Research
Our research focuses on the development of new approaches for turbulent combustion modeling, including the following:
- Data-Based Modeling
- Multiscale Large-Eddy Simulation of Turbulent Reacting Flows
- Combustion Physics and Theory
Data-Based Modeling:
Data-based modeling refers to the construction of turbulent combustion models using either numerical or experimental data. The approach is important when traditional closure models based on standard reactor models (e.g. flamelets) are not sufficient to bridge transitions in combustion mode (e.g. the presence of premixed, non-premixed and partially-premixed combustion), combustion regime (e.g. flamelet vs. distributed reaction) or dominant chemistry (e.g. transition of ignition to flame formation).
We have recently implemented data-based modeling in different contexts, which include:
The Development of a Novel Experiment-Based Framework in Turbulent Combustion
We have recently demonstrated a framework for developing closure models in turbulent combustion using experimental multi-scalar measurements. The framework is based on the construction of conditional means and joint scalar PDFs from experimental data based on the parameterization of the composition space using principal component analysis (PCA). The resulting principal components (PCs) act as both conditioning variables and transported variables. Their chemical source terms are constructed starting from instantaneous temperature and species measurements using a variant of the pairwise mixing stirred reactor (PMSR) approach. A multi-dimensional kernel density estimation (KDE) approach is used to construct the joint PDFs in PC space. Convolutions of these joint PDFs with conditional means are used to determine the unconditional means for the closure terms: the mean PCs chemical source terms and the density. These means are parameterized in terms of the mean PCs using artificial neural networks (ANN). The framework was demonstrated a priori and a posteriori using the data from the Sandia piloted turbulent jet flames D, E and F and the Sydney piloted flames with inhomogeneous inlets.
Representative Publications
R. Ranade & T. Echekki, A framework for data-based turbulent combustion closure: A priori validation, Combustion and Flame 206, 490-505 (2019). https://doi.org/10.1016/j.combustflame.2019.05.028
R. Ranade & T. Echekki, A framework for data-based turbulent combustion closure: A posteriori validation, Combustion and Flame 210, 279-291 (2019). https://doi.org/10.1016/j.combustflame.2019.08.039
Machine-Learning Strategies with Hybrid Chemistry
The oxidation chemistry of complex hydrocarbons involves large mechanisms with hundreds or thousands of chemical species and reactions. For practical applications and computational ease, it is desirable to reduce their chemistry. To this end, high-temperature fuel oxidation for large carbon number fuels may be described as comprising two steps, fuel pyrolysis and small species oxidation. Such an approach has recently been adopted as ‘hybrid chemistry’ or HyChem to handle hightemperature chemistry of jet fuels by utilizing time-series measurements of pyrolysis products. investigated in our studies, a shallow Artificial Neural Network (ANN) is used to fit temporal profiles of fuel fragments to directly extract chemical reaction rate information. This information is then correlated with the species concentrations to build an ANN-based model for the fragments’ chemistry during the pyrolysis stage. Finally, this model is combined with a C-0-C-4 chemical mechanism to model high-temperature fuel oxidation.
Representative Publications
R. Ranade, S. Alqahtani, A. Farooq & T. Echekki, An extended hybrid chemistry framework for complex hydrocarbon fuels, FUEL, 251, 276-284 (2019). https://doi.org/10.1016/j.fuel.2019.04.053
R. Ranade, S. Alqahtani, A. Farooq & T. Echekki, An ANN based hybrid chemistry framework for complex fuels, FUEL, 241, 625–636 (2019). https://doi.org/10.1016/j.fuel.2018.12.082
T. Echekki & H. Mirgolbabaei, Principal component transport in turbulent combustion: A posteriori analysis, Combustion and Flame, 162, 1919-1933 (2015). https://doi.org/10.1016/j.combustflame.2014.12.011
Principle Component Transport in Direct Numerical Simulations (DNS) or DNS-Like Simulations of Combustion Flows
In an effort to accelerate the integration of chemistry and multiscalar transport, principal component transport is investigated. As discussed above, PC transport has been demonstrated within the context of RANS and LES. However, PC transport also can be used to accelerate DNS or lower-dimensional models, such as one-dimensional turbulence.
Representative Publications
O. Owoyele, T. Echekki, Toward computationally efficient combustion DNS with complex fuels via principal component transport, Combustion Theory and Modelling, 21(4), 770–798 (2017). (doi: 10.1080/13647830.2017.1296976)
H. Mirgolbabaei, T. Echekki, The reconstruction of thermo-chemical scalars in combustion from a reduced set of their principal components, Combustion and Flame 162, 1650-1652 (2015). https://doi.org/10.1016/j.combustflame.2014.11.027
T. Echekki, H. Mirgolbabaei, Principal component transport in turbulent combustion: A posteriori analysis, Combustion and Flame 162, 1919-1933 (2015). https://doi.org/10.1016/j.combustflame.2014.12.011
H. Mirgolbabaei, T. Echekki, N. Smaoui, A nonlinear principal component analysis approach for turbulent combustion composition space, International Journal of Hydrogen Energy 39, 4622-4633 (2014). https://doi.org/10.1016/j.ijhydene.2013.12.195
H. Mirgolbabaei, T. Echekki, Nonlinear reduction of combustion space with kernel principal component analysis, Combustion and Flame 161, 118-126 (2014). https://doi.org/10.1016/j.combustflame.2013.08.016
H. Mirgolbabaei, T. Echekki, A novel principal component analysis-based acceleration scheme for LES-ODT: An a priori study, Combustion and Flame 160, 898-908 (2013). https://doi.org/10.1016/j.combustflame.2013.01.007
Machine Learning Strategies for Turbulent Combustion Closure
The goal is to use machine-learning approaches to improve turbulent combustion closure. Our most recent work involves collaborations with Ansys (Ranade et al, 2019) as well as Argonne National Lab (Owoyele et al, 2019) to improve the “tabulation” of flamelet models using machine-learning approaches.
Another effort involves the use of low-dimensional stochastic simulations, such as one-dimensional turbulence (ODT) to construct closure models for the thermo-chemical scalars statistical distributions (or PDFs) or for their conditional means. ODT represents a computationally effective simulation approach to generate statistical data at a much lower expense than multi-dimensional DNS.
Representative Publications
R. Ranade, G. Li, S. Li, T. Echekki, An efficient machine-learning approach for PDF tabulation in turbulent combustion closure, Combustion Science and Technology, Nov. 2019. (doi: 10.1080/00102202.2019.1686702).
Multiscale Large-Eddy Simulation of Turbulent Reacting Flows:
The approach is based on the coupling of 3D LES with embedded 1D fine-grained solutions based on the one-dimensional turbulence (ODT) model.
Different approaches and physical models have been developed within the context of the LES-ODT approach. The first approach is based on stationary 1D solutions embedded in the 3D computational domain. The second approach, termed flame embedding, is based on the embedding of 1D ODT solutions on the flame surface.
Different physical models have been developed within the context of LES-ODT, including the incorporation of chemistry, a multiscale radiation approach based on photon Monte-Carlo methods, and an adaptive grid methodology, which has been implemented for turbulent shear-driven flows.
Representative Publications
A.F. Hoffie, T. Echekki, A coupled LES-ODT model for spatially-developing turbulent reacting shear layers, International Journal of Heat and Mass Transfer,
127, 458–473 (2018). https://doi.org/10.1016/j.ijheatmasstransfer.2018.06.105
Y. Fu, T. Echekki, Upscaling and downscaling approaches in les-odt for turbulent combustion flows, International Journal for Multiscale Computational Engineering, 16(1), 45–76 (2018).
S. Srivastava, T. Echekki, Particle-filter based upscaling for turbulent reacting flow simulations, International Journal for Multiscale Computational Engineering, 15(1), 1–17 (2017).
S. Ben Rejeb, T. Echekki, Thermal radiation modeling using the LES-ODT framework for turbulent combustion flows, International Journal of Heat and Mass Transfer, 104, 1300–1316 (2017). https://doi.org/10.1016/j.ijheatmasstransfer.2016.09.074
J. Park, T. Echekki, LES-ODT study of turbulent premixed interacting flames, Combustion and Flame 159, 609-620 (2012). https://doi.org/10.1016/j.combustflame.2011.08.002
T. Echekki, The emerging role of multiscale methods in turbulent combustion, in Turbulent Combustion Modeling: Advances, New Trends and Perspectives, pp. 177-192 (T. Echekki, E. Mastorakos, Editors), Springer 2011. https://doi-org.prox.lib.ncsu.edu/10.1007/978-94-007-0412-1_8
S. Cao, T. Echekki, A low-dimensional stochastic closure model for combustion large-eddy simulation, Journal of Turbluence 9, 1-35 (2008). (doi: 10.1080/14685240701790714).
Combustion Physics and Theory:
Representative Publications
P. Kundu, T. Echekki, Y.J. Pei, S. Som, An equivalent dissipation rate model for capturing history effects in non-premixed flames, Combustion and Flame, 176, 202–212 (1017).
T. Echekki, Asymptotic analysis of steady two-reactant premixed flames using a step-function reaction rate model, Combustion and Flame, 172, 280–288 (2016).