Industrialization of High-Order Unstructured Methods for Computational Fluid Dynamics

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Canada has committed to reducing greenhouse gas emissions by 40-45% by 2030, and reaching net-zero by 2050. These goals will rely on improving transportation systems, power generation, alternative fuels, Hydrogen combustion, and industrial processes. Improving these systems relies, increasingly, on our ability to make accurate performance predictions using Computational Fluid Dynamics (CFD).The vast majority of CFD practitioners rely on the Reynolds-Averaged Navier-Stokes (RANS) approach for design, resolving only the time-average flow and relying on approximate turbulence modelling. However, the well-known limitations of RANS turbulence models restricts their use to a relatively small region of the engineering design space, as they are unable to accurately predict separated or transitional turbulent flows. To address these limitations, a new generation of unsteady scale-resolving CFD techniques, including Large-Eddy Simulation (LES), have been proposed as enabling technologies for next-generation design.Using LES significantly improves accuracy, but also greatly increases computational cost. To address this, a new generation of high-order unstructured CFD methods, including the Discontinuous Galerkin and Flux Reconstruction approaches, have been developed. These schemes can leverage the compute capability of modern hardware architectures, such as Graphical Processing Units, to provide orders of magnitude more accurate results at reduced computational cost. Nevertheless, their industrial adoption is limited by three primary factors: the availability of efficient time-stepping techniques, the formulation of provably non-linearly stable schemes, and bespoke non-linear solver technologies. It follows that these three factors are currently active areas of research, with each being the direct focus of one applicant to this program. Importantly, these factors are all highly coupled and it is expected that, by addressing these as a team, we will be able to seed a step change in the numerical methods used for industrial CFD. Ultimately, this will enable the use of LES at industrial scale, improve the accuracy of engineering performance predictions, and contribute to meeting Canada's emissions reduction targets.