Passive scalar interface in a spatially evolving mixing layer (A. Attili and D. Denker)

Quartz nozzle sampling (D. Felsmann)

Dissipation element analysis of a planar diffusion flame (D. Denker)

Turbulent/non-turbulent interface in a temporally evolving jet (D. Denker)

Dissipation elements crossing a flame front (D. Denker and B. Hentschel)

Particle laden flow (E. Varea)

Turbulent flame surface in non-premixed methane jet flame (D. Denker)

DNS of primary break up (M. Bode)

Diffusion flame in a slot Bunsen burner (S. Kruse)

Various quantities in spatially evolving jet diffusion flame (D. Denker)

OH layer in a turbulent wall bounded flame (K. Niemietz)

Gandolfo Scialabba, M. Sc.

E-Mail:  g.scialabba(at)itv.rwth-aachen.de

Adresse:
              Institut für Technische Verbrennung
              RWTH Aachen University
              Templergraben 64
              52056 Aachen

Telefon: +49 (0)241 80-94544
Telefax: +49 (0)241 80-92923

Büro:  219 (2. Etage)

Arbeitsgebiete

Forschung: - Soot Modelling

Lehre: - Numerische Strömungssimulation (Übungsassistent)

Publikationen

  • G. Scialabba, A. Attili, L. Berger and H. Pitsch. SootDNS: Studying soot oxidation based on direct numerical simulation of a turbulent non-premixed ethylene/air jet flame. HLRS Annual Review Workshop, Stuttgart, 2023.
  • L. Nista, C.D.K Schumann, G. Scialabba, T. Grenga, A. Attili and H. Pitsch. The influence of adversarial training on turbulence closure modelling. AIAA SciTech 2022 Forum, 4-9 January 2022, 2022.
  • T. Grenga, L. Nista, C. Schumann, A. N. Karimi, G. Scialabba, A. Attili and H. Pitsch. Predictive Data-Driven Model Based on Generative Adversarial Network for Premixed Turbulence-Combustion Regimes. Combustion Science and Technology, pages 1-24, 03 2022. [DOI]
  • G. Scialabba, A. Attili, L. Berger and H. Pitsch. Direct numerical simulation of soot oxidation in a three-dimensional turbulent non-premixed ethylene jet flame. 18th International Conference on Numerical Combustion, 2022.
  • T. Grenga, L. Nista, C. D. K. Schumann, A. N. Karimi, G. Scialabba, M. Bode, A. Attili and H. Pitsch. Predictive data driven turbulence-combustion model through Super Resolution Generative Adversarial Network. In Proceedings of the 10th European Combustion Meeting, April 14-15, Naples (Italy) / Online, 2021.
  • L. Nista, C. D. K. Schumann, T. Grenga, A. N. Karimi, G. Scialabba, M. Bode, A. Attili and H. Pitsch. Turbulent mixing predictive model with physics-based Generative Adversarial Network. In Proceedings of the 10th European Combustion Meeting, April 14-15, Naples (Italy) / Online, 2021.
  • A. Attili, N. Sorace, L. Nista, C. Schumann, A. Karimi, G. Scialabba, T. Grenga and H. Pitsch. Investigation of the Extrapolation Performance of Machine Learning Models for LES of Turbulent Premixed Combustion. In Proceedings of the 10th European Combustion Meeting, April 14-15, Naples (Italy) / Online, 2021.
  • G. Scialabba, R. Langer, A. Attili, L. Berger and H. Pitsch. The relevance of soot diffusion in counterflow laminar flame configurations. In Proceedings of the 10th European Combustion Meeting, April 14-15, Naples (Italy) / Online, 2021.

Offene Stellen/Arbeiten

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