Department of Chemical and Process Engineering  ¦ Engler-Bunte-Institute ¦ Deutsch ¦ Impressum ¦ Data Protection ¦ KIT
Kolloquium Verbrennungstechnik

Programm des "Kolloquium Verbrennungstechnik" für das WS-2019/20 ist verfügbar.
more...

Gasphase
Equilibrium calculator

Please try out our program for calculating the gas phase equilibrium state.
more...

Contact

Engler-Bunte-Ring 7
76131 Karlsruhe 

Building number 40.13.I 

Tel: +49(0)721 608-42571
Fax: +49(0)721 608-47770

E-Mail: Secretariat

Verbrennungstechnisches Seminar

Programm des "Verbrennungstechnischen Semiars" für das WS-2019/20 ist verfügbar.
more ...

Bachelor- and Masterthesis

Current proposals for topics of bachelor- and master thesis you find on the following page.
more ...

Influence of an oscillating air flow on the atomization process at the airblast atomization

Project: MAGISTER

Machine learning for Advanced Gas turbine Injection SysTems to Enhance combustoR performance.

Air transportation is expected to grow persistently over the next decades. Clean combustion technology for aircraft engines is a key enabler to reduce the impact of this growth on ecosystems and humans’ health. The vision for European aviation is shaped by the Advisory Council for Aviation Research and Innovation in Europe in the Flight Path 2050 goals, which define stringent regulations on pollutant emissions.

To meet these goals, the major engine manufacturers develop lean premixed combustors operated at very high pressure. This development introduces a large risk for reduced reliability and lifetime of engines: pressure oscillations in the combustor called thermoacoustics.

Aviation industry encounters currently the fourth industrial revolution: cyber-physical systems analyze and monitor technical systems and take automated decisions. This industrial revolution is known as “Industry 4.0” in Germany and “Industrial Internet” in the USA. An essential enabler of the fourth industrial revolution is Machine Learning.

The ITN MAGISTER will utilize Machine Learning to predict and understand thermoacoustics in aircraft engine combustors, and lead combustion research a revolutionary new approach in this area.

 

Research

Aim of this work is to provide the validation experimental data, which will be combined with combustion and acoustic models, in order for the thermoacoustic simulation for aircraft engines to be achieved through Machine Learning.

The experiments will investigate the influence of well defined air excitation on droplet dimension and liquid to air distribution and its response compared to steady state conditions.

Using a device for a forced oscillating air excitation, there will be measurements of the unsteady flow field, the droplet dimension and velocity, for various air flow speeds, excitation frequencies and excitation amplitudes. These measurements will be performed for 2 air blast nozzle designs, one generic and one application oriented. The research will take place at an atmospheric test rig.

The measurement techniques that will be used are optical: LDA and PDA measurements can be applied.

Expected result is the quantification of the influence of acoustic air modulation on the liquid spray characteristics, and its dependence on the modulation frequency and amplitude.