A method for collaborative optimization of key internal structure parameters and external heat sink thermal resistance in TEC application process

Abstract

A kind of method for the synergic optimization of key internal structure parameters and external radiator thermal resistance in TEC application process, step 1: determine experimental response variable and design variable, clear design variable range and level, experimental design is carried out to sample point in specified design space by response surface experimental design method;Step 2: the value of response variable under different design variables in experimental design table is calculated by simulation calculation;Step 3: the mathematical model of TEC performance parameter in TEC cooling system is established as response variable about design parameter by response surface algorithm, and the accuracy of model is determined by variance analysis;Step 4: response surface analysis is carried out to mathematical model, and the variation trend of TEC performance parameter about design variable and the interaction between design variables are determined.The best working current of TEC cooling system in the actual working process of TEC is determined, which provides a solution for determining the best working condition in actual application process.

Description

Technical Field

[0001] This invention relates to a method for synergistic optimization of radiator thermal resistance in the design and application of thermoelectric coolers. Technical Background

[0002] Currently, with the miniaturization and enhanced functionality of electronic devices, the requirements for heat dissipation are becoming increasingly stringent. Increased heat flux density in electronic devices leads to higher junction temperatures, severely reducing their output performance and reliability. Thermoelectric coolers (TECs) are widely used for cooling electrical equipment. TECs combined with air or liquid cooling, known as TEC cooling systems, are gaining popularity in electrical equipment cooling.

[0003] Studies of TEC and TEC cooling systems reveal that, in practical applications, TEC manufacturers often fail to consider the infinitesimal thermal resistance of the hot and cold side heatsinks during product design. This thermal resistance is often underestimated. In the design and development of TEC cooling systems, TEC application manufacturers typically optimize the structure or cooling parameters of the hot and cold side heatsinks, such as heatsink size, structure, airflow, or air inlet temperature, after determining the TEC itself, to achieve optimal cooling performance. Consequently, effective collaboration between TEC manufacturers and application users is lacking. Whether optimizing heatsink structure or fluid parameters during TEC applications, the essence is optimizing the thermal resistance of the hot and cold side heatsinks. Therefore, transforming the performance of the hot side heatsink from its geometric structure into a numerical form of thermal resistance, and establishing the relationship between thermal resistance, key internal structural parameters of the TEC, and transport performance within the TEC cooling system, will facilitate rapid matching between the TEC and the heatsink during TEC applications. Summary of the Invention

[0004] This invention addresses the problem of ineffective coordination between TEC structural design and heatsink optimization in practical applications by providing a method for the coordinated optimization of key internal structural parameters and external heatsink thermal resistance during TEC applications. This offers a solution for rapid matching of heatsinks and TEC structures in TEC applications.

[0005] A method for synergistic optimization of key internal structural parameters and external heat sink thermal resistance in TEC applications, characterized by comprising the following steps: Step 1: Determine the experimental response variables and design variables, clarify the range and level of the design variables, and conduct experimental design on the sample points within the specified design space using the response surface methodology, and determine the experimental design table; Step 2: Using simulation calculations, calculate the values ​​of the response variables under different design variable conditions in the experimental design table, and add them to the output variables of the experimental design table; Step 3: Use response surface methodology to establish a mathematical model of the TEC cooling system with TEC performance parameters as response variables and design parameters, and determine the accuracy of the model through analysis of variance; Step 4: Perform response surface analysis on the mathematical model to determine the changing trends of TEC performance parameters with respect to design variables and the interactions between design variables.

[0006] The response surface methodology in step 1 uses the Box-Behnken design method to determine the response variable, design variable, and the range and levels of the design variable. Among them, the performance parameters of the TEC cooling system are response variables. The response variables mainly include the optimal operating current, the cooling capacity under the maximum temperature difference or maximum cooling capacity of the TEC cooling system, the heat release of the TEC hot surface under the optimal current, the coefficient of performance (COP) under the optimal current, the TEC cold surface temperature under the optimal current, and the temperature difference between the hot and cold surfaces under the optimal current. Design variables include the number of grain pairs, grain height, grain width, grain length, and hot-side heatsink thermal resistance; the range of design variables is: 2-300 grain pairs, 0.2-2mm grain width, 0.2-2mm grain length, 0.2-2mm grain height, and 0.05-1℃ / W hot-side heatsink thermal resistance; the design variables are at levels 3-5; in step 1, the key structural parameters of the TEC and the performance parameters of the heatsink are simultaneously used as design variables to achieve collaborative design of key parameters in the TEC design process and application process.

[0007] In step 2, the simulation calculation process is as follows: Step 2.1: First, establish a simulation calculation model according to the design parameters specified in the experimental plan, and set the material properties of each component of TEC, and specify the ambient temperature for calculation; Step 2.2: Then, using the lumped heat system, a convective heat transfer resistance is added between the two nodes of the TEC hot surface and the environment. This convective heat transfer resistance is the thermal resistance of the hot surface radiator. The value of the convective heat transfer resistance is determined according to the experimental scheme in Step 1. Step 2.3: Calculate the TEC performance parameters under different heat sink thermal resistances and TEC structural parameters, obtain the TEC performance parameters under different current conditions, select the current corresponding to the maximum cooling capacity or the maximum temperature difference as the optimal operating current under this experimental scheme, and the corresponding cooling capacity, temperature difference, heat release, and lowest cold surface temperature are the performance parameters under the optimal current conditions; and add the above optimal parameters to the table corresponding to the experimental scheme.

[0008] The experimental verification method is as follows: Step 2.4: Select a TEC with known structural parameters, fix an actual convection heat exchanger on the hot side of the TEC, and fix a temperature sensor on both the cold and hot sides of the TEC. Step 2.5: Input different currents to the TEC and test the temperature and humidity of the inlet and outlet of the hot end heat sink, as well as the temperature of the cold and hot sides of the TEC under different current conditions; Step 2.6: Calculate the inlet and outlet enthalpy values ​​of the hot-end radiator based on the inlet and outlet air temperatures and humidity. Calculate the heat generation of the TEC hot surface under different output current conditions using the enthalpy difference method. Divide the difference between the ambient temperature and the hot surface temperature by the heat generation of the hot surface to obtain the thermal resistance of the radiator. The thermal resistance of the radiator is related to the radiator structure and fan components, and therefore is a constant value under different current conditions.

[0009] Step 3 specifically involves selecting a polynomial mathematical model for the numerical relationship between the response variable and the design variable. ,in, Y This represents the response value for each experiment; β 0 This is the system's baseline response value when all parameters are at the center level; β i To determine the linear effect of the parameters, a mathematical regression model was constructed between the response variable and the design variable, and an analysis of variance (ANOVA) was performed on the mathematical model. The ANOVA method is as follows: In the ANOVA summary, the coefficient of determination R of the model is... 2 ≥95%, accuracy is the ratio of signal to noise in the model, greater than 4, taking into account R... 2 If both the accuracy value and the above conditions are met, then the selected mathematical model is considered reasonable.

[0010] The beneficial effects of this invention, employing the above technical solution, are as follows: This invention determines the optimal operating current of the TEC cooling system during actual TEC operation, providing a solution for determining optimal operating conditions in practical applications. Simultaneously, the constructed mathematical model facilitates rapid matching of TEC structural parameters and the thermal resistance of the hot-end heatsink, establishing a bridge between TEC and TEC applications. Specifically: 1. A method is provided to correlate the output performance parameters of a TEC with the internal structural parameters of the TEC and the thermal resistance of the external heat sink under actual operating conditions, providing a solution for quickly matching the TEC with the heat sink during TEC application.

[0011] 2. A method is provided to quickly determine the optimal operating current when the TEC structure and heat sink structure are determined in actual TEC use conditions, so as to achieve the best performance of TEC under specific operating conditions.

[0012] 3. A link has been established between TEC structural design and TEC application, which is conducive to accelerating the rapid application of TEC. Attached Figure Description

[0013] Figure 1 To determine the response model and variable interaction flowchart; Figure 2 Graph showing the calculation process of TEC cooling system performance parameters; Figure 3 This is a simulation diagram of the experimental results; Figure 4 Thermal resistance of external heat sink ( Rh ) and TEC internal structure ( h, w Response surface plot between (, N). Detailed Implementation

[0014] like Figure 1 As shown, a method for synergistic optimization of key internal structural parameters and external heatsink thermal resistance in TEC applications specifically includes: Step 1: Determine the experimental response variables and design variables, clarify the range and level of the design variables, and conduct experimental design on the sample points within the specified design space using the response surface methodology, and determine the experimental design table; Step 2: Using simulation calculations, calculate the values ​​of the response variables under different design variable conditions in the experimental design table, and add them to the output variables of the experimental design table; Step 3: Use response surface methodology to establish a mathematical model of the TEC cooling system with TEC performance parameters as response variables and design parameters, and determine the accuracy of the model through analysis of variance (ANOVA).

[0015] Step 4: Perform response surface analysis on the mathematical model to determine the changing trends of TEC performance parameters with respect to design variables and the interactions between design variables.

[0016] In step 1, the response surface methodology employs the Box-Behnken design (BBD) method to determine the response variable, design variable, and the range and level of the design variable. Among them, the performance parameters of the TEC cooling system are response variables, which mainly include the optimal operating current corresponding to the maximum temperature difference or maximum cooling capacity of the TEC cooling system. I max ), Cooling capacity at optimal current ( Q cmax ), Heat dissipation of TEC hot surface under optimal current ( Q h ), Coefficient of performance (COP) at optimal current, and TEC (cooling surface temperature) at optimal current.T cmin ) and the temperature difference between hot and cold surfaces under optimal current ( dT max ).

[0017] Design variables include the number of grain pairs, grain height, grain width, grain length, and thermal resistance of the hot-side heatsink. Rh The design variables range as follows: 2-300 grain pairs, 0.2-2mm grain width, 0.2-2mm grain length, 0.2-2mm grain height, and 0.05-1℃ / W thermal resistance of the heatsink. The design variables are divided into 3-5 levels, meaning the design variables are averaged across the design range. In step 1, key structural parameters of the TEC and heatsink performance parameters are simultaneously used as design variables to achieve collaborative design of key parameters in the TEC design and application processes.

[0018] In step 2, the simulation calculation process is as follows: Step 2.1: First, establish a simulation calculation model according to the design parameters specified in the experimental plan, and set the material properties of each component of TEC, and specify the ambient temperature for calculation; Step 2.2: Then, using the lumped heat system, a convective heat transfer resistance is added between the two nodes of the TEC hot surface and the environment. This convective heat transfer resistance is the thermal resistance of the hot surface radiator. The value of the convective heat transfer resistance is determined according to the experimental scheme in Step 1.

[0019] Step 2.3: Calculate the TEC performance parameters under different heatsink thermal resistances and TEC structural parameters. During the calculation, a parameter scanning module is used, with a scanning current range of 0.1-50A and a scanning step size of 0.02A, to obtain the TEC performance parameters under different current conditions. The current corresponding to the maximum cooling capacity or maximum temperature difference is selected as the optimal operating current for this experimental scheme. The corresponding cooling capacity, temperature difference, heat dissipation, and lowest cold surface temperature are the performance parameters under the optimal current condition. These optimal parameters are then added to the table corresponding to the experimental scheme.

[0020] To verify the correctness of the simulation calculation model in step 2, an experimental verification model needs to be built to validate the calculation model. The specific experimental verification method is as follows: Step 2.4: Select a TEC with known structural parameters, fix an actual convection heat exchanger on the hot side of the TEC, and fix a temperature sensor on both the cold and hot sides of the TEC. Step 2.5: Input different currents to the TEC and test the temperature and humidity of the inlet and outlet of the hot end heat sink, as well as the temperature of the cold and hot sides of the TEC under different current conditions; Step 2.6: Based on the inlet and outlet temperatures and humidity of the hot-end radiator, calculate the inlet and outlet enthalpy values ​​of the hot-end radiator. Calculate the heat generation of the TEC hot surface under different output current conditions using the enthalpy difference method. The thermal resistance of the radiator is obtained by dividing the difference between the ambient temperature and the hot surface temperature by the heat generation of the hot surface. The thermal resistance of the radiator is related to the radiator structure and fan components; therefore, it is a constant value under different current conditions.

[0021] Therefore, experiments can be conducted to obtain the temperature difference between the hot and cold sides, the heat output of the hot side, and the temperature of the cold side under different input current conditions, assuming constant TEC structural parameters and the thermal resistance of the hot side heat sink. A simulation model with the same structural parameters and thermal resistance is then constructed to calculate the temperature difference between the hot and cold sides, the heat output of the hot side, and the temperature of the cold side under different current conditions. The values ​​obtained from the simulation model are compared with those obtained from experiments; if the error is within 3%, the simulation model is considered accurate. Figure 3 As shown in the simulation, the error between the numerical simulation data and the experimental test data is within the threshold.

[0022] Step 3: Select a polynomial mathematical model for the numerical relationship between the response variable and the design variable. ,in, Y This represents the response value for each experiment; β 0 This is the system's baseline response value when all parameters are at the center level (coded as 0); β i To assess the linear effects of the parameters, a mathematical regression model is constructed between the response variable and the design variable. An analysis of variance (ANOVA) is then performed on the mathematical model to determine its reliability. The ANOVA method is as follows: In the ANOVA summary, the model's coefficient of determination R0 is... 2 ≥95%, accuracy is the ratio of signal to noise in the model, greater than 4, taking into account R... 2 If both the accuracy value and the above conditions are met, then the selected mathematical model is considered reasonable.

[0023] Step 4: Based on the constructed mathematical model, the Box-Behnken design (BBD) method of response surface analysis is used to determine the changing trend of the response variables with the design variables and the interaction between the design variables, and to generate response surface curves, providing a basis for fine-tuning the design variables in subsequent practical applications.

[0024] like Figure 4 As shown, the thermal resistance of the external heat sink ( Rh ) and TEC internal structure ( h, w The interaction between N and N was analyzed. Rh Key parameters of internal structure h, w , Response surface plot of N.

Claims

1. A method for synergistic optimization of key internal structural parameters and external heat sink thermal resistance in TEC applications, characterized in that, Includes the following steps: Step 1: Determine the experimental response variables and design variables, clarify the range and level of the design variables, and conduct experimental design on the sample points within the specified design space using the response surface methodology, and determine the experimental design table; Step 2: Using simulation calculations, calculate the values ​​of the response variables under different design variable conditions in the experimental design table, and add them to the output variables of the experimental design table; Step 3: Use response surface methodology to establish a mathematical model of the TEC cooling system with TEC performance parameters as response variables and design parameters, and determine the accuracy of the model through analysis of variance; Step 4: Perform response surface analysis on the mathematical model to determine the changing trends of TEC performance parameters with respect to design variables and the interactions between design variables.

2. The method for synergistic optimization of key internal structural parameters and external heat sink thermal resistance in TEC applications according to claim 1, characterized in that: The response surface methodology in step 1 uses the Box-Behnken design method to determine the response variable, design variable, and the range and levels of the design variable. Among them, the performance parameters of the TEC cooling system are response variables. The response variables mainly include the optimal operating current, the cooling capacity under the maximum temperature difference or maximum cooling capacity of the TEC cooling system, the heat release of the TEC hot surface under the optimal current, the coefficient of performance (COP) under the optimal current, the TEC cold surface temperature under the optimal current, and the temperature difference between the hot and cold surfaces under the optimal current. Design variables include the number of grain pairs, grain height, grain width, grain length, and hot-side heatsink thermal resistance; the range of design variables is: 2-300 grain pairs, 0.2-2mm grain width, 0.2-2mm grain length, 0.2-2mm grain height, and 0.05-1℃ / W hot-side heatsink thermal resistance; the design variables are at levels 3-5; in step 1, the key structural parameters of the TEC and the performance parameters of the heatsink are simultaneously used as design variables to achieve collaborative design of key parameters in the TEC design process and application process.

3. The method for synergistic optimization of key internal structural parameters and external heat sink thermal resistance in TEC applications according to claim 1, characterized in that: In step 2, the simulation calculation process is as follows: Step 2.1: First, establish a simulation calculation model according to the design parameters specified in the experimental plan, and set the material properties of each component of TEC, and specify the ambient temperature for calculation; Step 2.2: Then, using the lumped heat system, a convective heat transfer resistance is added between the two nodes of the TEC hot surface and the environment. This convective heat transfer resistance is the thermal resistance of the hot surface radiator. The value of the convective heat transfer resistance is determined according to the experimental scheme in Step 1. Step 2.3: Calculate the TEC performance parameters under different heat sink thermal resistances and TEC structural parameters, obtain the TEC performance parameters under different current conditions, select the current corresponding to the maximum cooling capacity or the maximum temperature difference as the optimal operating current under this experimental scheme, and the corresponding cooling capacity, temperature difference, heat release, and lowest cold surface temperature are the performance parameters under the optimal current conditions; and add the above optimal parameters to the table corresponding to the experimental scheme.

4. The method for synergistic optimization of key internal structural parameters and external heat sink thermal resistance in TEC application according to claim 3, characterized in that: The experimental verification method is as follows: Step 2.4: Select a TEC with known structural parameters, fix an actual convection heat exchanger on the hot side of the TEC, and fix a temperature sensor on both the cold and hot sides of the TEC. Step 2.5: Input different currents to the TEC and test the temperature and humidity of the inlet and outlet of the hot end heat sink, as well as the temperature of the cold and hot sides of the TEC under different current conditions; Step 2.6: Calculate the inlet and outlet enthalpy values ​​of the hot-end radiator based on the inlet and outlet air temperatures and humidity. Calculate the heat generation of the TEC hot surface under different output current conditions using the enthalpy difference method. The thermal resistance of the radiator is obtained by dividing the difference between the ambient temperature and the hot surface temperature by the heat generated by the hot surface. The thermal resistance of the radiator is related to the radiator structure and the fan components. Therefore, it is a constant value under different current conditions.

5. The method for synergistic optimization of key internal structural parameters and external heat sink thermal resistance in TEC applications according to claim 1, characterized in that: Step 3 specifically involves: ， For the numerical relationship between the response variable and the design variable, a multinomial mathematical model is selected, where, Y This represents the response value for each experiment; β 0 This is the system's baseline response value when all parameters are at the center level; β i To determine the linear effect of the parameters, a mathematical regression model was constructed between the response variable and the design variable, and an analysis of variance (ANOVA) was performed on the mathematical model. The ANOVA method is as follows: In the ANOVA summary, the coefficient of determination R of the model is... 2 ≥95%, accuracy is the ratio of signal to noise in the model, greater than 4, taking into account R... 2 If both the accuracy value and the above conditions are met, then the selected mathematical model is considered reasonable.

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