Method and system for predicting mixed convective heat transfer performance of heat exchanger

Abstract

The invention provides a method and system for predicting the mixed convective heat transfer performance of a heat exchanger, and relates to the technical field of heat transfer science and engineering thermophysics, and the method comprises the steps: obtaining pure natural convective experimental data, and fitting a natural convective heat transfer correlation formula; obtaining mixed convection experiment data; constructing a hybrid convective heat transfer correlation type prototype based on a nonlinear superposition model; substituting the natural convective heat transfer correlation into the prototype, traversing the coupling indexes, reversely separating out forced convective components from the mixed data, and fitting to obtain forced convective heat transfer correlation and errors under each coupling index; screening an optimal coupling index and an optimal forced convective heat transfer correlation formula corresponding to the optimal coupling index based on an optimization criterion, and substituting the optimal coupling index and the optimal forced convective heat transfer correlation formula into the prototype to obtain a final mixed convective heat transfer correlation formula; and obtaining a Rayleigh number and a Reynolds number of a to-be-predicted working condition, and substituting the Rayleigh number and the Reynolds number into the final correlation formula to predict the According to the method, the mixed convection heat transfer performance of the heat exchanger can be accurately predicted under the condition that only pure natural convection and mixed convection data exist.

Description

A method and system for predicting the mixed convective heat transfer performance of heat exchangers Technical Field

[0001] This invention relates to the fields of heat transfer and engineering thermophysics, and in particular to a method and system for predicting the mixed convective heat transfer performance of heat exchangers. Background Technology

[0002] Mixed convection is widely used in various engineering heat exchange equipment. Its total heat transfer is determined by natural convection driven by buoyancy and forced convection driven by external force. Accurately predicting the heat transfer performance of mixed convection is the key to the design and operation optimization of heat exchangers.

[0003] Heat transfer correlations are a core technical means for predicting the heat transfer performance of mixed convection. Through correlations, operating parameters (such as Rayleigh number and Reynolds number) can be directly mapped to heat transfer intensity (Nuschert number), thus providing a quantitative basis for engineering design. Existing methods for establishing mixed convection correlations mostly employ superposition, that is, obtaining two independent heat transfer correlations—one for pure natural convection and one for pure forced convection—and then combining them linearly or nonlinearly to obtain the mixed convection heat transfer correlation. Therefore, having experimental conditions with both independent data from pure natural convection and pure forced convection is a prerequisite for establishing mixed convection correlations using traditional methods.

[0004] However, in many practical engineering experiments or constrained facilities, only pure natural convection experimental data and some mixed convection data influenced by external driving forces are often available. Independent pure forced convection data are either unavailable or difficult to obtain. If traditional superposition methods are directly applied to such data, the lack of a pure forced convection baseline makes it impossible to accurately separate the forced convection contribution from the mixed data. This can easily lead to unclear identification of the forced term and severe coupling between the natural and forced term parameters, resulting in unstable fitting of the constructed mixed convection correlation or physically inconsistent results, ultimately affecting the prediction accuracy of the mixed convection heat transfer performance. Summary of the Invention

[0005] This invention provides a method and system for predicting the mixed convection heat transfer performance of heat exchangers, in order to solve the technical problems existing in the background art.

[0006] To achieve the above objectives, a first aspect of the present invention provides a method for predicting the mixed convection heat transfer performance of a heat exchanger, comprising: acquiring pure natural convection experimental data and fitting a natural convection heat transfer correlation based on the data; acquiring mixed convection experimental data, the data including the mixed convection Nuschelt number and its corresponding Rayleigh number and Reynolds number; constructing a preliminary model of the mixed convection heat transfer correlation based on a nonlinear superposition model, the preliminary model expressing the mixed convection Nuschelt number as a superposition of the natural convection heat transfer correlation and an undetermined forced convection heat transfer correlation; substituting the natural convection heat transfer correlation into the preliminary model, and combining it with the mixed convection heat transfer performance of the heat exchanger. Convection experimental data are used to iterate through coupling indices, separating the forced convection Nuschelt number component from the mixed convection Nuschelt number. This forced convection Nuschelt number component is then fitted to the Reynolds number to obtain the forced convection heat transfer correlation and fitting error for each coupling index. Based on a preset optimization criterion, the optimal coupling index and its corresponding optimal forced convection heat transfer correlation are selected from all coupling indices and substituted into the initial model to obtain the final mixed convection heat transfer correlation. Finally, the Rayleigh number and Reynolds number for the operating condition to be predicted are obtained and substituted into the final mixed convection heat transfer correlation to predict the mixed convection Nuschelt number for that operating condition.

[0007] Furthermore, the functional form of the natural convection heat transfer correlation is: ,in, and These are the characteristic coefficients and exponents obtained by fitting the purely natural convection experimental data using the least squares method. For natural convection Nusselt number, It is a Rayleigh number.

[0008] Furthermore, the nonlinear superposition model is as follows: ,in, The coupling index is... For mixed convection Nuschelt number, For natural convection Nusselt number, The forced convection Nusselt number; the functional form of the forced convection heat transfer correlation is: ,in, For forced convection Nuschelt number, Let Reynolds number be 1. and The characteristic coefficients and exponents are to be determined.

[0009] Furthermore, the corresponding forced convection Nuschelt number component is separated from the mixed convection Nuschelt number, specifically calculated using the following formula: ,in, The fitted natural convection heat transfer correlation is determined based on the current Rayleigh number.

[0010] Furthermore, the forced convection Nusselt number component is fitted to the Reynolds number to obtain the forced convection heat transfer correlation and its fitting error under each coupling index, including: the separated components... With the corresponding Take the logarithm and fit a linear relationship using the least squares method. To determine the current Value and ; to determine the current , and Substitute back into the initial model of the mixed convection heat transfer correlation to calculate the predicted value of the mixed convection Nusselt number; compare the predicted value with the measured value in the mixed convection experimental data to obtain the fitting error index.

[0011] Furthermore, the preset optimization criteria include one or more combinations of the following: selecting the option with the smallest average relative error. Value as the optimal coupling index *; Select the option with the smallest maximum relative error. Value as the optimal coupling index *; Select the option where the sum of the average relative error and the maximum relative error is minimized. Value as the optimal coupling index *; Select an integer provided the error meets the preset threshold. Value as the optimal coupling index *

[0012] A second aspect of the present invention provides a system for predicting the mixed convection heat transfer performance of a heat exchanger, comprising: a first data acquisition module for acquiring pure natural convection experimental data and fitting a natural convection heat transfer correlation based on the data; a second data acquisition module for acquiring mixed convection experimental data, the data including the mixed convection Nusselt number and its corresponding Rayleigh number and Reynolds number; a model building module for constructing a preliminary model of the mixed convection heat transfer correlation based on a nonlinear superposition model, the preliminary model expressing the mixed convection Nusselt number as a superposition of the natural convection heat transfer correlation and an undetermined forced convection heat transfer correlation; and a parameter traversal and separation fitting module for applying the natural convection heat transfer correlation... Substituting the formula into the initial model and combining it with the mixed convection experimental data, the coupling indices are traversed to separate the forced convection Nuschelt number component from the mixed convection Nuschelt number. The component is then fitted with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error for each coupling index. An optimization and screening module is used to screen the optimal coupling index and its corresponding optimal forced convection heat transfer correlation from all coupling indices based on preset optimization criteria. Substituting this into the initial model, the final mixed convection heat transfer correlation is obtained. A prediction module is used to obtain the Rayleigh number and Reynolds number of the operating condition to be predicted, and substituting them into the final mixed convection heat transfer correlation to predict the mixed convection Nuschelt number under that operating condition.

[0013] A third aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the method for predicting the mixed convection heat transfer performance of a heat exchanger as described in the first aspect of the present invention.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps in the method for predicting the mixed convection heat transfer performance of a heat exchanger as described in the first aspect of the present invention.

[0015] A fifth aspect of the present invention provides a computer program product comprising software code, wherein the program in the software code performs the steps of the heat exchanger mixed convection heat transfer performance prediction method as described in the first aspect of the present invention.

[0016] Compared with existing technologies, the present invention provides a method and system for predicting the mixed convection heat transfer performance of heat exchangers, which has the following advantages: The present invention solves the technical problem of difficulty in constructing a reliable mixed convection heat transfer correlation when independent pure forced convection data is missing by constructing a preliminary mixed convection heat transfer correlation based on a nonlinear superposition model and using an ergonomic coupling index to separate the forced convection component in reverse. It realizes the accurate separation and quantification of the forced convection contribution from the mixed convection data under the condition of only pure natural convection and mixed convection data, thereby constructing a mixed convection heat transfer correlation with clear physical meaning and stable fitting, which significantly improves the prediction accuracy of mixed convection heat transfer performance. Attached Figure Description

[0017] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0018] Figure 1 is a flowchart of the heat exchanger mixed convection heat transfer performance prediction method provided in Embodiment 1 of the present invention; Figure 2 is an architecture diagram of the heat exchanger mixed convection heat transfer performance prediction system provided in Embodiment 2 of the present invention. Detailed Implementation

[0019] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0022] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0023] As shown in Figure 1, this embodiment provides a method for predicting the heat transfer performance of a heat exchanger through mixed convection, including: S1, acquiring pure natural convection experimental data, and fitting a natural convection heat transfer correlation based on the data.

[0024] Specifically, the experiment involves setting up a purely natural convection experimental setup, collecting and filtering valid data, and calculating the Rayleigh number Ra and the natural convection Nusselt number for each group based on the obtained data. After taking the logarithm of the data, the least squares method was used to regress and fit the correlation of natural convection heat transfer.

[0025] S2. Obtain mixed convection experimental data, including the mixed convection Nusselt number and its corresponding Rayleigh number and Reynolds number.

[0026] Specifically, the following steps are taken: Forced convection drive devices (pumps, fans, etc.) are activated; a mixed convection experimental condition is set up based on a purely natural convection experiment, and experiments are conducted. Data is collected and its validity is screened. Based on the valid data, the Rayleigh number Ra, Reynolds number Re, and mixed convection Nusselt number Nu under the mixed convection condition are calculated. o .

[0027] S3. Based on the nonlinear superposition model, a preliminary model of the mixed convection heat transfer correlation is constructed. The preliminary model expresses the mixed convection Nusselt number as the superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation.

[0028] The nonlinear superposition model is constructed based on the Churchill-type nonlinear superposition concept, and incorporates the mixed convection Nusselt number. o Expressed as the natural convection Nusselt number. n With forced convection Nuschelt number The power superposition form.

[0029] S4. Substitute the natural convection heat transfer correlation into the initial model, and combine it with the mixed convection experimental data. Traverse the coupling indices, separate the forced convection Nuschelt number component from the mixed convection Nuschelt number, and fit the forced convection Nuschelt number component with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error under each coupling index.

[0030] Specifically, this includes: setting the coupling index. The candidate range and step size (recommended 1.0–3.0, step size 0.1); for each candidate The fitted natural convection heat transfer correlation is used to calculate the corresponding data points for each mixed convection data point. The forced convection Nuschelt number components are obtained by inverse separation based on a nonlinear superposition model. ;right and Taking the logarithm, a least-squares linear fit is used to obtain the corresponding characteristic coefficients C and exponent m of the forced convection term; substituting n, C, and m back into the initial model of the mixing convection heat transfer correlation, the predicted value of the mixing convection Nusselt number is calculated and compared with the experimentally measured value to calculate the relative error index (including mean relative error MRE, maximum relative error MAXRE, etc.); each The values ​​of the correlation equation correspond to C, m, and various error indices.

[0031] S5. Based on the preset optimization criteria, select the optimal coupling index and its corresponding optimal forced convection heat transfer correlation from all coupling indices, substitute them into the initial model, and obtain the final hybrid convection heat transfer correlation.

[0032] The optimization criteria include the following priority recommendations: when there are no special engineering requirements, the n value with the minimum MRE should be selected as n*; for application scenarios sensitive to extreme working conditions, the n value with the minimum MAXRE should be selected as n*; when simplified calculation is required and the accuracy loss is within an acceptable range, the integer n value with an error close to the optimal value should be selected as n*; when both overall fitting accuracy and adaptability to extreme working conditions need to be considered, the n value with the minimum MRE+MAXRE should be selected as n*.

[0033] The final hybrid convection heat transfer correlation is constructed as follows: .

[0034] S6. Obtain the Rayleigh number and Reynolds number of the operating condition to be predicted, substitute them into the final mixed convection heat transfer correlation, and predict the mixed convection Nusselt number under the operating condition.

[0035] In one specific embodiment, the implementation process of the present invention is described in detail in conjunction with the application scenario of "mixing and convection of water outside the tube of a water source heat pump capillary heat exchanger". This embodiment is only used to illustrate the technical solution of the present invention, and not to limit its scope of protection.

[0036] This embodiment takes the water outside the tube of an immersion capillary heat exchanger as the research object, and demonstrates how to establish a mixed convection heat transfer correlation and accurately identify the forced term according to the six-step process of this invention under the condition of only having pure natural convection data and mixed convection data (without independent pure forced convection test points).

[0037] The capillary heat exchanger used in this embodiment is a U-shaped capillary network with 9 vertically arranged capillary tubes. The length of a single capillary tube is 2m, the tube spacing is 20 mm, and the spacing between the tubes is 60 mm. The experiment is set with a basic operating condition and the following operating parameters: the inlet temperature inside the tube is about 32℃, the outside temperature is about 25℃, and the average flow velocity inside the tube is about 0.05m / s.

[0038] This embodiment designs an experiment for the heat exchanger's heat release operation, clearly distinguishing between two types of conditions: pure natural convection and mixed convection. Under pure natural convection, all external driving devices are shut off (no pump / fan drives the flow of water outside the pipe), and heat exchange through convection in the water outside the pipe is achieved solely through buoyancy. The experiment uses "variable inlet temperature + variable outside temperature" as the core adjustment variables, with the inlet temperature at a basic operating condition of 32℃ and an adjustment range of 30-36℃, and the outside temperature at a basic operating condition of 25℃ and an adjustment range of 23-29℃. Under mixed convection, based on the pure natural convection condition, a forced convection driving device (pump) is activated for the water outside the pipe. Mixed convection heat exchange is achieved through a combination of external driving force and buoyancy. The experiment uses "variable outside velocity" as the core adjustment variable, with an adjustment range of 0.012-0.018 m / s (low velocity condition).

[0039] The specific steps for establishing the hybrid convection correlation in this embodiment are as follows: First, set up the experimental platform, complete the installation and debugging of the experimental device, clearly record the core geometric dimensions of the heat exchanger (such as tube length, tube spacing, mat spacing, etc.), and determine the characteristic length of this experiment (in this example, the outer diameter of the capillary tube is selected); clarify the measurement parameters, and measure the inlet and outlet temperatures of the capillary heat exchanger, the outer wall temperature of the heat exchanger, the water temperature, the flow rate of the capillary heat exchanger, and the flow rate of the water outside the tube based on the experimental platform.

[0040] The second step involves conducting experiments under natural convection conditions with all forced actuation disabled, collecting and selecting effective data from purely natural convection. Based on the obtained data, Ra and Nu are calculated for each group, and the Nusselt number Nu is fitted using least squares regression on logarithmic coordinates. n Association The fitting yielded A=0.00002 and p=0.66.

[0041] The correlation between the number of seats A and p and conventional laminar natural convection such as vertical slabs Or vertical slab turbulent natural convection The difference is mainly due to the compact structure and dense arrangement of multiple tubes in the capillary heat exchanger. Each capillary tube has a diameter of 4.3*0.85mm, and the spacing between tubes is only 20mm. This results in a double limitation on the development of the thermal boundary layer in the water outside the tubes: Radial limitation: the boundary layer has not yet fully developed (not reaching the "fully developed boundary layer" state) before it touches the tube wall or the boundary layer of adjacent capillaries, making it impossible to form a complete boundary layer structure as in classic operating conditions; Interference effect: the wakes of adjacent capillaries overlap with the boundary layer, disrupting the continuity of the boundary layer and weakening the local heat transfer intensity.

[0042] Compared to a vertical cylindrical wall in an infinitely large space, the natural convection between different capillaries in a capillary heat exchanger interferes with each other. Therefore, changes in the Rayleigh number simultaneously affect the boundary layer integrity, with a more significant impact on the natural convection heat transfer outside the capillary heat exchanger tubes. This results in the Rayleigh number exponent term in the correlation for natural convection outside the capillary heat exchanger tubes being higher than existing correlations, while the constant term is lower.

[0043] Step 3: Turn on the water pump and conduct the experiment according to the mixed convection experimental conditions. Collect and filter out the effective mixed convection data. Calculate Ra, Re, and Nu under mixed convection based on the effective data. o .

[0044] Step 4: Construct a hybrid convection nonlinear superposition model based on the Churchill-type nonlinear superposition concept, and establish the general form of the hybrid convection correlation equation. The Nu obtained in the second step n Substituting the relation into the relation, i.e. Where C and m are the coefficients and exponents of the forced convection term to be determined, and n is the coupling exponent to be determined. Its specific value will be determined through subsequent optimization steps.

[0045] The fifth step is to optimize and select the coupling index n and Nu. f Correlational fitting: (1) Set the candidate range and step size of the coupling index n (1.0 to 3.0 recommended, step size 0.1); (2) For each candidate n, process it according to the following steps: 1) Calculate the Nu corresponding to each mixed convection data point using the fitted A and p parameters (A=0.00002, p=0.66). n 2) Based on the model established in step 4, the Nusselt number component of forced convection is obtained. f The calculation formula is: ;3) For Nu f Logarithm of Re, least squares fitting, according to the calculation formula 4) Substitute n, C, and m into the mixed convection correlation in step 4 to calculate the predicted value Nu, and compare the predicted value with the experimentally measured Nu. o Calculate the relative error indices (including mean relative error MRE, maximum relative error MAXRE, etc.); record the corresponding error indices C, m, and Nu for each value of n. Table 1 shows the values ​​of the correlation parameters and the values ​​of each error index for different n values ​​in this embodiment.

[0046] Step 6: Determine the optimal value of n based on engineering requirements: This embodiment needs to balance overall fitting accuracy and adaptability to extreme working conditions. Therefore, the value of n when MRE+MAXRE is minimized is selected as the optimal coupling index n*. Based on the data in Table 1, when n=1.8, the average relative error MRE≈5.17% and the maximum relative error MAXRE≈15.78%, which meets the requirements. Therefore, n*=1.8 is determined, corresponding to parameters C*=20.87 and m*=0.791.

[0047]

[0048] Constructing the final correlation: The final hybrid convection correlation determined in this embodiment is as follows:

[0049] The method provided by this invention systematically traverses the coupling index n, performs forced term separation and parameter fitting for each candidate n value, and selects the optimal coupling index and its corresponding forced convection heat transfer correlation parameters based on engineering requirements (such as minimum average relative error, minimum maximum relative error, or a combination of both). This process avoids the problems of unstable fitting or physical inconsistency caused by severe parameter coupling in traditional methods, making the constructed correlation more adaptable to engineering and robust.

[0050] This invention is particularly applicable to practical engineering scenarios where independent pure forced convection experimental data are unavailable or difficult to obtain, such as low-disturbance systems, structurally constrained equipment, or renovation projects. It can fully utilize existing pure natural convection data and limited mixed convection data to establish high-precision mixed convection heat transfer correlations, providing reliable technical support for heat exchanger design optimization and operational evaluation, reducing experimental costs and time, and has broad industrial application prospects.

[0051] In this embodiment, the optimal coupling index n*=1.8 was obtained through optimization screening, which made the final constructed correlation average relative error MRE≈5.17% and maximum relative error MAXRE≈15.78%, verifying the effectiveness and accuracy of the method in dealing with heat exchangers with complex geometries.

[0052] Example 2, as shown in Figure 2, provides a system for predicting the mixed convection heat transfer performance of a heat exchanger, comprising: a first data acquisition module for acquiring pure natural convection experimental data and fitting a natural convection heat transfer correlation based on the data; a second data acquisition module for acquiring mixed convection experimental data, the data including the mixed convection Nusselt number and its corresponding Rayleigh number and Reynolds number; a model building module for constructing a preliminary model of the mixed convection heat transfer correlation based on a nonlinear superposition model, the preliminary model expressing the mixed convection Nusselt number as a superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation; and a parameter traversal and separation fitting module for converting the natural convection heat transfer... The correlation is substituted into the initial model, and combined with the mixed convection experimental data, the coupling indices are traversed to separate the forced convection Nuschelt number component from the mixed convection Nuschelt number. The component is then fitted with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error for each coupling index. The optimization and screening module is used to screen the optimal coupling index and its corresponding optimal forced convection heat transfer correlation from all coupling indices based on preset optimization criteria. This is substituted into the initial model to obtain the final mixed convection heat transfer correlation. The prediction module is used to obtain the Rayleigh number and Reynolds number of the operating condition to be predicted, and substitute them into the final mixed convection heat transfer correlation to predict the mixed convection Nuschelt number under that operating condition.

[0053] Example 3: This invention provides an electronic device.

[0054] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. The processor includes, but is not limited to, at least one of a central processing unit (CPU), a graphics processing unit (GPU), a neural network processor (NPU), a tensor processor (TPU), or an artificial intelligence acceleration chip. The program is used to execute the steps in the heat exchanger hybrid convection heat transfer performance prediction method as described in Embodiment 1 of the present invention.

[0055] The detailed steps are the same as those of the heat exchanger mixing convection heat transfer performance prediction method provided in Example 1, and will not be repeated here.

[0056] Example 4 of the present invention provides a computer-readable storage medium.

[0057] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the heat exchanger hybrid convection heat transfer performance prediction method as described in Embodiment 1 of the present invention.

[0058] The detailed steps are the same as those of the heat exchanger mixing convection heat transfer performance prediction method provided in Example 1, and will not be repeated here.

[0059] Example 5: This invention provides a computer program product.

[0060] A computer program product includes software code, wherein the program in the software code performs the steps of the heat exchanger mixed convection heat transfer performance prediction method as described in Embodiment 1 of the present invention.

[0061] The detailed steps are the same as those of the heat exchanger mixing convection heat transfer performance prediction method provided in Example 1, and will not be repeated here.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages. For example, in one implementation, the methods and systems can be developed based on deep learning frameworks (such as TensorFlow, PyTorch, etc.) and using the Python language. Those skilled in the art will understand that other suitable programming languages ​​or tools can also be used for implementation without departing from the core ideas of the present invention.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0066] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for predicting the mixed convective heat transfer performance of a heat exchanger, characterized in that, include: Natural convection experimental data were obtained, and a natural convection heat transfer correlation was fitted based on the data. Mixed convection experimental data were also obtained, including the mixed convection Nuschelt number and its corresponding Rayleigh and Reynolds numbers. Based on a nonlinear superposition model, a preliminary mixed convection heat transfer correlation was constructed, which expressed the mixed convection Nuschelt number as a superposition of the natural convection heat transfer correlation and an undetermined forced convection heat transfer correlation. The natural convection heat transfer correlation was substituted into the preliminary model, and combined with the mixed convection experimental data, the coupling indices were iterated to obtain the mixed convection Nuschelt number. The forced convection Nuschelt number component is separated from the Nuschelt number, and the forced convection Nuschelt number component is fitted with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error under each coupling index. Based on the preset optimization criteria, the optimal coupling index and its corresponding optimal forced convection heat transfer correlation are selected from all coupling indices and substituted into the initial model to obtain the final mixed convection heat transfer correlation. The Rayleigh number and Reynolds number of the operating condition to be predicted are obtained and substituted into the final mixed convection heat transfer correlation to predict the mixed convection Nuschelt number under that operating condition.

2. The method according to claim 1, characterized in that, The functional form of the natural convection heat transfer correlation is: ,in, and These are the characteristic coefficients and exponents obtained by fitting the purely natural convection experimental data using the least squares method. For natural convection Nusselt number, It is a Rayleigh number.

3. The method according to claim 1, characterized in that, The nonlinear superposition model is as follows: ,in, The coupling index is... For mixed convection Nuschelt number, For natural convection Nusselt number, The forced convection Nusselt number; the functional form of the forced convection heat transfer correlation is: ,in, For forced convection Nuschelt number, Let Reynolds number be 1. and The characteristic coefficients and exponents are to be determined.

4. The method according to claim 3, characterized in that, The forced convection Nusselt number component is separated from the mixed convection Nusselt number, specifically calculated using the following formula: ,in, The fitted natural convection heat transfer correlation is determined based on the current Rayleigh number.

5. The method according to claim 3, characterized in that, The forced convection Nusselt number component is fitted to the Reynolds number to obtain the forced convection heat transfer correlation and its fitting error for each coupling index, including: the separated components... With the corresponding Take the logarithm and fit a linear relationship using the least squares method. To determine the current Value and ; to determine the current 、 and Substitute back into the initial model of the mixed convection heat transfer correlation to calculate the predicted value of the mixed convection Nusselt number; compare the predicted value with the measured value in the mixed convection experimental data to obtain the fitting error index.

6. The method according to claim 5, characterized in that, The preset optimization criteria include one or more combinations of the following: selecting the option with the smallest average relative error. Value as the optimal coupling index *; Select the option with the smallest maximum relative error. Value as the optimal coupling index *; Select the option where the sum of the average relative error and the maximum relative error is minimized. Value as the optimal coupling index *; Select an integer provided the error meets the preset threshold. Value as the optimal coupling index *。 7. A system for predicting the mixed convective heat transfer performance of a heat exchanger, characterized in that, include: The first data acquisition module is used to acquire pure natural convection experimental data and fit a natural convection heat transfer correlation based on the data; the second data acquisition module is used to acquire mixed convection experimental data, including the mixed convection Nusselt number and its corresponding Rayleigh number and Reynolds number; the model building module is used to construct a preliminary model of the mixed convection heat transfer correlation based on a nonlinear superposition model, wherein the preliminary model expresses the mixed convection Nusselt number as a superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation; the parameter traversal and separation fitting module is used to substitute the natural convection heat transfer correlation into the preliminary model and combine it with the mixed convection experimental data. The system iterates through the coupling indices, separates the forced convection Nuschelt number component from the mixed convection Nuschelt number, and fits the forced convection Nuschelt number component with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error for each coupling index. An optimization and screening module is used to select the optimal coupling index and its corresponding optimal forced convection heat transfer correlation from all coupling indices based on preset optimization criteria. These are then substituted into the initial model to obtain the final mixed convection heat transfer correlation. A prediction module is used to obtain the Rayleigh number and Reynolds number of the operating condition to be predicted, substitute them into the final mixed convection heat transfer correlation, and predict the mixed convection Nuschelt number for that operating condition.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for predicting the mixed convective heat transfer performance of a heat exchanger according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method for predicting the mixed convective heat transfer performance of a heat exchanger according to any one of claims 1 to 6.

10. A computer program product, comprising software code, characterized in that, The program in the software code executes the steps of the method for predicting the mixed convective heat transfer performance of heat exchangers according to any one of claims 1 to 6.

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