CN110309589B - Parameter optimization method and device for fin heat exchanger - Google Patents
Parameter optimization method and device for fin heat exchanger Download PDFInfo
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- CN110309589B CN110309589B CN201910579161.3A CN201910579161A CN110309589B CN 110309589 B CN110309589 B CN 110309589B CN 201910579161 A CN201910579161 A CN 201910579161A CN 110309589 B CN110309589 B CN 110309589B
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Abstract
The invention discloses a parameter optimization method for a fin heat exchanger, which comprises the following steps: determining a flow resistance factor and a heat transfer factor under each structural parameter according to the structural parameters of the fin heat exchanger; establishing a neural network model by taking the corresponding relation between the structural parameters and the flow resistance factors and the heat transfer factors corresponding to the structural parameters as training samples; solving the neural network model; and determining structural parameters corresponding to the optimal solution of the comprehensive parameters so as to optimize the parameters of the fin heat exchanger. The flow resistance factor and the heat transfer factor under the corresponding parameters are obtained by calculation according to the structural parameters of the fin heat exchanger and are used as training samples to train and establish a neural network model, the model takes the structural parameters of the fin heat exchanger as input, and the flow resistance factor and the heat transfer factor under the corresponding parameters as output, and then the model is solved to obtain an optimal solution, so that the optimal structure of the fin heat exchanger is obtained, the workload is reduced, and the optimal structure is obtained with more convincing power.
Description
Technical Field
The invention relates to the technical field of energy, in particular to a method and a device for optimizing parameters of a fin heat exchanger.
Background
An evaporator and a condenser in a refrigeration industry water chilling unit or a fan coil at the tail end of an air conditioner and other heat exchangers are indispensable system components. Air conditioning equipment is more and more widely applied, not only consumes a large amount of materials, but also increases energy consumption year by year. Factors influencing the performance of the fin tube type heat exchanger are many, such as the geometric structure of fins, the spacing of the fins, the diameter of tubes, the arrangement and number of the tubes, the material performance and the like, and the geometric structure of the fins plays an important role in the heat exchange performance of the heat exchanger, so that the research on the influence of fin parameters on the performance of the heat exchanger is of great significance.
At present, most fin parameter optimization technologies are to establish a simulation model and modify single variables to compare with an original model. Another fin parameter optimization is to directly search for an optimized structure through trial-manufacture experiments. The optimization process is complex, the work is complex, the optimization method can only be suitable for a few variables, the optimal solution cannot be found, and only the optimal solution can be found.
Disclosure of Invention
The invention provides a method and a device for optimizing parameters of a fin heat exchanger.
In a first aspect, the present invention provides a method for optimizing parameters of a fin heat exchanger, the method including:
determining a flow resistance factor and a heat transfer factor under each structural parameter according to the structural parameters of the fin heat exchanger so as to establish a corresponding relation between any structural parameter and the corresponding flow resistance factor and heat transfer factor; establishing a neural network model by taking the corresponding relation between the structural parameters and the flow resistance factors and the heat transfer factors corresponding to the structural parameters as training samples, wherein the neural network model takes any structural parameter as input and the corresponding flow resistance factors and the corresponding heat transfer factors as output; solving the neural network model to obtain an optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor; and determining structural parameters corresponding to the optimal solution of the comprehensive parameters so as to optimize the parameters of the fin heat exchanger.
Preferably, the first and second electrodes are formed of a metal,
the step of determining the flow resistance factor and the heat transfer factor under each structural parameter according to the structural parameters of the finned heat exchanger comprises the following steps:
obtaining a fin break angle, a break distance, a fin inclination angle, a fin thickness and a fin distance of the fin heat exchanger; and calculating corresponding flow resistance factors and heat transfer factors according to the fin break angle, the break distance, the fin inclination angle, the fin thickness and the fin spacing of the fin heat exchanger.
Preferably, the first and second electrodes are formed of a metal,
the step of calculating the corresponding flow resistance factor and the heat transfer factor according to the fin break angle, the break distance, the fin inclination angle, the fin thickness and the fin spacing of the fin heat exchanger comprises the following steps:
performing parameter univariate on any structural parameter of a fin folding angle, a folding distance, a fin inclination angle, a fin thickness and a fin distance of the fin heat exchanger; and determining the corresponding flow resistance factor and heat transfer factor under the univariate structural parameters.
Preferably, the first and second liquid crystal display panels are,
the establishing of the neural network model by taking the corresponding relation between the structural parameters and the flow resistance factors and the heat transfer factors corresponding to the structural parameters as training samples comprises the following steps:
and selecting the structural parameters in the corresponding relations with the preset number and the corresponding flow resistance factors and heat transfer factors from all the corresponding relations as training samples to establish a neural network model.
Preferably, the first and second liquid crystal display panels are,
the solving the neural network model to obtain the optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor comprises the following steps:
and carrying out optimization solution on the neural network model through an optimization algorithm to obtain an optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor.
Preferably, the method further comprises:
determining a test sample from the corresponding relation; and testing the prediction performance of the neural network model based on the test samples, wherein the test samples are the structural parameters in the corresponding relations except for the training samples and the flow resistance factors and the heat transfer factors corresponding to the structural parameters.
The invention also provides a parameter optimization device for the fin heat exchanger, which comprises:
the calculation module is used for determining the flow resistance factor and the heat transfer factor under each structural parameter according to the structural parameters of the fin heat exchanger so as to establish the corresponding relationship between any structural parameter and the corresponding flow resistance factor and heat transfer factor;
the model establishing module is used for establishing a neural network model by taking the corresponding relation between the structural parameters and the corresponding flow resistance factors and heat transfer factors as training samples, wherein the neural network model takes any structural parameter as input and the corresponding flow resistance factors and heat transfer factors as output;
the model solving module is used for solving the neural network model to obtain the optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor;
and the optimization module is used for determining structural parameters corresponding to the optimal solution of the comprehensive parameters so as to optimize the parameters of the fin heat exchanger.
Preferably, the first and second electrodes are formed of a metal,
the calculation module comprises:
the acquiring unit is used for acquiring a fin break angle, a break distance, a fin inclination angle, a fin thickness and a fin distance of the fin heat exchanger; and the calculating unit is used for calculating corresponding flow resistance factors and heat transfer factors according to the fin break angle, the break distance, the fin inclination angle, the fin thickness and the fin spacing of the fin heat exchanger.
Preferably, the first and second electrodes are formed of a metal,
the model building module comprises:
and the model establishing unit is used for selecting the structural parameters in the corresponding relations with the preset number and the flow resistance factors and the heat transfer factors corresponding to the structural parameters from all the corresponding relations as training samples so as to establish a neural network model.
Preferably, the first and second liquid crystal display panels are,
the model solution model comprises:
and the solving unit is used for carrying out optimization solving on the neural network model through an optimization algorithm so as to obtain the optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor.
According to the method and the device for optimizing the parameters of the fin heat exchanger, the flow resistance factor and the heat transfer factor under the corresponding parameters are obtained by calculation according to the structural parameters of the fin heat exchanger and are used as training samples to train and establish the neural network model, the neural network model takes the structural parameters of the fin heat exchanger as input and takes the flow resistance factor and the heat transfer factor under the corresponding parameters as output, and then the model is solved to obtain an optimal solution, so that the optimal structure of the fin heat exchanger is obtained, the workload is reduced, and the optimal structure is obtained with more convincing power.
Drawings
In order to more clearly illustrate the embodiments or prior art solutions in the present specification, the drawings used in the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings may be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for optimizing parameters of a fin heat exchanger according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a parameter optimization device for a fin heat exchanger according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without making any creative effort belong to the protection scope of the present specification.
The method and the device for optimizing the parameters of the fin heat exchanger provided by the invention are described in detail below with reference to the accompanying drawings, so that a person skilled in the art can clearly and accurately understand the technical scheme of the invention.
Fig. 1 is a schematic flow chart of a method for optimizing parameters of a fin heat exchanger according to an embodiment of the present invention.
As shown in fig. 1, the parameters of a fin heat exchanger provided by an embodiment of the present invention may include the following steps:
and step 110, determining the flow resistance factor and the heat transfer factor under each structural parameter according to the structural parameters of the finned heat exchanger so as to establish the corresponding relationship between any structural parameter and the corresponding flow resistance factor and heat transfer factor.
In the embodiment of the present invention, step 110 may be implemented by, for example, obtaining a fin break angle, a break distance, a fin inclination angle, a fin thickness, and a fin pitch of the fin heat exchanger, and calculating a corresponding flow resistance factor and a corresponding heat transfer factor according to the fin break angle, the break distance, the fin inclination angle, the fin thickness, and the fin pitch of the fin heat exchanger.
Further, carrying out parameter univariate on any structural parameter of a fin folding angle, a folding distance, a fin inclination angle, a fin thickness and a fin distance of the fin heat exchanger; and determining the corresponding flow resistance factor and heat transfer factor under the univariate structural parameters. Illustratively, the folding angle, the folding distance, the inclination angle and the thickness of the fins are unchanged, and the spacing of the fins is changed to obtain the corresponding flow resistance factor and the heat transfer factor under the set of parameters. For another example, the fin folding distance, the fin inclination angle, the fin thickness and the fin spacing are unchanged, and the fin folding angle is changed to obtain the corresponding flow resistance factor and heat transfer factor under the group of parameters. One example is not given here.
In some embodiments of the present invention, a fin structure parameter may be simulated by a certain step size, and one or more other structure parameters may be used as variables to perform calculation simulation, so as to obtain corresponding flow resistance factors and heat transfer characteristic factors.
In other embodiments, the flow resistance factor f and the heat transfer factor j may be converted to composite parameters
And (6) outputting.
And step 120, establishing a neural network model by taking the corresponding relation between the structural parameters and the corresponding flow resistance factors and heat transfer factors as training samples, wherein the neural network model takes any structural parameter as input, and the corresponding flow resistance factors and heat transfer factors as output.
In the embodiment of the present invention, a neural network model is established based on the corresponding relationship between the set of structural parameters determined in step 110 and the corresponding flow resistance factor and heat transfer factor as training samples or training data. The neural network model may be a BP neural network model.
And step 130, solving the neural network model to obtain an optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor.
Illustratively, this step may be an optimization solution of the neural network model by an optimization algorithm to obtain an optimal solution of the integrated parameters of the flow resistance factor and the heat transfer factor. The optimization algorithm shown may be, for example, a gradient descent method, a newton method, etc., which is not limited by the present invention.
And 140, determining structural parameters corresponding to the optimal solution of the comprehensive parameters so as to optimize the parameters of the fin heat exchanger.
In the embodiment of the invention, the neural network model takes the structural parameters of the finned heat exchanger as input and takes the flow resistance factor and the heat transfer factor as output, and the output flow resistance factor and the heat transfer factor or the comprehensive parameters thereof are optimal solutions, and the optimal solutions correspond to the optimal structural parameters. And optimally designing the fin heat exchanger based on the structural parameters.
Illustratively, the embodiment of the present invention may further include the steps of: determining a test sample from the corresponding relation; and testing the prediction performance of the neural network model based on the test samples, wherein the test samples are the structural parameters in the corresponding relations except for the training samples and the flow resistance factors and the heat transfer factors corresponding to the structural parameters. In some embodiments, the structural parameters, the corresponding flow resistance factors, and the heat transfer factors in all the corresponding relationships may also be used as test samples. The invention is not limited in this regard.
In summary, according to the parameter optimization method for the fin heat exchanger provided by the invention, the flow resistance factor and the heat transfer factor under the corresponding parameters are obtained by calculation according to the structural parameters of the fin heat exchanger, and are used as training samples to train and establish the neural network model, the neural network model takes the structural parameters of the fin heat exchanger as input, and takes the flow resistance factor and the heat transfer factor under the corresponding parameters as output, and then the model is solved to obtain an optimal solution, so that the optimal structure of the fin heat exchanger is obtained, the workload is reduced, and the optimal structure is obtained with more persuasion.
Fig. 2 is a schematic structural diagram of a parameter optimization device for a fin heat exchanger according to an embodiment of the present invention.
As shown in fig. 2, a fin heat exchanger parameter optimization device 200 in the embodiment of the present invention may include a calculation module 210, a model building module 220, a model solving module 230, and an optimization module 240.
The calculation module 210 may be configured to determine a flow resistance factor and a heat transfer factor under each structural parameter according to the structural parameters of the fin heat exchanger, so as to establish a corresponding relationship between any structural parameter and its corresponding flow resistance factor and heat transfer factor.
In some embodiments, the calculation module 210 may include an acquisition unit and a calculation unit. The obtaining unit can be used for obtaining a fin break angle, a break distance, a fin inclination angle, a fin thickness and a fin distance of the fin heat exchanger, and the calculating unit can calculate a corresponding flow resistance factor and a corresponding heat transfer factor according to the fin break angle, the break distance, the fin inclination angle, the fin thickness and the fin distance of the fin heat exchanger.
The model establishing module 220 may be configured to establish a neural network model by using a corresponding relationship between the structural parameter and the flow resistance factor and the heat transfer factor corresponding thereto as a training sample, where the neural network model takes any structural parameter as an input and the corresponding flow resistance factor and the heat transfer factor as an output.
In an embodiment of the present invention, the model establishing module 220 may include a model establishing unit, and the model establishing unit may be configured to select, from all the corresponding relationships, the structural parameters in the preset number of corresponding relationships and the flow resistance factors and the heat transfer factors corresponding to the structural parameters as training samples to establish the neural network model.
The model solution module 230 may be configured to solve the neural network model to obtain an optimal solution for the integrated parameters of the flow resistance factor and the heat transfer factor.
The model solving module 230 may include a solving unit for performing an optimization solution on the neural network model through an optimization algorithm to obtain an optimal solution of the integrated parameters of the flow resistance factor and the heat transfer factor.
The optimization module 240 may be configured to determine structural parameters corresponding to the optimal solution of the comprehensive parameters, so as to optimize the fin heat exchanger parameters.
According to the fin heat exchanger parameter optimization device provided by the invention, the flow resistance factor and the heat transfer factor under the corresponding parameters are obtained by calculation according to the structural parameters of the fin heat exchanger and are used as training samples to train and establish a neural network model, the neural network model takes the structural parameters of the fin heat exchanger as input and takes the flow resistance factor and the heat transfer factor under the corresponding parameters as output, and then the model is solved to obtain an optimal solution, so that the optimal structure of the fin heat exchanger is obtained, the workload is reduced, and the optimal structure is obtained with more convincing effect.
For convenience of description, the above system is described as being divided into various units or modules by function, respectively. Of course, the functionality of the various elements or modules may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The description has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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 a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
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 the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present disclosure, and is not intended to limit the present disclosure. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (2)
1. A method for optimizing parameters of a fin heat exchanger, the method comprising:
obtaining a fin break angle, a break distance, a fin inclination angle, a fin thickness and a fin interval of the fin heat exchanger, and determining a corresponding flow resistance factor and a corresponding heat transfer factor under the structural parameter univariate by performing parameter univariate on any structural parameter of the fin break angle, the break distance, the fin inclination angle, the fin thickness and the fin interval of the fin heat exchanger so as to establish a corresponding relation between any structural parameter and the corresponding flow resistance factor and the corresponding heat transfer factor;
selecting structural parameters in a preset number of corresponding relations and corresponding flow resistance factors and heat transfer factors from the corresponding relations as training samples to establish a neural network model, wherein the neural network model takes any structural parameter as input and the corresponding flow resistance factors and heat transfer factors as output; or determining a test sample from the corresponding relation, and testing the prediction performance of the neural network model based on the test sample, wherein the test sample is a structural parameter in the corresponding relation except for serving as the training sample, and a flow resistance factor and a heat transfer factor corresponding to the structural parameter;
carrying out optimization solution on the neural network model through an optimization algorithm to obtain an optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor;
and determining structural parameters corresponding to the optimal solution of the comprehensive parameters so as to optimize the parameters of the fin heat exchanger.
2. A fin heat exchanger parameter optimization device, the device comprising:
the calculation module is used for acquiring a fin break angle, a break distance, a fin inclination angle, a fin thickness and a fin interval of the fin heat exchanger, and determining a corresponding flow resistance factor and a corresponding heat transfer factor under the structural parameter univariate by performing parameter univariate on any structural parameter of the fin break angle, the break distance, the fin inclination angle, the fin thickness and the fin interval of the fin heat exchanger so as to establish a corresponding relation between any structural parameter and the corresponding flow resistance factor and the corresponding heat transfer factor;
the model establishing module is used for selecting the structural parameters in the corresponding relations of the preset number and the flow resistance factors and the heat transfer factors corresponding to the structural parameters as training samples from the corresponding relations so as to establish a neural network model, wherein the neural network model takes any structural parameter as input, and the corresponding flow resistance factors and the corresponding heat transfer factors as output; or determining a test sample from the corresponding relation, and testing the prediction performance of the neural network model based on the test sample, wherein the test sample is a structural parameter in the corresponding relation except for serving as the training sample, and a flow resistance factor and a heat transfer factor corresponding to the structural parameter;
the model solving module is used for carrying out optimization solving on the neural network model through an optimization algorithm so as to obtain an optimal solution of the comprehensive parameters of the flow resistance factor and the heat transfer factor;
and the optimization module is used for determining structural parameters corresponding to the optimal solution of the comprehensive parameters so as to optimize the parameters of the fin heat exchanger.
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| CN102135511A (en) * | 2010-12-29 | 2011-07-27 | 浙江银轮机械股份有限公司 | Method and device for testing heat transfer performance of fin surface of plate-fin heat exchanger |
| CN108052752A (en) * | 2017-12-20 | 2018-05-18 | 杭州电子科技大学 | A kind of method of split fin structure parameter optimization for LED light source heat-dissipating |
| CN109781043A (en) * | 2019-02-13 | 2019-05-21 | 广东省计量科学研究院（华南国家计量测试中心） | Heat exchange fin key parameter calculation method and analytical calculation platform |
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| US8046200B2 (en) * | 2006-09-05 | 2011-10-25 | Colorado State University Research Foundation | Nonlinear function approximation over high-dimensional domains |
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| CN102135511A (en) * | 2010-12-29 | 2011-07-27 | 浙江银轮机械股份有限公司 | Method and device for testing heat transfer performance of fin surface of plate-fin heat exchanger |
| CN108052752A (en) * | 2017-12-20 | 2018-05-18 | 杭州电子科技大学 | A kind of method of split fin structure parameter optimization for LED light source heat-dissipating |
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