Publikationen

Institute of Combustion Technology for Aerospace Engineering

Hier finden Sie eine Liste der am IVLR veröffentlichten Publikationen.

IVLR Publications

  1. Kang, Y., & Hampp, F. (2024). Effect of Swirl-Afflicted Turbulence on Pressure Swirl Spray in High-Momentum Jet-Stabilized Burners. In 16th International Workshop on Measurement and Computation of Turbulent Nonpremixed Flames (TNF). https://tnfworkshop.org/
  2. Kang, Y., Ahn, J., & Hampp, F. (2024). Low Swirl Effect on Compact Spray and Combustion Systems Using Additive Manufactured Dual Airblast Injectors. Journal of Engineering for Gas Turbines and Power. https://doi.org/10.1115/1.4066005
  3. Jose, B., & Hampp, F. (2024). ML-based diagnostics on experimental data. In Measurement and Observation Techniques for Aerospace Research.
  4. Hampp, F., Schäfer, D., & Lammel, O. (2024). Spray flame characterisation under lean blow-out conditions. Combustion and Flame. https://doi.org/10.1016/j.combustflame.2024.113623
  5. Hampp, F. (2024). IVLR DataVerse on ML-augmented diagnostics. In DaRUS. https://darus.uni-stuttgart.de/dataverse/ivlr
  6. Griebel, P., Hampp, F., Lückerath, R., Lammel, O., Grein, T., Sallinen, R., Vilja, J., & Sandberg, K. (2024). Evaluation of Current and Future Aviation Fuels at High-Pressure RQL-Type Combustor Conditions. Journal of Engineering for Gas Turbines and Power. https://doi.org/10.1115/1.4066350
  7. Kang, Y., & Hampp, F. (2023). Quantification of Spray Experiments via Machine Learning. In Statusseminar SimTech.
  8. Kang, Y., & Hampp, F. (2023, September). Experimental Characterisation of Pressure Swirl and Airblast Sprays in Multiscale Turbulence. 32nd ILASS Europe Conference.
  9. Jose, B., & Hampp, F. (2023). Physics-informed domain randomisation for synthetic training data generation. In 9th WAW Workshop Machine Learning.
  10. Jose, B., & Hampp, F. (2023). DNN model development for experimental characterisation of multi-phase flows. In Statusseminar SimTech.
  11. Jose, B., & Hampp, F. (2023, September). Machine learning based spray process quantification. 32nd ILASS Europe Conference.
  12. Jose, B., & Hampp, F. (2023, Oktober). Training computer vision models without labelled data using physics informed domain randomisation. Int. Conf. on Data-Integrated Simulation Science.
  13. Hampp, F., Schäfer, D., & Lammel, O. (2023). Spray Flame Characterization of a Dual Injector for Compact Combustion Systems. Combustion Science and Technology. https://doi.org/10.1080/00102202.2023.2249222
  14. Hampp, F. (2023). Panel discussion: Fuel flexibility and H2-enrichment in gas turbine engines. In 15th International Workshop on Measurement and Computation of Turbulent Nonpremixed Flames (TNF).
  15. Hampp, F., Schäfer, D., & Lammel, O. (2023, September). Liquid fuel combustion for compact GT combustors. 31st Deutscher Flammentag.
  16. Jose, B., Geigle, K. P., & Hampp, F. (2022). Supervised learning without labelling: Applied domain randomization on scientific images. In 8th WAW Workshop Machine Learning.
  17. Hampp, F. (2022). Experimental data-driven flow simulations. In Statusseminar SimTech.
  18. Hampp, F. (2022). Machine Learning-based Data Analysis for Optical Diagnostics. In LACSEA.
Veröffentlichungen des DLR Instituts für Verbrennungstechnik sortiert nach Abteilungen
Zum Seitenanfang