CN113537804A - Turbine complex flow simulation evaluation method based on Reynolds average turbulence model - Google Patents
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Abstract
A turbine complex flow simulation evaluation method based on a Reynolds average turbulence model belongs to the field of fluid simulation software for calculating the internal flow of an aircraft engine. The method is characterized in that a mean N-S equation during solving of a turbulence model is applied to research the complex flow in the impeller machinery, plays an important role in improving the performance of the impeller for the pneumatic design of the impeller, and provides an evaluation method of the simulation precision of the turbulence model of typical impeller flow; and predicting and judging the accuracy of different turbulence models in the typical flow of the turbine by using the conventional CFD simulation software.
Description
Technical Field
The invention belongs to the field of fluid simulation software for calculation of flow in an aircraft engine, and particularly relates to a complex flow simulation evaluation method for a turbine based on a Reynolds average turbulence model (RANS).
Background
At present, a turbulence simulation method suitable for aircraft engine engineering design is also an RANS method, and common methods comprise Sparar-Allmoras (SA), k-epsilon (k-epsilon), k-omega (k-omega), Shear Stress Transport (SST), Reynolds Stress (RSM) and the like, so that the development of the applicability research of the RANS turbulence method based on the characteristics of the internal flow of the aircraft engine has important significance for improving the CFD simulation precision; in addition, as new turbulence models develop, systematic testing is required to be carried out for the applicability research of some new turbulence models in the engine. In addition, since there is a significant flow characteristic difference between laminar flow and turbulent flow, the prediction of transition phenomenon plays a very important role in engine design. And complicated three-dimensional flow exists in the turbine blade cascade channel, and phenomena such as transition, secondary flow, shock wave and boundary layer mutual interference can occur. The shock wave induces the separation and transition of the boundary layer, and the heat exchange coefficient can be greatly changed. Therefore, the RANS turbulence and turbulence transition based on the method have important significance for the flow prediction of the aircraft engine turbine.
Disclosure of Invention
The invention aims to provide a complex flow simulation evaluation method of a turbine based on a Reynolds average turbulence model.
The invention researches the complex flow in the impeller machinery by applying the mean N-S equation when solving the turbulence model, plays an important role in improving the performance of the impeller for the pneumatic design of the impeller, and provides an evaluation method of the simulation precision of the turbulence model of the typical impeller flow, which comprises the steps of combing the existing domestic and foreign test databases based on the algorithm of machine learning, and analyzing the precision of the existing turbulence model; the accuracy of different turbulence models in typical turbine flows was predicted and judged using existing commercial CFD simulation software.
The invention comprises the following steps:
1) combing the existing domestic and foreign test database based on the algorithm of machine learning, and analyzing the accuracy of the existing turbulence model;
2) predicting the precision of different turbulence models in the typical flow of the turbine by using commercial CFD simulation software;
3) and comparing the prediction result with the precision of the international simulation precision database for judgment.
In step 1), the specific steps of combing the existing domestic and foreign test databases and analyzing the accuracy of the existing turbulence model may be:
(1) combing the geometric, flow parameters, test and simulation results of the domestic and foreign turbines, classifying the data and establishing a database. And obtaining typical simulated precision data by using a machine learning algorithm, learning data such as sizes of different working conditions and examples, test and simulation results and the like, and obtaining a turbulence simulation precision database.
(2) The existing domestic and foreign test database is combed based on the algorithm of machine learning, the accuracy of the existing turbulence model is analyzed, and a problem description library and a parameter library are formed.
In step 2), the accuracy of different turbulence models in the typical flow of the turbine is predicted by using commercial CFD simulation software, and the specific steps can be as follows: inputting different turbulence models or correction models, importing commercial CFD simulation software, performing typical flow simulation analysis on the different turbulence models or correction models, and performing comparative analysis mainly by calculating typical parameters and parameter precision obtained by machine learning.
In the step 3), the prediction result is compared with the precision of an international simulation precision database for judgment, key parameters including impeller efficiency and total pressure ratio are judged, and a precision and usability evaluation report is formed.
Compared with the prior art, the invention has the following outstanding advantages:
1) firstly, combing the existing domestic and foreign test database based on the algorithm of machine learning, and analyzing the accuracy of the existing turbulence model;
2) and predicting and judging the accuracy of different turbulence models in the typical flow of the turbine by using commercial CFD simulation software, so that the evaluation level of the prediction accuracy of the turbulence models is improved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention;
FIG. 2 is a multi-block structural grid of Rotor37 transonic flow computation;
FIG. 3 is a close-up view of the Rotor37 transonic flow computational grid. Wherein a is a tip intermittent grid (red region) near the leading edge; and b is a cross-section blade tip intermittent grid diagram.
Detailed Description
The following examples will further illustrate the present invention with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present invention includes the steps of:
1. the method comprises the steps of combing the geometric, flow parameters, test and simulation results of a turbine with blade tip intermittent flow of a Rotor37 transonic axial compressor at home and abroad, carrying out data classification, and establishing a database. Obtaining typical simulated precision data by using a machine learning algorithm, learning data such as sizes of different working conditions and examples, test and simulation results and the like, and obtaining a turbulence simulation precision database;
2. combing the existing domestic and foreign test database based on the algorithm of machine learning, and analyzing the accuracy of the existing turbulence model to form a problem description library and a parameter library;
3. inputting different turbulence models including an SA model, an SST model, a k-omega model, a k-epsilon model and an RSM model or a correction model, introducing commercial CFD simulation software, carrying out typical flow simulation analysis on intermittent flow with a blade tip of a Rotor37 transonic axial compressor under the conditions of different turbulence models or correction models, and mainly carrying out comparative analysis by calculating typical parameters and parameter precision obtained by machine learning. FIG. 2 is a three-dimensional computational domain and a multi-block structural grid for transonic flow numerical simulation of Rotor37, and FIG. 3 is a partial enlarged view of a transonic flow computational grid of Rotor 37;
4. and judging key parameters including impeller efficiency and total pressure ratio, and forming an accuracy and usability evaluation report.
Table 1 shows the evaluation results of the turbulence model evaluation method of the present invention.
TABLE 1
| Turbulence model | SA | SST | k-ω | k-ε | RSM | Correction model | Database with a plurality of databases |
| Impeller efficiency (%) | 85.1 | 85.5 | 85.4 | 85.5 | 86.9 | 85.6 | 87.3 |
| Error (%) | 2.52 | 2.06 | 2.18 | 2.06 | 0.46 | 1.9 | |
| Total pressure ratio | 2.068 | 2.003 | 2.067 | 2.067 | 2.081 | 2.067 | 2.083 |
| Error (%) | 0.72 | 3.84 | 0.77 | 0.77 | 0.09 | 0.7 |
The NASA rotator 37 serves as a blind test algorithm that many researchers use as an algorithm to check the accuracy of the CFD program and also help to recognize the internal flow of the transonic compressor. Inputting a correction model, judging turbulence key parameters to obtain typical flow problem judgment, verifying a turbulence model by adopting transonic axial compressor blade tip intermittent flow, obtaining performance parameters of impeller efficiency and total pressure ratio through calculation, checking test values and other different turbulence model results by inputting examples and the performance parameters, finding that the accuracy of the correction model is improved to a certain extent relative to S-A, k-epsilon, k-omega and SST, and showing that the turbulence models can be replaced by transonic axial compressor blade tip intermittent flow.
The invention forms a turbulence model evaluation method, and has important significance for developing and evaluating turbulence models and related typical flow calculation.
Claims (4)
1. The turbine complex flow simulation evaluation method based on the Reynolds average turbulence model is characterized by comprising the following steps of:
1) combing the existing domestic and foreign test database based on the algorithm of machine learning, and analyzing the accuracy of the existing turbulence model;
2) predicting the precision of different turbulence models in the typical flow of the turbine by using commercial CFD simulation software;
3) and comparing the prediction result with the precision of the international simulation precision database for judgment.
2. The Reynolds average turbulence model-based turbine complex flow simulation evaluation method of claim 1, wherein in step 1), the specific steps of combing the existing domestic and foreign test databases and analyzing the accuracy of the existing turbulence model are as follows:
(1) combing the geometric parameters, the flow parameters, the test and simulation results of the domestic and foreign turbines, classifying the data, and establishing a database; obtaining typical simulated precision data by using a machine learning algorithm, learning data such as sizes of different working conditions and examples, test and simulation results and the like, and obtaining a turbulence simulation precision database;
(2) the existing domestic and foreign test database is combed based on the algorithm of machine learning, the accuracy of the existing turbulence model is analyzed, and a problem description library and a parameter library are formed.
3. The reynolds average turbulence model-based turbine complex flow simulation evaluation method according to claim 1, wherein in step 2), the accuracy of different turbulence models in turbine typical flow is predicted by using commercial CFD simulation software, and the specific steps are as follows: inputting different turbulence models or correction models, importing commercial CFD simulation software, performing typical flow simulation analysis on the different turbulence models or correction models, and performing comparative analysis mainly by calculating typical parameters and parameter precision obtained by machine learning.
4. The method for evaluating the complex flow simulation of the turbine based on the reynolds average turbulence model of claim 1, wherein in the step 3), the predicted result is compared with the precision of an international simulation precision database to determine key parameters including the efficiency and the total pressure ratio of the turbine, and a precision and usability evaluation report is formed.
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| JP2017004103A (en) * | 2015-06-05 | 2017-01-05 | 株式会社東芝 | Turbulence simulation method |
| CN108416075A (en) * | 2017-08-29 | 2018-08-17 | 沈阳工业大学 | Wind energy conversion system aerodynamics model modeling method based on CFD technologies |
| US20200387579A1 (en) * | 2019-06-10 | 2020-12-10 | General Electric Company | Deep learning surrogate for turbulent flow |
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- 2021-07-26 CN CN202110845688.3A patent/CN113537804A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2017004103A (en) * | 2015-06-05 | 2017-01-05 | 株式会社東芝 | Turbulence simulation method |
| CN105701272A (en) * | 2015-12-30 | 2016-06-22 | 中国航空工业集团公司沈阳发动机设计研究所 | Processing method of GAO-YONG rational turbulence model |
| CN108416075A (en) * | 2017-08-29 | 2018-08-17 | 沈阳工业大学 | Wind energy conversion system aerodynamics model modeling method based on CFD technologies |
| US20200387579A1 (en) * | 2019-06-10 | 2020-12-10 | General Electric Company | Deep learning surrogate for turbulent flow |
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