December 2-4, 2019
Auditorium between building 4 and 5, level 0
KAUST community members are invited to attend the 2019 workshop on Physics of Turbulent Combustion, hosted and organized by the Clean Combustion Research Center (CCRC) at KAUST. The workshop will bring 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. The workshop invites experts in combustion theory, direct numerical simulations, large eddy simulations, mathematical modeling, combustion kinetics, laser diagnostics, reduced order modeling, big data and machine learning. The outcome will be a community-wide consensus on high impact research toward sustainable energy, and building a synergistic research network between academia and industry.
Scope and Objectives
The workshop aims to improve our current knowledge and capabilities in the description of turbulent combustion on the following themes:
- Physics of turbulent combustion: This theme will investigate observation and analysis of high fidelity simulation and experimental measurements, typically of canonical flame configurations, thereby providing insights into the unknown characteristics pertaining to turbulent combustion at extreme conditions. We will assess their implications, thus revise the existing theory, and identify challenges in broadening our understanding for the unexplored fuels and conditions in future research.
- Advances in modeling and diagnostics for practical applications: The fidelity of simulations and diagnostic information need to be utilized in the development of predictive simulation capabilities for industrial applications. The state-of-the-art RAN and LES practice in the academia and industry today will be assessed and future direction for high impact predictive model developments will be discussed.
- Enabling technologies for large data and analytics: The massive amount of computational and experimental data offers an opportunity of a new paradigm of non-physics based submodels relying on machine learning algorithms. Cutting-edge computational methodologies for machine learning from large data will be discussed and the strategies to utilize these tools for academic and industrial applications will be identified
Invited speakers include:
- Alexei Poludnenko, University of Connecticut, U.S.
- Antonio Attili, RWTH Aachen, Germany
- Francesco Creta, University of Rome, Sapienza, Italy
- Vladimir Sabelnikov, ONERA, France
- Xinyu Zhao, University of Connecticut, U.S.
- Xue-Song Bai, Lund University, Sweden
- Yuki Minamoto, Tokyo Institute of Technology, Japan
- Cesar Dopazo, University of Zaragoza, Spain
- Zhongshan Li, Lund University, Sweden
- Kelly Senecal, Convergent Science, U.S.
- Andrei Liptatnikov, Chalmers University, Sweden
- Bruno Renou, INSA Rouen, France
- Xiaoyi He, Air Products, U.S.
- Isaac Boxx, DLR, Germany
- Venkat Raman, University of Michigan, U.S.
- Tarek Echekki, North Carolina State University, U.S.
- Alessandro Parente, Universite Libre de Bruxelles, Belgium
- Mauro Valorani, University of Rome, Sapienza, Italy
For more details, please visit https://ccrc.kaust.edu.sa/ptc.