Droplet characterisation

Under gas turbine conditions, aerodynamic forces strongly influence the atomization performance of liquid fuels. Our research aims to enhance the fundamental understanding of these complex interactions through advanced quantitative diagnostics and data analysis. By developing state-of-the-art AI-based detection models, we analyse experimental data to accurately identify ligaments and droplets, thereby clearly describing the spray processes from primary atomization through turbulent dispersion. This comprehensive approach provides crucial insights that guide the optimization of injection systems, ensuring excellent atomization quality. Improved atomization enables rapid evaporation and optimal mixture formation, essential for reducing non-CO2​ emissions such as nitrogen oxides (NOX) and particulates in advanced combustion systems.

The developed model precisely distinguishes ligaments from droplets to quantitatively determine geometric shape and droplet size distributions. Validation against independent diagnostic techniques ensures high accuracy, robustness, and reliable predictions across diverse operating conditions.

If you are interested in our project, have further questions, or would like to support us through student work, internships, or thesis projects, we would be delighted to hear from you via email or phone. Contact details can be found below.