Tribology & Surface Engineering

investigation of tribological characteristics to improve the lifespan and efficiency of next-generation internal combustion engine vehicles (NG-ICEVs) by lowering wear and friction coefficient in liner-piston interactions. These NG-ICEVs are fueled with carbon-free/neutral fuels, such as ammonia and ethanol. Even though there are many studies focusing on the combustion phenomenon or engine performance, there are only a few studies focusing on the effect of tribological pairs when these fuels are used. Due to the differences in chemical properties, in the long run conventional engine components tend to corrode and new coating/materials are need to be introduced to the automotive sector. In our lab, wear-friction-corrosion analyses are being conducted on an ammonia fueled ICE.    

Micro-machining

it is well-known fact that micro-textures under different lubricating regimes can act differently. In our laboratory we are assessing the usability of laser surface texturing, micro-electrical discharge machining (μEDM) and micro-milling techniques to create various micro-textures on tribological pairs. Currently, the focus is on the piston ring-cylinder liner pair , where the overall goal is to develop a comprehensive guideline for micro-texture characteristics (shape, size, density, etc...) depending on the piston's speed and spatial position. CFD simulations, and tribology experiments are being conducted concurrently. 

Deep Learning

development of neural network-based "heat transfer models" to utilize nucleate-boiling (NB) phenomenon in coolant systems for batteries. In theory, NB can enhance the heat transfer coefficient immensely, around 10^4～5 times, thanks to the air bubbles. However, the thermal interval for NB to work is limited, around 10-30°C, which needs a precise control algorithm for cooling systems. In addition, it is rather difficult to model these complex physical phenomena of multiphase fluid flow. This is where "deep learning" (DL) comes into the spotlight. The goal is to develop physics-based DL frameworks to predict heat flux during coolant flow and make necessary adjustments in the cooling system to ensure that the heat transfer is indeed in the NB regime. The physics-based model will be backed by visual data of air bubbles captured by high speed cameras during the experiments. By utilizing NB phenomenon, it is possible to scale down the size and weight of conventional cooling systems, which will eventually result in higher mileage for EVs and cut down the emissions for NG-ICEVs .