Mino is currently involved in the Air.Car project aiming to predict a real-time NOx emissions from real-world driving conditions for a wide range of diesel vehicles. His research is focused on developing a instantaneous vehicle NOx emissions by Artificial Neural Network (ANN), that is one of the machine learning techniques. Findings from this project will contribute to real-time emissions monitoring, with applications in urban air quality management, enhanced driver training and pay-as-you-pollute charging. The emissions models will also be coupled to traffic simulation software to evaluate the effect of different driving behaviours on network-level road transport emissions.
Mino has expertise in numerical modelling and simulation of reacting flow especially for the catalytic reactions such as diesel after-treatment system. Previously, he worked at KWEnC in Korea where he developed a number of computer codes for after-treatment system (DOC, LNT, SCR and TWC) within several industrial projects.
Woo M, Choi BC, 2021, Numerical study on fuel-NO formation characteristics of ammonia-added methane fuel in laminar non-premixed flames with oxygen/carbon dioxide oxidizer, Energy, Vol:226, ISSN:0360-5442
et al., 2021, Open-source modelling of aerosol dynamics and computational fluid dynamics: nodal method for nucleation, coagulation, and surface growth, Computer Physics Communications, Vol:261, ISSN:0010-4655
et al., 2021, Multiscale numerical modeling of solid particle penetration and hydrocarbons removal in a catalytic stripper, Aerosol Science and Technology, Vol:55, ISSN:0278-6826, Pages:987-1000
et al., 2021, A step toward the numerical simulation of catalytic hydrogenation of nitrobenzene in Taylor flow at practical conditions, Chemical Engineering Science, Vol:230, ISSN:0009-2509, Pages:116132-116132
et al., 2020, A Qualitative Numerical Study on Catalytic Hydrogenation of Nitrobenzene in Gas-Liquid Taylor Flow with Detailed Reaction Mechanism, Fluids, Vol:5, Pages:234-234