PhD/Post-Doctoral Position on biomass combustion

Biomass combustion under oxy-fuel environment could be a future generation combustion technology with ultra-low CO2 emission in power generation sector. However, current understanding of the biomass combustion chemistry on devolatilization, gas phase combustion, and pollutant formation is insufficient. Fundamental experimental investigations on biomass solid phase combustion, and biomass tar gas phase combustion may help to understand the chemistry and support kinetic model development. Within scope of the SFB/TRR 129 “Oxyflame” project, we would like to understand and describe the biomass combustion chemistry in devolatilization, gas phase combustion, and pollutant formation by experiments and kinetic modelling. Measured speciation data will support the gas phase kinetic model development, and also support the overall biomass combustion model named as OxySim-129 with other projects in SFB/TRR 129 project.

Following burner configurations will be used:

• Counterflow burner
• Plate burner

Our diagnostic equipment covers:

• Gas chromatograph mass spectrometer (GC/MS)
• Time-of-Flight molecular beam mass spectrometer (ToF-MBMS)

• Do you want to support the development of next generation combustion systems?
• Are you looking for new challenges in the field of experiments?
• Is your spectrum of experimental research topics piquing your attention?

NOW is the action time! Send your initiative application to jobs@itv.rwth-aachen.de.

Postdoc position in kinetic model development and quantum chemistry calculation for PAH chemistry and renewable fuels

• Development of chemical kinetic models for PAH formation and fuel oxidation
• Quantum chemistry calculation of important rate coefficients and species thermochemistry data
• Development and application of methods and simulation frameworks for kinetic model analysis and development (sensitivity analysis, uncertainty quantification, optimization)
• Project management and interdisciplinary research (within ITV and with external collaborators & sponsors)
• Publication of research results in high-impact journals (Combustion and Flame, Proceedings of the Combustion Institute, etc.)

Promotion im experimentellen Bereich

Du suchst Herausforderungen im Bereich der experimentellen Analyse von Verbrennungsprozessen? Wir suchen Verstärkung für unser Team und bieten:

• Entwicklung neuartiger Verbrennungskonzepte zur Effizienzsteigerung und Emissionsreduktion
• Projekte zum Design und zur Optimierung von Energieträgern der Zukunft
• Aufbau, Entwicklung und Anwendung hoch-komplexer und einzigartiger Messtechnik
• Modernste Forschungsanlagen zur Beantwortung von top-aktuellen Fragestellungen im Bereich zukünftiger Energiewandlungssysteme

Thermodynamik Dieselmotoren (TV-L 13 Vollzeit)

• Thermodynamische Analyse von motorischen VerbrennungsprozessenGrundlagenforschung an innovativen VerbrennungskonzeptenFreikolben-Lineargenerator)
• Planung, Durchfuhrung sowie Auswertung der Experimente
• Weiterfuhrende Analysen zur Prozessoptimierung und Effizienzsteigerung
• Applikation von Sondermesstechniken zur Online-Analyse der Verbrennungsvorgänge
• Interdisziplinäre Zusammenarbeit im Bereich der Motorreglung

Computational Simulations of Turbulent Reacting Flows (TV-L 13 Vollzeit)

• Development of in-house codes (CIAO, FlameMaster) for high-fidelity predictive simulations;
• DNS of turbulent fields and consequent theoretical investigation for the development of closure models of turbulence;
• DNS of combustion instabilities and theoretical characterization;
• DNS and LES of multiphase flows for the investigation, characterization and modeling of the mechanisms driving the breakup and evaporation phenomena;
• Soot modeling;
• Machine learning: development of physics guided neural networks;
• Machine learning: use of artificial neural network as model into predictive simulations;
• Simulation of combustion systems (e.g. internal combustion engines, nano-particle synthesis burner, heating devices)

Numerical Model Development and Uncertainty Quantification for Fuel Combustion Kinetics (TV-L 13 Vollzeit)

• Numerical modeling of the fundamental chemical processes of fuel combustion
• Development of chemical kinetic models for conventional and alternative fuels
• Development of advanced methods and simulation frameworks for kinetic model
  generation, optimization, and uncertainty quantification
• Interdisciplinary research on reduced kinetic models for CFD simulations
• Machine learning: Use of artificial neural networks for predictive modeling of
  combustion kinetics

Use of machine learning to optimise turbulence modelling