CN114818472A - Supercritical pressure fluid heat exchange performance prediction method, device and equipment - Google Patents
Supercritical pressure fluid heat exchange performance prediction method, device and equipment Download PDFInfo
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Abstract
The application provides a method, a device and equipment for predicting heat exchange performance of supercritical pressure fluid. The method comprises the following steps: acquiring a heat exchange data set of the supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid; preprocessing a heat exchange data set to determine a first data set; performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set, and determining a heat exchange performance prediction model of the supercritical pressure fluid; and sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting heat exchange performance parameters of the target supercritical pressure fluid. The method realizes the prediction of the heat exchange performance of the supercritical pressure fluid with high accuracy, high speed and low cost.
Description
Technical Field
The present disclosure relates to the field of predicting convective heat exchange performance, and in particular, to a method, an apparatus, and a device for predicting supercritical pressure fluid heat exchange performance.
Background
The supercritical pressure fluid has the characteristics of large specific heat capacity, low viscosity, good fluidity, good cooling performance, no corrosion, no combustion and no toxicity, and has wide application prospect. However, the physical properties of the supercritical pressure fluid in the vicinity of the quasi-critical temperature may be changed drastically, which brings great influence to the flowing heat exchange system, interferes with the balance and stable operation of the system, and even causes potential safety hazard, so that the heat exchange performance parameters of the supercritical pressure fluid need to be predicted.
In the prior art, the convection heat exchange performance parameters of the supercritical pressure fluid are generally calculated by using a supercritical pressure fluid convection heat exchange rule correlation formula, or the flow of the fluid is simulated by using a numerical simulation method by using computational fluid mechanics, so that the supercritical pressure fluid convection heat exchange performance parameters are predicted.
However, in the prior art, the accuracy is poor when the convection heat exchange performance of the supercritical pressure fluid is predicted by using a supercritical pressure fluid convection heat exchange criterion correlation formula; the cost of the numerical simulation method is high.
Disclosure of Invention
The application provides a method, a device and equipment for predicting the heat exchange performance of supercritical pressure fluid, which are used for solving the problems of poor accuracy and high cost of predicting the heat exchange performance of the supercritical fluid.
In a first aspect, the present application provides a method for predicting heat exchange performance of supercritical pressure fluid, the method comprising:
acquiring a heat exchange data set of supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid;
preprocessing the heat exchange data set to determine a first data set;
performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set to determine a heat exchange performance prediction model of the supercritical pressure fluid, wherein the preset real heat exchange performance parameter set comprises a real heat exchange performance parameter of each heat exchange working condition in the first data set;
and sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting the heat exchange performance parameters of the target supercritical pressure fluid.
In an optional embodiment, each heat exchange working condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, and inlet reynolds number; the first data set comprises the heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data and dimensionless parameters under each heat exchange working condition; preprocessing the heat exchange data set, and determining a first data set, wherein the preprocessing comprises the following steps:
according to preset screening logic, rejecting abnormal heat exchange data in the heat exchange data set to obtain a processed heat exchange data set;
determining fluid wall surface thermal property parameter data under each heat exchange condition and mainstream fluid thermal property parameter data under each heat exchange condition according to the surface pressure under each heat exchange condition, the wall surface temperature under each heat exchange condition, the mainstream fluid temperature under each heat exchange condition and a preset fluid property data table in the processed heat exchange data set;
determining the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data according to the fluid wall surface thermophysical property parameter data under each heat exchange working condition and the mainstream fluid thermophysical property parameter data under each heat exchange working condition;
and determining dimensionless parameters under each heat exchange working condition according to the heat exchange data and preset definition information, wherein the dimensionless parameters comprise Reynolds numbers, Plantt numbers, Bo numbers and Kv numbers.
In an alternative embodiment, the thermophysical parameters include: including density, dynamic viscosity, specific heat capacity at constant pressure, thermal conductivity, coefficient of thermal expansion, and enthalpy.
In an optional embodiment, performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set, and determining a heat exchange performance prediction model of the supercritical pressure fluid includes:
dividing the first data set into a training data set and a testing data set, wherein data in the training data set and data in the testing data set are not intersected with each other;
constructing and fitting a preset model according to the preset algorithm, the preset real heat exchange performance parameter set and the training data set to generate an initial prediction model;
and evaluating the precision of the initial prediction model according to the test data set and the preset real heat exchange performance parameter set, and determining the heat exchange performance prediction model of the supercritical pressure fluid.
In an optional embodiment, the evaluating the accuracy of the initial prediction model according to the test data set and the preset real heat exchange performance parameter set to determine the heat exchange performance prediction model of the supercritical pressure fluid includes:
determining the test data set as a target test data set;
calculating to obtain a heat exchange performance parameter of the heat exchange working condition according to heat exchange data of any heat exchange working condition in the target test data set, fluid wall surface thermophysical property parameter data of the heat exchange working condition, mainstream fluid thermophysical property parameter data of the heat exchange working condition, a ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data under the heat exchange working condition, a dimensionless parameter of the heat exchange working condition and the initial prediction model;
judging whether the difference value between the heat exchange performance parameter and a preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not;
if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is larger than a preset threshold value, adding part of heat exchange data in the target test data set into the training data set according to a preset proportion to form a new test data set and a new training data set, constructing and fitting the initial prediction model according to the new training data set to generate a new initial prediction model, determining the new test data set as a target test data set, and repeatedly executing the heat exchange data according to any heat exchange working condition in the target test data set, the fluid wall surface thermal physical property parameter data under the heat exchange working condition, the mainstream fluid thermal physical property parameter data under the heat exchange working condition, the ratio of each fluid wall surface thermal physical property parameter data to each mainstream fluid thermal property parameter data under the heat exchange working condition, the ratio of each fluid wall surface thermal physical property parameter data under the heat exchange working condition and each mainstream fluid thermal property parameter data under the heat exchange working condition, Calculating the heat exchange performance parameter of the heat exchange working condition and the initial prediction model to determine whether the difference between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is greater than a preset threshold value or not until the difference between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is less than or equal to the preset threshold value;
and if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is smaller than or equal to a preset threshold value, determining that the initial prediction model is the heat exchange performance prediction model of the supercritical pressure fluid.
In a second aspect, the present application provides a device for predicting heat exchange performance of supercritical pressure fluid, the device comprising:
the device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring a heat exchange data set of the supercritical pressure fluid, and the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid;
the first determining unit is used for preprocessing the heat exchange data set and determining a first data set;
a second determining unit, configured to perform model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set, and determine a heat exchange performance prediction model of the supercritical pressure fluid, where the preset real heat exchange performance parameter set includes a real heat exchange performance parameter of each heat exchange condition in the first data set;
and the processing unit is used for sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting the heat exchange performance parameters of the target supercritical pressure fluid.
In an optional embodiment, each heat exchange working condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, and inlet reynolds number; the first data set comprises the heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data and dimensionless parameters under each heat exchange working condition; the first determination unit includes:
the first processing subunit is used for eliminating abnormal heat exchange data in the heat exchange data set according to preset screening logic to obtain a processed heat exchange data set;
the first determining subunit is configured to determine fluid wall thermophysical property parameter data under each heat exchange condition and mainstream fluid thermophysical property parameter data under each heat exchange condition according to a surface pressure under each heat exchange condition, a wall temperature under each heat exchange condition, a mainstream fluid temperature under each heat exchange condition, and a preset fluid physical property data table in the processed heat exchange data set;
the second determining subunit is configured to determine, according to the fluid wall surface thermophysical property parameter data under each heat exchange condition and the mainstream fluid thermophysical property parameter data under each heat exchange condition, a ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data;
and the third determining subunit is used for determining dimensionless parameters under each heat exchange working condition according to the heat exchange data and preset definition information, wherein the dimensionless parameters comprise a Reynolds number, a Plantt number, a Bo number and a Kv number.
In an optional embodiment, the second determining unit includes:
the second processing subunit is used for dividing the first data set into a training data set and a testing data set, wherein the data in the training data set and the data in the testing data set are not intersected with each other;
the third processing subunit is used for constructing and fitting a preset model according to the preset algorithm, the preset real heat exchange performance parameter set and the training data set to generate an initial prediction model;
and the fourth determining subunit is configured to evaluate the accuracy of the initial prediction model according to the test data set and the preset real heat exchange performance parameter set, and determine a heat exchange performance prediction model of the supercritical pressure fluid.
In a third aspect, the present application provides an electronic device, comprising: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the method of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon computer-executable instructions for implementing the method according to the first aspect when executed by a processor.
According to the method, the device and the equipment for predicting the heat exchange performance of the supercritical pressure fluid, a heat exchange data set of the supercritical pressure fluid is obtained, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid; preprocessing a heat exchange data set, and determining a first data set; according to the first data set and a preset real heat exchange performance parameter set, model training is carried out based on a preset algorithm, and a heat exchange performance prediction model of the supercritical pressure fluid is determined; and sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting heat exchange performance parameters of the target supercritical pressure fluid. The method realizes the prediction of the heat exchange performance of the supercritical pressure fluid with high accuracy, high speed and low cost.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is a flowchart of a method for predicting heat exchange performance of a supercritical pressure fluid according to an embodiment of the present application;
FIG. 2 is a flow chart of another method for predicting heat exchange performance of a supercritical pressure fluid according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a device for predicting heat exchange performance of a supercritical pressure fluid according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another device for predicting heat exchange performance of supercritical pressure fluid according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure;
fig. 6 is a block diagram of a terminal device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The supercritical pressure fluid has the characteristics of both liquid and gas in physical properties: the density of the supercritical pressure fluid is higher and is close to liquid, and the viscosity and the diffusion coefficient of the supercritical pressure fluid are small and are close to those of gas, so that the supercritical pressure fluid has good fluidity and transport property, has the characteristics of large specific heat capacity, low viscosity, good fluidity, good cooling performance, no corrosion, no combustion and no toxicity, is applied to a flowing heat exchange system, and has wide prospect. However, if the supercritical pressure fluid is near the quasi-critical temperature or crosses the quasi-critical temperature during operation, the physical properties of the fluid may be changed drastically, which may bring great influence to the flowing heat exchange system, interfere with the balance and stable operation of the system, and even cause a safety hazard, so that the heat exchange performance parameters of the supercritical pressure fluid need to be predicted.
The heat exchange mechanism of the supercritical pressure fluid is very complex and has a plurality of influencing factors, and the heat transfer deterioration of different degrees can be caused by the change of any working condition parameter. The existing methods for predicting the convection heat exchange performance of the supercritical pressure fluid mainly comprise two methods: 1) correlation prediction method: calculating the convective heat exchange performance parameters (such as convective heat exchange coefficient and convective heat exchange Nussel number) of the supercritical pressure fluid by using a convective heat exchange rule correlation formula; 2) numerical simulation method: the flow of the fluid can be simulated through computational fluid mechanics or direct numerical simulation, and the convective heat exchange performance can be predicted.
However, in the prior art, 1) due to the coupling influence of a plurality of factors such as buoyancy force, flow acceleration and the like, the accuracy of the correlation of the convective heat transfer criterion is often poor, and generally deviates from a true value by more than 30%; 2) in computational fluid mechanics, the deviation between a calculated predicted value and a true value is more than 20% or even higher due to the inaccuracy of a turbulence model, while a direct numerical simulation method can predict the heat exchange performance more accurately, calculation and simulation are required on a supercomputer, the equipment cost is high, the cost of calculation resources generally hundreds of thousands is spent, the calculation period is also long, and generally months or even longer are spent. Therefore, although the convection heat exchange rule correlation can quickly predict the convection heat exchange performance of the supercritical pressure fluid, the accuracy is poor, and the correlation can be regarded as low-precision data; although the numerical simulation method has higher accuracy, the obtained data is generally high-precision data, but the cost of the calculation equipment and time is very high.
The related research of the turbulent flow heat exchange law of the supercritical pressure fluid has been historical for decades, a large amount of experimental and numerical simulation research data has been accumulated in the related research so far, the method for predicting the heat exchange performance of the supercritical pressure fluid, provided by the application, is used for sorting and summarizing the historical experimental and numerical simulation research data of the supercritical pressure fluid, standardizing and normalizing the data to obtain a high-quality and high-precision data set, and on the basis of the data set, a substitute model with high calculation efficiency, low cost and high precision is provided and trained by means of an open source distributed high-performance Gradient Boosting Machine (LightGBM) algorithm in Machine learning, so that the accurate prediction can be given to the turbulent flow heat exchange performance of the supercritical pressure fluid, and the technical problem in the prior art is solved.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for predicting heat exchange performance of a supercritical pressure fluid according to an embodiment of the present application, where as shown in fig. 1, the method includes:
101. and acquiring a heat exchange data set of the supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid.
Illustratively, a heat exchange data set of the supercritical pressure fluid is obtained, wherein the heat exchange data set includes heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid, the richer the heat exchange working conditions included in the heat exchange data set are, the wider the application of the supercritical pressure fluid heat exchange performance prediction model obtained based on the heat exchange data set is, and the heat exchange data may be obtained through experiments or through numerical calculation and numerical simulation.
102. And preprocessing the heat exchange data set to determine a first data set.
For example, to ensure the accuracy of the method, the first data set is determined by preprocessing, i.e., normalization and normalization, the preprocessing including, but not limited to, screening data and normalization data.
103. And performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set to determine a heat exchange performance prediction model of the supercritical pressure fluid, wherein the preset real heat exchange performance parameter set comprises the real heat exchange performance parameters of each heat exchange working condition in the first data set.
Illustratively, in order to verify the usability and accuracy of the subsequently obtained prediction model, a real heat exchange performance parameter set is preset, the set comprises real heat exchange performance parameters of each heat exchange working condition in a first data set, and according to the first data set and the preset real heat exchange performance parameter set, model training is performed based on a preset algorithm, such as a LightGBM algorithm, so as to determine the heat exchange performance prediction model of the supercritical pressure fluid.
104. And sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting heat exchange performance parameters of the target supercritical pressure fluid.
Illustratively, an obtaining instruction is sent to the terminal equipment based on the prediction model to obtain heat exchange data of the target supercritical pressure fluid, and the heat exchange performance parameters of the target supercritical pressure fluid are predicted according to the prediction model and the heat exchange data to obtain the heat exchange performance parameters of the target supercritical pressure fluid and apply the heat exchange performance parameters to actual production.
In this embodiment, the following steps are performed: acquiring a heat exchange data set of the supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid; preprocessing a heat exchange data set, and determining a first data set; performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set, and determining a heat exchange performance prediction model of the supercritical pressure fluid; and sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting heat exchange performance parameters of the target supercritical pressure fluid. The method realizes the prediction of the heat exchange performance of the supercritical pressure fluid with high accuracy, high speed and low cost.
Fig. 2 is a flowchart of another method for predicting heat exchange performance of a supercritical pressure fluid according to an embodiment of the present application, and as shown in fig. 2, the method includes:
201. and acquiring a heat exchange data set of the supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid.
In one example, each heat exchange condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, and inlet reynolds number.
Illustratively, a heat exchange data set of the supercritical pressure fluid is obtained, wherein the heat exchange data set includes heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid, and each heat exchange working condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, and inlet reynolds number of the fluid under the heat exchange working condition. The richer the heat exchange working conditions contained in the heat exchange data set are, the wider the application of the supercritical pressure fluid heat exchange performance prediction model obtained based on the heat exchange data set is, and the heat exchange data can be obtained through experiments or numerical calculation and numerical simulation.
202. And preprocessing the heat exchange data set to determine a first data set.
In one example, the first data set includes heat exchange data, fluid wall thermophysical property parameter data under each heat exchange condition, mainstream fluid thermophysical property parameter data under each heat exchange condition, a ratio of each fluid wall thermophysical property parameter data to each mainstream fluid thermophysical property parameter data, and a dimensionless parameter under each heat exchange condition.
In one example, step 202 includes the steps of:
and rejecting abnormal heat exchange data in the heat exchange data set according to preset screening logic to obtain a processed heat exchange data set.
And determining fluid wall surface thermal physical property parameter data under each heat exchange condition and mainstream fluid thermal physical property parameter data under each heat exchange condition according to the surface pressure under each heat exchange condition, the wall surface temperature under each heat exchange condition, the mainstream fluid temperature under each heat exchange condition and a preset fluid physical property data table in the processed heat exchange data set.
And determining the ratio of the thermophysical parameter data of each fluid wall surface to the thermophysical parameter data of each mainstream fluid according to the thermophysical parameter data of the fluid wall surface under each heat exchange working condition and the thermophysical parameter data of the mainstream fluid under each heat exchange working condition.
And determining dimensionless parameters under each heat exchange working condition according to the heat exchange data and preset definition information, wherein the dimensionless parameters comprise Reynolds numbers, Plantt numbers, Bo numbers and Kv numbers.
In one example, the thermophysical parameters include: including density, dynamic viscosity, specific heat capacity at constant pressure, thermal conductivity, coefficient of thermal expansion, and enthalpy.
Illustratively, in order to ensure the accuracy of the method, abnormal heat exchange data and outlier heat exchange data in the heat exchange data set are removed according to a preset screening logic, so as to obtain a processed heat exchange data set; determining fluid wall surface thermal property parameter data under each heat exchange condition and mainstream fluid thermal property parameter data under each heat exchange condition according to the surface pressure under each heat exchange condition, the wall surface temperature under each heat exchange condition, the mainstream fluid temperature under each heat exchange condition and a preset fluid property data table in the processed heat exchange data set; determining the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data according to the fluid wall surface thermophysical property parameter data under each heat exchange working condition and the mainstream fluid thermophysical property parameter data under each heat exchange working condition; and determining dimensionless parameters under each heat exchange working condition according to the heat exchange data and preset definition information, wherein the dimensionless parameters comprise Reynolds numbers, Plantt numbers, Bo numbers and Kv numbers. After the heat exchange data set is subjected to standardization and normalization processing, a first data set is determined, wherein the first data set comprises heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data and dimensionless parameters under each heat exchange working condition. Wherein the thermophysical parameters include: including density, dynamic viscosity, specific heat capacity at constant pressure, thermal conductivity, coefficient of thermal expansion, and enthalpy.
203. The first data set is divided into a training data set and a testing data set, wherein data in the training data set and data in the testing data set are not intersected with each other.
Illustratively, the first data set is divided into a training data set and a testing data set, wherein data in the training data set and data in the testing data set are not crossed with each other, the training data set is used for generating, fitting and training the prediction model based on a preset algorithm, and the testing data set is used for evaluating and judging accuracy and feasibility of the prediction model.
204. And constructing and fitting a preset model according to a preset algorithm, a preset real heat exchange performance parameter set and a training data set to generate an initial prediction model.
In the process, input parameters of the preset model are all parameters in the training set, output parameters are convection heat exchange coefficients or Nursell numbers, the output convection heat exchange coefficients or the Nursell numbers are compared with a preset real heat exchange performance parameter set, when the deviation is minimum, an initial prediction model of the convection heat exchange performance parameters of the supercritical fluid is obtained, and finally relevant parameters in the training result are stored.
205. And evaluating the precision of the initial prediction model according to the test data set and a preset real heat exchange performance parameter set, and determining the heat exchange performance prediction model of the supercritical pressure fluid.
In one example, step 205 includes the steps of:
determining the test data set as a target test data set;
calculating to obtain heat exchange performance parameters of the heat exchange working condition according to heat exchange data of any heat exchange working condition in the target test data set, fluid wall surface thermophysical parameter data of the heat exchange working condition, mainstream fluid thermophysical parameter data of the heat exchange working condition, a ratio of each fluid wall surface thermophysical parameter data to each mainstream fluid thermophysical parameter data under the heat exchange working condition, dimensionless parameters of the heat exchange working condition and an initial prediction model;
judging whether the difference value between the heat exchange performance parameter and a preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not;
if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is larger than the preset threshold value, adding part of heat exchange data in the target test data set into the training data set according to a preset proportion to form a new test data set and a new training data set, constructing and fitting the initial prediction model according to the new training data set to generate a new initial prediction model, determining the new test data set as the target test data set, and repeatedly executing the heat exchange data according to any heat exchange working condition in the target test data set, the fluid wall surface thermal physical property parameter data of the heat exchange working condition, the mainstream fluid thermal physical property parameter data of the heat exchange working condition, the ratio of each fluid wall surface thermal physical property parameter data to each mainstream fluid thermal property parameter data under the heat exchange working condition, the dimensionless parameter of the heat exchange working condition and the initial prediction model, calculating to obtain a heat exchange performance parameter of the heat exchange working condition, and judging whether the difference value between the heat exchange performance parameter and a preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not until the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is smaller than or equal to the preset threshold value;
and if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is smaller than or equal to a preset threshold value, determining that the initial prediction model is the heat exchange performance prediction model of the supercritical pressure fluid.
Exemplarily, determining a test data set as a target test data set, inputting heat exchange data of any heat exchange working condition in the target test data set, fluid wall thermal physical property parameter data of the heat exchange working condition, mainstream fluid thermal physical property parameter data of the heat exchange working condition, a ratio of each fluid wall thermal physical property parameter data to each mainstream fluid thermal physical property parameter data under the heat exchange working condition and dimensionless parameters of the heat exchange working condition into an initial prediction model, and calculating to obtain heat exchange performance parameters of the heat exchange working condition; if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is smaller than or equal to a preset threshold value, determining the initial prediction model as a prediction model; and if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is larger than a preset threshold value, adding part of heat exchange data in the target test data set into the training data set to form a new training data set, constructing and fitting the initial prediction model according to the new training data set to generate a new initial prediction model, and performing evaluation test on the new initial prediction model according to the heat exchange data in the rest test data set until the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is smaller than or equal to the preset threshold value.
206. And sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting heat exchange performance parameters of the target supercritical pressure fluid.
For example, this step is referred to as step 104, and is not described again.
In this embodiment, the following steps are performed: the method comprises the steps of obtaining a heat exchange data set of the supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid, screening and standardizing the heat exchange data set to obtain a first data set, each heat exchange working condition comprises various heat exchange data including surface pressure, wall surface temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature and inlet Reynolds number, and the first data set comprises heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data and dimensionless parameters under each heat exchange working condition; dividing the first data set into a training data set and a testing data set, and constructing and fitting a preset model according to a preset algorithm, a preset real heat exchange performance parameter set and the training data set to generate an initial prediction model; evaluating the precision of the initial prediction model according to the test data set and a preset real heat exchange performance parameter set, and determining a heat exchange performance prediction model of the supercritical pressure fluid; and sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting heat exchange performance parameters of the target supercritical pressure fluid. The method realizes the prediction of the heat exchange performance of the supercritical pressure fluid with high accuracy, high speed and low cost.
Fig. 3 is a schematic structural diagram of a device for predicting heat exchange performance of a supercritical pressure fluid according to an embodiment of the present application, and as shown in fig. 3, the device includes:
the acquiring unit 31 is configured to acquire a heat exchange data set of the supercritical pressure fluid, where the heat exchange data set includes heat exchange data of at least one heat exchange condition of the supercritical pressure fluid.
The first determining unit 32 is configured to pre-process the heat exchange data set to determine a first data set.
The second determining unit 33 is configured to perform model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set, and determine a heat exchange performance prediction model of the supercritical pressure fluid, where the preset real heat exchange performance parameter set includes a real heat exchange performance parameter of each heat exchange condition in the first data set.
And the processing unit 34 is configured to send an acquisition instruction to the terminal device based on the prediction model, where the acquisition instruction is used to acquire heat exchange data of the target supercritical pressure fluid and predict a heat exchange performance parameter of the target supercritical pressure fluid.
Fig. 4 is a schematic structural diagram of another device for predicting heat exchange performance of supercritical pressure fluid according to an embodiment of the present application, and based on the embodiment shown in fig. 3, as shown in fig. 4, the device includes:
in one example, each heat exchange condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, and inlet reynolds number; the first data set comprises heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of the fluid wall surface thermophysical property parameter data to the mainstream fluid thermophysical property parameter data, and dimensionless parameters under each heat exchange working condition; the first determination unit 32 includes:
the first processing subunit 321 is configured to, according to a preset screening logic, reject abnormal heat exchange data in the heat exchange data set to obtain a processed heat exchange data set.
The first determining subunit 322 is configured to determine fluid wall thermophysical property parameter data under each heat exchange condition and mainstream fluid thermophysical property parameter data under each heat exchange condition according to the surface pressure under each heat exchange condition, the wall temperature under each heat exchange condition, the mainstream fluid temperature under each heat exchange condition, and a preset fluid physical property data table in the processed heat exchange data set.
The second determining subunit 323 is configured to determine, according to the fluid wall thermophysical parameter data under each heat exchange condition and the mainstream fluid thermophysical parameter data under each heat exchange condition, a ratio of each fluid wall thermophysical parameter data to each mainstream fluid thermophysical parameter data.
And a third determining subunit 324, configured to determine a dimensionless parameter under each heat exchange condition according to the heat exchange data and preset definition information, where the dimensionless parameter includes a reynolds number, a prandtl number, a Bo number, and a Kv number.
In one example, the thermophysical parameters include: including density, dynamic viscosity, specific heat capacity at constant pressure, thermal conductivity, coefficient of thermal expansion, and enthalpy.
In one example, the second determining unit 33 includes:
the second processing subunit 331 is configured to divide the first data set into a training data set and a test data set, where data in the training data set and data in the test data set do not intersect with each other.
And the third processing subunit 332 is configured to construct and fit a preset model according to a preset algorithm, a preset set of real heat exchange performance parameters, and a training data set, so as to generate an initial prediction model.
And the fourth processing subunit 333 is configured to evaluate the accuracy of the initial prediction model according to the test data set and the preset real heat exchange performance parameter set, and determine a heat exchange performance prediction model of the supercritical pressure fluid.
In one example, the fourth processing subunit 333 is specifically configured to:
determining the test data set as a target test data set;
calculating to obtain heat exchange performance parameters of the heat exchange working condition according to heat exchange data of any heat exchange working condition in the target test data set, fluid wall surface thermophysical property parameter data of the heat exchange working condition, mainstream fluid thermophysical property parameter data of the heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data under the heat exchange working condition, dimensionless parameters of the heat exchange working condition and an initial prediction model;
judging whether the difference value between the heat exchange performance parameter and a preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not;
if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is larger than the preset threshold value, adding part of heat exchange data in the target test data set into the training data set according to a preset proportion to form a new test data set and a new training data set, constructing and fitting the initial prediction model according to the new training data set to generate a new initial prediction model, determining the new test data set as the target test data set, and repeatedly executing the heat exchange data according to any heat exchange working condition in the target test data set, the fluid wall surface thermal physical property parameter data of the heat exchange working condition, the mainstream fluid thermal physical property parameter data of the heat exchange working condition, the ratio of each fluid wall surface thermal physical property parameter data to each mainstream fluid thermal property parameter data under the heat exchange working condition, the dimensionless parameter of the heat exchange working condition and the initial prediction model, calculating to obtain a heat exchange performance parameter of the heat exchange working condition, and judging whether the difference value between the heat exchange performance parameter and a preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not until the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is smaller than or equal to the preset threshold value;
and if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is smaller than or equal to a preset threshold value, determining that the initial prediction model is the heat exchange performance prediction model of the supercritical pressure fluid.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 5, the electronic device includes: memory 51, processor 52.
A memory 51; a memory for storing instructions executable by the processor 52.
Wherein the processor 52 is configured to perform the method as provided in the above embodiments.
Fig. 6 is a block diagram of a terminal device, which may be a mobile phone, a computer, a messaging device, a tablet device, or the like, according to an embodiment of the present disclosure.
The apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the apparatus 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed status of the device 800, the relative positioning of the components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration/deceleration of the device 800, and a change in the temperature of the device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, communications component 816 further includes a Near Field Communications (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the device 800 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Embodiments of the present application also provide a non-transitory computer-readable storage medium, where instructions in the storage medium, when executed by a processor of an electronic device, enable the electronic device to perform the method provided by the above embodiments.
An embodiment of the present application further provides a computer program product, where the computer program product includes: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for predicting heat exchange performance of supercritical pressure fluid, the method comprising:
acquiring a heat exchange data set of supercritical pressure fluid, wherein the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid;
preprocessing the heat exchange data set to determine a first data set;
performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set to determine a heat exchange performance prediction model of the supercritical pressure fluid, wherein the preset real heat exchange performance parameter set comprises a real heat exchange performance parameter of each heat exchange working condition in the first data set;
and sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting the heat exchange performance parameters of the target supercritical pressure fluid.
2. The method of claim 1, wherein each heat exchange condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, inlet reynolds number; the first data set comprises the heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data and dimensionless parameters under each heat exchange working condition; preprocessing the heat exchange data set, and determining a first data set, wherein the preprocessing comprises the following steps:
according to preset screening logic, rejecting abnormal heat exchange data in the heat exchange data set to obtain a processed heat exchange data set;
determining fluid wall surface thermal property parameter data under each heat exchange condition and mainstream fluid thermal property parameter data under each heat exchange condition according to the surface pressure under each heat exchange condition, the wall surface temperature under each heat exchange condition, the mainstream fluid temperature under each heat exchange condition and a preset fluid property data table in the processed heat exchange data set;
determining the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data according to the fluid wall surface thermophysical property parameter data under each heat exchange working condition and the mainstream fluid thermophysical property parameter data under each heat exchange working condition;
according to the heat exchange data and preset definition information, dimensionless parameters under each heat exchange working condition are determined, wherein the dimensionless parameters comprise Reynolds numbers, Prandtl numbers and Bo * Number and Kv number.
3. The method of claim 2, wherein the thermophysical parameters include: including density, dynamic viscosity, specific heat capacity at constant pressure, thermal conductivity, coefficient of thermal expansion, and enthalpy.
4. The method according to claim 1, wherein the step of performing model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set to determine a heat exchange performance prediction model of the supercritical pressure fluid comprises:
dividing the first data set into a training data set and a testing data set, wherein data in the training data set and data in the testing data set are not intersected with each other;
constructing and fitting a preset model according to the preset algorithm, the preset real heat exchange performance parameter set and the training data set to generate an initial prediction model;
and evaluating the precision of the initial prediction model according to the test data set and the preset real heat exchange performance parameter set, and determining the heat exchange performance prediction model of the supercritical pressure fluid.
5. The method according to claim 4, wherein evaluating the accuracy of the initial prediction model from the test data set and the preset set of true heat exchange performance parameters, determining a heat exchange performance prediction model for the supercritical pressure fluid, comprises:
determining the test data set as a target test data set;
calculating to obtain a heat exchange performance parameter of the heat exchange working condition according to heat exchange data of any heat exchange working condition in the target test data set, fluid wall surface thermophysical property parameter data of the heat exchange working condition, mainstream fluid thermophysical property parameter data of the heat exchange working condition, a ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data under the heat exchange working condition, a dimensionless parameter of the heat exchange working condition and the initial prediction model;
judging whether the difference value between the heat exchange performance parameter and a preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not;
if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is larger than a preset threshold value, adding part of heat exchange data in the target test data set into the training data set according to a preset proportion to form a new test data set and a new training data set, constructing and fitting the initial prediction model according to the new training data set to generate a new initial prediction model, determining the new test data set as a target test data set, and repeatedly executing the heat exchange data according to any heat exchange working condition in the target test data set, the fluid wall surface thermal physical property parameter data under the heat exchange working condition, the mainstream fluid thermal physical property parameter data under the heat exchange working condition, the ratio of each fluid wall surface thermal physical property parameter data to each mainstream fluid thermal property parameter data under the heat exchange working condition, the ratio of each fluid wall surface thermal physical property parameter data under the heat exchange working condition and each mainstream fluid thermal property parameter data under the heat exchange working condition, Calculating the heat exchange performance parameter of the heat exchange working condition according to the dimensionless parameter of the heat exchange working condition and the initial prediction model, and judging whether the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is larger than a preset threshold value or not until the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter of the heat exchange working condition is smaller than or equal to the preset threshold value;
and if the difference value between the heat exchange performance parameter and the preset real heat exchange performance parameter under the heat exchange working condition is smaller than or equal to a preset threshold value, determining that the initial prediction model is the heat exchange performance prediction model of the supercritical pressure fluid.
6. A device for predicting heat exchange performance of supercritical pressure fluid, the device comprising:
the device comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring a heat exchange data set of the supercritical pressure fluid, and the heat exchange data set comprises heat exchange data of at least one heat exchange working condition of the supercritical pressure fluid;
the first determining unit is used for preprocessing the heat exchange data set and determining a first data set;
a second determining unit, configured to perform model training based on a preset algorithm according to the first data set and a preset real heat exchange performance parameter set, and determine a heat exchange performance prediction model of the supercritical pressure fluid, where the preset real heat exchange performance parameter set includes a real heat exchange performance parameter of each heat exchange condition in the first data set;
and the processing unit is used for sending an acquisition instruction to the terminal equipment based on the prediction model, wherein the acquisition instruction is used for acquiring heat exchange data of the target supercritical pressure fluid and predicting the heat exchange performance parameters of the target supercritical pressure fluid.
7. The apparatus of claim 6, wherein each heat exchange condition has a plurality of heat exchange data, including surface pressure, wall temperature, mainstream fluid temperature, mass flow rate, heat flow density, inlet and outlet temperature, inlet Reynolds number; the first data set comprises the heat exchange data, fluid wall surface thermophysical property parameter data under each heat exchange working condition, mainstream fluid thermophysical property parameter data under each heat exchange working condition, the ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data and dimensionless parameters under each heat exchange working condition; the first determination unit includes:
the first processing subunit is used for eliminating abnormal heat exchange data in the heat exchange data set according to preset screening logic to obtain a processed heat exchange data set;
the first determining subunit is configured to determine fluid wall thermophysical property parameter data under each heat exchange condition and mainstream fluid thermophysical property parameter data under each heat exchange condition according to a surface pressure under each heat exchange condition, a wall temperature under each heat exchange condition, a mainstream fluid temperature under each heat exchange condition, and a preset fluid physical property data table in the processed heat exchange data set;
the second determining subunit is configured to determine, according to the fluid wall surface thermophysical property parameter data under each heat exchange condition and the mainstream fluid thermophysical property parameter data under each heat exchange condition, a ratio of each fluid wall surface thermophysical property parameter data to each mainstream fluid thermophysical property parameter data;
and the third determining subunit is used for determining dimensionless parameters under each heat exchange working condition according to the heat exchange data and preset definition information, wherein the dimensionless parameters comprise a Reynolds number, a Plantt number, a Bo number and a Kv number.
8. The apparatus according to claim 6, wherein the second determining unit comprises:
the second processing subunit is used for dividing the first data set into a training data set and a test data set, wherein the data in the training data set and the data in the test data set are not intersected with each other;
the third processing subunit is used for constructing and fitting a preset model according to the preset algorithm, the preset real heat exchange performance parameter set and the training data set to generate an initial prediction model;
and the fourth determining subunit is configured to evaluate the accuracy of the initial prediction model according to the test data set and the preset real heat exchange performance parameter set, and determine a heat exchange performance prediction model of the supercritical pressure fluid.
9. An electronic device, characterized in that the electronic device comprises: a memory, a processor;
a memory; a memory for storing the processor-executable instructions;
wherein the processor is configured to perform the method of any one of claims 1-5.
10. A computer-readable storage medium having computer-executable instructions stored thereon, which when executed by a processor, perform the method of any one of claims 1-5.
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| CN116955933B (en) * | 2023-09-21 | 2024-01-09 | 深圳市丰瑞德机电技术有限公司 | Variable flow microchannel heat exchanger performance detection method, device and equipment |
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