NeRF-Inspired Neural Fields for 3D Super-resolution of Reacting-Flows

The multiscale nature of turbulent reacting flows and the importance of fine-scale structures pose challenges for super-resolution. We investigate a 3D super-resolution framework inspired by Neural Radiance Fields (NeRF), called Neural Field Super-Resolution (NFSR), to reconstruct fine-resolution fields from coarsened reacting-flow fields. The proposed formulation adapts the continuous coordinate-conditioned representation of NeRF to physical flow-field reconstruction by conditioning spatial coordinates with local coarse-grid thermochemical information, while eliminating radiance prediction and volume rendering. Principal component analysis is used to provide a compact and low-dimensional representation of the thermochemical state, and a hierarchical extension, H-NFSR, progressively reconstructs the solution through intermediate resolution levels. The framework is evaluated using three-dimensional direct numerical simulation data for a turbulent premixed hydrogen-air flame, with an overall spatial coarsening factor of eight in each coordinate direction. The results demonstrate consistently better prediction measures and a higher ability to reconstruct fine-scale information than tricubic interpolation on the unseen test realization. The hierarchical formulation reduces visible coarse-cell imprinting. In contrast, biased sampling mainly benefits the most localized structures. Three-dimensional isosurface comparisons further show that H-NFSR recovers much of the small and intermediate features of the flow, including surface wrinkling and local flame curvature effects. These results highlight the potential of coordinate-conditioned neural fields as a flexible framework for three-dimensional coarse-to-fine reconstruction of reacting-flow simulations.