Federico Ghioldi

A species-dependent transport formulation incorporating non-unity Lewis numbers and thermal diffusion is employed within a reacting-flow large eddy simulation (LES) framework for hydrogen–air combustion, extending the standard unity Lewis-number closure while maintaining consistency with detailed kinetics and energy transport. The formulation is applied to a lean premixed hydrogen–air Bunsen flame, and the structural response to differential diffusion is characterized through comparison with the unity Lewis baseline. Non-unity transport produces a systematic displacement of the flame front, local mixture enrichment near the reaction zone, and a broader thermal layer. Computational singular perturbation (CSP) diagnostics are then applied to examine how transport modeling affects the intrinsic chemical dynamical structure of the flame. While the dominant reaction pathways remain largely unchanged, differential diffusion reorganizes the spatial distribution of chemical modes. In the inner core, the explosive mode region shifts downstream and widens while its growth rate decreases. HO2-mediated chemistry assumes a dominant role that persists beyond the flame front, substantially delaying the transition toward the NO-controlled slow manifold. These results demonstrate that simplified transport assumptions affect not only macroscopic flame observables but also the hierarchy of chemical time scales governing lean hydrogen flame evolution.

This study presents the implementation and validation of C++ library modules within the OpenFOAM Technology for high-fidelity simulations of multi-regime combustion in aero-engines. The dynamic thickened flame combustion model for large eddy simulations is integrated with ordinary differential equation solvers to enable fast direct integration of finite-rate chemistry on graphics processing units. The reacting solvers are extended to incorporate a mixture-averaged formulation for viscosity and thermal conductivity. Additionally, diffusion models are generalized to multicomponent mixtures to account for individual species contributions, along with corrections that support non-unity Lewis numbers in the diffusion terms of the convection-diffusion equations for chemical species. The methodology is validated against experimental data from the hydrogen/air flame produced by the hydrogen low NOx burner. The numerical results demonstrate the robustness, accuracy, and computational efficiency of the proposed approach, paving the way for future investigations into the complex physics of aero-engine installations powered by sustainable aviation fuels.

The solution of reactive computational fluid dynamics (CFD) simulations is accelerated by the implementation of a hybrid central processing unit/graphics processing units (CPU/GPU) Finite Volume solver based on the operator‐splitting strategy, where the chemistry integration is treated independently of the flow solution. The integration of ordinary differential equations (ODEs) describing the finite‐rate chemical kinetics is solved by an adaptive multi‐block explicit solver on GPUs, while the load of the fluid solution is distributed on a multicore CPU algorithm. The resulting speed‐up for reactive CFD simulations is up to 10; the performance gain increases with the size of the mechanism. The proposed implementation is general and can be applied to any CFD problem where the governing equations for the fluid transport are coupled with an ODE system. Code validation is performed against reference solutions on a selection of test cases involving reacting flows.

We introduce algorithmic advancements designed to expedite simulations in OpenFOAM using GPUs. These developments include the following. (a) The amgx4Foam library, which connects the open-source AmgX library from NVIDIA to OpenFOAM. Matrix generation, involving tasks such numerical integration and assembly, is performed on CPUs. Subsequently, the assembled matrix is processed on the CPU. This approach accelerates the computationally intensive linear solver phase of simulations on GPUs. (b) Enhancements to code performance in reactive flow simulations, by relocating the solution of finite-rate chemistry to GPUs, which serve as co-processors. We present code verification and validation along with performance metrics targeting two distinct application sets, namely, aerodynamics calculations and supersonic combustion with finite-rate chemistry.