MACHINE LEARNING FOR THE DEVELOPMENT OF DATA DRIVEN TURBULENCE CLOSURES IN COOLANT SYSTEMS

Abstract

This work shows the application of Gene &pression Pro­gramming to augment RANS turbulence closure modelling for flows through complex geometries, designed for additive manu­facturing. Specifically, for the design of optimised internal cool­ing channels in turbine blades. One of the challenges in internal coolant design is the heat transfer accuracy of the RANS formu­lation in comparison to higher fidelity methods, which are still not used in design on account of their computational cost. How­ever, high fidelity data can be extremely valuable for improving cu"ent lower fidelity models and this work shows the application of data driven approaches to develop turbulence closures for an internall..