CN116805518B - Method and device for constructing calculation model of characteristic parameters of solid-liquid phase change fuzzy region - Google Patents
Method and device for constructing calculation model of characteristic parameters of solid-liquid phase change fuzzy region Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for constructing a calculation model of a characteristic parameter of a solid-liquid phase fuzzy area, wherein the method for constructing the calculation model of the characteristic parameter of the solid-liquid phase fuzzy area is provided by the embodiment of the application, a plurality of influence factors of the characteristic parameter of the fuzzy area are obtained, M key influence factors of the characteristic parameter of the fuzzy area are determined from the influence factors based on an evaluation model, a first dimensionless criterion number, N second dimensionless criterion numbers and a first correlation between the first dimensionless criterion number and the characteristic parameter of the fuzzy area are determined based on the dimensions of the M key influence factors and the dimensions of the characteristic parameter of the fuzzy area, a second correlation between the first dimensionless criterion number and the N second dimensionless criterion numbers are fitted, and a calculation model of the characteristic parameter of the solid-liquid phase fuzzy area is constructed based on the first correlation and the second correlation. The calculation model of the characteristic parameters of the solid-liquid phase fuzzy area constructed by the embodiment of the application can accurately and efficiently calculate the characteristic parameters of the fuzzy area.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a device for constructing a calculation model of characteristic parameters of a solid-liquid phase fuzzy region.
Background
The fields of metallurgy, building energy conservation, solar heat storage, electronic equipment heat management and the like generally have the requirements of analyzing the thermophysical phenomenon, key characteristics and heat transfer-phase change-flow coupling of a solid-liquid phase change process of a material. The phase transition point of many solid-liquid phase transition materials is a temperature interval, so that a fuzzy area exists between a liquid phase area and a solid phase area, the fuzzy area can be regarded as a 'porous medium', and when the numerical simulation analysis is carried out on the solid-liquid phase transition of the materials by using a method such as an enthalpy-porous medium method, a fuzzy area characteristic parameter corresponding to the fuzzy area is usually required to be set, and the parameter is very important to directly determine the reliability of the numerical simulation of the solid-liquid phase transition. In the prior art, a fuzzy region characteristic parameter of a solid-liquid phase change material is usually determined by adopting a material analogy or trial-and-error method, so that the problems of large consumption of trial-and-error time and low cost and low accuracy exist, and the high-efficiency application of a solid-liquid phase change value simulation technology in the fields of metallurgy, electronic equipment thermal management and the like is seriously hindered.
Disclosure of Invention
The embodiment of the application provides a method and a device for constructing a calculation model of characteristic parameters of a solid-liquid phase fuzzy region, which are used for solving the problems of time consumption and lower accuracy of the related technology that the characteristic parameters of the fuzzy region of a material are set by adopting a material analogy or trial-and-error method.
In order to solve the technical problems, the application is realized as follows:
in a first aspect, an embodiment of the present application provides a method for constructing a calculation model of a characteristic parameter of a fuzzy region in solid-liquid phase, including:
acquiring a plurality of influencing factors of characteristic parameters of a fuzzy area;
m key influence factors of the characteristic parameters of the fuzzy area are determined from a plurality of influence factors based on an evaluation model, wherein M is an integer greater than 1;
determining a first dimensionless number of criteria, N second dimensionless numbers of criteria and a first association between the first dimensionless number of criteria and the characteristic parameters of the fuzzy region based on the dimensionalities of the M key influence factors and the dimensionalities of the characteristic parameters of the fuzzy region, wherein N is a positive integer less than M;
fitting a second correlation between the first dimensionless number of criteria and the N second dimensionless number of criteria;
and constructing a calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy area based on the first correlation formula and the second correlation formula.
In a second aspect, an embodiment of the present application further provides a device for constructing a calculation model of a characteristic parameter of a solid-liquid phase fuzzy area, including:
the first acquisition module is used for acquiring a plurality of influence factors of the characteristic parameters of the fuzzy area;
the first determining module is used for determining M key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors based on the evaluation model, wherein M is an integer greater than 1;
the second determining module is used for determining a first dimensionless number of criteria, N second dimensionless numbers of criteria and a first association formula between the first dimensionless number of criteria and the characteristic parameters of the fuzzy area based on the dimensionalities of the M key influence factors and the dimensionalities of the characteristic parameters of the fuzzy area, wherein N is a positive integer smaller than M;
the fitting module is used for fitting a second association formula between the first dimensionless number of criteria and the N second dimensionless number of criteria;
the construction module is used for constructing a calculation model of the characteristic parameters of the solid-liquid phase change fuzzy area based on the first correlation formula and the second correlation formula.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method described above when executing the computer program.
In a fourth aspect, embodiments of the present application further provide a computer readable storage medium storing a computer program, which when executed by a processor, implements the above method.
According to the method for constructing the calculation model of the characteristic parameters of the fuzzy area in the solid-liquid phase transformation, a plurality of influence factors of the characteristic parameters of the fuzzy area are obtained, M key influence factors of the characteristic parameters of the fuzzy area are determined from the influence factors based on the evaluation model, the first dimensionless criterion number, the N second dimensionless criterion numbers and the first correlation between the first dimensionless criterion number and the characteristic parameters of the fuzzy area are determined based on the dimensions of the M key influence factors and the dimensions of the characteristic parameters of the fuzzy area, the second correlation between the first dimensionless criterion number and the N second dimensionless criterion numbers is fitted, and the calculation model of the characteristic parameters of the fuzzy area in the solid-liquid phase transformation is constructed based on the first correlation and the second correlation, wherein M is an integer greater than 1, and N is a positive integer less than M. The calculation model of the characteristic parameters of the solid-liquid phase change fuzzy area constructed by the embodiment of the application provides a new thought and method for calculating the characteristic parameters of the fuzzy area, and in the construction process of the characteristic parameters calculation model of the solid-liquid phase change fuzzy area, the general rule between the characteristic parameters of the fuzzy area and the solid-liquid phase change influence factors is quantized by using an evaluation model and dimension analysis.
Drawings
FIG. 1 is a schematic flow chart of a method for constructing a calculation model of characteristic parameters of a solid-liquid phase-change fuzzy region according to an embodiment of the present application;
FIG. 2 is a view of the selection A m A hierarchical model schematic diagram of the value influencing factors;
FIG. 3 is a schematic flow chart of an application example of a method for constructing a calculation model of characteristic parameters of a fuzzy region with solid-liquid phase change according to an embodiment of the present application;
FIG. 4 is CaCl 2 ·6H 2 Schematic of the effect of multiple regression between the dimensionless criterion number A of O and Ste and Gr;
fig. 5 is a schematic diagram of a calculation model construction device for characteristic parameters of a solid-liquid phase-change fuzzy area according to an embodiment of the present application.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided merely to facilitate a thorough understanding of embodiments of the application. It will therefore be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the application. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Likewise, the terms "a" or "an" and the like do not denote a limitation of quantity, but rather denote the presence of at least one.
The solid-liquid phase change technology can be applied to more fields such as metallurgy, construction, heat storage, thermal management of electronic equipment and the like. Taking the field of thermal management of electronic equipment as an example, the constant temperature can be maintained and a large amount of latent heat can be absorbed during solid-liquid phase transition, the constant-temperature thermal management of the electronic equipment can be realized, and the design efficiency of the thermal management of the electronic equipment can be improved by analyzing the solid-liquid phase transition in a mode such as numerical simulation by an enthalpy-porous medium method. Because some materials have a fuzzy region between the liquid phase region and the solidification region, the characteristic parameters of the fuzzy region of the solid-liquid phase are generally required to be set in the numerical simulation analysis process. In the prior art, the characteristic parameters of the fuzzy region are set by using a blind value taking method and a trial-and-error method, so that the problems of time consumption and lower accuracy of the value taking method exist, and the efficiency and accuracy of a solid-liquid phase change analysis result are further affected.
In order to overcome the above problems, as shown in fig. 1, an embodiment of the present application provides a method for constructing a calculation model of a characteristic parameter of a solid-liquid phase-change fuzzy region, including:
step 101, obtaining a plurality of influencing factors of characteristic parameters of a fuzzy area;
step 102, determining M key influence factors of characteristic parameters of a fuzzy area from a plurality of influence factors based on an evaluation model, wherein M is an integer greater than 1;
step 103, determining a first dimensionless number, N second dimensionless numbers and a first correlation between the first dimensionless number and the characteristic parameters of the fuzzy area based on the dimensionalities of the M key influence factors and the dimensionalities of the characteristic parameters of the fuzzy area, wherein N is a positive integer smaller than M;
step 104, fitting a second association formula between the first dimensionless number of criteria and the N second dimensionless number of criteria;
and 105, constructing a calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy area based on the first correlation formula and the second correlation formula.
The method provided by the embodiment of the application can be applied to electronic equipment with operation capability, such as a personal computer or a server, and the like, and is not particularly limited herein. For simplicity of description, the following will mainly take an execution subject of the method as an electronic device as an example.
In step 101, the electronic device may obtain a plurality of influencing factors of the characteristic parameters of the fuzzy area, and in general, the characteristic parameters of the fuzzy area of the material may be influenced by a plurality of factors, such as a material type, a container structure, a thermal boundary condition, etc., and these factors may be divided into a plurality of sub-factors, etc., and by counting the factors influencing the characteristic parameters of the fuzzy area, a plurality of influencing factors of the characteristic parameters of the fuzzy area may be obtained.
In some examples, the electronic device may obtain the influencing factors in response to user input, or may obtain the influencing factors by reading information in the storage medium.
In step 102, the electronic device may determine M key influencing factors of the fuzzy region characteristic parameter from among a plurality of influencing factors by an evaluation model.
In some examples, the evaluation model may be an evaluation model based on methods such as a hierarchical analysis method, a principal component analysis method, a gray correlation analysis method, a fuzzy comprehensive evaluation method, or a neural network comprehensive evaluation method. In some embodiments, the electronic device may also integrate M key influencing factors using multiple types of evaluation models.
It is easy to understand that each key influencing factor and the characteristic parameters of the fuzzy area may have corresponding dimensions, in step 103, the electronic device may perform dimensional analysis based on the dimensions of the M key influencing factors and the dimensions of the characteristic parameters of the fuzzy area, and process the characteristic parameters of the fuzzy area and part of the key influencing factors into dimensionless numbers according to the relationships between the dimensions, where the dimensionless numbers may be subjected to criterion to obtain dimensionless criterion numbers.
In combination with some examples, a dimension analysis method such as the platinum-han pi theorem may be used to analyze the dimensions of each key influencing factor and the characteristic parameters of the fuzzy area, and obtain a dimensionless criterion number, where the dimensionless criterion number corresponding to the characteristic parameters of the fuzzy area may be considered as the first dimensionless criterion number, and the dimensionless criterion number corresponding to some key influencing factors may be considered as the N second dimensionless criterion numbers.
In addition, the process of processing the characteristic parameters of the fuzzy area into the first dimensionless number of criteria may use a transformation formula, which may be regarded as a first correlation between the first dimensionless number of criteria and the characteristic parameters of the fuzzy area.
To facilitate a more intuitive understanding of the dimensionless numbers of criteria in solid-liquid phase change analysis, the following is illustrative of the inferences of some of the dimensionless numbers of criteria that may be available:
1) Different types of phase change materials can be represented by Plandter numbers (Pr) and the like; 2) Constant wall temperature boundary conditions are represented by Stefan number (Ste) and Grignard number (Gr); 3) Constant heat flow boundary conditions are determined by the modified Gray dawn number (Gr * ) Representation (Gr) * =grnu, nu is noose number); 4) The Lakoff number (Gr) or the Rayleigh number (Ra=GrPr) can measure the natural convection intensity of the liquid phase change material, and also can contain the influences of the characteristic size, the inclination angle and the like of a container for packaging the phase change material; 5) Delta T for phase change material fuzzy area temperature difference m /(T w -T m ) Representation (DeltaT in the formula) m 、T w 、T m Respectively the fuzzy region temperature difference, the container outer wall surface temperature and the phase change temperature of the phase change materialDegree); 6) The dimensionless criterion number of the blur area characteristic parameter Am can be expressed as a=a m D 2 /(4 μ); (wherein D is the characteristic size of the packaging phase change material container or the solid particle diameter of the fuzzy area, and mu is the dynamic viscosity of the liquid phase change material), wherein the expression also corresponds to the first correlation.
Of course, the above is some examples of the dimensionless number, and in practical applications, the N second dimensionless number may be all or part of the dimensionless number in the above examples, and may also include other dimensionless numbers.
In step 104, the electronic device may fit a second association between the first dimensionless number of criteria and the N second dimensionless number of criteria.
For example, the first dimensionless number of criteria may be represented by a described above, and the N second dimensionless number of criteria may include Gr, ste, and the like. Establishing a functional relationship between a first dimensionless number of criteria and N second dimensionless number of criteria, e.g. A=Gr α1 Ste α2 And fitting coefficients alpha 1 and alpha 2 in the functional relation by combining the test data related to the solid-liquid phase transition, so as to obtain the second relation.
In step 105, the electronic device may construct a calculation model of the characteristic parameters of the solid-liquid phase-change blur region based on the first correlation equation and the second correlation equation, and the calculation model of the characteristic parameters of the solid-liquid phase-change blur region may be subsequently used for calculating the characteristic parameters of the blur region of the material to be analyzed.
By way of example above, by combining a=gr α1 Ste α2 And (3) fitting to obtain a second correlation, determining Gr and Ste of the material to be analyzed in an existing mode before carrying out numerical simulation on the solid-liquid phase transformation process of the material to be analyzed, calculating a first dimensionless criterion number A by combining the second correlation, and further combining the first correlation on the basis of the first dimensionless criterion number A to obtain the characteristic parameters of the fuzzy region.
According to the method for constructing the calculation model of the characteristic parameters of the fuzzy area in the solid-liquid phase transformation, a plurality of influence factors of the characteristic parameters of the fuzzy area are obtained, M key influence factors of the characteristic parameters of the fuzzy area are determined from the influence factors based on the evaluation model, the first dimensionless criterion number, the N second dimensionless criterion numbers and the first correlation between the first dimensionless criterion number and the characteristic parameters of the fuzzy area are determined based on the dimensions of the M key influence factors and the dimensions of the characteristic parameters of the fuzzy area, the second correlation between the first dimensionless criterion number and the N second dimensionless criterion numbers is fitted, and the calculation model of the characteristic parameters of the fuzzy area in the solid-liquid phase transformation is constructed based on the first correlation and the second correlation, wherein M is an integer greater than 1, and N is a positive integer less than M. The solid-liquid phase change fuzzy region characteristic parameter calculation model constructed by the embodiment of the application provides a new thought and method for calculating the characteristic parameters of the fuzzy region, and in the construction process of the solid-liquid phase change fuzzy region characteristic parameter calculation model, the general rule between the characteristic parameters of the fuzzy region and solid-liquid phase change influence factors is quantized by utilizing an evaluation model and dimension analysis, and compared with the existing mode of determining the characteristic parameters of the fuzzy region by means of reference, analogy, artificial value taking and trial and error, the solid-liquid phase change fuzzy region characteristic parameter calculation model constructed by the embodiment of the application can calculate the characteristic parameters of the fuzzy region more accurately and efficiently, and accelerate the application of the solid-liquid phase change theory in the fields such as electronic equipment thermal management and the like.
In some examples, the solid-liquid phase-change fuzzy region characteristic parameter calculation model may be a modularized calculation model, which may be used to independently calculate fuzzy region characteristic parameters, or may be embedded into fluid dynamics software as a calculation module, or the like.
Optionally, the evaluation model comprises a first evaluation model and a second evaluation model of different types;
determining M key influence factors of the fuzzy region characteristic parameters from a plurality of influence factors based on the evaluation model, wherein the M key influence factors comprise:
determining m1 key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors through a first evaluation model;
determining m2 key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors through a second evaluation model;
and obtaining intersection sets of the M1 key influence factors and the M2 key influence factors to obtain M key influence factors, wherein M1 and M2 are positive integers.
The first evaluation model and the second evaluation model are different types of evaluation models, for example, the first evaluation model and the second evaluation model may be respectively an analytic hierarchy process model and a gray correlation analysis process model, or may be respectively a fuzzy comprehensive evaluation process model, a neural network comprehensive evaluation process model, or the like.
Of course, the above are some examples of the first evaluation model and the second evaluation model, and in practical application, the two may be any combination of existing different evaluation models.
Through different evaluation models, corresponding sets of key influence factors can be obtained respectively, and the sets of key influence factors can be the same or different. In this embodiment, the set of key influence factors obtained by the first evaluation model may include the above-mentioned m1 key influence factors, and the set of key influence factors obtained by the second evaluation model may include the above-mentioned m2 key influence factors. M key influencing factors can be obtained through intersection operation among the sets.
It should be noted that, the description manner of the first evaluation model and the second evaluation model used in the embodiment is to use different evaluation models to determine key influence factors for expressing the method of the present application, and the method is not limited to use of two different evaluation models, but may also be three or more evaluation models, and M key influence factors are obtained by performing intersection operation on key influence factors obtained by a plurality of different evaluation models, so that the rationality of the determined M key influence factors can be effectively improved.
In one embodiment, the first evaluation model comprises a hierarchical model and the second evaluation model comprises a gray correlation model.
The basic implementation principle of the two evaluation models is briefly described below.
(1) Hierarchical analysis method for screening key influencing factors
For influence A m Diversity of value selection factorsAnd the key influencing factors are analyzed and screened by constructing a hierarchical structure model of the influencing factors. In A way m Analyzing key influence factors selected by the values as target layers, and firstly dividing the influence factors into three categories of phase change material types, container structures and boundary conditions; then, the influencing factors are divided into three layers, which respectively correspond to primary (phase change material type, etc.), secondary (paraffin RT27, etc.) and tertiary (fuzzy region temperature difference, etc.) factor indexes. On this basis, a hierarchical model is built that influences the Am value selection factor, as shown in fig. 2.
In the weight distribution of the acquisition analytic hierarchy process, a five-scale analytic hierarchy process, a three-scale analytic hierarchy process or a nine-scale analytic hierarchy process can be used, and the five-scale analytic hierarchy process can be preferably selected in some application scenes, so that on one hand, enough distinction is made among all influence factors, on the other hand, the boundary judgment ambiguity among all influence factors can be avoided, and the follow-up iteration times can be avoided.
The five-scale analytic hierarchy process (which may be simply referred to as five-scale process) calculates the index weight by:
a) Establishing an hierarchical structure model of Am value influence factors (shown in FIG. 2);
b) Constructing a comparison matrix B= (B) according to a five-scale assignment principle ij ) n×n ；
Where n is the number of influencing factors (hereinafter may be simply referred to as factors), bij is the importance assignment of factor i compared with factor j based on the five-scale assignment principle, i, j=1, 2, …, n.
c) Calculating an importance ranking index r i ；
d) Constructing a judgment matrix C= (C) ij ) n×n ；
Wherein r is i And r j The importance ranking index of factor i and factor j, respectively; r is (r) max And r min The maximum and minimum values in the importance ranking index, respectively.
e) Solving for the optimal transfer matrix d= (D) ij ) n×n ；
f) Solving for quasi-optimal consistent matrix E= (E) ij ) n×n ；
g) The key factors are determined. First, the maximum eigenvalue λ of the matrix E is calculated max And its corresponding feature vector χ. Then, the feature vector is normalized to obtain index weight values of paraffin RT27, cylinder, constant wall temperature and other single layers, and index weight values of phase change material types, container structures and other total layers. And finally, determining key factors influencing the selection of the Am value through the size sorting of the weight values.
It should be noted that, the explanation of each letter in the formulas (1) - (5) above may be independent with respect to the explanation of the other formulas or embodiments, so as to represent a general process of screening key influencing factors by analytic hierarchy process.
(2) Gray correlation analysis method for screening key influencing factors
The gray correlation analysis method is a multi-factor statistical analysis method, which adopts gray comprehensive correlation degree to describe the strength, the size and the order of the correlation relationship among the factors based on the sample data of the factors. If the two factors change in a consistent situation (i.e. the degree of synchronous change is higher), the two factors can be considered to be related more; otherwise, the two are less correlated.
The gray correlation analysis method comprises the following specific steps:
a) Set X i As a system factor, its observed data at sequence number k is x i (k) (k=1, 2, …, n), then X is referred to as i = [x i (1), x i (2),…, x i (n)](k=1, 2, …, m) is the behavioral sequence of the factor.
b) Gray absolute correlation calculation
Design sequence χ 0 With χ i Is the same length, behavior sequence χ i The initial point zero image is X i 0 =[x i (1)-x i (1), x i (2)- x i (1),…,x i (n)-x i (1)]=[x i 0 (1), x i 0 (2) …, x i 0 (n)] (i=0,1,2, …,m)。
Order the
Then χ is 0 With χ i Gray absolute correlation epsilon 0i Is that
c) Gray relative correlation calculation
Design sequence χ 0 With χ i Is identical in length and has an initial value other than zero, behavior sequence χ i The initial value image formula of (1) is
Wherein X is i ' starting point zero-ized image is X i ' 0 =[x i ' (1)- x i ' (1), x i ' (2)- x i ' (1),…, x i ' (n)- x i ' (1)]=[x i ' 0 (1), x i ' 0 (2) …, x i ' 0 (n)] (i=0,1,2, …,m).
Then X is 0 And X is i Gray relative correlation gamma 0i Is that
d) Gray comprehensive association degree χ 0i Is that
By comparing the gray comprehensive relevance χ of each factor 0i Determining influence A m The importance of the value factor.
Finally, analyzing the influence A of each factor on the basis of the analytic hierarchy process and the gray correlation analysis process m Importance of value selection, and influence factors screened by the two methods are used as A finally identified m Key influencing factors for value selection.
It should be noted that the explanation of each letter in the formulas (6) - (10) above may be independent with respect to the explanation of the other formulas or embodiments, so as to represent the general process of screening the key influencing factors by the gray correlation analysis method.
The analytic hierarchy process and the gray correlation analysis process are combined, so that the shortcoming that the analytic hierarchy process excessively depends on weights is overcome, the disadvantage of the gray correlation analysis process in the process of carrying out the weighting treatment is reduced, the advantages of the two methods are fully exerted, and the influence of subjective factors is reduced to the greatest extent.
Optionally, in step 103, determining the first dimensionless number of criteria, the N second dimensionless number of criteria, and a first association between the first dimensionless number of criteria and the feature parameter of the fuzzy area based on the dimensions of the M key influencing factors and the dimensions of the feature parameter of the fuzzy area, including:
Based on the platinum-Han pi theorem, obtaining M-N reference dimensions corresponding to M-N key influence factors by carrying out dimension representation on the dimensions of the M key influence factors and the characteristic parameters of the fuzzy region;
dimensionless processing is carried out on the dimensionality of the characteristic parameters of the fuzzy region and the dimensionality of the rest N key influence factors through M-N reference dimensionalities, so that a first dimensionality criterion number corresponding to the characteristic parameters of the fuzzy region, N second dimensionality criterion numbers corresponding to the N key influence factors and a first association between the first dimensionality criterion number and the characteristic parameters of the fuzzy region are obtained.
The key influencing factors and A are determined according to the platinum-Han pi theorem m The general procedure of the association law between them is explained.
And constructing a dimensionless association between the key influencing factors and Am based on the platinum-Han pi theorem. The parameter A is established by adopting a classical dimension analysis method, namely the platinum-Han pi theorem m Dimensionless correlation with key influencing factors. The platinum-Han theorem can be briefly described as: one expressed as a relation u between m variables 1 = f(u 2 , u 3 , …, u m ) If n is the minimum reference dimension number referred to by the m variables, the relationship can be converted to an associative pi containing m-n independent dimensionless groups 1 = f(π 2 , π 3 , … , π m-n )。
The detailed steps for obtaining the dimensionless criterion number by adopting the dimension analysis method, namely the platinum-Chinese pi theorem are as follows:
a) Based on the key influencing factors of the analytical model screening, key variables (u 1 , u 2 , … , u m ) The total variable number is recorded as m;
b) Each variable is represented in a dimensional form according to the dimensional representation of { MLT Θ }, the minimum number of reference dimensions involved being noted as n. Wherein M, L, T and Θ represent the dimensions of mass, length, time and temperature, respectively;
c) The number of pi terms required is m-n;
d) From the m variables, n of them are selected as repetition variables (u 1 , u 2 , … , u n )；
e) The remaining variables (u) n+1 , u n+2 , … , u n+i , … , u m ) One by oneA multiplication with the n repetition variables and adding a pending index, e.g. pi, to each repetition variable n+i =u n+i u 1 i1 u 2 i2 …u n in (wherein i 1 , i 2 , … , i n Representing pending indexes), the pending index of each pi term is not necessarily the same, forming m-n pi terms altogether;
f) Checking the m-n pi terms by adopting a dimension harmony principle, and determining the numerical value of the index to be determined of the repeated variable, so that the pi terms are in a dimensionless form;
g) Obtaining the criterion numbers (namely dimensionless criterion numbers) corresponding to the m-n pi items;
h) Parameter A expressed in terms of the criterion number of these m-n pi terms m The correlation is pi 1 = f(π 2 , π 3 , … , π m-n ) And gives the physical meaning of its representation. Some of the possible dimensionless numbers of criteria have been exemplified above (e.g., pr, ste, gr, etc.), and will not be repeated here.
It should be noted that the above explanation of each letter in the platinum-chinese pi theorem explanation may be independent with respect to the explanation in the rest of the formulas or embodiments, in order to represent the general process of screening key influencing factors by the gray correlation analysis method. The following text, where there are different interpretations of the same letter, can be understood in conjunction with the context semantics and will not be emphasized again.
In combination with the descriptions in steps 101-105, the following relationships may exist between some letters: 1) m=m+1, namely, the key variables related to the Am value selection problem comprise fuzzy region characteristic parameters and M key influence factors; 2) n=m-N, i.e. M-N reference dimensions correspond to N repetition variables. In addition, pi 1 Corresponds to a first dimensionless criterion number, pi 2 , π 3 , … , π m-n Corresponding to N second dimensionless criterion numbers, from A m To pi 1 Can be converted to a first correlation, pi 1 = f(π 2 , π 3 , … , π m-n ) I.e. a further fitting of the resulting second correlation is required.
Optionally, step 104, fitting a second association between the first dimensionless number of criteria and the N second dimensionless number of criteria includes:
constructing a linear regression model between the first dimensionless number of criteria and the N second dimensionless number of criteria;
And solving the linear regression model through a least square method to obtain a second association between the first dimensionless number of criteria and the N second dimensionless numbers of criteria.
The procedure of dimensionless correlation based on the least squares multiple linear regression Am is described below on the basis of the above explanation of the platinum-Han theorem.
First, dimensionless pi formed by key influencing factors 2 , π 3 , … , π m-n Under the condition, the non-dimensionless number pi of Am 'paired' with the numerical simulation and experimental verification means are obtained 1 The method comprises the steps of carrying out a first treatment on the surface of the At the same time, the above-mentioned related pi obtained by platinum-Han pi theorem 1 = f(π 2 , π 3 , … , π m-n ) Is rewritten as pi 1 =β 1 π 2 β2 π 3 β3 …π m-n βm-n Taking the logarithm of both sides of the equation, and converting into a multi-element linear equation based on the logarithm:
log α (π 1 )=log α (β 1 )+β 2 log α (π 2 ) +β 3 log α (π 3 )+…+β m-n log α (π m-n ) (alpha is the base and beta is the coefficient of uncertainty).
Then, a least square method is adopted to obtain a criterion number pi 1 , π 2 , π 3 , … , π m-n The undetermined coefficient beta in the multiple linear equation formed between 1 , β 2 , β 3 , … , β m-n To obtain A m Dimensionless regression model of (a). And finally, giving the goodness of fit and standard error of the dimensionless Am criterion association. In addition, the A of the regression model linear extrapolation condition is determined by combining the verification means such as numerical simulation and experiments m Value prediction essenceDegree.
The multiple linear regression modeling based on the least square method comprises the following steps:
a) Let the sample size be N, the dependent variable be y, the independent variable be x j (j=1, 2, …, m-n-1), the overall linear regression model of the logarithmic-based multi-element linear equation obtained by the platinum-Han pi theorem is y i =β 1 +β 2 x i2 +β 3 x i3 +…+β (m-n) x i(m-n) +ε i (wherein. Beta.) 1 '=log α (β 1 ) The method comprises the steps of carrying out a first treatment on the surface of the i=1, 2, …, N; epsilon is the random error). In the form of a matrix y=xb+e, the matrices Y, X, B, E being expressed as
The cross-product V of the augmentation matrix (X, Y) is
b) The least square of the undetermined coefficient β is b= (V) 11 ) -1 V 12 Thus, a regression equation is obtained as
c) Sum of squares of residual errors S SSE Sum of squares S SST Represented as
Wherein V is 12, 1 Is V 12 Is the first component of (c).
d) Goodness of fit R 2 Standard error S e Represented as
Based on the method and means, firstly, the obtained Am value is extrapolated in a dimensionless associated mode, and the correctness of the dimensionless Am associated mode is verified by collaborative numerical simulation, corresponding experiments and the like. Then, the applicability under the extrapolated condition and the correction method thereof are determined. Finally, the limiting conditions of its use are defined.
The general procedure of the multiple linear regression process based on the least square method is described above, and in the formulas (11) - (15), the dependent variable y corresponds to the first dimensionless criterion number A, and the independent variable x j The respective second dimensionless numbers correspond to the respective second dimensionless numbers, and the correspondence between the other parameters is foreseen, which will not be described in detail here. The second correlation formula is fitted in a mode of solving the linear regression model, so that the second correlation formula can reflect a general rule between the first dimensionless criterion number and the N second dimensionless criterion numbers more accurately, and applicability of the solid-liquid phase fuzzy region characteristic parameter calculation model and accuracy of fuzzy region characteristic parameter calculation are improved.
Optionally, after constructing the calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy area based on the first correlation formula and the second correlation formula, the method further comprises:
determining a fuzzy region characteristic parameter calculation value of the material to be analyzed based on a calculation model of the solid-liquid phase change fuzzy region characteristic parameter;
inputting the characteristic parameter calculation value of the fuzzy region into fluid dynamics software, and carrying out numerical simulation on the solid-liquid phase change process of the material to be analyzed to obtain a simulation result.
As indicated above, the computational model of the solid-liquid phase-change blur area characteristic parameters may be built based on the first and second correlations.
In one example, the first association may be a=a m D 2 /(4 μ), where A is a first dimensionless criterion number, A m And D is the characteristic size of a container for packaging the phase change material or the diameter of solid particles in the fuzzy area, and mu is the dynamic viscosity of the liquid phase change material.
The second association may be a=gr α1 Ste α2 Where Gr is the Gellan number, ste is the Stefan number, α1 and α2 are coefficients, and specific values have been obtained by prior fitting.
As can be seen from the combination of the first correlation and the second correlation, in the calculation model of the characteristic parameters of the fuzzy region of solid-liquid phase change, if D, mu, gr and Ste of the material to be analyzed are input, the characteristic parameters A of the fuzzy region can be automatically calculated m The calculated value can be further input into fluid dynamics software for carrying out numerical simulation on the solid-liquid phase change process of the material to be analyzed to obtain a simulation result.
The fluid dynamics software may be ANSYS Fluent, COMSOL Multiphysics, openFOAM, or the like, which is not particularly limited herein. In some examples, the fluid dynamics software may have a solid-liquid phase transformation module that may perform solid-liquid phase transformation simulation of the material to be analyzed in combination with fuzzy region characteristic parameters to direct, for example, the design of electronic devices to employ solid-liquid phase thermal change management.
Optionally, inputting the calculated value of the characteristic parameter of the fuzzy area into fluid dynamics software, performing numerical simulation on the solid-liquid phase change process of the material to be analyzed, and after obtaining the simulation result, the method further comprises the steps of:
obtaining a test result obtained by testing a solid-liquid phase-change process of a material to be analyzed;
and correcting the calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy region according to the simulation result and the test result.
In this embodiment, to expand the application scenario of the characteristic parameter calculation model of the solid-liquid phase fuzzy area, the difference between the test result and the simulation result may be combined to continuously correct the calculation model of the characteristic parameter of the solid-liquid phase fuzzy area, for example, correct the coefficient in the second association type.
In some embodiments, under typical conventional conditions that the calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy region is widely applicable, the calculation model is expanded to wider ranges of atypical conditions, extreme conditions and the like, so as to further test the robustness, universality and the like of the calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy region.
1) Atypical condition A m Verification of value dimensionless associations
Atypical conditions include, but are not limited to: 1) Novel phase change materials (including novel nano-class phase change materials, bio-based phase change materials, and organic, organic and inorganic, inorganic and inorganic phase change material blends, etc.); 2) Abnormal container structure (including ellipse, truncated cone, sector, etc.); 3) Variable boundaries, mixed boundary conditions (including transient or non-uniformly distributed wall temperatures, heat flows, and multiple boundary condition mixtures, etc.).
2) Non-dimensional correlation test of Am value under extreme conditions
Extreme conditions include, but are not limited to: 1) The ultrahigh temperature operation (more than 900 ℃) comprises the steps of taking silicon, aluminum-silicon alloy and the like as phase change materials, comprehensively considering the influences of radiation heat exchange, solid-liquid phase heating expansion stress and the like, and providing a theoretical basis of a parameter Am for a solid-liquid phase heating management technology of electronic equipment; 2) Microgravity environment (10) -6 g to 10 -3 g, g is the ground gravity acceleration 9.81 ms -2 ) Etc. The method provides a theoretical basis for parameters Am for an advanced solid-liquid phase transition heat management technology of an electronic device for aerospace.
Firstly, under the atypical or extreme conditions, adopting numerical simulation, and combining verification means such as experimental test data or related test data of existing literature data and the like to verify the universality of dimensionless relevance of the Am value. Then, the correction methods such as calibration and compensation are determined according to the main reasons of error generation of the non-dimensionless correlation of the Am values which do not meet atypical or extreme conditions, such as an equivalent coefficient method. Finally, the general calculation formula of Am value is generalized to expand A m The application range of the dimensionless association value is such that A m The value dimensionless correlation is more generalized.
Refining the total A m The value association rule and the physical meaning of the representation of the rule are defined from the point of view of dimensionless criterion number; thereby forming A m And (3) a general value calculation framework, and then writing an Am value calculation program, and implanting computational fluid dynamics software such as ANSYS Fluent, COMSOL Multiphysics, openFOAM and the like. In addition, in the form of 2D and 3D response surfaces, the method is characterized byReflecting the change rule of the Am value along with the key influence factors; quantitatively reflecting the intrinsic law between the Am value and the key influencing factors thereof in a table form; and a database under the Am value general calculation framework is established according to the classification, so that users such as ANSYS Fluent can search or correct the database directly. Providing a parameter A for simulating solid-liquid phase change by an enthalpy-porous medium method m And promote the high-efficiency application of solid-liquid phase transition theory in the fields such as electronic equipment heat management and the like.
Referring to fig. 3, fig. 3 is a flowchart of an application example of a method for constructing a calculation model of a characteristic parameter of a solid-liquid phase-change fuzzy region according to an embodiment of the present application, and steps 301 to 304 are performed.
Step 301, determining key influencing factors for selecting the value of the characteristic parameter Am of the fuzzy area.
The analytic hierarchy process and the gray correlation analysis process are combined, so that the shortcoming that the analytic hierarchy process excessively depends on weights is overcome, the disadvantage of the gray correlation analysis process in the process of carrying out the weighting treatment is reduced, the advantages of the two methods are fully exerted, and the influence of subjective factors is reduced to the greatest extent. Typical conventional conditions selected mainly include: 1) The widely used class of phase change materials involves the transition from organic (paraffin RT 27) to inorganic (water, etc.), from inorganic nonmetallic (CaCl 2 ·6H 2 O, etc.) to metal (gallium), a series of typical phase change materials from low temperature (water, etc.) to high temperature (sodium nitrate); 2) Typical container structures of cylindrical, square, etc. packaging phase change materials; 3) Constant wall temperature, constant heat flow and other typical boundary conditions; 4) The operation temperature To of the solid-liquid phase change system is less than or equal To 900 ℃, and the gravitational acceleration g=9.81 ms -2 And the like under conventional conditions. The key factors influencing the selection of the Am value are determined for the melting process (solid phase-liquid phase transition) and the solidification process (liquid phase-solid phase transition), respectively.
Step 302, determining key influencing factors and A m The association rule between them.
This step involves constructing A based on the platinum-Han pi theorem m Dimensionless correlation (corresponding to the first correlation), and multiple linear regression A based on least squares m Dimensionless association (corresponding to the second association).
Step 303, A m The commonality of the dimensionless relevance of the values and their correction.
The method comprises the following steps: 1) Under atypical conditions, checking the dimensionless relevance of the Am value; 2) Under extreme conditions A m Checking a value dimensionless association type; 3) A is that m Correcting the dimensionless value association; 4) Total A m And (5) refining the dimensionless association rules of the values.
Step 304, A m And establishing a general computing framework and a classification database of the values.
Application example:
1) For CaCl 2 ·6H 2 O is melted in a cylindrical container, and based on the dimension of the parameter Am, the characteristic parameter A of the fuzzy area m Is custom defined as a=a m D 2 /(4 μ) in combination with the number of steven (ste=c), a criterion for measuring the ratio of sensible heat to latent heat in the solid-liquid phase change process p DeltaT/L) and liquid CaCl measurements 2 ·6H 2 Criterion number of strong and weak O natural convection-glas dawn number (gr=gβΔtd 3 ρ 2 /(8μ 2 ) )；
2) Fuzzy region characteristic parameter A m There is a perfect correlation between the dimensionless criterion number a and Ste and Gr as follows:
A=Gr 0.639 Ste -2.947 (R 2 =0.9999)。
the multiple regression effect is shown in FIG. 4. The result shows that the required characteristic parameters of the fuzzy area can be obtained rapidly through the technical scheme of the application. And then, the obtained fuzzy region characteristic parameters are implanted into solid-liquid phase transformation modules of computational fluid dynamics software such as ANSYS Fluent, COMSOL Multiphysics, openFOAM and the like to perform solid-liquid transformation simulation so as to guide the electronic equipment to adopt the design of solid-liquid phase transformation management.
Note that: cp in the formulas of criterion numbers A, ste and Gr represents CaCl 2 ·6H 2 Specific heat of O (J kg) -1 K -1 ) DT represents the temperature T of the outer wall surface of the cylindrical container w With phase transition temperature T m The difference (K), L represents the latent heat of phase change (J kg) -1 ) G represents the gravitational acceleration (m s) -2 ) B represents CaCl 2 ·6H 2 Coefficient of thermal expansion of O (K -1 ) D denotes packaging CaCl 2 ·6H 2 Characteristic dimension (m) of O packaging container, r and m respectively represent liquid CaCl 2 ·6H 2 Density of O (kg m) -3 ) And dynamic viscosity (kg m) -1 s -1 )。
As shown in fig. 5, the embodiment of the present application further provides a device for constructing a calculation model of a characteristic parameter of a solid-liquid phase-change fuzzy area, including:
a first obtaining module 501, configured to obtain a plurality of influencing factors of feature parameters of a fuzzy area;
A first determining module 502, configured to determine M key influencing factors of the characteristic parameters of the fuzzy area from a plurality of influencing factors based on the evaluation model, where M is an integer greater than 1;
a second determining module 503, configured to determine a first dimensionless number of criteria, N second dimensionless numbers of criteria, and a first association between the first dimensionless number of criteria and the feature parameter of the fuzzy area based on the dimensionalities of the M key influencing factors and the dimensionalities of the feature parameter of the fuzzy area, where N is a positive integer less than M;
a fitting module 504, configured to fit a second association between the first dimensionless number of criteria and the N second dimensionless number of criteria;
the construction module 505 is configured to construct a calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy area based on the first correlation and the second correlation.
Optionally, the evaluation model comprises a first evaluation model and a second evaluation model of different types;
accordingly, the first determining module 502 may be specifically configured to:
determining m1 key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors through a first evaluation model;
determining m2 key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors through a second evaluation model;
and obtaining intersection sets of the M1 key influence factors and the M2 key influence factors to obtain M key influence factors, wherein M1 and M2 are positive integers.
Optionally, the first evaluation model comprises an analytic hierarchy process model and the second evaluation model comprises a gray associative process model.
Alternatively, the second determining module 503 may be specifically configured to:
based on the platinum-Han pi theorem, obtaining M-N reference dimensions corresponding to M-N key influence factors by carrying out dimension representation on the dimensions of the M key influence factors and the characteristic parameters of the fuzzy region;
dimensionless processing is carried out on the dimensionality of the characteristic parameters of the fuzzy region and the dimensionality of the rest N key influence factors through M-N reference dimensionalities, so that a first dimensionality criterion number corresponding to the characteristic parameters of the fuzzy region, N second dimensionality criterion numbers corresponding to the N key influence factors and a first association between the first dimensionality criterion number and the characteristic parameters of the fuzzy region are obtained.
Alternatively, the fitting module 504 may be specifically configured to:
constructing a linear regression model between the first dimensionless number of criteria and the N second dimensionless number of criteria;
and solving the linear regression model through a least square method to obtain a second association between the first dimensionless number of criteria and the N second dimensionless numbers of criteria.
Optionally, the calculation model construction device of the characteristic parameters of the solid-liquid phase-change fuzzy area may further include:
The third determining module is used for determining a fuzzy region characteristic parameter calculation value of the material to be analyzed based on a calculation model of the solid-liquid phase fuzzy region characteristic parameter;
and the simulation module is used for inputting the characteristic parameter calculation value of the fuzzy region into fluid dynamics software, and carrying out numerical simulation on the solid-liquid phase change process of the material to be analyzed to obtain a simulation result.
Optionally, the calculation model construction device of the characteristic parameters of the solid-liquid phase-change fuzzy area may further include:
the second acquisition module is used for acquiring a test result obtained by testing the solid-liquid phase-change process of the material to be analyzed;
and the correction module is used for correcting the calculation model of the characteristic parameters of the solid-liquid phase fuzzy area according to the simulation result and the test result.
The device for constructing the calculation model of the characteristic parameters of the solid-liquid phase fuzzy area provided by the embodiment of the application is the device authority corresponding to the method for constructing the calculation model of the characteristic parameters of the solid-liquid phase fuzzy area in the above embodiment, and the method embodiment can be applied to the device embodiment and achieve the same technical effects, and is not repeated here.
The embodiment of the application also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize the method for constructing the calculation model of the characteristic parameters of the solid-liquid phase fuzzy area.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the method for constructing the calculation model of the characteristic parameters of the solid-liquid phase fuzzy area when being executed by a processor.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (6)
1. A method for constructing a calculation model of characteristic parameters of a solid-liquid phase change fuzzy area is characterized by comprising the following steps:
acquiring a plurality of influencing factors of the characteristic parameters of the fuzzy region, wherein the plurality of influencing factors comprise factors related to material types, container structures and thermal boundary conditions;
determining M key influence factors of the characteristic parameters of the fuzzy area from the influence factors based on an evaluation model, wherein M is an integer greater than 1;
determining a first dimensionless number of criteria, N second dimensionless numbers of criteria, and a first correlation between the first dimensionless number of criteria and the fuzzy region feature parameters based on the dimensionality of the M key influence factors and the dimensionality of the fuzzy region feature parameters, N being a positive integer less than M;
Fitting a second correlation between the first dimensionless number of criteria and the N second dimensionless number of criteria;
constructing a calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy area based on the first correlation formula and the second correlation formula;
the evaluation model comprises a first evaluation model and a second evaluation model of different types;
the determining, based on the evaluation model, M key influence factors of the fuzzy region characteristic parameters from the plurality of influence factors includes:
determining m1 key influence factors of the fuzzy region characteristic parameters from the influence factors through the first evaluation model;
determining m2 key influence factors of the fuzzy region characteristic parameters from the influence factors through the second evaluation model;
obtaining intersection sets of the M1 key influence factors and the M2 key influence factors to obtain the M key influence factors, wherein M1 and M2 are positive integers;
the first evaluation model is an analytic hierarchy process model, and the second evaluation model is a gray correlation process model;
the determining a first dimensionless number of criteria, N second dimensionless numbers of criteria, and a first association between the first dimensionless number of criteria and the fuzzy region feature parameter based on the dimensions of the M key influencing factors and the dimensions of the fuzzy region feature parameter, includes:
Based on the platinum-Han pi theorem, obtaining M-N reference dimensions corresponding to M-N key influence factors by carrying out dimension representation on the dimensions of the M key influence factors and the characteristic parameters of the fuzzy region;
dimensionless processing is carried out on the dimension of the characteristic parameters of the fuzzy region and the dimension of the rest N key influence factors through the M-N reference dimensions, so that a first dimensionless criterion number corresponding to the characteristic parameters of the fuzzy region, N second dimensionless criterion numbers corresponding to the N key influence factors and a first correlation formula between the first dimensionless criterion number and the characteristic parameters of the fuzzy region are obtained;
said fitting a second relationship between said first dimensionless number of criteria and said N second dimensionless number of criteria comprises:
constructing a linear regression model between the first dimensionless number of criteria and the N second dimensionless number of criteria;
solving the linear regression model through a least square method to obtain a second association between the first dimensionless number of criteria and the N second dimensionless numbers of criteria;
the first association isA=A m D 2 /(4μ) Wherein, the method comprises the steps of, wherein,Aas a first number of dimensionless criteria,A m in order to make the characteristic parameters of the fuzzy area, DTo encapsulate the feature size or the solid particle diameter of the fuzzy region of the phase change material container,μdynamic viscosity of the liquid phase change material;
the second association isA=Gr α1 Ste α2 Wherein, the method comprises the steps of, wherein,Grfor the purpose of the gara dawn number,Stein order to provide a still number of the present invention,α1 andα2 is a coefficient, and a specific numerical value is obtained through pre-fitting;
the calculation model of the characteristic parameters of the solid-liquid phase change fuzzy area combines the first correlation formula and the second correlation formula to input materials to be analyzedD、μ、GrAndStecalculating to obtain the characteristic parameters of the fuzzy areaA m Is a calculated value of (a).
2. The method of claim 1, wherein after the constructing a calculation model of the characteristic parameters of the solid-liquid phase-change blur area based on the first correlation formula and the second correlation formula, the method further comprises:
determining a fuzzy region characteristic parameter calculation value of the material to be analyzed based on the calculation model of the solid-liquid phase change fuzzy region characteristic parameter;
inputting the calculated value of the characteristic parameter of the fuzzy region into fluid dynamics software, and performing data simulation on the solid-liquid phase change process of the material to be analyzed to obtain a simulation result.
3. The method according to claim 2, wherein the inputting the calculated values of the characteristic parameters of the fuzzy area into the hydrodynamic software performs data simulation on the solid-liquid phase transformation process of the material to be analyzed, and after obtaining the simulation result, the method further comprises:
Obtaining a test result obtained by testing the solid-liquid phase-change process of the material to be analyzed;
and correcting a calculation model of the characteristic parameters of the solid-liquid phase-change fuzzy region according to the simulation result and the test result.
4. The device for constructing the calculation model of the characteristic parameters of the solid-liquid phase change fuzzy area is characterized by comprising the following components:
a first acquisition module for acquiring a plurality of influencing factors of the characteristic parameters of the fuzzy region, wherein the plurality of influencing factors comprise factors related to the material type, the container structure and the thermal boundary condition;
the first determining module is used for determining M key influence factors of the fuzzy region characteristic parameters from the influence factors based on an evaluation model, wherein M is an integer greater than 1;
the second determining module is used for determining a first dimensionless number, N second dimensionless numbers and a first association between the first dimensionless number and the characteristic parameters of the fuzzy area based on the dimensionality of the M key influence factors and the dimensionality of the characteristic parameters of the fuzzy area, wherein N is a positive integer smaller than M;
a fitting module for fitting a second association between the first dimensionless number of criteria and the N second dimensionless number of criteria;
The construction module is used for constructing a calculation model of the characteristic parameters of the solid-liquid phase change fuzzy area based on the first correlation formula and the second correlation formula;
the evaluation model comprises a first evaluation model and a second evaluation model of different types;
the first determining module is specifically configured to:
determining m1 key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors through a first evaluation model;
determining m2 key influence factors of the characteristic parameters of the fuzzy area from a plurality of influence factors through a second evaluation model;
obtaining intersections of M1 key influence factors and M2 key influence factors to obtain M key influence factors, wherein M1 and M2 are positive integers;
the first evaluation model is an analytic hierarchy process model, and the second evaluation model is a gray correlation process model;
the second determining module is specifically configured to:
based on the platinum-Han pi theorem, obtaining M-N reference dimensions corresponding to M-N key influence factors by carrying out dimension representation on the dimensions of the M key influence factors and the characteristic parameters of the fuzzy region;
dimensionless processing is carried out on the dimensionality of the characteristic parameter of the fuzzy region and the dimensionality of the rest N key influence factors through M-N reference dimensionalities, so that a first dimensionality criterion number corresponding to the characteristic parameter of the fuzzy region, N second dimensionality criterion numbers corresponding to the N key influence factors and a first association type between the first dimensionality criterion number and the characteristic parameter of the fuzzy region are obtained;
The fitting module is specifically used for:
constructing a linear regression model between the first dimensionless number of criteria and the N second dimensionless number of criteria;
solving the linear regression model through a least square method to obtain a second association between the first dimensionless number of criteria and N second dimensionless numbers of criteria;
the first association isA=A m D 2 /(4μ) Wherein, the method comprises the steps of, wherein,Aas a first number of dimensionless criteria,A m in order to make the characteristic parameters of the fuzzy area,Dto encapsulate the feature size or the solid particle diameter of the fuzzy region of the phase change material container,μdynamic viscosity of the liquid phase change material;
the second association isA=Gr α1 Ste α2 Wherein, the method comprises the steps of, wherein,Grfor the purpose of the gara dawn number,Stein order to provide a still number of the present invention,α1 andα2 is a coefficient, and a specific numerical value is obtained through pre-fitting;
the calculation model of the characteristic parameters of the solid-liquid phase change fuzzy area combines the first correlation formula and the second correlation formula to input materials to be analyzedD、μ、GrAndStecalculating to obtain the characteristic parameters of the fuzzy areaA m Is a calculated value of (a).
5. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 3 when executing the computer program.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 3.
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