Zacharias Nikolaou
Zacharias Nikolaou
University of Cambridge
Verified email at strath.ac.uk
Title
Cited by
Cited by
Year
Heat release rate markers for premixed combustion
ZM Nikolaou, N Swaminathan
Combustion and flame 161 (12), 3073-3084, 2014
622014
A 5-step reduced mechanism for combustion of CO/H2/H2O/CH4/CO2 mixtures with low hydrogen/methane and high H2O content
ZM Nikolaou, JY Chen, N Swaminathan
Combustion and flame 160 (1), 56-75, 2013
592013
Heat release rate estimation in laminar premixed flames using laser-induced fluorescence of CH2O and H-atom
IA Mulla, A Dowlut, T Hussain, ZM Nikolaou, SR Chakravarthy, ...
Combustion and Flame 165, 373-383, 2016
312016
Direct numerical simulation of complex fuel combustion with detailed chemistry: physical insight and mean reaction rate modeling
ZM Nikolaou, N Swaminathan
Combustion Science and Technology 187 (11), 1759-1789, 2015
192015
Evaluation of a reduced mechanism for turbulent premixed combustion
ZM Nikolaou, N Swaminathan, JY Chen
Combustion and flame 161 (12), 3085-3099, 2014
162014
Progress Variable Variance and Filtered Rate Modelling Using Convolutional Neural Networks and Flamelet Methods
ZM Nikolaou, C Chrysostomou, L Vervisch, RS Cant
Flow Turbulence and Combustion, 2019
122019
A priori assessment of an iterative deconvolution method for LES sub-grid scale variance modelling
ZM Nikolaou, L Vervisch
Flow, Turbulence and Combustion 101 (1), 33-53, 2018
122018
Direct mapping from LES resolved scales to filtered-flame generated manifolds using convolutional neural networks
A Seltz, P Domingo, L Vervisch, ZM Nikolaou
Combustion and Flame 210, 71-82, 2019
112019
Scalar flux modelling in turbulent flames using iterative deconvolution
ZM Nikolaou, RS Cant, L Vervisch
Phys. Rev. Fluids 3 (043201), 2018
92018
Modelling turbulent premixed flames using convolutional neural networks: application to sub-grid scale variance and filtered reaction rate.
ZM Nikolaou, C Chrysostomou, L Vervisch, S Cant
arXive:1810.07944v1, 1-19, 2018
52018
Assessment of FSD and SDR closures for turbulent flames of alternative fuels
ZM Nikolaou, N Swaminathan
Flow, Turbulence and Combustion 101 (3), 759-774, 2018
32018
Assessment of deconvolution-based flamelet methods for progress variable rate modelling.
LV Z.M. Nikolaou
Aeronautics and Aerospace Open Access Journal 2 (5), 274-281, 2018
32018
Neural network-based modelling of unresolved stresses in a turbulent reacting flow with mean shear
ZM Nikolaou, C Chrysostomou, Y Minamoto, L Vervisch
arXiv:1904.08167 [physics.flu-dyn], 2019
22019
Study of multi-component fuel premixed combustion using direct numerical simulation
ZM Nikolaou
University of Cambridge, 2014
22014
Unresolved stress tensor modeling in turbulent premixed V-flames using iterative deconvolution: An a priori assessment
ZM Nikolaou, Y Minamoto, L Vervisch
Physical Review Fluids 4 (6), 063202, 2019
12019
Accelerating simulations using REDCHEM_v0. 0 for atmospheric chemistry mechanism reduction
ZM Nikolaou, JY Chen, Y Proestos, J Lelieveld, R Sander
Geoscientific Model Development 11 (8), 3391-3407, 2018
12018
Evaluation of a Neural Network-Based Closure for the Unresolved Stresses in Turbulent Premixed V-Flames
Z Nikolaou, C Chrysostomou, Y Minamoto, L Vervisch
Flow Turbulence and Combustion, 2020
2020
From Discrete and Iterative Deconvolution Operators to Machine Learning for Premixed Turbulent Combustion Modeling
P Domingo, Z Nikolaou, A Seltz, L Vervisch
Data Analysis for Direct Numerical Simulations of Turbulent Combustion, 215-232, 2020
2020
Assessment of FSD and SDR closures for turbulent flames of alternative fuels. Flow, Turbulence and Combustion. ISSN 1573-1987
ZM Nikolaou, N Swaminathan
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Articles 1–19