Associate Department Head
- Phone: (919) 515-5238
- Email: firstname.lastname@example.org
- Office: Engineering Building III (EB3) 3252
- Website: https://echekki.wordpress.ncsu.edu/
In addition to his duties as Associate Department Head, Dr. Tarek Echekki also serves as MAE’s Director of Undergraduate Programs.
At the graduate level, Dr. Echekki has taught Fluid Dynamics of Combustion I (MAE 504) and the follow up advanced combustion course, Fluid Dynamics of Combustion II (MAE 704). He also has taught the graduate Fluid Dynamics course, Foundations of Fluid Dynamics (MAE 550) and an introduction to Turbulence, Turbulence (MAE 776).
At the undergraduate level, he has taught Engineering Thermodynamics I and II (MAE 201 and MAE 302) and fluid Mechanics I (MAE 308).
Combustion plays an important role in the solution of many of the engineering problems that we face today. Graduate students who work with Dr. Echekki are also drawn to this area because of its breadth. The reliance of combustion on thermodynamics, heat transfer, and fluid mechanics means that the subject is never boring and provides a foundation from which the student can later branch out.
Outside of work, Dr. Echekki spends time with his family and friends.
- On the application of principal component transport for compression ignition of lean fuel/air mixtures under engine relevant conditions
- Jung, K. S., Kumar, A., Echekki, T., & Chen, J. H. (2024), Combustion and Flame. https://doi.org/10.1016/j.combustflame.2023.113204
- Acceleration of turbulent combustion DNS via principal component transport
- Kumar, A., Rieth, M., Owoyele, O., Chen, J. H., & Echekki, T. (2023), COMBUSTION AND FLAME, 255. https://doi.org/10.1016/j.combustflame.2023.112903
- Derived scalar statistics from multiscalar measurements via surrogate composition spaces
- Taassob, A., & Echekki, T. (2023), COMBUSTION AND FLAME, 250. https://doi.org/10.1016/j.combustflame.2023.112641
- Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements
- Taassob, A., Ranade, R., & Echekki, T. (2023, October 31), ENERGY & FUELS, Vol. 10. https://doi.org/10.1021/acs.energyfuels.3c02410
- CFD multiphase combustion modelling of oleic by-products pellets in a counter-current fixed bed combustor
- Mami, M. A., Lajili, M., & Echekki, T. (2022), COMPTES RENDUS CHIMIE, 25, 113–127. https://doi.org/10.5802/crchim.170
- Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion
- Gitushi, K. M., Ranade, R., & Echekki, T. (2022), COMBUSTION AND FLAME, 236. https://doi.org/10.1016/j.combustflame.2021.111814
- Principal component analysis based combustion model in the context of a lifted methane/air flame: Sensitivity to the manifold parameters and subgrid closure
- Malik, M. R., Coussement, A., Echekki, T., & Parente, A. (2022), COMBUSTION AND FLAME, 244. https://doi.org/10.1016/j.combustflame.2022.112134
- A data-based hybrid model for complex fuel chemistry acceleration at high temperatures
- Alqahtani, S., & Echekki, T. (2021), COMBUSTION AND FLAME, 223, 142–152. https://doi.org/10.1016/j.combustflame.2020.09.022
- A numerical study of backdraft phenomena under normal and reduced gravity
- Ashok, S. G., & Echekki, T. (2021), FIRE SAFETY JOURNAL, 121. https://doi.org/10.1016/j.firesaf.2020.103270
- An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure
- Ranade, R., Li, G., Li, S., & Echekki, T. (2021), Combustion Science and Technology, 193(7), 1258–1277. https://doi.org/10.1080/00102202.2019.1686702
- Reduced Order Surrogate Models for Direct Numerical Simulation for Exascale Computing in Turbulent Combustion
- From Experiments to Models: A Data-Science Approach for Low-Temperature Chemistry
- Liquid Rocketry Lab
- EAGER: An Experiment-Based Framework for Turbulent Combustion Modeling
- Acquisition of a Computational Code from Sandia National Laboratories
- Modelling Combustion Noise Spectrum for Lean-Burn Engines
- Multiphysics Simulation of Injection and Combustion of Supercritical Fuels
- Multiscale Turbulent Reacting Flows and Data-Based Modeling
- Computational Methods For Multiscale Turbulent Reacting Flows
- A Multiscale Approach For Turbulence, Chemistry and Radiative Heat Transport Modeling in Combustion