A method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network

Abstract

The present invention belongs to the field of digital twin technology, and discloses a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network. The digital twin model construction method involves the generation of a liquid cooling cold plate model, the generation of a pipe network single-row branch channel sub-module, and the generation of a supply / return liquid main channel sub-module. The present invention establishes a system model based on hybrid modeling of physical mechanisms and machine learning methods, which greatly shortens the simulation time from the original hours-day simulation time to seconds-minute simulation time; the present invention provides key internal flow, temperature, and pressure data for a multi-node cold plate parallel liquid cooling pipe network, and realizes real-time monitoring of a large amount of internal unmeasurable data in the operation and maintenance of the liquid cooling pipe network; the present invention provides the necessary internal pipe network data for the operation fault alarm function of the digital twin system of the multi-node cold plate parallel liquid cooling pipe network, greatly reducing the electromagnetic interference and false alarms and false alarms caused by the arrangement of sensor monitoring.

Description

Technical Field

[0001] The present invention mainly relates to the field of digital twin technology, and in particular to a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network. Background Art

[0002] Liquid cooling of electronic equipment involves circulating liquid to continuously remove heat from electronic components through convection heat transfer between the liquid and the surfaces of the electronic components or the solid surface of a cold plate, thereby ensuring that the temperature of the electronic components remains within the normal operating range. Due to its advantages such as efficient heat transfer, low noise, and compactness, it has been widely used to cool a variety of electronic devices, such as data centers, electric vehicles, mobile devices, IGBTs, laser weapons, and phased array radars.

[0003] Liquid-cooled cold plates are widely used to dissipate heat from electronic equipment. A cooling medium at a specific flow rate, temperature, and pressure is supplied to the outside of the cold plate. Through convection and conduction within the cold plate, the heat generated by the electronic equipment is transferred to the cooling medium, ensuring that the electronic equipment or device operates within an optimal temperature range for long-term reliability and longevity. Multi-node parallel liquid cooling pipe network systems are widely used in equipment such as liquid-cooled data centers and liquid-cooled phased array radars. Their design involves analyzing fluid properties, pipe network flow resistance and flow distribution, heat conduction, and fluid-structure interaction. This design process is often carried out using fluid or thermal design software simulation. The design process repeatedly iterates the results under multiple input conditions to evaluate heat dissipation performance and ultimately determine the design parameters. Conducting combined thermal and electrical simulations and software trial-and-error iterations mitigates design risks, but this inevitably places high demands on simulation accuracy, which in turn reduces simulation efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for constructing a digital twin model of a multi-node cold plate parallel liquid-cooling pipe network. Digital twin technology has the characteristics of high fidelity, multi-physics, multi-disciplinary, and multi-scale. By establishing this digital twin model of a multi-node cold plate parallel liquid-cooling pipe network, the traditional three-dimensional CFD simulation model can be reduced to a high-fidelity reduced-order model (ROM). By performing multi-operating condition analysis on the digital twin ROM model to evaluate the cold plate heat dissipation performance, the design iteration time can be greatly shortened. By establishing a real-time mapping between the physical pipe network and the reduced-order ROM model, the digital twin model can be corrected, improving accuracy while significantly shortening the design cycle. In addition, during the thermal testing phase, the internal flow characteristics of the parallel liquid-cooled cold plate, the flow resistance characteristics of the pipe network system, and the flow distribution, which cannot be directly seen, can also be visualized through its twin model simulation.

[0005] To achieve the above objectives, the present invention provides a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network, comprising the following steps:

[0006] Step 1: Build a detailed mechanism-based simulation model of the internal flow path of the liquid-cooled cold plate. Run the detailed mechanism-based simulation model multiple times according to the operating range of the liquid-cooled cold plate to obtain the input-output dataset of the liquid-cooled cold plate.

[0007] The input-output dataset is used as training data and a machine learning algorithm is used to generate the liquid-cooled cold plate model ROM1.

[0008] Step 2: Create a single-row liquid supply model around the liquid-cooled cold plate model, including a liquid supply manifold and a liquid-cooled cold plate;

[0009] The liquid supply channel supplies liquid to a row of j basic units. Each basic unit includes k liquid-cooled cold plates. The liquid-cooled cold plate model ROM1 generated in step 1 is nested in the single-row liquid supply model to establish an interface model for the liquid supply channel to flow into each liquid-cooled cold plate.

[0010] Step 3: Establish a single-row liquid return model including a liquid return branch channel and a liquid return interface;

[0011] Step 4: Assemble the liquid-cooled cold plate model, the single-row liquid supply model, and the single-row liquid return model to form a decoupled single-row diversion channel model;

[0012] Step 5: Based on the working range of the liquid cooling network, obtain multiple sets of input-output data sets of the decoupled single-row branch channel model running in the working state;

[0013] The input-output data set is used as training data and a machine learning algorithm is used to generate a decoupled single-row runner reduced-order model ROM2.

[0014] Step 6: The pipe network includes a total of i rows of liquid supply diversion channels. According to step 5, i decoupled single-row diversion channel reduced-order models are generated to form a pipe network single-row diversion channel sub-module;

[0015] Step 7: Generate the main liquid supply / return channel submodule: The main liquid supply channel submodule calculates the flow distribution of each liquid supply branch channel and performs a bidirectional coupling calculation with the single-row branch channel submodule of each pipe network row. The main liquid supply channel submodule provides the calculated flow rate to the single-row branch channel submodule of the pipe network, and the single-row branch channel submodule of the pipe network feeds back the single-row flow resistance data to the main liquid supply channel submodule.

[0016] The liquid return main channel submodule calculates the flow distribution of each liquid return branch channel and performs bidirectional coupling calculation with the pipe network single-row branch channel submodule of each row. The liquid return main channel submodule provides the calculated flow to the pipe network single-row branch channel submodule, and the pipe network single-row branch channel submodule feeds back the single-row flow resistance data to the liquid return main channel submodule.

[0017] Step 8: Scan output submodule generation: The scan output submodule separates the information of the specified liquid cooling plate of the specified basic unit according to the predefined liquid cooling plate position number and the specified flow information calculated by the supply / return liquid main channel submodule.

[0018] Furthermore, it also includes step 9: combining the supply / return liquid main channel sub-module, the pipe network single-row branch channel sub-module and the scanning output sub-module to establish a liquid cooling pipe network digital twin model based on row attributes and encapsulate it.

[0019] Furthermore, it also includes step 10: liquid cooling pipe network digital twin model SDK generation: input real-time measurement data and output SDK file.

[0020] Furthermore, the interface model of the liquid supply channel established in step 2 flowing into each liquid-cooled cold plate is encapsulated.

[0021] Furthermore, the liquid supply branch channel corresponding to each group of basic units in step 2 is established by the Modelica model.

[0022] Furthermore, the k liquid-cooled cold plates included in each basic unit in step 2 are connected in parallel.

[0023] Furthermore, the pipe network includes 81 basic units in 9 rows and 9 columns, and each basic unit includes 5 parallel liquid-cooled cold plates.

[0024] Furthermore, in step 10, the TwinDeployer module is used to output the SDK file.

[0025] Beneficial effects:

[0026] The present invention provides a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network, and proposes a method for modeling the main flow channel and branch flow channel inside the multi-node cold plate parallel liquid cooling pipe network. The present invention establishes a system model based on hybrid modeling of physical mechanisms and machine learning methods, which greatly shortens the simulation time from the original hours-day simulation time to seconds-minutes simulation time; the present invention provides key internal flow, temperature, and pressure data for the multi-node cold plate parallel liquid cooling pipe network, realizing real-time monitoring of a large amount of internal unmeasurable data in the operation and maintenance of the liquid cooling pipe network; the present invention provides the necessary internal pipe network data for the operation fault alarm function of the digital twin system of the multi-node cold plate parallel liquid cooling pipe network, greatly reducing the electromagnetic interference and false alarms and false alarms caused by the arrangement of sensor monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a diagram of the composition and flow path pipelines of a multi-node cold plate parallel liquid cooling network involved in an embodiment of the present invention;

[0028] Figure 2It is a basic unit in the liquid cooling network involved in the embodiment of the present invention;

[0029] Figure 3 It is the input / output interface of the digital twin model of the liquid cooling pipe network involved in the embodiment of the present invention;

[0030] Figure 4 This is a flow chart of a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to an embodiment of the present invention;

[0031] Figure 5 It is a single-line liquid supply model diagram;

[0032] Figure 6 It is a composition diagram of a single-line liquid return model;

[0033] Figure 7 This is a decoupled single-row branch channel model diagram;

[0034] Figure 8 This is a comparative analysis of the calculation results of the decoupled single-row branch channel model and the multi-row coupled model;

[0035] Figure 9 This is a diagram of the numbering method for parallel liquid-cooled cold plates;

[0036] Figure 10 It is the composition and flow channel distribution diagram of the liquid cooling pipe network embodiment;

[0037] Figure 11 It is a flow chart for generating liquid-cooled cold plate model;

[0038] Figure 12 This is a model diagram of a liquid-cooled cold plate;

[0039] Figure 13 It is a diagram of the single-line liquid supply model;

[0040] Figure 14 It is a diagram of the single-line liquid return model;

[0041] Figure 15 It is a diagram of the decoupled single-row diversion channel model;

[0042] Figure 16 This is the training flow chart of the decoupled single-row split-channel reduced-order model;

[0043] Figure 17 It is a submodule diagram of a single-row diversion channel of a pipe network;

[0044] Figure 18 It is the scanned source submodule diagram;

[0045] Figure 19 This is a digital twin model diagram of the liquid cooling pipe network;

[0046] Figure 20This is the SDK interface diagram for the liquid cooling network digital twin model;

[0047] Figure 21 This is the liquid cooling pipe network digital twin model SDK file. DETAILED DESCRIPTION

[0048] The preferred structure and implementation method of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0049] like Figures 1 to 21 As shown, an embodiment of the present invention discloses a technical solution for a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network. The present invention relates to liquid cooling cold plate model generation, single-row liquid supply model generation, single-row liquid return model generation, decoupled single-row branch channel model generation, decoupled single-row branch channel reduced-order model generation, pipe network single-row branch channel sub-module generation, liquid supply / return main channel sub-module generation, scanning output sub-module generation, liquid cooling pipe network digital twin model generation, and liquid cooling pipe network digital twin model SDK generation.

[0050] The multi-node cold plate parallel liquid cooling pipe network is a unit array composed of several parallel cold plates. Its composition and pipe network pipeline diagram are as follows: Figure 1 As shown. The pipe network consists of i×j basic units, such as Figure 2 The diagram is shown within the yellow dashed line. Each basic unit contains k parallel liquid-cooled cold plates (five in the figure), meaning the entire liquid-cooled network contains a total of i × j × K parallel liquid-cooled cold plates. Cold plates are numbered according to their location in the network, where "cold plate ijk" refers to the kth liquid-cooled cold plate in the basic unit in the i-th row and j-th column of the network.

[0051] The liquid cooling network is equipped with supply and return channels. This includes one main supply channel and one return channel. Coolant enters the main supply channel through the network's supply port, flows through row i of supply branch channels, and supplies the i-th row of basic units. It then flows into the liquid-cooled cold plate to cool the components. It then enters rows i and i+1 of return branch channels, converges, and enters the main return channel. Finally, it flows out through the network's return port, completing a complete liquid cooling cycle.

[0052] The input and output interfaces of the digital twin model of the multi-node cold plate parallel liquid cooling pipe network are as follows: Figure 3As shown in the figure. The model's input data includes the coolant temperature and flow rate at the network inlet, the coolant pressure at the network outlet, the heating power of different cold plate positions, and the cold plate position data i, j, and K. This data can all be acquired through real-time monitoring by sensors in physical form. This input data drives its digital twin model to perform real-time calculations and simulations, outputting calculation results that can calculate the outlet pressure, inlet flow rate, inlet enthalpy, heating power, and coolant outlet temperature of a specified "cold plate ijk" in the network. In phase change cooling systems, this data can also be used to calculate the coolant's vapor mass fraction. This allows for a comprehensive understanding of the flow distribution and temperature distribution of each cold plate within the network, significantly reducing electromagnetic interference and false alarms caused by the deployment of sensor monitoring.

[0053] Among the input data, the coolant temperature at the network inlet, the coolant flow rate at the network inlet, and the coolant pressure at the network outlet are flow parameters for the supply and return channels of the liquid cooling network. These are the physical quantities that drive the flow mechanisms within the entire model. These signals can originate from sensor signals or cooling equipment connected externally to the liquid cooling network. The heating power of each cold plate in the liquid cooling network can be individually controlled, so the model interface is configured separately for the heating power of each row and column of cold plates. Because the cold plates involved in the liquid cooling network digital twin model are all connected in parallel and numerous, the physical quantities required to be calculated for each cold plate are numerous, making it impossible to output all of them in parallel. Therefore, a scanning mechanism is implemented. First, all cold plates are numbered. Then, the liquid cooling network digital twin model outputs all the physical quantities for each cold plate based on the number. This scanning mechanism scans the cold plate numbers, providing input interfaces for row and column numbers, as well as the cold plate number within the basic unit.

[0054] The output interfaces of this model include: outlet pressure of a specified cold plate, inlet flow rate of a specified cold plate, inlet enthalpy of a specified cold plate, heating power of a specified cold plate, coolant temperature of a specified cold plate outlet, temperature of a specified cold plate, and mass fraction of coolant vapor at the outlet of a specified cold plate.

[0055] The overall architecture of the digital twin model construction method for the multi-node cold plate parallel liquid cooling pipe network is as follows: Figure 4 As shown, the model is generated from three submodules: the "supply / return main channel submodule," the "pipeline network single-row branch channel submodule," and the "scanning output submodule." Of these three modules, the supply / return main channel submodule and the single-row branch channel submodule operate in a bidirectionally coupled manner. The supply / return main channel submodule transmits the coolant flow rate signal for each row to the single-row branch channel submodule, and the single-row branch channel submodule transmits the equivalent flow resistance signal for each row to the supply / return main channel submodule, thereby calculating the flow within the entire liquid cooling network. The scanning output module uses the single-row flow rate and supply / return main channel pressure data, along with the input basic unit position number, to output specific information for the cold plate at that position, including flow rate, temperature, pressure, and heat output.

[0056] Example 1

[0057] This example takes a liquid cooling network comprising 9 rows and 9 columns of basic units as an example, wherein each basic unit comprises 5 cold plates connected in parallel, and provides an implementation method and a specific implementation process. This example is carried out using single-phase liquid cooling as an example, but is also applicable to two-phase liquid cooling systems, and this cannot be used to limit the scope of protection of the present invention.

[0058] Example Pipeline network distribution Figure 10 As shown, the network contains 81 basic units and 405 cold plates. The location number information is shown in Figure 10 shown.

[0059] The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network in this embodiment includes the following steps:

[0060] Step 1: Build a detailed mechanism-based simulation model of the liquid-cooled cold plate's internal flow path. Generate a certain number of input boundary conditions based on the cold plate's operating range. Run the detailed mechanism-based simulation model multiple times to obtain corresponding simulation results, thereby obtaining an input-output dataset for the liquid-cooled cold plate.

[0061] The input-output dataset is used as training data and a machine learning algorithm is used to generate the liquid-cooled cold plate model ROM1.

[0062] Step 2: Create a single-row liquid supply model including a liquid supply manifold and a liquid cooling cold plate around the liquid cooling cold plate model;

[0063] Single-line liquid supply model Figure 5 The yellow-framed liquid manifolds supply liquid to a row of j basic units. Each basic unit contains k liquid-cooled cold plates (k is 5 in the figure). As shown in each group, the liquid supply manifolds corresponding to the basic units are established by the Modelica model. The liquid-cooled cold plate model (ROM1) is generated by step 1 and nested in the single-row liquid supply model. Finally, the interface model for the liquid supply manifolds flowing into each cold plate is established.

[0064] The specific single-line liquid supply model is as follows: Figure 13 As shown in the figure, the blue module is the pipeline between the two basic units in the liquid supply manifold, the rose-red module is the liquid cooling cold plate model, and the pink module is the basic unit interface model with two and three interfaces.

[0065] Step 3: Establish a single-row liquid return model including a liquid return branch channel and a liquid return interface;

[0066] Single-line liquid return model Figure 6 As shown in the yellow box, it consists of a return liquid shunt channel and a return liquid interface between the return liquid shunt channel and the basic unit. The model is as follows Figure 14The orange module is the pipeline between two units in the return flow channel.

[0067] Step 4: To characterize the flow characteristics of multiple parallel pipes in the pipe network, this model construction method first decouples the complex multiple parallel branch channels in the pipe network. The decoupling method is as follows: Figure 7 As shown in the figure, the specific analysis method is: taking the liquid cooling cold plate model (ROM1) as the core and coordinating its flow mechanism, a complex flow network model of the liquid cooling pipe network is constructed. Single-row decoupled and multi-row coupled pipe network models are established respectively, and their key output parameters are examined under the same boundary conditions. The comparison results of the output parameters are shown in the figure. Figure 8 The error between the decoupled single-row result and the 3-row / 5-row coupled result is approximately 0.0002%. Therefore, the decoupling of the complex flow network within the official website is acceptable, and the impact on the accuracy of the multi-row parallel calculation is negligible.

[0068] The liquid-cooled cold plate model, the single-row liquid supply model, and the single-row liquid return model are assembled to form a decoupled single-row diversion channel model;

[0069] The model is composed of Figure 15 As shown, the capacitor element groups on the upper and lower sides are used to construct the decoupling split surface.

[0070] Step 5: Based on the working range of the liquid cooling network, obtain multiple sets of input-output data sets of the decoupled single-row branch channel model running in the working state;

[0071] The input-output data set is used as training data and a machine learning algorithm is used to generate a decoupled single-row runner reduced-order model ROM2.

[0072] The specific work process is as follows Figure 16 shown.

[0073] Step 6: The pipe network includes a total of i rows of liquid supply diversion channels. According to step 5, i decoupled single-row diversion channel reduced-order models are generated to form a pipe network single-row diversion channel sub-module;

[0074] Since the pipe network contains 9 rows in total, it is necessary to generate 9 single-row branch channel reduced-order models to form the pipe network single-row branch channel sub-module, such as Figure 17 shown.

[0075] Step 7: Generate the main liquid supply / return channel submodule: The main liquid supply channel submodule calculates the flow distribution of each liquid supply branch channel and performs a bidirectional coupling calculation with the single-row branch channel submodule of each pipe network row. The main liquid supply channel submodule provides the calculated flow rate to the single-row branch channel submodule of the pipe network, and the single-row branch channel submodule of the pipe network feeds back the single-row flow resistance data to the main liquid supply channel submodule.

[0076] The liquid return main channel submodule calculates the flow distribution of each liquid return branch channel and performs bidirectional coupling calculation with the pipe network single-row branch channel submodule of each row. The liquid return main channel submodule provides the calculated flow to the pipe network single-row branch channel submodule, and the pipe network single-row branch channel submodule feeds back the single-row flow resistance data to the liquid return main channel submodule.

[0077] Step 8: Scan output submodule generation: The scan output submodule separates the information of the specified liquid cooling plate of the specified basic unit according to the pre-defined liquid cooling plate position number and the specified flow information calculated by the supply / return liquid main channel submodule; the specific numbering method of the scan output module is as follows Figure 9 As shown in the figure. i represents the row number, j represents the column number, and k represents the cold plate number in each basic unit. By inputting i, j, and k, each cold plate can be accurately located. The packaged scanning output submodule is shown in the figure. Figure 18 shown.

[0078] Step 9: Combine the supply / return liquid main channel submodule, the pipe network single-row branch channel submodule and the scanning output submodule to establish a liquid cooling pipe network digital twin model based on row attributes and encapsulate it; the encapsulated configuration is as follows Figure 19 shown.

[0079] Step 10: Liquid cooling pipe network digital twin model SDK generation: Use TwinDeployer module to output SDK file, which can realize the rapid calculation and output of liquid cooling pipe network digital twin model calculation results. The specific process is as follows: Use TwinDeployer 2021R1 module to output SDK file. Import the twin file generated by Twin Bulider into the TwinDeployer environment and define the input data. Its interface is as follows: Figure 20 As shown; after completing the settings, you can directly output the SDK folder as Figure 21 shown.

[0080] The present invention provides a method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network. The digital twin model of the cold plate parallel liquid cooling pipe network is established using one-dimensional Modelica. A liquid-cooled cold plate model is constructed, and its reduced-order model (ROM1) is formed through working condition training. A "decoupled single-row shunt channel model" is constructed and its reduced-order model (ROM2) is formed through working condition training. A ROM model nesting technology and a parallel cold plate scan output traversal algorithm are proposed. This method is used in the single-row shunt channel submodule and the scan output submodule of this model. In the single-row shunt channel submodule, the above-mentioned single-row decoupling method is used to train and reduce the "decoupled single-row shunt channel model". In the scan output module, each parallel cold plate model (ROM1) in the above-mentioned decoupled single-row shunt channel reduced-order model (ROM2) is nested, thereby realizing the function of outputting the cold plate calculation results of the corresponding position number based on the input cold plate position number.

[0081] The above method interconnects the liquid cooling network with all parallel cold plates, building a digital twin model of a multi-node cold plate parallel liquid cooling network. Ultimately, this allows for rapid operational monitoring and simulation of key data such as temperature, flow, and pressure within the liquid cooling network and parallel cold plates. Furthermore, the input boundaries of this digital twin model can be measured using a limited number of sensors. By driving the model with measured data, real-time simulation and visualization of liquid cooling performance driven by measured data can be achieved. Compared to deploying a large number of temperature, flow, and pressure sensors for monitoring, this significantly reduces design costs and has engineering significance for evaluating heat dissipation, improving performance, and enhancing reliability of electronic equipment within the cold plates.

[0082] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. However, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network, characterized in that: The following steps are involved: Step 1: Build a detailed mechanism-based simulation model of the internal flow path of the liquid-cooled cold plate. Run the detailed mechanism-based simulation model multiple times according to the operating range of the liquid-cooled cold plate to obtain the input-output dataset of the liquid-cooled cold plate. The input-output dataset is used as training data and a machine learning algorithm is used to generate the liquid-cooled cold plate model ROM1. Step 2: Create a single-row liquid supply model including a liquid supply manifold and a liquid cooling cold plate around the liquid cooling cold plate model; The liquid supply channel supplies liquid to a row of j basic units. Each basic unit includes k liquid-cooled cold plates. The liquid-cooled cold plate model ROM1 generated in step 1 is nested in the single-row liquid supply model to establish an interface model for the liquid supply channel to flow into each liquid-cooled cold plate. Step 3: Establish a single-row liquid return model including a liquid return branch channel and a liquid return interface; Step 4: Assemble the liquid-cooled cold plate model, the single-row liquid supply model, and the single-row liquid return model to form a decoupled single-row diversion channel model; Step 5: Based on the working range of the liquid cooling network, obtain multiple sets of input-output data sets of the decoupled single-row branch channel model running in the working state; The input-output data set is used as training data and a machine learning algorithm is used to generate a decoupled single-row runner reduced-order model ROM2. Step 6: The pipe network includes a total of i rows of liquid supply diversion channels. According to step 5, i decoupled single-row diversion channel reduced-order models are generated to form a pipe network single-row diversion channel sub-module; Step 7: Generate the main liquid supply / return channel submodule: The main liquid supply channel submodule calculates the flow distribution of each liquid supply branch channel and performs a bidirectional coupling calculation with the single-row branch channel submodule of each pipe network row. The main liquid supply channel submodule provides the calculated flow rate to the single-row branch channel submodule of the pipe network, and the single-row branch channel submodule of the pipe network feeds back the single-row flow resistance data to the main liquid supply channel submodule. The liquid return main channel submodule calculates the flow distribution of each liquid return branch channel and performs bidirectional coupling calculation with the pipe network single-row branch channel submodule of each row. The liquid return main channel submodule provides the calculated flow to the pipe network single-row branch channel submodule, and the pipe network single-row branch channel submodule feeds back the single-row flow resistance data to the liquid return main channel submodule. Step 8: Scan output submodule generation: The scan output submodule separates the information of the specified liquid cooling plate of the specified basic unit according to the predefined liquid cooling plate position number and the specified flow information calculated by the supply / return liquid main channel submodule.

2. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 1, characterized in that: It also includes step 9: combining the supply / return liquid main channel sub-module, the pipe network single-row branch channel sub-module and the scanning output sub-module to establish a row-attribute-based liquid cooling pipe network digital twin model and encapsulate it.

3. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 1, characterized in that: It also includes step 10: Liquid cooling pipe network digital twin model SDK generation: input real-time measurement data and output SDK file.

4. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 1, characterized in that: The interface model of the liquid supply channel established in step 2 flowing into each liquid-cooled cold plate is encapsulated.

5. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 1, characterized in that: The liquid supply channel corresponding to each group of basic units in step 2 is established by the Modelica model.

6. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 1, characterized in that: The k liquid-cooled cold plates included in each basic unit in step 2 are connected in parallel.

7. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 1, characterized in that: The pipe network consists of 81 basic units in 9 rows and 9 columns, and each basic unit includes 5 parallel liquid-cooled cold plates.

8. The method for constructing a digital twin model of a multi-node cold plate parallel liquid cooling pipe network according to claim 3, characterized in that: In step 10, use the TwinDeployer module to output the SDK file.

Images