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

No common laminar kinetic energy (LKE) transition model has to date been able to predict both separation-induced and bypass transition, both phenomena commonly found in low-pressure turbines and high-pressure turbines. Here, a data-driven approach is adopted to develop a more general LKE transition model suitable for both transition modes. To achieve this, two strategies are adopted. The first is to extend the computational fluid dynamics (CFD)-driven model training framework for simultaneously training models on multiple turbine cases, subject to multiple objectives. By increasing the training data set, different transition modes can be considered. The second strategy employed is the use of a newly derived set of local non-dimensionalized variables as training inputs to reduce the search space. Because one of the training turbine cases is characterized by strong unsteady effects, 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 results show that the data-driven models do perform better, in terms of their predictions of pressure coefficient, wall shear stress, and wake losses, than the baseline model. The models were then tested on two previously unseen testing cases, one at a higher Reynolds number and one with a different geometry. For both testing cases, stable solutions were obtained with results improved over the predictions using the baseline models.