Turbulent Spray Combustion: Mechanism Research and Modeling
A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Mathematical Modelling and Numerical Simulation of Combustion and Fire".
Deadline for manuscript submissions: 18 February 2026 | Viewed by 208
Special Issue Editor
Special Issue Information
Dear Colleagues,
Turbulent spray combustion plays a key role in energy efficiency and emission control in propulsion and power systems, involving complex interactions between multiphase flows, turbulence, and chemical kinetics. Challenges span from resolving multiscale phenomena (e.g., droplet dynamics, evaporation, and turbulence–chemistry coupling) to validating models against high-fidelity experiments. Advancing predictive capabilities in this field is vital for optimizing clean combustion technologies and enabling sustainable engineering solutions.
This Special Issue seeks contributions addressing fundamental mechanisms, innovative modeling approaches, and experimental advancements in turbulent spray combustion. Topics include, but are not limited to, the following:
- High-fidelity numerical methods for gas–liquid multiphase flows.
- Turbulent combustion modeling for two-phase reactive flows.
- Atomization/evaporation model development and calibration.
- Multiscale modeling of spray combustion processes.
- Droplet interaction mechanisms and modeling.
- Computational analysis of spray combustion in practical combustors.
- Machine learning-enhanced modeling and validation.
- Innovative diagnostic techniques for spray combustion.
By bridging theory, simulation, and experiments, this Special Issue aims to accelerate breakthroughs in spray combustion research for next-generation applications.
Dr. Jianfeng Zou
Guest Editor
Manuscript Submission Information
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Keywords
- turbulent spray combustion
- multiphase flows
- turbulence–chemistry interaction
- droplet collision
- droplet evaporation
- droplet dynamics
- atomization and evaporation
- combustion diagnostics
- spray combustion modeling
- machine learning in combustion
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