A method and system for predicting the heat transfer performance of a heat exchanger mixed convection
By using a nonlinear superposition model and optimization screening, the hybrid convection heat transfer correlation is optimized, which solves the problem of lacking independent pure forced convection data and achieves high-precision prediction of hybrid convection heat transfer performance.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- QINGDAO UNIV OF TECH
- Filing Date
- 2026-03-26
- Publication Date
- 2026-06-23
AI Technical Summary
In the absence of independent pure forced convection data, existing technologies struggle to accurately construct hybrid convection heat transfer correlations, leading to unstable and low-accuracy heat transfer performance predictions.
A nonlinear superposition model was adopted to construct a mixed convection heat transfer correlation by acquiring experimental data of pure natural convection and mixed convection. The forced convection component was separated in reverse by using the nonlinear superposition model, and the optimal coupling index was selected by optimization criteria to establish the final mixed convection heat transfer correlation.
Under conditions where only pure natural convection and mixed convection data are available, the contribution of forced convection is accurately separated and quantified, and a mixed convection heat transfer correlation with clear physical meaning and stable fitting is constructed, which significantly improves the accuracy of heat transfer performance prediction.
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Figure CN121963975B_ABST
Abstract
Description
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 convective heat transfer performance of a heat exchanger, comprising:
[0007] Obtain experimental data of pure natural convection, and fit the data to obtain the heat transfer correlation of natural convection;
[0008] Acquire mixed convection experimental data, including the mixed convection Nusselt number and its corresponding Rayleigh and Reynolds numbers;
[0009] 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 Nuschelt number as a superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation.
[0010] 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.
[0011] 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 hybrid convection heat transfer correlation.
[0012] Obtain the Rayleigh number and Reynolds number for the operating condition to be predicted, substitute them into the final mixed convection heat transfer correlation, and predict the mixed convection Nusselt number for that operating condition.
[0013] Furthermore, the functional form of the natural convection heat transfer correlation is:
[0014] ,
[0015] 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.
[0016] Furthermore, the nonlinear superposition model is as follows:
[0017] ,
[0018] in, The coupling index is... For mixed convection Nuschelt number, For natural convection Nusselt number, For forced convection Nuschelt number;
[0019] The functional form of the forced convection heat transfer correlation is:
[0020] ,
[0021] in, For forced convection Nuschelt number, Let Reynolds number be 1. and The characteristic coefficients and exponents are to be determined.
[0022] Furthermore, the corresponding forced convection Nuschelt number component is separated from the mixed convection Nuschelt number, specifically calculated using the following formula:
[0023] ,
[0024] in, The fitted natural convection heat transfer correlation is determined based on the current Rayleigh number.
[0025] 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 for each coupling index, including:
[0026] For the separated With the corresponding Take the logarithm and fit the linear relationship using the least squares method. To determine the current Value and ;
[0027] The currently determined , 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;
[0028] By comparing the predicted values with the measured values in the mixed convection experimental data, a fitting error index is obtained.
[0029] Furthermore, the preset optimization criteria include one or more combinations of the following:
[0030] Choose the option with the smallest average relative error Value as the optimal coupling index * ;
[0031] Choose the option with the smallest maximum relative error. Value as the optimal coupling index * ;
[0032] Choose the option where the sum of the average relative error and the maximum relative error is minimized. Value as the optimal coupling index * ;
[0033] Provided the error meets the preset threshold, select an integer. Value as the optimal coupling index * .
[0034] A second aspect of the present invention provides a system for predicting the mixed convection heat transfer performance of a heat exchanger, comprising:
[0035] The first data acquisition module is used to acquire pure natural convection experimental data and to obtain a natural convection heat transfer correlation based on the data.
[0036] 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;
[0037] The model building module is used to construct a preliminary model of the hybrid convection heat transfer correlation based on the nonlinear superposition model. The preliminary model expresses the hybrid convection Nusselt number as a superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation.
[0038] The parameter traversal and separation fitting module is used to substitute the natural convection heat transfer correlation into the initial model, and combine the mixed convection experimental data to traverse the coupling index, separate the forced convection Nusselt number component from the mixed convection Nusselt number, and fit the component with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error under each coupling index.
[0039] The optimization and filtering 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, and substitute them into the initial model to obtain the final hybrid convection heat transfer correlation.
[0040] 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 Nusselt number under that operating condition.
[0041] 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.
[0042] 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.
[0043] 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.
[0044] Compared with the prior art, the method and system for predicting the mixed convection heat transfer performance of heat exchangers provided by the present invention have the following beneficial effects:
[0045] This invention solves the technical problem of constructing a reliable mixed convection heat transfer correlation based on a nonlinear superposition model and using an ergonomic coupling index to separate the forced convection component. This addresses the difficulty in constructing a reliable mixed convection heat transfer correlation when independent pure forced convection data is missing. It enables the accurate separation and quantification of the forced convection contribution from 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
[0046] 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.
[0047] Figure 1 This is a flowchart of the method for predicting the mixed convection heat transfer performance of a heat exchanger provided in Embodiment 1 of the present invention;
[0048] Figure 2 This is an architecture diagram of the heat exchanger hybrid convection heat transfer performance prediction system provided in Embodiment 2 of the present invention. Detailed Implementation
[0049] 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.
[0050] 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.
[0051] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0052] 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.
[0053] Example 1
[0054] like Figure 1 This embodiment provides a method for predicting the mixed convective heat transfer performance of a heat exchanger, including:
[0055] S1. Obtain experimental data of pure natural convection, and obtain the correlation of natural convection heat transfer based on the data.
[0056] Specifically, the experiment involves setting up a purely natural convection experimental setup, collecting and filtering valid data, and calculating the Rayleigh number for each group based on the obtained data. Ra Nuschelt number with natural convection After taking the logarithm of the data, the least squares method was used to regress and fit the correlation of natural convection heat transfer.
[0057] S2. Obtain mixed convection experimental data, including the mixed convection Nusselt number and its corresponding Rayleigh number and Reynolds number.
[0058] Specifically, the process involves: activating the forced convection drive device (pump, fan, etc.), setting up a mixed convection experimental condition based on the pure natural convection experiment, conducting the experiment, collecting data, and then screening for validity; calculating the Rayleigh number under the mixed convection condition based on the valid data. Ra Reynolds number Re With mixed convection Nuschelt number Nu o .
[0059] 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.
[0060] The nonlinear superposition model is constructed based on the Churchill-type nonlinear superposition concept, and incorporates the mixed convection Nuschelt number. Nu o Expressed as natural convection Nusselt number Nu n With forced convection Nuschelt number The power superposition form.
[0061] 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.
[0062] 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 and using the least squares method for linear fitting, the characteristic coefficients of the corresponding forced convection term are obtained. C With index m ;Will n , C , m Substitute the initial model of the mixed convection heat transfer correlation, calculate the predicted value of the mixed convection Nusselt number, compare it with the experimentally measured value, and calculate the relative error index (including mean relative error MRE, maximum relative error MAXRE, etc.); record each Value-based correlations C , m and various error indicators.
[0063] 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.
[0064] The optimization criteria include the following priority recommendations:
[0065] When there are no special engineering requirements, the option with the smallest MRE should be selected first. n Value as n* ;
[0066] For applications sensitive to extreme operating conditions, select the minimum MAXRE setting. n Value as n* ;
[0067] When simplified calculations are required and the loss of accuracy is within an acceptable range, choose an integer whose error is close to the optimal value. n Value as n* ;
[0068] When balancing overall fitting accuracy and adaptability to extreme operating conditions, select the option with the minimum MRE+MAXRE value. n Value as n* .
[0069] The final hybrid convection heat transfer correlation is constructed as follows: .
[0070] 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.
[0071] 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.
[0072] 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).
[0073] 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.
[0074] 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).
[0075] The specific steps for establishing the hybrid convection correlation in this embodiment are as follows:
[0076] The first step is to 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.
[0077] The second step involves conducting experiments under natural convection conditions with all forced convection turned off, collecting and filtering out effective data from purely natural convection. Based on the obtained data, calculations are performed for each group. Ra and NuThe natural convection Nuschelt number was fitted using least squares regression in logarithmic coordinates. Nu n Association The fitting yielded A =0.00002、 p =0.66.
[0078] Number of seats A , p Compared with conventional laminar flow and natural convection in vertical slabs Or vertical slab turbulent natural convection The difference is mainly due to the compact structure and dense arrangement of multiple capillary tubes in the capillary heat exchanger. Each capillary tube measures 4.3*0.85mm, with a small diameter and a spacing of only 20mm, which leads to a double limitation on the development of the thermal boundary layer in the water outside the tubes:
[0079] Radial confinement: The boundary layer has not yet fully developed (not reaching the state of "fully developed boundary layer") before it touches the tube wall or the boundary layer of the adjacent capillary, and it is impossible to form a complete boundary layer structure under the classic working condition;
[0080] Interference effect: The wakes of adjacent capillaries superimpose with the boundary layer, disrupting the continuity of the boundary layer and weakening the local heat transfer intensity.
[0081] 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.
[0082] 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 the mixed convection under the effective data. Ra , Re and Nu o .
[0083] 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 result obtained in the second step Nu n Substituting the relation into the relation, i.e. .in C, m The coefficients and exponents of the forced convection term are to be determined. n The coupling index is to be determined, and its specific value will be determined through subsequent optimization steps.
[0084] Fifth step, perform coupling index calculation.n Optimized filtering and Nu f Correlational fitting:
[0085] (1) Set the coupling index n Candidate range and step size (recommended 1.0 to 3.0, step size 0.1).
[0086] (2) For each candidate n Please follow these steps:
[0087] 1) Utilizing the already fitted A, p parameter( A =0.00002、 p =0.66), calculate the corresponding data points of each mixed convection data point. Nu n ;
[0088] 2) Based on the model established in step 4, the forced convection Nuschelt number components are separated. Nu f The calculation formula is: ;
[0089] 3) To Nu f and Re Take the logarithm, fit using the least squares method, and calculate according to the formula. The corresponding characteristic coefficients of the forced convection term are obtained. C With index m ;
[0090] 4) n, C, m Substitute the values into the mixed convection correlation from step four to calculate the predicted values. Nu Compare the predicted value with the experimental results. Nu o Calculate the relative error indices (including mean relative error MRE, maximum relative error MAXRE, etc.).
[0091] Record each n Value-based correlation correspondence C, m and Nu Various error indicators. In this embodiment... n The values of the correlation parameters and various error indices for different values are shown in Table 1.
[0092] Step 6: Determine the optimal solution based on engineering requirements n value:
[0093] This embodiment needs to balance overall fitting accuracy and adaptability to extreme working conditions, therefore, the minimum value of MRE+MAXRE is selected. n Value as the optimal coupling index n *. Based on the data in Table 1, whenn When the value is 1.8, the mean relative error MRE ≈ 5.17% and the maximum relative error MAXRE ≈ 15.78%, which meets the requirements for consideration. Therefore, the value is determined as follows. n *=1.8, corresponding parameter C *=20.87、 m *=0.791.
[0094]
[0095] Construct the final association:
[0096] The final hybrid convection correlation determined in this embodiment is:
[0097]
[0098] The method provided by this invention systematically traverses coupling indices. n For each candidate n The system performs forced term separation and parameter fitting, 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.
[0099] 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.
[0100] In this embodiment, the optimal coupling index is obtained through optimized screening. n The value of *=1.8 makes the final constructed correlation mean relative error MRE≈5.17% and maximum relative error MAXRE≈15.78%, which verifies the effectiveness and accuracy of the method in dealing with heat exchangers with complex geometries.
[0101] Example 2
[0102] like Figure 2 As shown, this embodiment provides a system for predicting the mixed convective heat transfer performance of a heat exchanger, including:
[0103] The first data acquisition module is used to acquire pure natural convection experimental data and to obtain a natural convection heat transfer correlation based on the data.
[0104] 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;
[0105] The model building module is used to construct a preliminary model of the hybrid convection heat transfer correlation based on the nonlinear superposition model. The preliminary model expresses the hybrid convection Nusselt number as a superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation.
[0106] The parameter traversal and separation fitting module is used to substitute the natural convection heat transfer correlation into the initial model, and combine the mixed convection experimental data to traverse the coupling index, separate the forced convection Nusselt number component from the mixed convection Nusselt number, and fit the component with the Reynolds number to obtain the forced convection heat transfer correlation and fitting error under each coupling index.
[0107] The optimization and filtering 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, and substitute them into the initial model to obtain the final hybrid convection heat transfer correlation.
[0108] 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 Nusselt number under that operating condition.
[0109] Example 3
[0110] Embodiment 3 of the present invention provides an electronic device.
[0111] 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.
[0112] 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.
[0113] Example 4
[0114] Embodiment 4 of the present invention provides a computer-readable storage medium.
[0115] 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.
[0116] 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.
[0117] Example 5
[0118] Embodiment 5 of the present invention provides a computer program product.
[0119] 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.
[0120] 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.
[0121] 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.
[0122] 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0123] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0124] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0125] 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: Obtain experimental data of pure natural convection, and fit the data to obtain the heat transfer correlation of natural convection; Acquire mixed convection experimental data, including the mixed convection Nusselt number and its corresponding Rayleigh and Reynolds numbers; 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 Nuschelt number as a superposition of the natural convection heat transfer correlation and the undetermined forced convection heat transfer correlation. 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. 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 hybrid convection heat transfer correlation. Obtain the Rayleigh number and Reynolds number for the operating condition to be predicted, substitute them into the final mixed convection heat transfer correlation, and predict the mixed convection Nusselt number for 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, For forced convection Nuschelt 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, By fitting the forced convection Nusselt number component to the Reynolds number, the forced convection heat transfer correlation and its fitting error under each coupling index are obtained, including: For the separated With the corresponding Take the logarithm and fit the linear relationship using the least squares method. To determine the current Value and ; The currently determined , 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; By comparing the predicted values with the measured values in the mixed convection experimental data, a fitting error index is obtained.
6. The method according to claim 5, characterized in that, The preset optimization criteria include one or more combinations of the following: Choose the option with the smallest average relative error Value as the optimal coupling index * ; Choose the option with the smallest maximum relative error. Value as the optimal coupling index * ; Choose the option where the sum of the average relative error and the maximum relative error is minimized. Value as the optimal coupling index * ; Provided the error meets the preset threshold, select an integer. 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 to obtain 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 hybrid convection heat transfer correlation based on the nonlinear superposition model. The preliminary model expresses the hybrid 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 initial model, and combine the mixed convection experimental data to traverse the coupling index, 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. The optimization and filtering 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, and substitute them into the initial model to obtain the final hybrid 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 Nusselt number under 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.