Bibliography¶
Parviz Moin and Krishnan Mahesh. Direct numerical simulation: a tool in turbulence research. Annual review of fluid mechanics, 30(1):539–578, 1998.
Stephen B Pope. Turbulent Flows. Cambridge University Press, Cambridge, England, June 2012.
Thierry Poinsot and Denis Veynante. Theoretical and Numerical Combustion. Volume 28. R.T. Edwards, Inc., Philadelphia, PA, USA, 01 2005.
Antonio Attili and Fabrizio Bisetti. Statistics and scaling of turbulence in a spatially developing mixing layer at reλ = 250. Physics of Fluids, March 2012. URL: http://dx.doi.org/10.1063/1.3696302, doi:10.1063/1.3696302.
Xiang Yang, Wen Zhang, Mahdi Abkar, and William Anderson. Computational fluid dynamics: its carbon footprint and role in carbon reduction. Journal of Renewable and Sustainable Energy, September 2024. URL: http://dx.doi.org/10.1063/5.0217320, doi:10.1063/5.0217320.
Wai Tong Chung, Matthias Ihme, Ki Sung Jung, Jacqueline H. Chen, Jack Guo, Davy Brouzet, Mohsen Talei, Bin Jiang, Bruno Savard, Alexei Y. Poludnenko, Bassem Akoush, Pushan Sharma, and Alex Tamkin. Blastnet simulation dataset. June 2023. URL: https://blastnet.github.io/. URL: https://doi.org/10.5281/zenodo.8034232, doi:10.5281/zenodo.8034232.
Wai Tong Chung, Ki Sung Jung, Jacqueline Chen, and Matthias Ihme. The bearable lightness of big data: towards massive public datasets in scientific machine learning. In ICML 2022 2nd AI for Science Workshop. 2022. URL: https://openreview.net/forum?id=LxGTZM7L6qn.
Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, and Matthias Ihme. Blastnet: a call for community-involved big data in combustion machine learning. Applications in Energy and Combustion Science, 12:100087, 2022. URL: https://www.sciencedirect.com/science/article/pii/S2666352X22000309, doi:https://doi.org/10.1016/j.jaecs.2022.100087.
Wai Tong Chung, Aashwin Ananda Mishra, and Matthias Ihme. Interpretable data-driven methods for subgrid-scale closure in les for transcritical lox/gch4 combustion. Combustion and Flame, 239:111758, May 2022. URL: http://dx.doi.org/10.1016/j.combustflame.2021.111758, doi:10.1016/j.combustflame.2021.111758.
D.A. Donzis and P.K. Yeung. Resolution effects and scaling in numerical simulations of passive scalar mixing in turbulence. Physica D: Nonlinear Phenomena, 239(14):1278–1287, July 2010. URL: http://dx.doi.org/10.1016/j.physd.2009.09.024, doi:10.1016/j.physd.2009.09.024.
Roshan J. Samuel, Mathis Bode, Janet D. Scheel, Katepalli R. Sreenivasan, and Jörg Schumacher. No sustained mean velocity in the boundary region of plane thermal convection. Journal of Fluid Mechanics, October 2024. URL: http://dx.doi.org/10.1017/jfm.2024.853, doi:10.1017/jfm.2024.853.
D. Brouzet, M. Talei, M.J. Brear, and B. Cuenot. The impact of chemical modelling on turbulent premixed flame acoustics. Journal of Fluid Mechanics, 2021. doi:10.1017/jfm.2020.1184.
Victor Coulon, Jessica Gaucherand, Victor Xing, Davide Laera, Corentin Lapeyre, and Thierry Poinsot. Direct numerical simulations of methane, ammonia-hydrogen and hydrogen turbulent premixed flames. Combustion and Flame, 256:112933, October 2023. URL: http://dx.doi.org/10.1016/j.combustflame.2023.112933, doi:10.1016/j.combustflame.2023.112933.
Ki Sung Jung, Seung Ook Kim, Tianfeng Lu, Jacqueline H. Chen, and Chun Sang Yoo. On the flame stabilization of turbulent lifted hydrogen jet flames in heated coflows near the autoignition limit: a comparative dns study. Combustion and Flame, 233:111584, November 2021. URL: http://dx.doi.org/10.1016/j.combustflame.2021.111584, doi:10.1016/j.combustflame.2021.111584.
Nedunchezhian Swaminathan and Alessandro Parente. Machine Learning and Its Application to Reacting Flows: ML and Combustion. Springer Nature, 2023.
Matthias Ihme, Wai Tong Chung, and Aashwin Ananda Mishra. Combustion machine learning: principles, progress and prospects. Prog. Energy Combust. Sci., 91(101010):101010, July 2022.
Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, Jenia Jitsev, and Heinz Pitsch. Using physics-informed super-resolution generative adversarial networks for subgrid modeling in turbulent reactive flows. Proceedings of the Combustion Institute, 38(2):2617–2625, 2019. doi:https://doi.org/10.1016/j.proci.2020.06.022.
Mathis Bode, Michael Gauding, Zeyu Lian, Dominik Denker, Marco Davidovic, Konstantin Kleinheinz, Jenia Jitsev, and Heinz Pitsch. Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows. Proc. Combust. Inst., 38(2):2617–2625, 2021.
L Nista, C D K Schumann, T Grenga, A Attili, and H Pitsch. Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows. Proc. Combust. Inst., 39(4):5279–5288, 2023.
Marc T. Henry de Frahan, Shashank Yellapantula, Ryan King, Marc S. Day, and Ray W. Grout. Deep learning for presumed probability density function models. Combust. Flame, 208:436–450, 2019. doi:10.1016/j.combustflame.2019.07.015.
Lorenzo Piu, Arthur Péquin, Rodolfo S. M. Freitas, Salvatore Iavarone, Heinz Pitsch, and Alessandro Parente. A data-driven approach to refine the partially stirred reactor closure for turbulent premixed flames. Flow, Turbulence and Combustion, January 2025. URL: http://dx.doi.org/10.1007/s10494-024-00626-3, doi:10.1007/s10494-024-00626-3.
Chun Sang Yoo, Ramanan Sankaran, and JH Chen. Three-dimensional direct numerical simulation of a turbulent lifted hydrogen jet flame in heated coflow: flame stabilization and structure. Journal of Fluid Mechanics, 640:453–481, 2009.
Juan Li, Zhenwei Zhao, Andrei Kazakov, and Frederick L. Dryer. An updated comprehensive kinetic model of hydrogen combustion. International Journal of Chemical Kinetics, 36(10):566–575, August 2004. URL: http://dx.doi.org/10.1002/kin.20026, doi:10.1002/kin.20026.
Mauro Valorani, Samuel Paolucci, Emanuele Martelli, Temistocle Grenga, and Pietro P. Ciottoli. Dynamical system analysis of ignition phenomena using the tangential stretching rate concept. Combustion and Flame, 162(8):2963–2990, 2015. URL: https://www.sciencedirect.com/science/article/pii/S0010218015001534, doi:https://doi.org/10.1016/j.combustflame.2015.05.015.
Riccardo Malpica Galassi. Pycsp: a python package for the analysis and simplification of chemically reacting systems based on computational singular perturbation. Computer Physics Communications, 276:108364, 2022. URL: https://www.sciencedirect.com/science/article/pii/S0010465522000832, doi:https://doi.org/10.1016/j.cpc.2022.108364.