A Data-Driven Approach for Generalizing the Laminar Kinetic Energy Model for Separation and Bypass Transition in Low- and High-Pressure Turbines

Abstract

Abstract The laminar kinetic energy (LKE) transition model recently has shown good predictions on separation-induced transition that frequently experienced by low pressure turbines (LPT). In contrast to LPTs, high-pressure turbines (HPT) often are subject to bypass transition which currently is not captured well with the LKE model. Because these two types are common transition modes for turbines, the current effort is focused on generalizing and improving the LKE model to be able to predict the different types of transition. In addition to modify the transition model to achieve better on-blade predictions, the turbulence model in the wake region is also revised for more accurately capturing the wake mixing. Hence, the main purpose of this study is to use a data-driven approach to simultaneously develop spatially separate transition and turbulence closures suitable for a range of different turbine configurations. To achieve this, two strategies are adopted. The first is to employ a multi-case multi-objective computational fluid dynamics (CFD) -driven model training framework. The training is performed on several LPT and HPT configurations, specifically the T108, T106A, and LS89 sections, that feature both separation-induced and bypass transition to ensure better generalizability of the models. The second strategy employed is the use of a newly derived set of local non-dimensionalized variables, that serves as the inputs for the LKE model corrections. Among the training cases, steady calculations are conducted for LPTs. The LS89 case is an HPT characterized by shocks, acoustic waves, and unsteady trailing edge vortex shedding. To capture these, for the first time an unsteady solver is utilized during the CFD-driven training, and the time-averaged results are used to calculate the cost function as part of the model development process. The model obtained from the new training process are tested on the a steady case - T108 with a higher Reynolds number and an unsteady case - PakB profile. Their performance are assessed in terms of pressure coefficient, wall shear stress and wake losses.