Forschung:

Lehre: -

• H. Chu, L. Berger, M. Gauding, A. Attili and H. Pitsch. Effects of dilatation and turbulence on tangential strain rates in premixed hydrogen and iso-octane flames. Journal of Fluid Mechanics, vol. 981, page A5, 2024. [DOI]
• T. Lehmann, M. Gauding, L. Berger, A. Attili and H. Pitsch. Intrinsic Flame Instabilities in Ammonia/Hydrogen Flames: Analysis of Temperature and Progress Variable. 2nd Symposium on Ammonia Energy, Orléans July 11, 2023.
• P. E. Lapenna, M. Gauding, L. Berger, S. A. Kassar, A. Attili, G. Troiani, A. Remiddi, F. Creta and H. Pitsch. Recent advancements in understanding and modelling of intrinsically unstable Hydrogen-Air premixed flames. ERCOFTAC Bulletin, vol. 136, pages 23-30, 2023.
• F Cameron, H. Xu, S. Nardi, S. Nerzak, J. Beeckmann, M. Gauding, Y. Ren, L Berger, B. Feller and H. Pitsch. Experimental and numerical study of the influence of fuel staging and hydrogen addition on turbulent methane flames in a lab-scale industrial burner. In 31. Deutscher Flammentag für nachhaltige Verbrennung, Berlin, 2023.
• H. Chu, L Berger, M. Gauding, S. Pieper and H Pitsch. Effects of equivalence ratio inhomogeneities on hydrogen and iso-octane flame kernel development under engine conditions. In 31. Deutscher Flammentag für nachhaltige Verbrennung, Berlin, 2023.
• T. Lehmann, L. Berger, M. Gauding, S. Girhe, F. Creta, B. Dally and H. Pitsch. Intrinsic Flame Instabilities in Laminar Ammonia/Hydrogen Flames: Effects of Hydrogen-Fraction and Equivalence Ratio. In Proceedings of the 11th European Combustion Meeting, 2023.
• T. Lehmann, M. Gauding, L. Berger, A. Atiili and H. Pitsch. Intrinsic Flame Instabilities in Ammonia/Hydrogen Flames: Analysis of Temperature and Progress Variable. 2nd Symposium on Ammonia Energy, 2023.
• H. Chu, L. Berger, T. Grenga, M. Gauding, L. Cai and H. Pitsch. Effects of turbulence on variations in early development of hydrogen and iso-octane flame kernels under engine conditions. Combustion and Flame, vol. 255, page 112914, 2023. [DOI]
• M. Bode, M. Gauding, Z. Lian, D. Denker, M. Davidovic, K. Kleinheinz, J. Jitsev and H. Pitsch. Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows. Proceedings of the Combustion Institute, vol. 38 no. 2, pages 2617-2625, 2021.
• D. Denker, A. Attili, M. Gauding, K. Niemietz, M. Bode and H. Pitsch. A new modeling approach for mixture fraction statistics based on dissipation elements. Proceedings of the Combustion Institute, vol. 38, pages 2681-2689, 2021. [DOI]
• D. Denker, A. Attili, J. Boschung, F. Hennig, M. Gauding, M. Bode and H. Pitsch. Dissipation element analysis of non-premixed jet flames. Journal of Fluid Mechanics, vol. 905, 12 2020. [DOI]
• M. Gauding, D. Denker, Y. Brahami, M. Bode, E. Varea and L. Danaila. A non-local analysis of strong fluctuations in non-premixed turbulent jet flames. In 11th U.S. National Combustion Meeting, March 24th - 27th, Pasadena, California, US, 2019.
• M. Gauding, Y. Brahami, L. Danaila, E. Varea, D. Denker and M. Bode. The effect of external intermittency on the self-similarity of reactive and non-reactive turbulent jet flows. In VAVIDEN Workshop (Variable Viscosity and Density Flows Workshop), June 27th - 28th, Rouen, France, 2019.
• D. Denker, A. Attili, S. Luca, F. Bisetti, M. Gauding and H. Pitsch. Dissipation Element Analysis of Turbulent Premixed Jet Flames. Combustion Science and Technology, vol. 191, pages 1-16, 2019. [DOI]
• M. Bode, M. Gauding, K. Kleinheinz and H. Pitsch. Modeling turbulent/reactive flows using high performance computing data and deep learning. In ISC High Performance, June 16th – 20th, Frankfurt, Germany, 2019.
• D. Denker, A. Attili, M. Bode, M. Gauding and H. Pitsch. Dissipation Element Analysis of Turbulent Non-Premixed Jet Flames. In VAVIDEN Workshop (Variable Viscosity and Density Flows Workshop), June 27th - 28th, Rouen, France, 2019.
• M. Bode, M. Gauding, J.H. Göbbert, B. Liao, J. Jitsev and H. Pitsch. Towards Prediction of Turbulent Flows at High Reynolds Numbers Using High Performance Computing Data and Deep Learning. In Lecture Notes in Computer Science, volume 11203, 2018.
• M. Bode, M. Gauding, J.H. Göbbert and H. Pitsch. Using deep learning for prediction of turbulent flow statistics at high Reynolds numbers. In APS DFD, November 18th-20th, Atlanta, USA, 2018.