Work

Involving non-equilibrium training dataset in data-driven turbulence modeling for turbomachinery

Year: 2024

Type: article

Abstract: In order to improve the performance of $k - \omega $ SST model in turbomachinery, previous studies have used the machine-learning (ML) technique to obtain turbulence models (for example, the ML-RANS EQ model). However, these models do not lead to satisfactory results in complex flows in turbomachinery. In this study, we use non-equilibrium training dataset to obtain a new turbulence model (i.e., the ML-RANS TR-NE-EQ model). Calculations in various cases of turbine cascade flows show that ML-RANS TR-NE-EQ model performs obviously better than ML-RANS EQ model as well as $k - \omega $ SST model.

Source: The Aeronautical Journal

DOI: 10.1017/aer.2024.123

Authors: J.L. Du, W.S. Liu, L. Fang, T.W. Bao

Institutions: Beihang University, Peking University

Language: English

Topic: Turbomachinery Performance and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Beihang University

Awards: G3121615513, G4345788479