Establishment of a generalizable model on a small-scale dataset to predict the surface pressure distribution of gas turbine blades

Qi Wang, Li Yang and Yu Rao

Energy, 2021, vol. 214, issue C

Abstract: The main challenge of establishing a model to predict the flow fields of turbomachinery was insufficient data. This study aimed to establish a generalizable and accurate model on a small-scale dataset to cost-effectively predict the surface pressure distribution of a turbine rotor cascade with widely varying geometries and boundary conditions. To meet this purpose, a novel concept of transfer learning was introduced, which was defined as transferring knowledge from a large-scale but low-fidelity dataset to a small-scale but high-fidelity dataset. A Conditional Generative Adversarial Neural Network was designed as the pre-trained network for the transfer learning to regress the surface pressure distributions. Two models transferred from datasets with different fidelity and an independent model were established and compared in detail. The results showed that the proposed method successfully reduced the modeling cost with a low error in predicting the surface pressure distributions. The model transferred from the higher-fidelity dataset had better generalization performance, which reduced the root mean square error and modeling cost by 40.2% and 9 times, respectively. The presented method could serve as a base framework for modeling surface pressure distribution of complex objects using a small-scale dataset.

Keywords: Pressure distribution; Gas turbine; Small-scale; Transfer learning (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:214:y:2021:i:c:s036054422031985x

DOI: 10.1016/j.energy.2020.118878

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