Professor and Director of Undergraduate Programs
- Engineering Building III (EB3) 3252
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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.
Washington University, St. Louis
Dr. Echekki primary interests are in the modeling and simulation of turbulent combustion with emphasis on models that overcome the challenges of closure related to turbulence-chemistry interactions. At present, Dr. Echekki is pursuing the development of multi-scale models for turbulent combustion, the development of data-based closure strategies, including experimental data. Multi-scale models involve the hybrid coupling of coarse solution schemes, such as large-eddy simulation (LES), to fine-grained low-dimensional simulations embedded in the coarse solution to capture unresolved physics. Examples of such hybrid approaches include the LES-ODT approach, which couples LES to 1D solutions for multi-component transport and chemistry based on the one-dimensional turbulence (ODT) model. Data-based models exploit the growing availability of high-fidelity simulations and experiments to construct turbulent combustion closure models.
Honors and Awards
- Associate Fellow, American Institute of Aeronautics and Astronautics, 2012
- Fellow, American Society of Mechanical Engineers, 2019
- 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, 111814. https://doi.org/10.1016/j.combustflame.2021.111814
- 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, 103270. 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. https://doi.org/10.1080/00102202.2019.1686702
- Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets
- Ranade, R., Echekki, T., & Masri, A. R. (2021, November 16), FLOW TURBULENCE AND COMBUSTION, Vol. 108, pp. 1043–1067. https://doi.org/10.1007/s10494-021-00304-8
- Large Eddy Simulation on the Effects of Coal Particles Size on Turbulent Combustion Characteristics and NOx Formation Inside a Corner-Fired Furnace
- Sun, W., Zhong, W., Zhang, J., & Echekki, T. (2021), JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 8. https://doi.org/10.1115/1.4048864
- Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames
- , (2020). International Journal of Engine Research. https://doi.org/10.1177/1468087419837770
- In the rain with and without an umbrella? The Reynolds transport theorem to the rescue
- Echekki, T. (2020), EUROPEAN JOURNAL OF PHYSICS, 41(1), 015002. https://doi.org/10.1088/1361-6404/ab4b62
- A framework for data-based turbulent combustion closure: A posteriori validation
- , (2019). Combustion and Flame. https://doi.org/10.1016/j.combustflame.2019.08.039
- A framework for data-based turbulent combustion closure: A priori validation
- , (2019). Combustion and Flame. https://doi.org/10.1016/j.combustflame.2019.05.028