CN113779706B - Impeller mechanical loss model construction method based on data credibility - Google Patents
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Abstract
The invention relates to the field of impeller machinery aerodynamic thermodynamics, in particular to a method for constructing an impeller machinery loss model based on data credibility. Evaluating the data reliability in the impeller machinery database to obtain an impeller machinery database accounting the data reliability; carrying out sensitivity analysis on geometric parameters and pneumatic parameters of the blade, and establishing an expression form of an impeller mechanical loss model; and solving the loss model expression coefficient by means of an optimization algorithm which is counted in the data reliability to obtain the impeller machinery loss model based on the data reliability. The experimental data reliability, the simulation data reliability, the geometric parameter reliability and the flow field parameter reliability are effectively brought into the establishment of the impeller machinery loss model, the customized loss model is established for the target blade profile based on a limited database, and the difficult problem that the loss model prediction difference is large due to different data precision, different blade configurations and different typical flow parameters is solved.
Description
Technical Field
The invention relates to the field of impeller machinery aerodynamic thermodynamics, in particular to a method for constructing an impeller machinery loss model based on data credibility.
Background
Impeller machines are widely used in energy and power applications such as aircraft engines, ground and marine gas turbines, steam turbines, and compressed air energy storage systems. The pneumatic design of the impeller machinery is a low-dimensional to high-dimensional progressive and repeated optimization process, the low-dimensional design is the basis of the high-dimensional design, and in the low-dimensional design stage, accurate loss prediction can lay a good basis for the high-dimensional design, and the design period is effectively shortened.
Because the aerodynamic performance of the impeller machinery is influenced by various geometric and aerodynamic parameters, the loss model presents strong nonlinear characteristics under the influence of the multiple parameters, the accuracy of the loss model depends on a database used for constructing the model to a great extent, the construction sources of the loss model are complex, the loss model mainly comprises test or numerical simulation data disclosed in literature, and the data have different test errors, different numerical simulation precision, different blade configurations and the like, so that the prediction results obtained by adopting different loss models for the same blade model have larger difference. That is, the accuracy of the database supported during model construction is different, and the difference between the data distribution and the target leaf profile may result in insufficient prediction accuracy of the prediction model.
However, at present, no systematic evaluation of the validity of the data constructed for the loss model exists, in the actual design process, a trial-and-error method is mostly adopted, if the test result of a certain leaf pattern under a certain flow field is consistent with the prediction of a certain loss model, the loss model is considered to be applicable, the experience is depended relatively, and the loss model is not effectively improved from the root. How to incorporate existing test and simulation data into the construction of the loss model effectively to improve the prediction accuracy of the loss model lacks a corresponding research method.
Disclosure of Invention
Accordingly, a primary object of the present invention is to provide a method for constructing a loss model of an impeller machine based on data reliability, so as to at least partially solve at least one of the above problems. The method comprises the steps of evaluating the reliability of data in an impeller machine database to obtain the impeller machine database with the reliability of the data; carrying out sensitivity analysis on geometric parameters and pneumatic parameters of the blade, and establishing an expression form of an impeller mechanical loss model; and solving the loss model expression coefficient by means of an optimization algorithm which is counted in the data reliability to obtain the impeller machinery loss model based on the data reliability. The experimental data reliability, the simulation data reliability, the geometric parameter reliability and the flow field parameter reliability are effectively brought into the establishment of the impeller machinery loss model, the customized loss model is established for the target blade profile based on a limited database, and the difficult problem that the loss model prediction difference is large due to different data precision, different blade configurations and different typical flow parameters is solved.
The technical scheme adopted by the invention for realizing the technical purpose is as follows:
a method for constructing a model of impeller mechanical loss based on data reliability, the method comprising at least the steps of:
The method comprises the steps of SS1, performing reliability assessment on blade data in an existing impeller machine database to form an impeller machine database with data reliability;
SS2, performing sensitivity analysis on related geometric parameters and pneumatic parameters of the impeller machine by using an impeller machine database which is formed in the step SS1 and is used for counting the data reliability, and determining parameters to be counted in each part of the loss model and expression forms of the loss model;
And SS3, solving the relevant empirical coefficients in the loss model determined in the step SS2 by utilizing the impeller machinery database with the calculated data reliability formed in the step SS1 and utilizing an optimization algorithm with the calculated data reliability to obtain a specific expression of the impeller machinery loss model.
Preferably, in step SS1, the confidence assessment consists of four parts: test data reliability, simulation data reliability, geometric reliability and flow field parameter reliability, wherein the test data reliability depends on whether test data is from a standard test bench, a measurement method, measurement accuracy, a test data processing method and the like; the credibility of the simulation data depends on the simulation precision of the numerical method, whether the numerical method has test verification or not and the calculation precision of a model selected by the numerical method; the credibility of the geometric parameters depends on the similarity degree of the target leaf geometry and the leaf geometry in a database; the credibility of the flow field parameters depends on the similarity degree of typical dimensionless flow field parameters of the target blade and corresponding parameters in a database.
Further, the calculation formula of the data reliability includes, but is not limited to, the following calculation method:
R＝q1·R1+q2·R2+q3·R3+q4·R4
R, R 1、R2、R3、R4 respectively represents the total credibility of data, the credibility of test data, the credibility of simulation data, the credibility of geometry and the credibility of flow field parameters; q 1、q2、q3、q4 represents the weight of the reliability of each part in the total reliability of the data.
Further, the reliability of the test data depends on whether the test data is derived from a standard test bench, a measurement method, a measurement precision, a test data processing method and the like; the reliability of the simulation data depends on the simulation precision of the numerical method itself, whether the numerical method has test verification or not, and the calculation precision of a model selected by the numerical method; the reliability of the geometric parameters depends on the similarity degree of the target leaf geometry and the leaf geometry in the database; the reliability of the flow field parameters depends on the similarity of typical dimensionless flow field parameters of the target blade with corresponding parameters in a database.
Further, the measurement standards of the geometric parameter reliability and the flow field parameter reliability include, but are not limited to, an included angle cosine method, the larger the cosine value is, the higher the reliability is, and the calculation formula is as follows:
Wherein x 1k、x2k represents the geometric or aerodynamic parameters of the target blade and the blade in the database, respectively; n represents the number of parameters.
Preferably, in step SS2, the geometric parameters and aerodynamic parameters are determined by methods including, but not limited to, principal component analysis, etc., such as: and performing sensitivity analysis on the influence of chord length, grid distance, blade turning angle, inlet and outlet airflow angle and the like on the blade loss, selecting parameters to be considered in the loss model, and finally determining the expression form of the loss model.
Preferably, in step SS3, according to the data reliability in S1, the data reliability is incorporated into an objective function for evaluating the quality of the optimization algorithm solution, where the expression of the objective function is as follows:
Wherein δY j represents the predicted deviation value of the model on the loss coefficient of the jth group of blade data; r j represents the credibility of the j-th group of blade data; n represents the total amount of blade data in the database.
Further, optimization algorithms that account for data reliability include, but are not limited to, particle swarm optimization algorithms that account for data reliability, genetic algorithms that account for data reliability, the steepest descent method that accounts for data reliability, and the like. The unknown coefficients in the loss model are solved by adopting the optimization algorithm for accounting the reliability of the data, so that the established loss model is preferentially fitted to the data with higher reliability in the database, and the aim of improving the prediction accuracy of the loss model is fulfilled.
Based on the technical scheme, the impeller mechanical loss model construction method based on the data reliability has at least one of the following beneficial effects compared with the prior art:
The invention uses the test data credibility, the simulation data credibility, the geometric credibility and the flow field parameter credibility as the evaluation standard of the data credibility, and has the advantages of good universality, strong practicability, effective utilization of the existing public data and obvious improvement of the prediction precision of the loss model.
Drawings
FIG. 1 is a workflow diagram of a method of constructing a loss model of an impeller machine based on data confidence in the present invention;
FIG. 2 is a schematic diagram of the expected model predictive effect of the method for constructing a model of impeller mechanical loss based on data confidence in the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments obtained by the method according to the present invention without inventive effort by a person skilled in the art based on the method and the embodiments according to the present invention are included in the scope of protection of the present invention. The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. 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 invention belongs.
The impeller machinery loss model construction method based on the data credibility combines the characteristics of the impeller machinery and the credibility fitting algorithm, so that a loss model with good universality and strong practicability is constructed, the existing public data is effectively utilized, and the prediction precision of the loss model is improved obviously.
Specifically, the method for constructing the impeller machinery loss model based on the data credibility is used for predicting the loss characteristic of the impeller machinery. Firstly, evaluating the credibility of data in a database, wherein an evaluation criterion consists of four parts: test data reliability, simulation data reliability, geometric parameter reliability and flow field parameter reliability. The reliability of the test data depends on whether the test data is from a standard test bed, a measurement method, measurement precision, a test data processing method and the like; the reliability of the simulation data depends on the simulation precision of the numerical method itself, whether the numerical method has test verification or not, and the calculation precision of a model selected by the numerical method; the reliability of the geometric parameters depends on the similarity degree of the target leaf geometry and the leaf geometry in the database; the reliability of the flow field parameters depends on the similarity of typical dimensionless flow field parameters of the target blade with corresponding parameters in a database. And further, performing sensitivity analysis on the geometric parameters and the pneumatic parameters based on the database to determine the expression form of the loss model. And finally, solving coefficients in the loss model expression by using an optimization algorithm which is calculated into the credibility evaluation, so as to obtain the constructed high-precision impeller machinery loss model based on the credibility of the data.
In order to better understand the high-precision impeller machinery loss model construction method based on the data reliability provided by the embodiment of the application, the embodiment of the application is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method of constructing a loss model of a turbomachine based on data reliability in accordance with an embodiment of the present invention. As shown in fig. 1, the construction method of the impeller machinery loss model based on the data reliability comprises the following steps:
The SS1 evaluates the reliability of the impeller machine data in the existing impeller machine database to form an impeller machine database with the reliability of the data.
Specifically, the test data credibility or the simulation data credibility is determined according to the data source. The reliability of the test data depends on whether the test data is derived from a standard test bench, a measurement method, measurement accuracy, a test data processing method and the like; the credibility of the simulation data depends on the simulation precision of the numerical method, whether the numerical method has test verification or not and the calculation precision of a model selected by the numerical method; the geometric credibility and the credibility of the flow field parameters are then evaluated, and the measurement standards include, but are not limited to, an included angle cosine method, wherein the larger the cosine value is, the higher the credibility is.
It will be appreciated that whether the test data originates from a standard test bench has a crucial impact on the reliability of the test. The selected measuring method and measuring precision determine the precision of the test data to a certain extent. In addition, the processing method of the test data also introduces reliability differences.
It can be further understood that the difference of the blade configuration and the difference of the typical dimensionless flow field parameters can cause the difference of loss characteristics, and the blade profile similarity of the target blade and the database and the typical dimensionless flow field parameter similarity of the target blade and the database are evaluated successively, for example, an angle cosine method is used, and the larger the cosine value, the higher the similarity, the higher the credibility of the data.
And SS2, performing sensitivity analysis on related geometric parameters and pneumatic parameters of the impeller machine by using an impeller machine database which is formed in the step SS1 and is used for counting the reliability of data, and determining the expression form of the loss model.
Specifically, sensitivity of the loss characteristics of the impeller machinery to geometric parameters and aerodynamic parameters is studied by adopting a principal component analysis method (PRINCIPAL COMPONENT ANALYSIS, PCA), and parameters to be counted in each part of a loss model and the expression form of the loss model are determined by combining a physical mechanism generated by internal loss of the impeller machinery.
For example, the present example selects the airflow turning angle, the relative pitch, the installation angle, the leading edge radius, the trailing edge radius, the attack angle, the reynolds number and the mach number as parameters calculated by the blade profile loss model through PCA analysis, and determines the expression form of the blade profile loss model by combining the blade profile loss generation mechanism of the impeller machine and the classical loss model, as follows:
Yp＝Ki·KRe·Yb+Yte+YMa
Wherein a 1～a12 represents the coefficient to be solved; y p、Yb、Yte、YMa represents the blade profile loss, blade profile base loss, trailing edge loss and shock loss, respectively; k i、KRe represents an attack angle loss correction coefficient and a Reynolds number correction coefficient respectively; Δβ, β 1、β2、α2, i represent the airflow turning angle, the geometric inlet angle, the geometric outlet angle, the outlet airflow angle, and the angle of attack, respectively; s, c, d 1、d2 represent pitch, chord length, leading edge radius and trailing edge radius, respectively; ma 2 denotes the exit Mach number; re is the Reynolds number based on chord length and exit velocity.
And SS3 utilizes the impeller machinery database formed in the step SS1 and counted with the data reliability, and utilizes an optimization algorithm counted with the data reliability to solve the relevant experience coefficient in the loss model determined in the step SS2, so as to obtain a specific expression of the impeller machinery loss model.
Specifically, the impeller machinery database which is calculated in the data reliability and described in the step SS1 is combined with an optimization algorithm to obtain the loss model expression coefficient, and the high-precision impeller machinery loss model based on the data reliability is obtained.
For example, the present example uses a particle swarm Optimization algorithm (PARTICAL SWARM Optimization, PSO) to incorporate the data reliability into an objective function for evaluating the merits of candidate solutions, taking the leaf loss as an example, the objective function expression is as follows:
Wherein δY j represents the model-predicted deviation value of the blade profile loss coefficient of the j-th group of blade data; r j represents the credibility of the j-th group of blade data; n represents the total amount of blade data in the database.
Fig. 2 shows an example prediction effect of the credibility-based loss model construction method provided by the invention applied to the construction of the leaf type loss model, and by taking the relation between the attack angle loss Ki and the attack angle i as an example, the constructed model has better overlapping degree on the leaf data with high credibility.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention 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 invention.
Claims (4)
1. A method of constructing a predictive model of a loss of impeller machine, comprising the steps of:
And SS1, evaluating the reliability of the existing impeller machine data to form an impeller machine database for accounting the reliability of the data, wherein the evaluation standard of the reliability of the data consists of the following four parts:
(1) The reliability of the test data depends on whether the test data is derived from a standard test bed, a measurement method, measurement precision and a test data processing method;
(2) The credibility of the simulation data depends on the simulation precision of the numerical method, whether the numerical method has test verification or not and the calculation precision of a model selected by the numerical method;
(3) The credibility of the geometric parameters depends on the similarity degree of the target leaf geometry and the leaf geometry in the database;
(4) The credibility of the flow field parameters depends on the similarity degree of typical dimensionless flow field parameters of the target blade and corresponding parameters in a database;
in the evaluation standard of the data credibility, the weight of each credibility can be adjusted according to the actual situation;
SS2, performing sensitivity analysis on related geometric parameters and pneumatic parameters of the impeller machinery by utilizing an impeller machinery database which is formed in the step SS1 and is used for counting the data reliability, and determining parameters to be counted by each part of the loss model and expression forms of the loss model;
And SS3, solving the relevant empirical coefficients in the loss model expression determined in the step SS2 by utilizing the impeller machinery database with the calculated data reliability formed in the step SS1 and utilizing an optimization algorithm with the calculated data reliability to obtain a specific expression of an impeller machinery loss model, wherein the optimization algorithm with the calculated data reliability has the following objective function expression:
Wherein, Representing a model predicted deviation value of a loss coefficient of the j-th group of blade data; r j represents the credibility of the j-th group of blade data; n represents the total amount of blade data in the database;
The optimization algorithm for accounting the data reliability is a particle swarm optimization algorithm for accounting the data reliability, a genetic algorithm for accounting the data reliability or a steepest descent method for accounting the data reliability, and the unknown coefficients in the loss model are solved by adopting the optimization algorithm for accounting the data reliability, so that the established loss model is preferentially fitted to the data with higher reliability in the database, and the purpose of improving the prediction accuracy of the loss model is achieved.
2. The method according to claim 1, wherein the calculation formula in the evaluation criteria of the data reliability includes the following calculation method:
R, R 1、R2、R3、R4 respectively represents the total credibility of data, the credibility of test data, the credibility of simulation data, the credibility of geometry and the credibility of flow field parameters; q 1、q2、q3、q4 represents the weight of the reliability of each part in the total reliability of the data.
3. The method of claim 1, wherein the measure of geometric confidence and flow field parameter confidence comprises an angle cosine method, and the greater the cosine value, the higher the confidence, the following formula is calculated:
Wherein x 1k、x2k represents the geometric or aerodynamic parameters of the target blade and the blade in the database, respectively; n represents the number of parameters.
4. The method according to claim 1, wherein in step SS2, the influence of the blade loss is sensitively analyzed by using principal component analysis on geometrical and aerodynamic parameters including chord length, pitch, blade turning angle and inlet and outlet air flow angle, parameters to be considered in the loss model are selected, and finally the loss model expression form is determined.
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