by differences in channel structure and location is compensated, ultimately enabling all branches to obtain a consistent target flow.

[0043] Specifically, this step may include the following two sub-steps that have a clear logical sequence.

[0044] Step S131: Determine the target flow rate for each branch channel based on the number of branch channels and the total flow rate.

[0045] First, a uniform target flow rate needs to be set for each branch channel in the system. The determination of this target flow rate depends on the system's macroscopic parameters: the total number of branch channels N and the total inlet flow rate Q0. The formula for calculating the target flow rate Q is as follows: Q = Q0 / N. For example, in the large containerized energy storage system embodiment involved in this invention, the total inlet water flow rate is 480 L / min, and the entire liquid cooling system contains 6 battery clusters, i.e., N = 6. Therefore, the target flow rate for each battery cluster branch is Q = 480 / 6 = 80 L / min. This means that the ideal optimization result is to stabilize the coolant flow rate of each battery cluster at 80 L / min.

[0046] Step S132: Determine the target inlet area of ​​each branch channel based on the allocated flow rate, cross-sectional area, and target flow rate of each branch channel.

[0047] Once the target flow rate for each branch is determined, the required target inlet area can be calculated based on the initial state of each branch. This calculation is based on the fundamental relationship between flow rate and flow area in fluid mechanics, and is solved by constructing an equation to conserve flow rate before and after optimization. For example, the target inlet area for each branch can be determined by the following formula: Q n *A n =Q*A.

[0048] Among them, Q n The allocated flow rate for the nth branch can be understood as the calculated flow rate of the nth battery cluster before optimization. This data comes directly from the CFD simulation results of step S122 and characterizes the actual flow rate obtained by this branch under the existing structure. A n The target inlet area of ​​the nth branch channel can be understood as the inlet and outlet area of ​​the nth battery cluster after optimization. This is the final goal to be solved in this step, referring to the ideal flow area that needs to be adjusted at the inlet of this branch to achieve equal flow distribution. Q is the target flow rate of the nth branch channel, which can be understood as the target flow rate of a single battery cluster. In this example, it is the calculation result of step S131, a fixed value for all branches, which is 1 / 6 of the total system flow rate, i.e., 80 L / min. A is the cross-sectional area of ​​the nth branch channel, which can be understood as the cross-sectional area of ​​the inlet and outlet pipes of a single battery cluster before optimization. This is the original physical cross-sectional area at the branch inlet. In this embodiment, the cross-sectional areas of the inlet and outlet pipes of all battery clusters can be equal before optimization.

[0049] The physical meaning of this formula is as follows: assuming that the local resistance coefficient of the branch is changed by adjusting the inlet area, and that the adjusted branch is located at its new inlet area A... n This allows the target flow rate Q to pass through. After formula transformation, the target inlet area A can be obtained. n The direct solution formula: A n=(Q*A) / Q n This formula allows for the calculation of a unique, customized target influent area for each branch with unevenly distributed flow. For example, an initial flow rate Q... n For a larger branch (e.g., 85 L / min), the calculated target inlet area A n A flow rate smaller than the original cross-sectional area A means that the throttle valve needs to be closed appropriately to increase resistance; conversely, a branch with a small initial flow rate needs a larger A. n That is, open the throttle valve wider.

[0050] The target inlet area determination method proposed in this invention has significant technical advantages and application value. It provides a precise and quantitative control method, eliminating the reliance on empirical "trial and error" for flow field optimization. The most crucial advantage is that it allows for precise flow distribution by adjusting the non-standard opening of the end-point throttle valve, while maintaining the standardized design of each branch main pipeline. This avoids the need to redesign and manufacture non-standard pipelines of different diameters to balance flow, greatly simplifying the production and assembly process and effectively controlling manufacturing costs. This method is not only applicable to system-level flow regulation between battery clusters but can also be recursively applied downwards to fine-tuning the flow between individual battery packs within a battery cluster, thus forming a complete and efficient hierarchical flow control scheme. Step S140: Optimize and adjust the flow rate distribution of each branch channel according to the target inlet area.

[0051] In this step, the flow rate distribution of each branch channel can be optimized and adjusted based on the calculated target inlet area, which is a key step in transforming theoretical design into practical engineering application. This process achieves precise flow control of the multi-branch system through accurate calculation and configuration of throttling devices, ensuring uniform distribution of the cooling medium among the battery clusters. The implementation of this step marks the transition from the analysis and diagnosis phase to the active intervention phase, achieving an overall improvement in system performance through the optimization of structural parameters. Step S140 can specifically include: Step S141: Determine the first optimized radius of each branch flow channel based on the target inlet area and the radius of each branch flow channel.

[0052] To achieve uniform flow distribution, the target inlet area must first be converted into geometric parameters directly applicable to engineering design. Considering ease of manufacturing and installation, circular cross-section pipes are typically used; therefore, the optimized radius for each branch needs to be calculated. Based on the physical relationship between flow rate and flow area in fluid mechanics, the optimized radius of each battery cluster can be calculated, i.e., the first optimized radius r of the nth branch flow channel. n Calculate r using the following formula: n =r*(Q / Q n )1 / 2 .

[0053] Where, r n Let r be the inlet and outlet water radius of the nth battery cluster after optimization, and r be the initial radius of the nth branch channel, which can be understood as the inlet and outlet water pipe radius of the battery cluster before optimization. In this example, the inlet and outlet water radii of each battery cluster can be equal before optimization. The derivation of this formula is based on the conservation relationship between flow rate and flow area before and after optimization, ensuring that the overall flow distribution requirements of the system are met while changing the flow resistance.

[0054] For example, before optimization, the radius of the inlet and outlet pipes for a single battery cluster is r = 12.5 mm, and Q is the target flow rate of this branch, 80 L / min. n The initial flow rate of this branch is the pre-optimized flow rate. Using the above formula, the optimized inlet and outlet pipe radii for each battery cluster are calculated as follows: r1 = 11.949 mm; r2 = 12.314 mm; r3 = 12.511 mm; r4 = 12.648 mm; r5 = 12.774 mm; r6 = 12.873 mm. These data clearly show that branches with larger initial flow rates, such as battery cluster 1, have higher Q... n The initial flow rate is necessarily larger, so a smaller optimization radius is needed to increase flow resistance; while branches with smaller initial flow rates can obtain larger optimization radii, thereby reducing resistance and allowing them to obtain more flow.

[0055] Step S142: Determine the second optimized radius of the inlet and outlet water pipes based on the maximum value in the first optimized radius.

[0056] After obtaining the initial optimized radius for all branches, a unified standard pipeline radius needs to be determined from the perspective of system integration and manufacturing feasibility. This invention defines R as the second optimized radius, and its selection principle is that it must be greater than or equal to all initial optimized radii r. n The maximum value in the range. This design strategy has significant engineering implications: it allows for the construction of a unified, standardized piping system, and by configuring adjustable throttle valves at the inlet of each branch, it can precisely meet the different flow area requirements of each branch, thereby avoiding the customization of non-standard piping and significantly reducing manufacturing costs and supply chain complexity.

[0057] Based on the calculation results of step S141, the maximum value of the first optimized radius is r6 = 12.873 mm. Therefore, the second optimized radius R should be a standard pipe diameter not less than this value. In this embodiment, it is recommended to select an inlet and outlet water pipe with R = 13 mm. This selection satisfies the physical space requirements of all branches and also provides sufficient operating space for the installation and adjustment of the throttle valve.

[0058] Step S143: Adjust the flow rate in each branch channel according to the first optimization radius and the second optimization radius. Specifically, the flow rate in each branch channel is adjusted by adjusting the opening degree of the throttle valve.

[0059] After determining the second optimization radius, the required throttle valve opening for each branch can be accurately calculated. The throttle valve opening for the nth branch is α. n α can be calculated using the following formula: n =(r n / R) 2 .

[0060] This formula directly reflects the proportion of the flow area that the throttle valve needs to cover. (The formula is then used to calculate R=13mm and r for each branch.) n Substituting the values ​​into the formula, the formula for calculating the opening degree of the battery cluster n is: α n =(r n / 13) 2 The calculated opening values ​​of the throttle valves for battery clusters 1-5 are: α1=84.48%; α2=89.72%; α3=92.62%; α4=94.66%; α5=96.55%; α6=98.06%.

[0061] It should be noted that in CFD simulation models, the calculated throttle valve openings can be physically equivalent using porous jump boundary conditions or porous media models. For example, by writing user-defined functions (UDFs), different opening values ​​can be dynamically assigned to the corresponding branch inlets, accurately simulating the changes in the resistance characteristics of the throttle valve. Through this simulation, the effectiveness of the optimization scheme can be verified, ensuring that after implementing these opening settings in the actual physical system, the flow rate in each branch can achieve the expected uniform distribution.

[0062] This invention achieves precise optimization of flow distribution in multi-branch flow system by combining the aforementioned mathematical calculations with CFD simulation. This method transforms the complex fluid distribution problem into calculable and executable engineering parameters, avoiding trial-and-error methods based on experience and greatly improving optimization efficiency and accuracy. Finally, based on these calculated throttle valve openings, the initial CFD model established in step S110 is updated and optimized to form a simulation model that includes precise resistance control and can realistically reflect the optimized system state, providing a reliable basis for final engineering implementation.

[0063] In another embodiment, to verify the flow uniformity of the optimized model, after optimizing and adjusting the flow rate of each branch channel according to the target inlet area in step S140, the present invention may further include steps S151-S152. This introduces a verification and iterative optimization mechanism, which aims to systematically evaluate the optimized simulation model to ensure that the flow field uniformity meets the design target.

[0064] Step S151: Verify the flow field uniformity of the simulation model of the optimized multi-branch flow channel system.

[0065] The core of this step is to re-simulate and analyze the optimized CFD model to quantitatively evaluate the optimization effect. Specifically, referring to step S120 in the aforementioned embodiment, the flow field calculation for the optimized model with integrated throttling valve configuration needs to be performed again. After the simulation is completed, again referring to step S123, an automated data processing script is used to quickly extract and analyze the flow data of each battery cluster under the new throttling conditions. By comparing the optimized flow distribution (including key parameters such as average flow, maximum / minimum flow, and flow range) with the data before optimization, it is possible to objectively determine whether the flow uniformity has been significantly improved.

[0066] Step S152: If the verification fails, iteratively optimize the allocated flow rate for each branch channel.

[0067] The above verification steps will produce a clear pass or fail conclusion. If the flow uniformity of each battery cluster is highly consistent, it indicates that the piping design of the large-capacity liquid cooling system has met the requirements and the design process is complete. If the verification result fails, meaning that there are still significant differences in flow rate among the branches, an iterative optimization program needs to be initiated. At this stage, the main adjustment methods include fine-tuning the opening of the throttle valve, or adjusting its installation position if necessary. This process is a closed loop of "simulation-verification-adjustment," which needs to be repeated until the flow uniformity of all battery clusters in the system meets the preset convergence criteria.

[0068] See also Figure 6 The optimized influent flow rate data for each battery cluster is shown below. It can be seen that after completing the calculations and model modifications in the above steps in this example, the calculated flow rate of each cluster is uniform, and the flow rate range of each cluster is reduced from 12.12 L / min before optimization to 1 L / min. The optimization effect is significant, and the flow rate uniformity of each battery cluster is highly consistent.

[0069] In summary, this invention provides a method for simulating and optimizing the flow field uniformity of large-scale energy storage systems. This method combines high-fidelity CFD simulation, automated data processing, and optimization algorithms to quickly and accurately evaluate and optimize flow channel design, achieving uniformity of the coolant flow field within the system and thus ensuring temperature field consistency. It is evident that this invention can change the traditional "trial and error" model of liquid cooling system design, which relies on engineers' experience, by constructing a complete technical closed loop of "high-fidelity CFD simulation → automated data analysis → theoretical formula calculation → iterative optimization verification." It transforms the design process into a systematic, data-driven scientific workflow. Specifically, as described in steps S110 to S130, this method first accurately reproduces the physical system through hierarchical modeling, then efficiently diagnoses the root cause of flow unevenness using automated scripts, and finally guides physical adjustments through precise mathematical calculations. This achieves a qualitative leap from qualitative analysis to quantitative optimization, significantly improving the reliability and success rate of the design.

[0070] Specifically, the beneficial effects of this invention may include: 1. Significant Optimization of Efficiency and Cost. In terms of efficiency, the core of this method lies in replacing uncertain guesswork with precise calculations. By directly solving for optimization parameters using rigorous mathematical formulas, the empirical flow channel design process is transformed into a systematic optimization process based on data and models. This significantly reduces the iterative process that typically requires dozens of simulations and fine-tuning iterations, greatly improving the scientific rigor and reliability of the design. Simultaneously, automated data processing technology replaces cumbersome manual post-processing, significantly shortening the overall design cycle. In terms of cost, the flexible solution of "standard pipe diameter + adjustable throttle valve" cleverly balances the contradiction between performance customization and mass production, avoiding the additional manufacturing costs and supply chain complexity caused by non-standard components. This significantly shortens the design cycle and computational costs while ensuring accuracy.

[0071] 2. Clear Hierarchical Optimization Architecture. Faced with a complex system composed of thousands of battery packs, this invention provides a clear path of "breaking down the whole into parts and managing in layers," with a clear design and optimization logic that simplifies complexity. Its strict adherence to the hierarchical optimization logic of "system → battery cluster → battery pack" decomposes the macroscopic systemic problem into a series of independently solvable sub-problems, simplifying the complex issue. Specifically, it first adjusts the flow uniformity of each battery cluster to be consistent, and then adjusts the flow uniformity of each battery pack within the battery cluster to be consistent, making the method highly practical. This structured approach not only makes the optimization objectives clear and the steps orderly, but also significantly enhances the operability and practicality of the solution in engineering practice.

[0072] 3. Clear and verifiable performance improvement. This optimization method effectively improves the flow uniformity of each battery cluster and battery pack, thereby ensuring battery temperature uniformity. The flow difference between branches can be effectively reduced from a high level before optimization (e.g., exceeding 10 L / min) to an ideal range (e.g., 1-2 L / min). This flow field homogenization is a fundamental physical prerequisite for ensuring the consistency of the battery system's temperature field, thus directly supporting the core performance indicators of battery cycle life and operational safety.

[0073] 4. Broad Industry Application Potential. Although this method uses the liquid cooling design of a large-scale energy storage system as a specific example, its core principles possess high versatility. That is, the precise modeling and control of flow distribution in multi-branch systems can also be extended to the design of other multi-branch flow channel systems requiring uniform flow field distribution, such as fuel cell stacks and servers. Its core processes and calculation methods can be effectively transferred and applied to any field with parallel flow channels and uniform flow field requirements, such as fuel cell stacks and high-end server clusters.

[0074] In another embodiment, the present invention also provides a flow field optimization device 200 for a multi-branch flow channel system, such as... Figure 7 As shown, the optimization device 200 may include: a model building module 210 for establishing a simulation model of the multi-branch flow channel system; a flow analysis module 220 for performing flow field uniformity analysis on the simulation model to determine the distributed flow of each branch flow channel in the multi-branch flow channel system; a data processing module 230 for determining the target inlet area of ​​each branch flow channel based on the number of branches in the multi-branch flow channel system, the distributed flow of each branch flow channel, and the cross-sectional area; and an optimization adjustment module 240 for optimizing and adjusting the distributed flow of each branch flow channel based on the target inlet area.

[0075] In another embodiment, the present invention also provides a multi-branch flow channel system 300, which can be applied to any of the following: energy storage system, fuel cell stack, server, etc. The multi-branch flow channel system 300 may include: the flow field optimization device 200 of the multi-branch flow channel system described above.

[0076] The beneficial effects of the multi-branch flow channel system 300 and its flow field optimization device 200 provided in this application can be referred to the above description of the flow field optimization method 100 for the multi-branch flow channel system, and will not be repeated here.

[0077] Furthermore, embodiments of this application also provide a machine-readable storage medium storing instructions for causing a machine to execute the aforementioned flow field optimization method for a multi-branch flow channel system. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0078] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The functional steps specified in one or more boxes.

[0081] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0082] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0083] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0084] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0085] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for optimizing the flow field of a multi-branch flow channel system, characterized in that, The optimization method includes: Establish a simulation model of the multi-branch flow channel system; The flow field uniformity analysis is performed on the simulation model to determine the distributed flow rate of each branch of the multi-branch flow channel system. Based on the number of branches in the multi-branch channel system, the allocated flow rate of each branch channel, and the cross-sectional area, the target inlet area of ​​each branch channel is determined; and Based on the target inlet area, the flow rate distribution of each branch channel is optimized and adjusted.

2. The optimization method according to claim 1, characterized in that, The multi-branch flow channel system is a liquid cooling system and is used in any of the following: energy storage systems, fuel cell stacks, and servers. The energy storage system includes multiple battery clusters, each battery cluster includes multiple battery packs, and the liquid cooling system of the energy storage system includes a first pipeline flow channel corresponding to each battery cluster and a second pipeline flow channel corresponding to each battery pack.

3. The optimization method according to claim 2, characterized in that, In the case where the multi-branch flow channel system is the liquid cooling system of the energy storage system, establishing a simulation model of the multi-branch flow channel system includes: A first simulation model of the multiple battery clusters and the first pipeline flow channel is established in the liquid cooling system of the energy storage system; and A second simulation model of the multiple battery packs and the second pipeline flow channel is established in the battery cluster.

4. The optimization method according to any one of claims 1-3, characterized in that, The simulation model is a CFD model. The flow field uniformity analysis performed on the simulation model to determine the distributed flow rate of each branch of the multi-branch flow system includes: The boundary conditions of the CFD model are set and the mesh is drawn; preferably, the drawn mesh includes at least one of the following: the size range of the mesh is set between 1 / 3 of the minimum geometric feature of the simulation model and 1 / 2 of the maximum pipe diameter of the simulation model; 3-5 boundary layer meshes are arranged in the near-wall region of the simulation model; the surface mesh skewness is less than 0.7 and the volume mesh orthogonality mass is greater than 0.

2. The CFD model is simulated and analyzed to output the distributed flow rate of each branch channel; Preferably, the optimization method further includes: performing automated data post-processing based on the allocated flow rate of each branch channel to output statistical tables and / or visual curves of multiple flow parameters. The flow parameters include at least one of the following: the average flow rate, maximum flow rate, minimum flow rate, and flow range of each branch channel.

5. The optimization method according to any one of claims 1-3, characterized in that, The determination of the target inlet area for each branch channel based on the number of branch channels, the allocated flow rate of each branch channel, and the cross-sectional area includes: The target flow rate for each branch flow channel is determined based on the number of branch flow channels and the total flow rate. Based on the allocated flow rate, cross-sectional area, and target flow rate of each branch channel, the target inlet area of ​​each branch channel is determined by the following formula: Q n *A n =Q*A, Among them, Q n Distribute the flow rate to the nth branch channel; A n Q is the target inlet area of ​​the nth branch channel; Q is the target flow rate of the nth branch channel; and A is the cross-sectional area of ​​the nth branch channel.

6. The optimization method according to claim 5, characterized in that, The optimization and adjustment of the flow distribution in each branch channel based on the target inlet area includes: Based on the target inlet area and the radius of each branch channel, determine the first optimized radius of each branch channel; Based on the maximum value among the first optimized radii, determine the second optimized radii of the inlet and outlet water pipes; and The flow rate in each branch channel is adjusted based on the first optimization radius and the second optimization radius.

7. The optimization method according to claim 6, characterized in that, The flow rate in each branch channel is adjusted by the opening degree of the throttle valve. The first optimized radius r of the nth branch channel n Calculate using the following formula: r n =r*(Q / Q n ) 1 / 2 , The throttle valve opening α of the nth branch flow channel n Calculate α using the following formula: n =(r n / R) 2 , Where r is the initial radius of the nth branch flow channel, and R is the second optimized radius, which is greater than or equal to the maximum value of the first optimized radius.

8. The optimization method according to any one of claims 1-3, characterized in that, After optimizing and adjusting the flow rate of each branch channel according to the target inlet area, the optimization method further includes: The flow field uniformity of the optimized multi-branch flow channel system simulation model was verified. If the verification fails, the allocated flow rate for each branch channel is iteratively optimized.

9. A flow field optimization device for a multi-branch flow channel system, characterized in that, The optimization device includes: The model building module is used to establish a simulation model of the multi-branch flow channel system. The flow analysis module is used to perform flow field uniformity analysis on the simulation model to determine the distributed flow of each branch of the multi-branch flow channel system. The data processing module is used to determine the target inlet area of ​​each branch channel based on the number of branches in the multi-branch channel system, the allocated flow rate of each branch channel, and the cross-sectional area; and The optimization and adjustment module is used to optimize and adjust the flow rate distribution of each branch channel according to the target inlet area.

10. A multi-branch flow channel system, characterized in that, Applied to any of the following: energy storage systems, fuel cell stacks, servers, The multi-branch flow channel system includes: a flow field optimization device for the multi-branch flow channel system according to claim 9.

11. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to execute: the flow field optimization method for a multi-branch flow channel system according to any one of claims 1-8.

12. A processor, characterized in that, Used to run programs The program is used to execute the flow field optimization method for a multi-branch flow channel system according to any one of claims 1-8.

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