Computational Fluid Dynamics is one of the most relied upon tools in the design and analysis of components in turbomachines. From the propulsion fan at the inlet, through the compressor and combustion sections, to the turbines at the outlet, CFD is used to perform fluid flow and heat transfer analyses to help designers extract the highest performance out of each component. In some cases, such as the design point performance of the axial compressor, current methods are capable of delivering good predictive accuracy. However, many areas require improved methods to give reliable predictions in order for the relevant design spaces to be further explored with confidence. This paper illustrates recent developments in CFD for turbomachinery which make use of machine learning techniques to augment prediction accuracy, speed up prediction times, analyse and manage uncertainty and reconcile simulations with available data. Such techniques facilitate faster and more robust searches of the design space, with or without the help of optimization methods, and enable innovative designs which keep pace with the demand for improved efficiency and sustainability as well as parts and asset operation cost reduction.

The new possibilities offered by additive manufacturing (AM) can be exploited in gas turbines to produce a new generation of complex and efficient internal coolant systems. The flexibility offered by this new manufacturing method needs a paradigm shift in the design approach, and a possible solution is offered by topology optimization. The overall goal of this work is to propose an innovative method to design internal channels in gas turbines that fully exploit AM capabilities. The present work contains a new application of a fluid topology sedimentation method to optimize the internal coolant geometries with minimal pressure losses while maximizing the heat exchange. The domain is considered as a porous medium with variable porosity: the solution is represented by the final solid distribution that constitutes the optimized structure. In this work, the governing equations for an incompressible flow in a porous medium are considered together with a conjugate heat transfer equation that includes porosity-dependent thermal diffusivity. An adjoint optimization approach with steepest descent method is used to build the optimization algorithm. The simulations are carried out on three different geometries: a U-bend, a straight duct, and a rectangular box. For the U-bend, a series of splitter is automatically generated by the code, minimizing the stagnation pressure losses. In the straight duct and in the rectangular box, the impact of different choices of the weights and of the definition of the porosity-dependent thermal diffusivity is analyzed. The results show the formation of splitters and bifurcations in the box and “riblike” structures in the straight duct, which enhance the heat transfer.