| Heat release rate markers for premixed combustion ZM Nikolaou, N Swaminathan Combustion and flame 161 (12), 3073-3084, 2014 | 100 | 2014 |
| 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 | 80 | 2013 |
| 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 | 72 | 2016 |
| 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 | 61 | 2019 |
| 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 | 57 | 2019 |
| Evaluation of a reduced mechanism for turbulent premixed combustion ZM Nikolaou, N Swaminathan, JY Chen Combustion and flame 161 (12), 3085-3099, 2014 | 26 | 2014 |
| Scalar flux modelling in turbulent flames using iterative deconvolution ZM Nikolaou, RS Cant, L Vervisch Phys. Rev. Fluids 3 (043201), 2018 | 25 | 2018 |
| A priori assessment of an iterative deconvolution method for LES sub-grid scale variance modelling ZM Nikolaou, L Vervisch Flow, Turbulence and Combustion 101, 33-53, 2018 | 24 | 2018 |
| 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 | 24 | 2015 |
| 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 | 17 | 2020 |
| 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 | 12 | 2018 |
| 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 | 10 | 2019 |
| 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: From …, 2020 | 6 | 2020 |
| Criteria to switch from tabulation to neural networks in computational combustion Z Nikolaou, L Vervisch, P Domingo Combustion and Flame 246, 112425, 2022 | 5 | 2022 |
| 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 | 5 | 2018 |
| 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 | 3 | 2018 |
| 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 | 3 | 2018 |
| An optimisation framework for the development of explicit discrete forward and inverse filters Z Nikolaou, L Vervisch, P Domingo Computers & Fluids 255, 105840, 2023 | 2 | 2023 |
| Study of multi-component fuel premixed combustion using direct numerical simulation ZM Nikolaou | 2 | 2014 |
| 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 | 1 | 2019 |
Luc VervischINSA Rouen Normandie & IUF在 insa-rouen.fr 的电子邮件经过验证
