Works (12)
Updated: December 22nd, 2023 05:00
2023 article
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.
author keywords: DeepONet; kernel density estimation; principal component analysis; joint scalar PDFs; turbulent combustion
Sources: Web Of Science, NC State University Libraries
Added: December 18, 2023
2023 article
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.
Sources: Web Of Science, ORCID, NC State University Libraries
Added: November 1, 2023
2022 journal article
Investigation of deep learning methods for efficient high-fidelity simulations in turbulent combustion
COMBUSTION AND FLAME, 236.
author keywords: DeepONet; Kernel density estimation; Principal component analysis; Independent component analysis
Sources: ORCID, Web Of Science, NC State University Libraries
Added: November 1, 2021
2021 journal article
Algorithmically-consistent deep learning frameworks for structural topology optimization
Engineering Applications of Artificial Intelligence.
author keywords: Topology optimization; Deep learning; Sequence models; Algorithmically-consistent learning
TL;DR:
Deep learning-based frameworks consistent with traditional topology optimization algorithms for 3D topological optimization with a reasonably high resolution are developed.
(via Semantic Scholar)
Source: ORCID
Added: October 13, 2021
2021 journal article
An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure
Combustion Science and Technology, 193(7), 1258–1277.
author keywords: PDF turbulent combustion; multi-layer perceptron; self-organized maps; machine-learning
TL;DR:
This work introduces an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOM) for clustering data to tabulate using different networks to address both the multi-dimensionality of the PDF table as well as the computational efficiency of the proposed algorithm.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Source: ORCID
Added: November 7, 2019
2021 journal article
DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization
Computer Methods in Applied Mechanics and Engineering, 378, 113722.
author keywords: Partial Differential Equations; Machine Learning; Discretization Methods; Physics-Informed Learning
TL;DR:
The ML-Solver is demonstrated to solve the steady, incompressible Navier-Stokes equations in 3-D for several cases such as, lid-driven cavity, flow past a cylinder and conjugate heat transfer.
(via Semantic Scholar)
arxiv.org (repository)
Source: ORCID
Added: February 27, 2021
2021 article
Experiment-Based Modeling of Turbulent Flames with Inhomogeneous Inlets
Ranade, R., Echekki, T., & Masri, A. R. (2021, November 16). FLOW TURBULENCE AND COMBUSTION, Vol. 11.
author keywords: Data-based modeling; Kernel density estimation; Principal component analysis; Artificial neural networks
Sources: Web Of Science, NC State University Libraries, ORCID
Added: November 18, 2021
2020 article
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization
(2020, May 17).
Source: ORCID
Added: September 13, 2020
2019 article
A Framework for Data-Based Turbulent Combustion Closure: A Priori Validation
Ranade, R., & Echekki, T. (2019, May 20). Combustion and Flame, Vol. 206, pp. 490–505.
Contributors: n & T. Echekki n
Source: ORCID
Added: June 17, 2019
2019 journal article
A framework for data-based turbulent combustion closure: A posteriori validation
Combustion and Flame, 210, 279–291.
Contributors: n & T. Echekki n
author keywords: Data-based modeling; Joint probability density function; Principal component analysis; Artificial neural networks
Source: ORCID
Added: September 17, 2019
2019 journal article
An ANN based hybrid chemistry framework for complex fuels
FUEL, 241, 625–636.
Contributors: n , S. Alqahtani n, A. Farooq * & T. Echekki n
author keywords: Chemistry reduction; Artificial neural networks; Hydrocarbon oxidation; Pyrolysis
Sources: ORCID, Web Of Science, NC State University Libraries
Added: March 4, 2019
2019 journal article
An extended hybrid chemistry framework for complex hydrocarbon fuels
FUEL, 251, 276–284.
Contributors: n , S. Alqahtani n, A. Farooq * & T. Echekki n
author keywords: Chemistry reduction; Artificial neural networks; Hydrocarbon oxidation; Pyrolysis
Sources: ORCID, Web Of Science, NC State University Libraries
Added: June 17, 2019
Employment
Updated: November 15th, 2019 20:16
2019 - present
Ansys Inc Pittsburgh, PA, US
Machine Learning Researcher
CTO Office
2018 - 2018
Ansys Inc Lebanon, NH, US
Software Development Intern
Fluent reacting flow team
Education
Updated: September 20th, 2019 23:07
2015 - 2019
North Carolina State University Raleigh, NC, US
PhD
Mechanical and Aerospace Engineering
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