| Autor: |
Lopez, D.I., Ghisu, T., Shahpar, S. |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Journal of Turbomachinery |
| Popis: |
The increased need to design higher performing aerodynamic shapes has led to design optimization cycles requiring high-fidelity CFD models and high-dimensional parametrizationschemes. The computational cost of employing global search algorithms on such scenarioshas typically been prohibitive for most academic and industrial environments. In this paper,a novel strategy is presented that leverages the capabilities of artificial neural networks forregressing complex unstructured data, while coupling them with dimensionality reductionalgorithms. This approach enables employing global-based optimization methods on high-dimensional applications through a reduced computational cost. This methodologyis demonstrated on the efficiency optimization of a modern jet engine fan blade with con- strained pressure ratio. The outcome is compared against a state-of-the-art adjoint-based approach. Results indicate that the strategy proposed achieves comparable improvements to its adjoint counterpart with a reduced computational cost and can scale better to multi-objective optimization applications. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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