Geometric Deep Learning for Surrogate Modelling of Air Flow in Conical Diffusers Using Computational Fluid Dynamics Simulation Data

Designing gas turbine combustors that operate effectively with turbomachinery components over a wide range of operating conditions is a challenging task, requiring detailed 3D computational fluid dynamics (CFD) analysis, which is computationally intensive. To alleviate this, surrogate models of the different sub-components like the pre-diffuser, combustion zone, dilution zone and transition ducting can be employed. These component-level models can then be integrated with surrogate models of turbomachinery components to enable comprehensive whole-engine multi-point optimization. This paper presents the development of a data-driven surrogate model for conical air pre-diffusers that can be utilized to perform design optimization, what-if analysis, and be integrated within larger system-level simulation models. The surrogate model is developed within the spectral graph convolution neural network (GCN) framework to predict the 2D symmetry plane velocity, temperature and pressure distributions. The model is trained on CFD simulation data encompassing diffusers with diverse geometric parameters, including a wide range of aspect ratios, diffuser lengths, and inlet diameters, as well as various combinations of boundary conditions. As input, the surrogate model receives graphs representing the symmetry plane meshes, along with vectorized boundary condition values for each training sample. Different layer architectures are investigated to share information between the GCN and fully connected layers that process the graph and boundary condition data respectively. The best-performing surrogate model achieves high predictive accuracy, with normalized mean absolute percentage errors below 1% for pressure and temperature, and below 3.9% for velocity.