CN113779706A - Impeller mechanical loss model construction method based on data reliability - Google Patents
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Abstract
本发明涉及叶轮机械气动热力学领域，尤其涉及一种基于数据可信度的叶轮机械损失模型构建方法。对叶轮机械数据库中的数据可信度进行评估，获得计入数据可信度的叶轮机械数据库；对叶片几何参数、气动参数进行敏感性分析，建立叶轮机械损失模型表达式形式；借助计入数据可信度的优化算法对损失模型表达式系数进行求解得到基于数据可信度的叶轮机械损失模型。将试验数据可信度、仿真数据可信度、几何参数可信度和流场参数可信度有效纳入叶轮机械损失模型的建立，基于有限的数据库为目标叶型建立定制化损失模型，解决了数据精度不同、叶片构型不同、典型流动参数不同引起的损失模型预测差异较大的难题。
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 reliability. Evaluate the reliability of the data in the turbomachinery database, and obtain the turbomachinery database included in the data reliability; conduct sensitivity analysis on the blade geometric parameters and aerodynamic parameters, and establish the expression form of the turbomachinery loss model; with the help of the included data The reliability optimization algorithm solves the loss model expression coefficient to obtain the impeller machinery loss model based on the data reliability. The reliability of the test data, the reliability of the simulation data, the reliability of the geometric parameters and the reliability of the flow field parameters are effectively incorporated into the establishment of the impeller mechanical loss model. Different data precisions, different blade configurations, and different typical flow parameters cause large differences in loss model predictions.
Description
Technical Field
The invention relates to the field of impeller machinery aerodynamic thermodynamics, relates to an impeller machinery loss model construction method, and particularly relates to an impeller machinery loss model construction method based on data reliability.
Background
Turbomachines are widely used in the energy and power fields, such as aircraft engines, ground and marine gas turbines, steam turbines, and compressed air energy storage systems, among others. The impeller mechanical pneumatic design is a process of gradual progressive and repeated optimization from low dimension to high dimension, the low dimension design is the basis of the high dimension design, and in the low dimension design stage, accurate loss prediction can lay a good foundation for the high dimension design, and the design period is effectively shortened.
The aerodynamic performance of the impeller machinery is affected by various geometric and aerodynamic parameters, the loss model presents strong nonlinear characteristics under the influence of multiple parameters, the accuracy of the loss model depends on a database used for constructing the model to a large extent, the forming source of the loss model is complex, the loss model mainly comprises experimental or numerical simulation data disclosed by documents, and the data have different experimental errors, different numerical simulation accuracies, different blade configurations and the like, so that the prediction results obtained by adopting different loss models for the same blade type have large difference. That is, the prediction accuracy of the prediction model is lost due to different database accuracies and differences between data distribution and the target leaf profile during model construction.
However, systematic evaluation aiming at the effectiveness of data constructed by a loss model does not exist at present, and in the actual design process, a trial and error method is mostly adopted, and if the test result of a certain leaf type under a certain flow field is consistent with the prediction of a certain loss model, the loss model is considered to be applicable, and depends on experience relatively, and the loss model is not effectively improved from the root. How to effectively incorporate the existing test and simulation data into the establishment of the loss model to improve the prediction accuracy of the loss model is short of corresponding research methods.
Disclosure of Invention
In view of the above, the present invention provides a method for constructing a turbomachine loss model based on data reliability, so as to at least partially solve at least one of the above technical problems. The method comprises the steps of evaluating the data reliability in the impeller mechanical database to obtain the impeller mechanical database with data reliability; carrying out sensitivity analysis on geometric parameters and pneumatic parameters of the blades, and establishing an expression form of a mechanical loss model of the impeller; and solving the loss model expression coefficient by means of an optimization algorithm with data reliability, so as to obtain the impeller mechanical loss model based on the data reliability. The reliability of test data, the reliability of simulation data, the reliability of geometric parameters and the reliability of flow field parameters are effectively brought into the establishment of the mechanical loss model of the impeller, and a customized loss model is established for a target blade profile based on a limited database, so that the problem of large prediction difference of the loss model caused by 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 turbomachine loss model based on data reliability is characterized by at least comprising the following steps:
SS1, evaluating the reliability of blade data in the existing impeller mechanical database to form an impeller mechanical database with data reliability included;
SS2, carrying out sensitivity analysis on relevant geometric parameters and pneumatic parameters of the impeller machine by using the impeller machine database which is formed in the step SS1 and is used for counting the data credibility, and determining the parameters to be counted by each part of the loss model and the expression form of the loss model;
and SS3, solving the relevant empirical coefficients in the loss model determined in the step SS2 by using the impeller mechanical database which is formed in the step SS1 and takes the data reliability into account and an optimization algorithm of the data reliability, so as to obtain a specific expression of the impeller mechanical loss model.
Preferably, in step SS1, the credibility assessment consists of four parts: the reliability of the test data is determined by whether the test data is from a standard test bench, 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, whether the numerical method has test verification or not and the calculation precision of the 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 degree of the typical dimensionless flow field parameters of the target blade and the corresponding parameters in the database.
Further, the calculation formula of the data reliability includes, but is not limited to, the following calculation methods:
R＝q1·R1+q2·R2+q3·R3+q4·R4
wherein, R, R1、R2、R3、R4Respectively representing the overall reliability of the data, the reliability of the test data, the reliability of the simulation data, the geometric reliability and the reliability of the flow field parameters; q. q.s1、q2、q3、q4And respectively representing the weight of each part of credibility in the overall credibility of the data.
Further, the reliability of the test data depends on whether the test data is from a standard test bench, 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, whether the numerical method has test verification or not and the calculation precision of the 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 degree of the typical dimensionless flow field parameters of the target blade and the corresponding parameters in the database.
Further, the measurement criteria of the reliability of the geometric parameters and the reliability of the flow field parameters include, but are not limited to, an included angle cosine method, and the larger the cosine value, the higher the reliability, the calculation formula is as follows:
wherein x is1k、x2kRespectively representing the geometric or aerodynamic parameters of the target blade and the blades in the database; n represents the number of parameters.
Preferably, in step SS2, geometric and pneumatic parameters such as: and carrying out sensitivity analysis on the influence of chord length, grid pitch, blade turning angle, inlet and outlet airflow angle and the like on 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, the data reliability described in S1 is included in an objective function for evaluating the solution of the optimization algorithm, where the objective function is expressed as follows:
wherein, δ YjRepresenting a loss coefficient prediction deviation value of the model to the jth group of blade data; rjRepresenting the credibility of the jth group of leaf data; n represents the total amount of blade data in the database.
Further, optimization algorithms that account for data confidence include, but are not limited to, particle swarm optimization algorithms that account for data confidence, genetic algorithms that account for data confidence, the steepest descent method that accounts for data confidence, and the like. The unknown coefficients in the loss model are solved by adopting the optimization algorithm for the reliability of the input 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 precision of the loss model is fulfilled.
Based on the technical scheme, compared with the prior art, the impeller mechanical loss model construction method based on the data reliability has at least one of the following beneficial effects:
the method applies the reliability of the test data, the reliability of the simulation data, the reliability of the geometry and the reliability of the flow field parameters as the evaluation standard of the data reliability, 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 work flow diagram of a method for constructing a data reliability-based turbomachine loss model according to the present invention;
fig. 2 is a schematic diagram of an expected model prediction effect of the method for constructing the turbomachinery loss model based on data reliability.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. Based on the method and the embodiment proposed by the present invention, all other embodiments obtained by using the method proposed by the present invention will fall within the protection scope of the present invention without any creative work of the ordinary skilled person in the art. 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 shall have 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 reliability combines the characteristics of impeller machinery and a reliability 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 accuracy of the loss model is obviously improved.
Specifically, the method for constructing the impeller mechanical loss model based on the data reliability is used for predicting the loss characteristics of the impeller machinery. Firstly, evaluating the reliability of data in a database, wherein the evaluation criterion comprises four parts: reliability of test data, reliability of simulation data, reliability of geometric parameters and reliability of flow field parameters. The reliability of the test data depends on whether the test data is from a standard test bench, 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, whether the numerical method has test verification or not and the calculation precision of the 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 degree of the typical dimensionless flow field parameters of the target blade and the corresponding parameters in the database. And then carrying out 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 including credibility evaluation to obtain the constructed high-precision impeller mechanical loss model based on the data credibility.
In order to better understand the method for constructing the high-precision impeller mechanical loss model based on the data reliability provided by the embodiment of the application, the embodiment of the invention is further described below with reference to the attached drawings.
FIG. 1 is a flow chart of the operation of a method for constructing a turbomachine loss model based on data reliability according to an embodiment of the invention. As shown in fig. 1, a method for constructing a turbomachine loss model based on data reliability includes the following steps:
and the SS1 evaluates the reliability of the impeller mechanical data in the existing impeller mechanical database to form an impeller mechanical database with the reliability of the data.
Specifically, the reliability of the test data or the reliability of the simulation data is determined according to the data source. 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, whether the numerical method has test verification or not and the calculation precision of the model selected by the numerical method; and then evaluating the geometrical reliability and the flow field parameter reliability, wherein the measuring standard comprises but is not limited to an included angle cosine method, and the reliability is higher when the cosine value is larger.
It will be appreciated that whether the test data originates from a standard test stand 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 of the test data introduces reliability differences.
It can also be understood that the difference of the blade configuration and the difference of the typical dimensionless flow field parameters both cause the difference of the loss characteristics, and the similarity of the blade profile of the target blade and the database and the similarity of the typical dimensionless flow field parameters of the target blade and the database are evaluated successively, for example, by using an included angle cosine method, the larger the cosine value is, the higher the similarity is, and the higher the reliability of the data is.
SS2 utilizes the database of turbomachinery with data reliability formed in step SS1 to perform sensitivity analysis on the relevant geometric parameters and aerodynamic parameters of turbomachinery and determine the expression form of loss model.
Specifically, a Principal Component Analysis (PCA) is adopted to research the sensitivity of the loss characteristics of the impeller machinery to geometric parameters and aerodynamic parameters, and parameters required to be included in each part of the loss model and the expression form of the loss model are determined by combining a physical mechanism generated by the internal loss of the impeller machinery.
For example, in the present embodiment, the turning angle of the airflow, the relative grid pitch, the installation angle, the radius of the leading edge, the radius of the trailing edge, the attack angle, the reynolds number, and the mach number are selected as parameters of the blade loss model through PCA analysis, and the expression form of the blade loss model is determined by combining the blade loss generation mechanism of the turbomachine and the classical loss model, as follows:
Yp＝Ki·KRe·Yb+Yte+YMa
wherein, a1～a12Representing the coefficients to be solved; y isp、Yb、Yte、YMaRespectively representing blade profile loss, blade profile basic loss, trailing edge loss and shock wave loss; ki、KReRespectively representing an attack angle loss correction coefficient and a Reynolds number correction coefficient; delta beta, beta1、β2、α2And i respectively represents an airflow turning angle, a geometric inlet angle, a geometric outlet angle, an outlet airflow angle and an attack angle; s, c, d1、d2Respectively representing the grid pitch, the chord length, the radius of a front edge and the radius of a tail edge; ma2Represents the exit mach number; re is the reynolds number based on chord length and exit velocity.
And SS3, solving the relevant empirical coefficients in the loss model determined in the step SS2 by using the turbomachinery database which is formed in the step SS1 and includes the reliability of the data, and obtaining a specific expression of the turbomachinery loss model.
Specifically, the impeller mechanical database with the data reliability included in the step SS1 is combined with an optimization algorithm to solve to obtain a loss model expression coefficient, so as to obtain a high-precision impeller mechanical loss model based on the data reliability.
For example, in the present embodiment, a Particle Swarm Optimization (PSO) algorithm is used, and the data reliability is incorporated into an objective function for evaluating the quality of a candidate solution, where the leaf loss is taken as an example, the objective function expression is as follows:
wherein, δ YjThe representation model predicts the deviation value of the blade profile loss coefficient of the jth group of blade data; rjRepresenting the credibility of the jth group of leaf data; n represents the total amount of blade data in the database.
Fig. 2 shows an example prediction effect of the method for constructing the loss model based on the credibility, which is applied to the construction of the leaf-shaped loss model, and by taking the relationship between the angle of attack loss Ki and the angle of attack i as an example, it can be seen that the constructed model has a better overlap ratio on the leaf data with high credibility.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A method for constructing a prediction model of mechanical loss of an impeller is characterized by comprising the following steps:
SS1, evaluating the reliability of the existing impeller mechanical data to form an impeller mechanical database with the reliability of the data;
SS2, carrying out sensitivity analysis on relevant geometric parameters and pneumatic parameters of the impeller machine by using the impeller machine database which is formed in the step SS1 and is used for counting the data credibility, and determining the parameters to be counted by each part of the loss model and the expression form of the loss model;
and SS3, solving the related empirical coefficients in the loss model expression determined in the step SS2 by using the impeller mechanical database which is formed in the step SS1 and takes the data reliability into account and an optimization algorithm of the data reliability, so as to obtain a specific expression of the impeller mechanical loss model.
2. The method according to claim 1, wherein in step SS1, the evaluation criterion of data reliability is composed of the following four parts: (1) the reliability of the test data depends on whether the test data is from a standard test bench, a measurement method, measurement precision, a test data processing method and the like; (2) the reliability 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 the model selected by the numerical method; (3) the reliability of the geometric parameters depends on the similarity degree of the target leaf geometry and the leaf geometry in the database; (4) and the reliability of the flow field parameters depends on the similarity degree of the typical dimensionless flow field parameters of the target blade and the corresponding parameters in the database.
3. The method of claim 2, wherein in the evaluation criterion of the data credibility, the weight of each credibility can be adjusted according to the actual situation, and the calculation formula includes but is not limited to the following calculation methods:
R＝q1·R1+q2·R2+q3·R3+q4·R4
wherein, R, R1、R2、R3、R4Respectively representing the overall reliability of the data, the reliability of the test data, the reliability of the simulation data, the geometric reliability and the reliability of the flow field parameters; q. q.s1、q2、q3、q4And respectively representing the weight of each part of credibility in the overall credibility of the data.
4. The method of claim 2, wherein the geometric confidence measure and the flow field parameter confidence measure includes, but is not limited to, an angle cosine method, and the confidence measure is higher as the cosine value is larger, and the calculation formula is as follows:
wherein x is1k、x2kRespectively representing the geometric or aerodynamic parameters of the target blade and the blades in the database; n represents the number of parameters.
5. The method according to claim 1, wherein in step SS2, geometric and pneumatic parameters such as: and carrying out sensitivity analysis on the influence of chord length, grid pitch, blade turning angle, inlet and outlet airflow angle and the like on blade loss, selecting parameters to be considered in the loss model, and finally determining the expression form of the loss model.
6. The method according to claim 1, wherein in step SS3, the objective function expression of the optimization algorithm for calculating the confidence level of the data is as follows:
wherein, δ YjRepresenting a loss coefficient prediction deviation value of the model to the jth group of blade data; rjRepresenting the credibility of the jth group of leaf data; n represents the total amount of blade data in the database.
7. The method of the preceding claims, wherein the optimization algorithms that account for data confidence include, but are not limited to, particle swarm optimization algorithms that account for data confidence, genetic algorithms that account for data confidence, steepest descent methods that account for data confidence, and the like. The unknown coefficients in the loss model are solved by adopting the optimization algorithm for the reliability of the input 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 precision of the loss model is fulfilled.
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