Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning
Design optimization of transonic airfoils for rotary blades is a challenging subject that remarkably affects the stage and overall performance of axial-flow compressors. This paper describes a surrogate-based multi-objective optimization process over a transonic rotary blade. This blade works in the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/19942060.2021.1895889 |
| _version_ | 1818754689870594048 |
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| author | A. Zeinalzadeh M.R. Pakatchian |
| author_facet | A. Zeinalzadeh M.R. Pakatchian |
| author_sort | A. Zeinalzadeh |
| collection | DOAJ |
| description | Design optimization of transonic airfoils for rotary blades is a challenging subject that remarkably affects the stage and overall performance of axial-flow compressors. This paper describes a surrogate-based multi-objective optimization process over a transonic rotary blade. This blade works in the first high-pressure stage of a pre-designed industrial axial compressor. It experiences a massive separation behind an impinging shock wave over its suction side, resulting in very low efficiency of the whole stage. The key components of the current approach involve the application of novel-objective functions over the pressure distribution of airfoils, called the location of the shock wave and a flat-roof-top factor, to design supercritical airfoils. Moreover, to ensure the advantages of having an attached boundary layer and a high efficient blade, the area of separated boundary layer is also defined alongside other well-known objective functions related to the polar loss diagram. Notably, a sequential feed-forward multi-layer perceptron is designed to construct a mapping between airfoil geometrical variables and the objective functions. A numerical simulation of the whole compressor has shown an efficiency improvement of about 10% and 0.17% for the first stage and the whole compressor, respectively, and an attached boundary layer with a supercritical pressure distribution when employing the optimized rotor blade at the design stage. |
| first_indexed | 2024-12-18T05:27:15Z |
| format | Article |
| id | doaj.art-4ba46deb72ac4aa1a99ea8fe77f134f1 |
| institution | Directory Open Access Journal |
| issn | 1994-2060 1997-003X |
| language | English |
| last_indexed | 2024-12-18T05:27:15Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj.art-4ba46deb72ac4aa1a99ea8fe77f134f12022-12-21T21:19:30ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2021-01-0115156158310.1080/19942060.2021.18958891895889Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learningA. Zeinalzadeh0M.R. Pakatchian1MAPNA Turbine Engineering and Manufacturing Company (TUGA),MAPNA Turbine Engineering and Manufacturing Company (TUGA),Design optimization of transonic airfoils for rotary blades is a challenging subject that remarkably affects the stage and overall performance of axial-flow compressors. This paper describes a surrogate-based multi-objective optimization process over a transonic rotary blade. This blade works in the first high-pressure stage of a pre-designed industrial axial compressor. It experiences a massive separation behind an impinging shock wave over its suction side, resulting in very low efficiency of the whole stage. The key components of the current approach involve the application of novel-objective functions over the pressure distribution of airfoils, called the location of the shock wave and a flat-roof-top factor, to design supercritical airfoils. Moreover, to ensure the advantages of having an attached boundary layer and a high efficient blade, the area of separated boundary layer is also defined alongside other well-known objective functions related to the polar loss diagram. Notably, a sequential feed-forward multi-layer perceptron is designed to construct a mapping between airfoil geometrical variables and the objective functions. A numerical simulation of the whole compressor has shown an efficiency improvement of about 10% and 0.17% for the first stage and the whole compressor, respectively, and an attached boundary layer with a supercritical pressure distribution when employing the optimized rotor blade at the design stage.http://dx.doi.org/10.1080/19942060.2021.1895889airfoil design optimizationtransonic rotordeep learningmisesaxial compressorsupercritical pressure distribution |
| spellingShingle | A. Zeinalzadeh M.R. Pakatchian Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning Engineering Applications of Computational Fluid Mechanics airfoil design optimization transonic rotor deep learning mises axial compressor supercritical pressure distribution |
| title | Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning |
| title_full | Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning |
| title_fullStr | Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning |
| title_full_unstemmed | Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning |
| title_short | Evaluation of novel-objective functions in the design optimization of a transonic rotor by using deep learning |
| title_sort | evaluation of novel objective functions in the design optimization of a transonic rotor by using deep learning |
| topic | airfoil design optimization transonic rotor deep learning mises axial compressor supercritical pressure distribution |
| url | http://dx.doi.org/10.1080/19942060.2021.1895889 |
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