In low pressure turbines (LPT), due to the low Reynolds number a large part of the blade boundary layer remains laminar and transition may occur due to flow separation. The boundary layer details at the blade trailing edge can change substantially depending on the transition region topology and can strongly influence the wake mixing occurring downstream. Accurately predicting these flow phenomena still poses a challenge for Reynolds averaged Navier-Stokes (RANS) and unsteady RANS methods. In this work a recently developed computational fluid dynamics (CFD) driven machine learning framework featuring multi-expression, multi-objective optimization is exploited for the first time to simultaneously develop transition models and turbulence closures in a fully coupled way, aimed at improving both transition and wake mixing predictions in LPTs. The T106A blade cascade with an isentropic Reynolds number of 100,000 is adopted as a training case. The baseline transition model is based on a laminar kinetic energy transport approach, and the machine learning approach is used to reformulate the source terms as functions of suitably defined non-dimensional ratios. Additionally, machine learning based explicit algebraic Reynolds stress models are used to improve wake mixing predictions, making use of a specifically and newly developed wake sensing function based strategy that allows an automated zonal application of the developed models. It is shown that both on-blade performance and wake mixing can be predicted accurately with data-driven transition and turbulence models that have benefited from CFD feedback in their development, ensuring that their mutual interactions are captured.

This content is only available via PDF.
You do not currently have access to this content.