Tarek Echekki

Associate Department Head

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.


A Data-Based Hybrid Chemistry Acceleration Framework for the Low-Temperature Oxidation of Complex Fuels
Alqahtani, S., Gitushi, K. M., & Echekki, T. (2024), Energies. https://doi.org/10.3390/en17030734
Combustion chemistry acceleration with DeepONets
Kumar, A., & Echekki, T. (2024), Fuel. https://doi.org/10.1016/j.fuel.2024.131212
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
Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data
Ranade, R., Gitushi, K. M., & Echekki, T. (2023, November 25), COMBUSTION SCIENCE AND TECHNOLOGY, Vol. 11. https://doi.org/10.1080/00102202.2023.2283816
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
ML for reacting flows _ editorial
Vervisch, L., & Echekki, T. (2023, December), APPLICATIONS IN ENERGY AND COMBUSTION SCIENCE, Vol. 16. https://doi.org/10.1016/j.jaecs.2023.100208
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

View all publications via NC State Libraries


  • 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
Tarek Echekki