Influence of turbulent inlet conditions on the flow inside a bulb turbine draft tube using Large-Eddy Simulations

• Published in: IOP conference series. Earth and environmental science 2021-06, Vol.774 (1), p.12014
• Main Authors: Véras, P, Balarac, G, Métais, O, Georges, D, Bombenger, A, Ségoufin, C
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Computer applications; Draft tubes; Experimental data; Finite element method; Flow; Flow simulation; Inlet flow; Large eddy simulation; Learning algorithms; Machine learning; Predictions; Simulation; Turbines; Turbulence; Vortices
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/774/1/012014

The head losses inside the draft tube of a bulb turbine can represent an important portion of the total energy losses due to the low heads at which these machines operate. However, the complexity of the flow makes it a numerical challenge to simulate. Previous studies have shown that Large Eddy Simulations (LES) based on mean inlet conditions improve flow prediction inside the draft tube’s cone but diverge further downstream [4]. Moreover, uncertainties and the lack of detailed experimental data at the inlet have been identified as the main reason for these discrepancies and precise measurements close to the walls are necessary to correctly validate the numerical simulations [8, 4]. Thanks to detailed experimental measurements, the objective of this paper is to enhance the accuracy of previous results by performing LES computations to numerically simulate the flow inside the draft tube of a bulb turbine, and to investigate the influence of inlet conditions using an innovative approach. A two-criteria based mesh adaptation [14] along with an element-wise masking strategy is used to assure a good spatial discretization level while reducing the computational cost. Partial experimental data imposed at the inlet are often not sufficient to achieve a proper downstream flow prediction with a LES. The real challenge thus consists in the economical generation of proper mean and fluctuating inlet flow fields. We first show that a simple homogeneous and isotropic synthetic turbulence field added to the mean experimental profiles may improve the prediction of the downstream flow, but this is only achieved through empirical adjustments. Therefore, we also investigate the use of machine learning procedure to automatically generate proper inlet mean and fluctuating fields.