Direct numerical simulation (DNS) and large eddy simulation (LES), incorporating detailed chemistry and transport properties, will be employed to investigate flame–wall interactions (FWI), the mechanisms governing near-wall emission formation, and the structure and dynamics of reaction front propagation for green fuels in dual-fuel marine engines. This project is driven by growing societal concerns regarding global warming caused by greenhouse gas (CO2) emissions, as well as the release of harmful pollutants (soot, NOx, CO, and unburned hydrocarbons) from fossil fuel combustion in hard-to-electrify sectors such as maritime transport. The overall objective is to advance the understanding of combustion and emission characteristics of green fuels—hydrogen, ammonia, and methanol—in internal combustion engines (ICEs), thereby supporting the transition toward sustainable transport and power generation. However, several technical challenges must be addressed. Hydrogen suffers from low volumetric energy density and storage limitations. Ammonia and methanol present ignition difficulties, slow flame propagation, and reduced combustion efficiency. In addition, ammonia combustion leads to significant NOx and N2O emissions due to the nitrogen content of the fuel. To address these challenges, the project combines industrial-scale Reynolds-averaged Navier–Stokes (RANS) simulations with GPU-accelerated DNS to systematically generate high-fidelity datasets of FWI and near-wall emissions, including N2O, CO, and CH2O. These data will be used to develop near-wall combustion models suitable for integration into industrial computational fluid dynamics (CFD) frameworks. The proposed work builds upon NAISS 2025/1-21, which utilized a mature CPU-based RANS/LES workflow. A key advancement in the present project is the transition to GPU-based DNS, motivated by the need for more accurate near-wall data and the broader shift toward GPU-accelerated computing within NAISS systems. The specific objectives of the project are: (i) to investigate near-wall flame dynamics of ammonia and methanol mixtures under engine-relevant turbulent conditions using DNS; (ii) to quantify pollutant formation mechanisms, focusing on NO, N2O, CO, CH2O, and unburned fuels across a range of wall temperatures, pressures, fuel–air mixtures, and turbulence intensities; (iii) to develop reduced-order models that accurately capture flame–wall interactions for implementation in engineering-scale CFD tools.