Cold plate surface anti-condensation control method and system based on model prediction

Abstract

The invention provides a cold plate surface anti-condensation control method and system based on model prediction, and the method comprises the steps: 1, obtaining cold plate surface temperature data and a cold plate surface condensation image when a liquid cooling plate operates for a preset period of time through a cold plate cooling experiment and CFD simulation, supercooling degree and condensation delay time data are obtained through the condensation image; 2, performing multi-parameter nonlinear fitting on the cold plate surface temperature data in the cold plate operation process to form a cold plate surface temperature prediction model; and step 3, utilizing the prediction model, controlling the MPC to control a fluid inlet valve of the cold plate through model prediction, and closing the valve before condensation on the surface of the cold plate. The MPC is controlled through model prediction to control the fluid inlet valve of the cold plate, the valve is closed before dew formation on the surface of the cold plate, the dew formation risk is remarkably reduced, the workload of a refrigeration system is reduced, the liquid cooling plate operates at a smaller safety temperature difference, and the reliability of the liquid cooling system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of cold plate surface anti-condensation control, and in particular to a cold plate surface anti-condensation control method and system based on model prediction. Background Art

[0002] During the actual operation of the liquid cooling plate, if condensation occurs on the outer surface of the liquid cooling plate (especially the cooling surface in contact with electronic components), it may cause metal corrosion, or even serious phenomena such as short circuit of electronic components, which may bring various risks to the equipment. Therefore, it is very necessary to analyze why condensation occurs on the liquid cooling plate in actual production and operation and to formulate corresponding control strategies based on the condensation phenomenon.

[0003] Previous studies have shown that when the temperature of the surface of the liquid cooling plate is lower than the dew point temperature of the outside air, water vapor will condense and condensation will begin to form on the surface of the liquid cooling plate. Therefore, in most actual projects, condensation is prevented by controlling the surface temperature of the liquid cooling plate to always be about 1-2°C higher than the indoor dew point temperature (empirical safety temperature difference). This anti-condensation method causes the start time of the anti-condensation control to be greatly advanced due to the large safety temperature difference, which in turn seriously limits the cooling capacity of the liquid cooling system. Especially in actual applications, condensation on the surface of the liquid cooling plate does not occur instantly. The temperature of the liquid cooling plate needs to be lower than the dew point temperature of the outside air by more than a certain range, and it takes some time for heat transfer. Summary of the invention

[0004] In view of the defects in the prior art, an object of the present invention is to provide a cold plate surface anti-condensation control method and system based on model prediction.

[0005] The cold plate surface anti-condensation control method based on model prediction provided by the present invention includes: Step 1: Through the cold plate cooling experiment and CFD simulation, the cold plate surface temperature data and the cold plate surface condensation image of the liquid cold plate running for a preset period of time are obtained, and the supercooling degree and condensation delay time data are obtained through the condensation image; Step 2: Perform multi-parameter nonlinear fitting on the cold plate surface temperature data during the cold plate operation process to form a cold plate surface temperature prediction model; Step 3: Using the prediction model, the fluid inlet valve of the cold plate is controlled through the model predictive control MPC, the valve is closed before condensation forms on the cold plate surface, and the valve change amplitude is minimized.

[0006] Preferably, step 2 comprises: The nonlinear regression model is confirmed by the change curve of the cold plate surface temperature over time, that is, the liquid cold plate surface temperature With time The functional relationship is: (1) Among them, n, k, and b are the surface temperatures of the liquid cooling plate. With time The coefficients in the functional relationship of ; The temperature change curves of different cooling conditions were regressed by SPSS data analysis software to obtain different cooling water temperatures. Tin , Cooling water flow Q , Ambient temperature Ta , relative humidity H The values ​​of n, k, and b under the above conditions are combined with the physical formula of the heat transfer process and the data characteristics. The corresponding models are selected and then multivariate nonlinear fitting is performed on n, k, and b. The fitting relationship is as follows: (2) (3) (4) The cold plate surface prediction model is as follows: (5).

[0007] Preferably, if liquid droplets with a diameter of 10 μm appear in the area of ​​the liquid cooling plate that is most susceptible to condensation, it indicates that condensation has occurred on the surface of the liquid cooling plate, otherwise the opposite is true.

[0008] Preferably, if the difference between the dew point temperature and the cold plate surface temperature is greater than the supercooling degree or the time after the cold plate surface temperature reaches the dew point temperature exceeds the condensation delay time, if either of the two conditions is met, it is determined that condensation is about to occur on the plate surface and the valve needs to be closed.

[0009] Preferably, the step 3 comprises: The objective function of model predictive control is: (6) in, Indicates the prediction time length set in the model prediction; represents the penalty weight; Indicates the rate of change of the valve state at time t; The constraints are: (7) (8) (9) (10) in, represents the surface temperature of the cold plate at time t; Indicates the dew point temperature of the environment at time t; Indicates the subcooling degree of the liquid cooling plate system at time t; Tin (t), Q (t), Ta (t), H (t) represents the cooling water temperature collected by the liquid cooling system at time t Tin , Cooling water flow Q , Ambient temperature Ta , relative humidity H ; The surface temperature of the cold plate within a period of time is predicted, and the optimal solution S=[S1,S2,…,S120] of the valve state in this period of time is obtained. The controller inputs the first solution S1 to the actual system for control, and obtains the actual surface temperature of the cold plate at that moment. The optimal solution process for the next period of time is performed based on the temperature. This process is repeated in the following time for continuous rolling optimization, thereby realizing model predictive control.

[0010] The cold plate surface anti-condensation control system based on model prediction provided by the present invention includes: Module M1: obtain the surface temperature data of the cold plate and the condensation image of the cold plate after the liquid cold plate runs for a preset period of time through cold plate cooling experiment and CFD simulation, and obtain the supercooling degree and condensation delay time data through the condensation image; Module M2: Perform multi-parameter nonlinear fitting on the cold plate surface temperature data during the cold plate operation to form a cold plate surface temperature prediction model; Module M3: Using the prediction model, the fluid inlet valve of the cold plate is controlled through the model predictive control MPC, the valve is closed before condensation forms on the cold plate surface, and the valve change amplitude is minimized.

[0011] Preferably, the module M2 comprises: The nonlinear regression model is confirmed by the change curve of the cold plate surface temperature over time, that is, the liquid cold plate surface temperature With time The functional relationship is: (1) Among them, n, k, and b are the surface temperatures of the liquid cooling plate. With time The coefficients in the functional relationship of ; The temperature change curves of different cooling conditions were regressed by SPSS data analysis software to obtain different cooling water temperatures. Tin , Cooling water flow Q , Ambient temperature Ta , relative humidity HThe values ​​of n, k, and b under the above conditions are combined with the physical formula of the heat transfer process and the data characteristics. The corresponding models are selected and then multivariate nonlinear fitting is performed on n, k, and b. The fitting relationship is as follows: (2) (3) (4) The cold plate surface prediction model is as follows: (5).

[0012] Preferably, if liquid droplets with a diameter of 10 μm appear in the area of ​​the liquid cooling plate that is most susceptible to condensation, it indicates that condensation has occurred on the surface of the liquid cooling plate, otherwise the opposite is true.

[0013] Preferably, if the difference between the dew point temperature and the cold plate surface temperature is greater than the supercooling degree or the time after the cold plate surface temperature reaches the dew point temperature exceeds the condensation delay time, if either of the two conditions is met, it is determined that condensation is about to occur on the plate surface and the valve needs to be closed.

[0014] Preferably, the module M3 comprises: The objective function of model predictive control is: (6) in, Indicates the prediction time length set in the model prediction; represents the penalty weight; Indicates the rate of change of the valve state at time t; The constraints are: (7) (8) (9) (10) in, represents the surface temperature of the cold plate at time t; Indicates the dew point temperature of the environment at time t; Indicates the subcooling degree of the liquid cooling plate system at time t; Tin (t), Q (t), Ta (t), H (t) represents the cooling water temperature collected by the liquid cooling system at time t Tin , Cooling water flow Q , Ambient temperature Ta , relative humidity H ; The surface temperature of the cold plate within a period of time is predicted, and the optimal solution S=[S1,S2,…,S120] of the valve state in this period of time is obtained. The controller inputs the first solution S1 to the actual system for control, and obtains the actual surface temperature of the cold plate at that moment. The optimal solution process for the next period of time is performed based on the temperature. This process is repeated in the following time for continuous rolling optimization, thereby realizing model predictive control.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention controls the fluid inlet valve of the cold plate through model predictive control (MPC), closes the valve before condensation forms on the cold plate surface, significantly reduces the risk of condensation, reduces the workload of the refrigeration system, enables the liquid cold plate to operate safely under a smaller temperature difference, improves the reliability of the liquid cooling system, and extends the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 Define graphs for subcooling and condensation delay time; Figure 2 Schematic diagram of model predictive control for liquid cooling plate system. DETAILED DESCRIPTION

[0017] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0018] Example The present invention proposes a cold plate surface anti-condensation control method based on model prediction, comprising: MPC builds a mathematical model of the liquid cooling system to simulate the possible states under various operating conditions. In the real-time control process, MPC optimizes the control strategy and predicts future operations based on the current environment and system temperature, thereby preventing the surface temperature of the liquid cooling plate from being lower than the actual condensation temperature of the liquid cooling plate. By minimizing the predicted condensation risk and system energy consumption, MPC can intelligently maintain the system in the optimal control state.

[0019] Monitor the surface temperature of the liquid cooling plate and the ambient dew point temperature in real time, and use mathematical models to predict future condensation risks.

[0020] Based on real-time data, the temperature threshold of the liquid cooling system is dynamically adjusted to ensure that the surface temperature of the liquid cooling plate is within a safe range without affecting the cooling performance.

[0021] By lowering the lower end of the system's operating temperature range, the system can maximize cooling capacity without condensation and reduce unnecessary control actions.

[0022] By building a complex nonlinear regression model to predict the surface temperature of the cold plate, combined with MPC control, the impact of different control variables (such as cooling water flow, cooling water temperature, ambient humidity, etc.) on the surface temperature of the liquid cold plate can be analyzed and predicted in real time. This method can flexibly respond to changes under different environmental conditions, thereby achieving more precise temperature control. MPC not only considers the current system state, but also can judge the future state of the system based on the prediction model, adjust the control strategy in advance, and reduce delayed response time. This feedforward control feature can significantly improve the response speed of the system and reduce the risk of condensation.

[0023] The specific process is: (1) Through cold plate cooling experiments and CFD simulation, 72 sets of cold plate surface temperature data and cold plate surface condensation images within 10 minutes of liquid cooling plate operation were obtained. Condensation occurred in 54 working conditions. The supercooling degree and condensation delay time data were obtained based on the condensation image information.

[0024] (2) Table 1 shows the values ​​of the variables for the working condition research, and a total of 72 working conditions in which the cold plate is cooled are selected.

[0025] Table 1 Working conditions arrangement

[0026] (3) Condensation on the surface of the liquid cooling plate does not occur instantly. It requires the temperature of the liquid cooling plate to be lower than the dew point temperature of the outside world by more than a certain range, and it takes a period of time for heat to be transferred. The temperature difference between the surface temperature of the liquid cooling plate and the dew point temperature required for condensation to occur is called the supercooling degree, and the time required is called the delay time.

[0027] (4) Perform multi-parameter nonlinear fitting on the cold plate surface temperature data during the cold plate operation process to form a good cold plate surface temperature prediction model. The specific regression process is as follows: First, the basic nonlinear regression model is confirmed by the change curve of the cold plate surface temperature over time, that is, the liquid cold plate surface temperature Tp With time t The functional relationship is: (1) Among them, n, k, and b are the surface temperatures of the liquid cooling plate. Tp With time t The coefficients in the functional relationship of .

[0028] The temperature change curves of 72 cooling conditions were regressed by SPSS data analysis software to obtain the temperature curves of different cooling water conditions. Tin , Cooling water flow Q , Ambient temperature Ta , relative humidity H The values ​​of n, k, and b under the condition are as follows: Combining the physical formula of the heat transfer process and the data characteristics, appropriate models are selected and then multivariate nonlinear fitting is performed on n, k, and b. The fitting relationship is as follows: (2) (3) (4) Among them, the regression fitting results of equations (2), (3) and (4) are good, and the prediction errors are low and within the allowable range.

[0029] Through the above, the cold plate surface prediction model is obtained as shown in formula (5): (5) Formula (5) performs well in the data of all working conditions, and the prediction error is low and within the allowable range.

[0030] (5) The criterion for judging whether the liquid cooling plate has condensation is: whether small droplets with a diameter of 10 μm appear in the area of ​​the liquid cooling plate that is most prone to condensation. If they appear, it indicates that condensation has occurred on the surface of the liquid cooling plate, otherwise it is the opposite. Through the collected image information, when condensation is observed on the surface of the cold plate, the supercooling degree and condensation delay time data under condensation conditions are obtained. Similarly, through the method of step (4), the prediction model of supercooling degree and condensation delay time under different working conditions is obtained.

[0031] (6) Based on the trend of environmental parameters at future moments, the obtained prediction model is used to control the fluid inlet valve of the cold plate through model predictive control (MPC). If the difference between the dew point temperature and the plate surface temperature is greater than the supercooling degree or the time after the plate surface temperature reaches the dew point temperature exceeds the condensation delay time, if either of the two is met, it is determined that the plate surface will condense and the valve is closed. According to this judgment standard, if it is determined that the change of environmental parameters in the future will not cause condensation, the valve remains open; if it is determined that the change of environmental parameters in the future will cause condensation, the valve is closed. The valve can be closed until the temperature value of the electronic component does not reach the acceptable upper limit. Once the temperature value of the electronic component approaches the acceptable upper limit, the valve is opened again.

[0032] (7) The objective function of using model predictive control in the liquid cooling plate cooling system is: (6) in, Indicates the prediction time length set in the model prediction; represents the penalty weight; Indicates the rate of change of the valve state at time t; The constraints are: (7) (8) (9) (10) Tp (t) represents the surface temperature of the cold plate at time t; Td (t) represents the dew point temperature of the environment at time t; Tsub (t) represents the subcooling degree of the liquid cooling plate system at time t; Tin (t), Q (t), Ta (t), H (t) represents the cooling water temperature collected by the liquid cooling system at time t Tin , Cooling water flow Q , Ambient temperature Ta , relative humidity H .

[0033] The surface temperature of the cold plate within 120s is predicted, and the optimal solution S=[S1,S2,…,S120] of the valve state in this time period is obtained. The controller inputs the first solution S1 to the actual system for control, and obtains the actual surface temperature of the cold plate at that moment. The optimal solution process for the next time period is performed based on the temperature. This process is repeated in the following time for continuous rolling optimization, thereby realizing model predictive control.

[0034] (8) The present invention designs a model predictive control method for the liquid cooling plate system by combining the degree of supercooling and the condensation delay time, which can ensure that no condensation occurs on the cold plate surface during the operation of the liquid cooling system. At the same time, this method can also reduce the frequency of valve operation and reduce maintenance costs.

[0035] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0036] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A cold plate surface anti-condensation control method based on model prediction, characterized in that: include: Step 1: Through the cold plate cooling experiment and CFD simulation, the cold plate surface temperature data and the cold plate surface condensation image of the liquid cold plate running for a preset period of time are obtained, and the supercooling degree and condensation delay time data are obtained through the condensation image; Step 2: Perform multi-parameter nonlinear fitting on the cold plate surface temperature data during the cold plate operation process to form a cold plate surface temperature prediction model; Step 3: Using the prediction model, the fluid inlet valve of the cold plate is controlled through the model predictive control MPC, the valve is closed before condensation forms on the cold plate surface, and the valve change amplitude is minimized.

2. The cold plate surface anti-condensation control method based on model prediction according to claim 1 is characterized in that: The step 2 comprises: The nonlinear regression model is confirmed by the change curve of the cold plate surface temperature over time, that is, the liquid cold plate surface temperature With time The functional relationship is: Among them, n, k, and b are the surface temperatures of the liquid cooling plate. With time The coefficients in the functional relationship of ; The temperature change curves of different cooling conditions were regressed by SPSS data analysis software to obtain different cooling water temperatures. , Cooling water flow , Ambient temperature , relative humidity The values ​​of n, k, and b under the above conditions are combined with the physical formula of the heat transfer process and the data characteristics. The corresponding models are selected and then multivariate nonlinear fitting is performed on n, k, and b. The fitting relationship is as follows: The cold plate surface prediction model is as follows: 。 3. The cold plate surface anti-condensation control method based on model prediction according to claim 1, characterized in that: If droplets with a diameter of 10 μm appear in the area of ​​the liquid cooling plate that is most prone to condensation, it indicates that condensation has occurred on the surface of the liquid cooling plate, otherwise the opposite is true.

4. The cold plate surface anti-condensation control method based on model prediction according to claim 1, characterized in that: If the difference between the dew point temperature and the cold plate surface temperature is greater than the supercooling degree or the time after the cold plate surface temperature reaches the dew point temperature exceeds the condensation delay time, if either of the two conditions is met, it is determined that condensation is about to occur on the plate surface and the valve needs to be closed.

5. The cold plate surface anti-condensation control method based on model prediction according to claim 1, characterized in that: The step 3 comprises: The objective function of model predictive control is: in, Indicates the prediction time length set in the model prediction; represents the penalty weight; express The rate of change of valve status at the moment; The constraints are: in, express The surface temperature of the cold plate at the moment; express The dew point temperature of the environment at the moment; express The subcooling degree of the liquid cooling plate system at all times; They represent the data collected by the liquid cooling system. Cooling water temperature, cooling water flow, ambient temperature, and ambient relative humidity at all times; Predict the surface temperature of the cold plate within a period of time, and get the optimal solution of the valve state in this period of time S=[ , ,…, ], the controller inputs the first solution to the actual system Control is carried out and the actual surface temperature of the cold plate at that moment is obtained. The optimal solution process for the next period is carried out based on the temperature. This process is repeated in the following time for continuous rolling optimization, thereby realizing model predictive control.

6. A cold plate surface anti-condensation control system based on model prediction, characterized in that: include: Module M1: obtain the surface temperature data of the cold plate and the condensation image of the cold plate after the liquid cold plate runs for a preset period of time through cold plate cooling experiment and CFD simulation, and obtain the supercooling degree and condensation delay time data through the condensation image; Module M2: Perform multi-parameter nonlinear fitting on the cold plate surface temperature data during the cold plate operation process to form a cold plate surface temperature prediction model; Module M3: Using the prediction model, the fluid inlet valve of the cold plate is controlled through the model predictive control MPC, the valve is closed before condensation forms on the cold plate surface, and the valve change amplitude is minimized.

7. The cold plate surface anti-condensation control system based on model prediction according to claim 6, characterized in that: The module M2 comprises: The nonlinear regression model is confirmed by the change curve of the cold plate surface temperature over time, that is, the liquid cold plate surface temperature With time The functional relationship is: Among them, n, k, and b are the surface temperatures of the liquid cooling plate. With time The coefficients in the functional relationship of ; The temperature change curves of different cooling conditions were regressed by SPSS data analysis software to obtain different cooling water temperatures. , Cooling water flow , Ambient temperature , relative humidity The values ​​of n, k, and b under the above conditions are combined with the physical formula of the heat transfer process and the data characteristics. The corresponding models are selected and then multivariate nonlinear fitting is performed on n, k, and b. The fitting relationship is as follows: The cold plate surface prediction model is as follows: 。 8. The cold plate surface anti-condensation control system based on model prediction according to claim 6, characterized in that: If droplets with a diameter of 10 μm appear in the area of ​​the liquid cooling plate that is most prone to condensation, it indicates that condensation has occurred on the surface of the liquid cooling plate, otherwise the opposite is true.

9. The cold plate surface anti-condensation control system based on model prediction according to claim 6, characterized in that: If the difference between the dew point temperature and the cold plate surface temperature is greater than the supercooling degree or the time after the cold plate surface temperature reaches the dew point temperature exceeds the condensation delay time, if either of the two conditions is met, it is determined that condensation is about to occur on the plate surface and the valve needs to be closed.

10. The cold plate surface anti-condensation control system based on model prediction according to claim 6, characterized in that: The module M3 comprises: The objective function of model predictive control is: in, Indicates the prediction time length set in the model prediction; represents the penalty weight; express The rate of change of valve status at the moment; The constraints are: in, express The surface temperature of the cold plate at the moment; express The dew point temperature of the environment at the moment; express The subcooling degree of the liquid cooling plate system at all times; They represent the data collected by the liquid cooling system. Cooling water temperature, cooling water flow, ambient temperature, and ambient relative humidity at all times; Predict the surface temperature of the cold plate within a period of time, and get the optimal solution of the valve state in this period of time S=[ , ,…, ], the controller inputs the first solution to the actual system Control is carried out and the actual surface temperature of the cold plate at that moment is obtained. The optimal solution process for the next period is carried out based on the temperature. This process is repeated in the following time for continuous rolling optimization, thereby realizing model predictive control.

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