Mohammed Dayili


​M.S Student

Research Interests

​In order to understand fuels for making cleaner and more efficient combustion, my research interests are in the area of combustion chemistry, chemical kinetics, and laser diagnostics of fuels. To this end, I use rapid compression machine and shock tubes experiments, as well as modeling, to understand the ignition delay time, ignition temperature, and the emission formation of additives in different fuel mixtures. In addition to the experiments, I use the machine learning on the mixtures to better understand the chemical kinetics and reaction rate of combustion.

Currently, I'm researching a new potential mixture of fuel additives (Nano-fuels), which is a fuel suspended with energetic oxygenated Nano-particles that can alter the heat transfer and combustion. My goal is to understand the ignition delay time and the homogeneity of the mixture in the rapid compression machine chamber. Nano fuel is a strong candidate for heavy applications in the future because it has a positive effect on decreasing pollutants such as NOx, Co2, and HC.



  • ​B.Sc., Mechanical Engineering, Jazan University, Jazan, Saudi Arabia, 2020

Professional Profile

  • July 2020-October 2020: Research and development engineer intern, Estonia, Europe.
  • January 2020-July 2020: Research assistant intern, KAUST, Thwual, Saudi Arabia.
  • July 2019-September 2019: Data science intern, Opus.Ai, California, USA.
  • August 2018-September 2018: Steel manufacturing engineer intern, Solb Steel, Jazan, Saudi Arabia.
  • Jun 2018-July 2018: Solar car engineer intern, Tokai University, Tokyo, Japan.

Scientific and Professional Membership

  • ​Saudi Arabian Section of the Combustion Institute (SAS-CI)
  • Combustion Institute (CI)
  • Saudi Council of Engineers (SCE)
  • American Institute of Aeronautics and Astronautics (AIAA)

KAUST Affiliations

  • ​Clean Combustion Research Center (CCRC)
  • Division of Physical Sciences and Engineering (PSE)

Research Interests Keywords

combustion1 Chemical kinetics Fuel additives Laser Diagnostics Machine learning