Data-Based Modal Decomposition and Reduced Order Model Development for Acoustically Forced Jet Diffusion Flames
Abstract
Data-based modal decomposition will be applied to temporally resolved images from the laminar UCLA EPRL and turbulent Edwards AFRL experiments on coaxial jet diffusion flames. Machine learning tools will be then employed to develop reduced order models with flame lift-off and extinction predictive capabilities. Such an effort will lead to a better understanding of oscillatory combustion. This is relevant for the development of efficient fuel and oxidizer injection systems for liquid rocket engines and air-breathing propulsion systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2023
- Source ID
- W911NF2210274
Entities
People
- Marcilio Alves
Organizations
- Army Contracting Command
- Fluminense Federal University
- United States Army