Gradient-Free and Gradient-Based Optimization of a Radial Turbine
A turbocharger’s radial turbine has a strong impact on the fuel consumption and transient response of internal combustion engines. This paper summarizes the efforts to design a new radial turbine aiming at high efficiency and low inertia by applying two different optimization techniques to a paramet...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
|
| Series: | International Journal of Turbomachinery, Propulsion and Power |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-186X/5/3/14 |
| _version_ | 1797563239288012800 |
|---|---|
| author | Nicolas Lachenmaier Daniel Baumgärtner Heinz-Peter Schiffer Johannes Kech |
| author_facet | Nicolas Lachenmaier Daniel Baumgärtner Heinz-Peter Schiffer Johannes Kech |
| author_sort | Nicolas Lachenmaier |
| collection | DOAJ |
| description | A turbocharger’s radial turbine has a strong impact on the fuel consumption and transient response of internal combustion engines. This paper summarizes the efforts to design a new radial turbine aiming at high efficiency and low inertia by applying two different optimization techniques to a parametrized CAD model. The first workflow wraps 3D fluid and solid simulations within a meta-model assisted genetic algorithm to find an efficient turbine subjected to several constraints. In the next step, the chosen turbine is re-parametrized and fed into the second workflow which makes use of a gradient projection algorithm to further fine-tune the design. This requires the computation of gradients with respect to the CAD parametrization, which is done by calculating and combining surface sensitivities and design velocities. Both methods are applied successfully, i.e., the first delivers a well-performing turbine, which, by the second method, is further improved by 0.34% in efficiency. |
| first_indexed | 2024-03-10T18:39:42Z |
| format | Article |
| id | doaj.art-956b00be10404a32a32c81e37259656e |
| institution | Directory Open Access Journal |
| issn | 2504-186X |
| language | English |
| last_indexed | 2024-03-10T18:39:42Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Turbomachinery, Propulsion and Power |
| spelling | doaj.art-956b00be10404a32a32c81e37259656e2023-11-20T05:57:02ZengMDPI AGInternational Journal of Turbomachinery, Propulsion and Power2504-186X2020-07-01531410.3390/ijtpp5030014Gradient-Free and Gradient-Based Optimization of a Radial TurbineNicolas Lachenmaier0Daniel Baumgärtner1Heinz-Peter Schiffer2Johannes Kech3MTU Friedrichshafen GmbH, Maybachplatz 1, 88045 Friedrichshafen, GermanyTechnical University of Munich, Chair of Structural Analysis, Arcisstr. 21, 80333 Munich, GermanyInstitute of Gas Turbines and Aerospace Propulsion, Technical University Darmstadt, Otto-Berndt-Str. 2, 64287 Darmstadt, GermanyMTU Friedrichshafen GmbH, Maybachplatz 1, 88045 Friedrichshafen, GermanyA turbocharger’s radial turbine has a strong impact on the fuel consumption and transient response of internal combustion engines. This paper summarizes the efforts to design a new radial turbine aiming at high efficiency and low inertia by applying two different optimization techniques to a parametrized CAD model. The first workflow wraps 3D fluid and solid simulations within a meta-model assisted genetic algorithm to find an efficient turbine subjected to several constraints. In the next step, the chosen turbine is re-parametrized and fed into the second workflow which makes use of a gradient projection algorithm to further fine-tune the design. This requires the computation of gradients with respect to the CAD parametrization, which is done by calculating and combining surface sensitivities and design velocities. Both methods are applied successfully, i.e., the first delivers a well-performing turbine, which, by the second method, is further improved by 0.34% in efficiency.https://www.mdpi.com/2504-186X/5/3/14Large Diesel Engineturbochargerradial turbineoptimizationmeta-modeladjoint sensitivity |
| spellingShingle | Nicolas Lachenmaier Daniel Baumgärtner Heinz-Peter Schiffer Johannes Kech Gradient-Free and Gradient-Based Optimization of a Radial Turbine International Journal of Turbomachinery, Propulsion and Power Large Diesel Engine turbocharger radial turbine optimization meta-model adjoint sensitivity |
| title | Gradient-Free and Gradient-Based Optimization of a Radial Turbine |
| title_full | Gradient-Free and Gradient-Based Optimization of a Radial Turbine |
| title_fullStr | Gradient-Free and Gradient-Based Optimization of a Radial Turbine |
| title_full_unstemmed | Gradient-Free and Gradient-Based Optimization of a Radial Turbine |
| title_short | Gradient-Free and Gradient-Based Optimization of a Radial Turbine |
| title_sort | gradient free and gradient based optimization of a radial turbine |
| topic | Large Diesel Engine turbocharger radial turbine optimization meta-model adjoint sensitivity |
| url | https://www.mdpi.com/2504-186X/5/3/14 |
| work_keys_str_mv | AT nicolaslachenmaier gradientfreeandgradientbasedoptimizationofaradialturbine AT danielbaumgartner gradientfreeandgradientbasedoptimizationofaradialturbine AT heinzpeterschiffer gradientfreeandgradientbasedoptimizationofaradialturbine AT johanneskech gradientfreeandgradientbasedoptimizationofaradialturbine |