CN112949166B - Gas turbine steady-state model coefficient determination method - Google Patents
Gas turbine steady-state model coefficient determination method Download PDFInfo
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- CN112949166B CN112949166B CN202110121599.4A CN202110121599A CN112949166B CN 112949166 B CN112949166 B CN 112949166B CN 202110121599 A CN202110121599 A CN 202110121599A CN 112949166 B CN112949166 B CN 112949166B
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Abstract
The application belongs to the technical field of gas turbine steady-state model coefficient determination, and particularly relates to a gas turbine steady-state model coefficient determination method, which comprises the following steps: based on the set steady-state model coefficient population of the gas turbine, correspondingly calculating to obtain section performance parameters; based on the calculated section performance parameters, correspondingly calculating the adaptability between the calculated section performance parameters and the actual section performance parameters; the gas turbine steady-state model coefficient population corresponding to the poor fitness is eliminated according to the selection probability, the rest gas turbine steady-state model coefficient populations are crossed and mutated to generate gas turbine steady-state model coefficient sub-populations, and the gas turbine steady-state model coefficient populations are updated and set by the sub-populations until the genetic algebra is reached; and selecting the steady-state model coefficients of the gas turbine in the steady-state model coefficient population of the rest gas turbines.
Description
Technical Field
The application belongs to the technical field of gas turbine steady-state model coefficient determination, and particularly relates to a gas turbine steady-state model coefficient determination method.
Background
The gas turbine is an important power component, steady-state simulation is carried out on the gas turbine, the section performance parameters of the gas turbine can be conveniently obtained, and the gas turbine has important guiding significance for design, optimization, debugging and test run of the gas turbine.
The steady-state simulation is carried out on the gas turbine, the accurate setting of the parameters of the gas turbine in the steady-state model, namely the accurate setting of the coefficients of the whole machine or parts of the gas turbine in the steady-state model, is an important guarantee for effective simulation steady-state simulation results, and when the steady-state simulation is carried out on the gas turbine, the parameters of the gas turbine are mostly calculated in a mode of manually adjusting the parameters.
The present application has been made in view of the existence of the above-mentioned technical drawbacks.
It should be noted that the above disclosure of the background art is only for aiding in understanding the inventive concept and technical solution of the present invention, which is not necessarily prior art to the present application, and should not be used for evaluating the novelty and the creativity of the present application in the case where no clear evidence indicates that the above content has been disclosed at the filing date of the present application.
Disclosure of Invention
It is an object of the present application to provide a method for determining steady state model coefficients of a gas turbine that overcomes or mitigates at least one of the technical drawbacks of the known art.
The technical scheme of the application is as follows:
a method for determining steady-state model coefficients of a gas turbine, comprising:
based on the set steady-state model coefficient population of the gas turbine, correspondingly calculating to obtain section performance parameters;
based on the calculated section performance parameters, correspondingly calculating the adaptability between the calculated section performance parameters and the actual section performance parameters;
the gas turbine steady-state model coefficient population corresponding to the poor fitness is eliminated according to the selection probability, the rest gas turbine steady-state model coefficient populations are crossed and mutated to generate gas turbine steady-state model coefficient sub-populations, and the gas turbine steady-state model coefficient populations are updated and set by the sub-populations until the genetic algebra is reached;
and selecting the steady-state model coefficients of the gas turbine in the steady-state model coefficient population of the rest gas turbines.
According to at least one embodiment of the present application, in the method for determining a steady-state model coefficient of a gas turbine, the steady-state model coefficient of the gas turbine in the steady-state model coefficient population of the gas turbine includes a converted flow rate of a gas turbine compressor, turbine efficiency, and bleed air amount of a high-pressure turbine.
According to at least one embodiment of the present application, in the above method for determining a steady-state model coefficient of a gas turbine, the section performance parameters are a rotational speed, a total temperature, a total pressure, and a power of the gas turbine.
According to at least one embodiment of the present application, in the above method for determining a steady-state model coefficient of a gas turbine, the fitness between the calculated section performance parameter and the actual section performance parameter is correspondingly calculated, specifically, the fitness is calculated according to the following formula:
wherein,
j is the adaptability between the calculated section performance parameters and the actual section performance parameters;
n is a section performance parameter;
e j and calculating the deviation of the j-th section performance parameter and the actual section performance parameter.
According to at least one embodiment of the present application, in the method for determining a steady-state model coefficient of a gas turbine described above, the selecting a steady-state model coefficient of the gas turbine in the remaining gas turbine steady-state model coefficient population specifically includes:
and selecting the gas turbine steady-state model coefficient in the gas turbine steady-state model coefficient population corresponding to the optimal fitness.
Drawings
FIG. 1 is a flow chart of a method for determining steady state model coefficients of a gas turbine provided by an embodiment of the present application;
fig. 2 is a schematic diagram provided in an embodiment of the present application.
For the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions; further, the drawings are for illustrative purposes, wherein the terms describing the positional relationship are limited to the illustrative description only and are not to be construed as limiting the present patent.
Detailed Description
In order to make the technical solution of the present application and the advantages thereof more apparent, the technical solution of the present application will be more fully described in detail below with reference to the accompanying drawings, it being understood that the specific embodiments described herein are only some of the embodiments of the present application, which are for explanation of the present application, not for limitation of the present application. It should be noted that, for convenience of description, only the portion relevant to the present application is shown in the drawings, and other relevant portions may refer to a general design, and without conflict, the embodiments and technical features in the embodiments may be combined with each other to obtain new embodiments.
Furthermore, unless defined otherwise, technical or scientific terms used in the description of this application should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs. The terms "upper," "lower," "left," "right," "center," "vertical," "horizontal," "inner," "outer," and the like as used in this description are merely used to indicate relative directions or positional relationships, and do not imply that a device or element must have a particular orientation, be configured and operated in a particular orientation, and that the relative positional relationships may be changed when the absolute position of the object being described is changed, and thus should not be construed as limiting the present application. The terms "first," "second," "third," and the like, as used in the description herein, are used for descriptive purposes only and are not to be construed as indicating or implying any particular importance to the various components. The use of the terms "a," "an," or "the" and similar referents in the description of the invention are not to be construed as limited in number to the precise location of at least one. As used in this description, the terms "comprises," "comprising," or the like are intended to cover an element or article that appears before the term and that is listed after the term and its equivalents, without excluding other elements or articles.
Furthermore, unless specifically stated and limited otherwise, the terms "mounted," "connected," and the like in the description herein are to be construed broadly and refer to either a fixed connection, a removable connection, or an integral connection, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can also be communicated with the inside of two elements, and the specific meaning of the two elements can be understood by a person skilled in the art according to specific situations.
The present application is described in further detail below with reference to fig. 1-2.
A method for determining steady-state model coefficients of a gas turbine, comprising:
based on the set steady-state model coefficient population of the gas turbine, correspondingly calculating to obtain section performance parameters;
based on the calculated section performance parameters, correspondingly calculating the adaptability between the calculated section performance parameters and the actual section performance parameters;
the gas turbine steady-state model coefficient population corresponding to the poor fitness is eliminated according to the selection probability, the rest gas turbine steady-state model coefficient populations are crossed and mutated to generate gas turbine steady-state model coefficient sub-populations, and the gas turbine steady-state model coefficient populations are updated and set by the sub-populations until the genetic algebra is reached;
and selecting the steady-state model coefficients of the gas turbine in the steady-state model coefficient population of the rest gas turbines.
For the method for determining the steady-state model coefficient of the gas turbine disclosed in the above embodiment, those skilled in the art can understand that the method is implemented based on a genetic algorithm, and through multiple iterations, a gas turbine steady-state model coefficient population conforming to the operation of the gas turbine is quickly searched, and the gas turbine coefficient is selected to perform steady-state simulation on the gas turbine, so that the method has higher precision.
For the method for determining the steady-state model coefficient of the gas turbine disclosed in the above embodiment, it can be further understood by those skilled in the art that the method can be implemented based on a MATLAB platform, and the adaptive function definition is completed on a SIMULINK platform, so that the method has higher efficiency.
For the method for determining the steady-state model coefficient of the gas turbine disclosed in the above embodiment, it can be further understood by those skilled in the art that the number of the steady-state model coefficient populations of the gas turbine, the size of the selection probability, the size of the crossover probability, the size of the mutation probability, and the number of the genetic algebra can be determined by those skilled in the relevant arts according to specific practical application when the present application is applied.
In some alternative embodiments, in the method for determining the steady-state model coefficient of the gas turbine, the steady-state model coefficient of the gas turbine in the steady-state model coefficient group of the gas turbine includes a converted flow rate of a gas turbine compressor, turbine efficiency and bleed air amount of a high-pressure turbine.
In some alternative embodiments, in the method for determining steady-state model coefficients of a gas turbine, the section performance parameters are rotation speed, total temperature, total pressure and power of the gas turbine.
In some optional embodiments, in the method for determining a steady-state model coefficient of a gas turbine, the fitness between the calculated section performance parameter and the actual section performance parameter is correspondingly calculated based on the calculated section performance parameter, specifically, the fitness is calculated according to the following formula:
wherein,
j is the adaptability between the calculated section performance parameters and the actual section performance parameters;
n is a section performance parameter;
e j and calculating the deviation of the j-th section performance parameter and the actual section performance parameter.
For the method for determining the steady-state model coefficient of the gas turbine disclosed in the above embodiment, it will be further understood by those skilled in the art that the fitness J between the calculated section performance parameter and the actual section performance parameter represents that the steady-state simulation of the gas turbine is performed by using the steady-state model coefficient of the gas turbine in the steady-state model coefficient population of the gas turbine, and the smaller the value thereof, the higher the fitness, and the higher the validity of the obtained result is for the steady-state simulation of the gas turbine by using the steady-state model coefficient of the gas turbine in the corresponding steady-state model coefficient population of the gas turbine.
In some optional embodiments, in the method for determining a steady-state model coefficient of a gas turbine described above, the selecting a steady-state model coefficient of the gas turbine in the remaining gas turbine steady-state model coefficient population specifically includes:
and selecting the gas turbine steady-state model coefficient in the gas turbine steady-state model coefficient population corresponding to the optimal fitness.
In a specific embodiment, the process of determining the coefficient in the steady-state model of the gas turbine according to the method for determining the coefficient of the steady-state model of the gas turbine is shown in fig. 2.
In the description, each embodiment is described in a progressive manner, and each embodiment is mainly described by the differences from other embodiments, so that the same similar parts among the embodiments are mutually referred.
Having thus described the technical aspects of the present application with reference to the preferred embodiments illustrated in the accompanying drawings, it should be understood by those skilled in the art that the scope of the present application is not limited to the specific embodiments, and those skilled in the art may make equivalent changes or substitutions to the relevant technical features without departing from the principles of the present application, and those changes or substitutions will now fall within the scope of the present application.
Claims (1)
1. A method for determining steady-state model coefficients of a gas turbine, comprising:
based on the set steady-state model coefficient population of the gas turbine, correspondingly calculating to obtain section performance parameters;
based on the calculated section performance parameters, correspondingly calculating the adaptability between the calculated section performance parameters and the actual section performance parameters;
the gas turbine steady-state model coefficient population corresponding to the poor fitness is eliminated according to the selection probability, the rest gas turbine steady-state model coefficient populations are crossed and mutated to generate gas turbine steady-state model coefficient sub-populations, and the gas turbine steady-state model coefficient populations are updated and set by the sub-populations until the genetic algebra is reached;
selecting the steady-state model coefficients of the gas turbine in the steady-state model coefficient population of the rest gas turbines;
the gas turbine steady-state model coefficients in the gas turbine steady-state model coefficient population comprise gas turbine compressor conversion flow, turbine efficiency and high-pressure turbine bleed air quantity;
the section performance parameters are the rotating speed, total temperature, total pressure and power of the gas turbine;
the adaptability between the corresponding calculated section performance parameters and the actual section performance parameters is calculated according to the following formula:
wherein,
j is the adaptability between the calculated section performance parameters and the actual section performance parameters;
n is a section performance parameter;
e j the j-th section performance parameter obtained by calculation and the deviation of the actual section performance parameter are calculated;
the gas turbine steady-state model coefficients in the rest gas turbine steady-state model coefficient population are selected, and specifically:
and selecting the gas turbine steady-state model coefficient in the gas turbine steady-state model coefficient population corresponding to the optimal fitness.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126902A (en) * | 2016-06-21 | 2016-11-16 | 上海电机学院 | Gas turbine component characteristic line modification method based on particle swarm optimization algorithm identification |
| CN107885891A (en) * | 2016-09-29 | 2018-04-06 | 中国航发商用航空发动机有限责任公司 | A kind of aero-engine overall performance optimization method |
| CN108416086A (en) * | 2018-01-25 | 2018-08-17 | 大连理工大学 | A kind of aero-engine whole envelope model adaptation modification method based on deep learning algorithm |
| CN109992880A (en) * | 2019-03-29 | 2019-07-09 | 中国石油大学(北京) | A kind of gas turbine performance adaptation method, equipment and system |
| CN110298060A (en) * | 2019-04-30 | 2019-10-01 | 哈尔滨工程大学 | It is a kind of based on gas turbine Stute space model identification method cold between improving expert inquiry method |
| CN112131670A (en) * | 2020-09-28 | 2020-12-25 | 大连理工大学 | Aero-engine model iterative algorithm based on hybrid adaptive differential evolution |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11144683B2 (en) * | 2016-12-06 | 2021-10-12 | General Electric Company | Real-time adaptation of system high fidelity model in feature space |
| FR3070761B1 (en) * | 2017-09-07 | 2019-08-23 | Safran Aircraft Engines | METHOD AND DEVICE FOR MEASURING A PUSH MARGIN OF A TURBOMACHINE |
-
2021
- 2021-01-28 CN CN202110121599.4A patent/CN112949166B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106126902A (en) * | 2016-06-21 | 2016-11-16 | 上海电机学院 | Gas turbine component characteristic line modification method based on particle swarm optimization algorithm identification |
| CN107885891A (en) * | 2016-09-29 | 2018-04-06 | 中国航发商用航空发动机有限责任公司 | A kind of aero-engine overall performance optimization method |
| CN108416086A (en) * | 2018-01-25 | 2018-08-17 | 大连理工大学 | A kind of aero-engine whole envelope model adaptation modification method based on deep learning algorithm |
| CN109992880A (en) * | 2019-03-29 | 2019-07-09 | 中国石油大学(北京) | A kind of gas turbine performance adaptation method, equipment and system |
| CN110298060A (en) * | 2019-04-30 | 2019-10-01 | 哈尔滨工程大学 | It is a kind of based on gas turbine Stute space model identification method cold between improving expert inquiry method |
| CN112131670A (en) * | 2020-09-28 | 2020-12-25 | 大连理工大学 | Aero-engine model iterative algorithm based on hybrid adaptive differential evolution |
Non-Patent Citations (2)
| Title |
|---|
| 一种基于粒子群优化算法的燃气轮机自适应热力计算方法;应雨龙;李淑英;;燃气轮机技术(第04期);54-60 * |
| 基于数据驱动的全工况下燃气轮机基准值确定;王仲;顾煜炯;韩旭东;朱俊杰;黄元平;;自动化仪表(第04期);9-12, 17 * |
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