We are actively looking for gradaute students.

Feel free to reach out via email. When you do, make sure to add your CV & additional necessary information about your interest.
Check out the below link for admission information & scholarship opportunities.
Admission Information for International Students
Email: yilmaz(at)sophia.ac.jp
＊ replace (at) with @ mark!

卒研配属について　　

ただいま取り組んでいる研究テーマの詳細については，以下のリンクからご覧いただけます．
また，進学・就職・研究室選びなどでお悩みの方は，ぜひお気軽にご相談ください．
E-mail: yilmaz(at)sophia.ac.jp
＊ (at)を@に置き換えてください．
詳細　

themes

Our studies are focused on creating functional surfaces for various engineering applications – from nucleate boiling to tribology. On-going projects are listed below:

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 needed to be introduced to the automotive sector. In our lab, wear–friction–corrosion analyses are being conducted on an ammonia-fueled ICE.

surface texturing via LST & EDM

It is well-known that micro-textures under different lubricating regimes can act differently. In our laboratory we are assessing the usability of laser surface texturing (LST), electrical discharge machining (EDM) 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 thermal management systems for engines. In theory, NB can enhance the heat transfer coefficient immensely, around 10^3–5 times, when controlled properly thanks to 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 reduced emissions for NG-ICEVs.