Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation
In a previous paper, a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work,...
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MDPI AG
2019-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/13/2679 |
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| author | Marco Berchiolli Gregory Guarda Glen Walsh Apostolos Pesyridis |
| author_facet | Marco Berchiolli Gregory Guarda Glen Walsh Apostolos Pesyridis |
| author_sort | Marco Berchiolli |
| collection | DOAJ |
| description | In a previous paper, a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer. |
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| format | Article |
| id | doaj.art-feeb8c0f8f3b42729fda7a9ed1938c86 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T06:21:43Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-feeb8c0f8f3b42729fda7a9ed1938c862022-12-22T01:17:49ZengMDPI AGApplied Sciences2076-34172019-06-01913267910.3390/app9132679app9132679Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine OptimisationMarco Berchiolli0Gregory Guarda1Glen Walsh2Apostolos Pesyridis3Department of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UKDepartment of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UKDepartment of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UKDepartment of Mechanical and Aerospace Engineering, Brunel University, London UB8 3PH, UKIn a previous paper, a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer.https://www.mdpi.com/2076-3417/9/13/2679turbochargeraxial turbinegenetic algorithmsmultidisciplinary design optimisation |
| spellingShingle | Marco Berchiolli Gregory Guarda Glen Walsh Apostolos Pesyridis Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation Applied Sciences turbocharger axial turbine genetic algorithms multidisciplinary design optimisation |
| title | Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation |
| title_full | Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation |
| title_fullStr | Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation |
| title_full_unstemmed | Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation |
| title_short | Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation |
| title_sort | turbocharger axial turbines for high transient response part 2 genetic algorithm development for axial turbine optimisation |
| topic | turbocharger axial turbine genetic algorithms multidisciplinary design optimisation |
| url | https://www.mdpi.com/2076-3417/9/13/2679 |
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