Overview
The Computational Reacting Flow Laboratory (CRFL), led by Professor Hong Im, develops and utilizes various high-fidelity simulation capabilities as predictive tools to unravel fundamental physics of laminar and turbulent combustion phenomena encountered in practical power generation systems. Recent and ongoing research projects include: direct numerical simulations and large eddy simulations of laminar and turbulent flames utilizing detailed kinetic models; modeling of pollutant formation; utilization of future fuels; auto-ignition in modern engine conditions; multi-phase modeling of liquid spray evaporation and desublimation; dynamics of bluff-body flame stabilization; full-cycle internal combustion engine modeling with advanced turbulent combustion sub-models; advanced algorithms for high-performance computing for hybrid computing architectures; machine learning application for combustion simulation; plasma effects on combustion; low-grade fuel utilization; cryogenic carbon capture.
Latest News
20 February, 2022
Hydrogen peroxide only forms in the presence of ozone in water microdroplets. A comprehensive investigation by KAUST researchers sets the record straight on the formation of hydrogen peroxide in micrometre-sized water droplets, or microdroplets, and shows that ozone is the key to this transformation.
09 December, 2019
CCRC hosted the first-ever KAUST Research Workshop on Physics of Turbulent Combustion in the first week of December 2019. Chaired by Prof. Hong G. Im, the workshop brought together worldwide experts in turbulent combustion research to identify the most significant scientific and engineering issues towards predictive modeling of turbulent combustion phenomena towards clean and efficient energy conversion.
12 August, 2018
Highly fuel-efficient new engine designs could significantly reduce the environmental impact of vehicles, especially if the engines run on renewable nonpetroleum-based fuels. Ensuring these unconventional fuels are compatible with next-generation engines was the aim of a new computational study on fuel ignition behavior at KAUST.