Many open questions remain in the study of turbulent combustion, hindering the design of next-generation energy systems. A key gap in our knowledge is the lack of proper experimental data that can be used to validate and inspire predictive models. Due to the inherent three-dimensional (3D) spatial structures of practical turbulent flames, instantaneous 3D measurements of fundamental flame properties have been long desired for use in model development. However, existing experimental efforts are predominately limited to obtaining data at a point, along a line, or across a 2D plane. This project is to develop novel diagnostics that can enable 3D measurements in flames. These valuable experimental data will be used for model validation and development. The knowledge generated in this project will help with the design and optimization of propulsion and energy systems utilizing combustion. Additional efforts in this project include establishing collaborations across boundaries of disciplines and institutions as well as engaging industry to accelerate technology transfer.

Turbulent combustion is governed by the intricate interactions between chemical reactions and turbulence. Such interactions inherently occur in 3D, and consequently measurements that can resolve the variations of combustion properties in all three dimensions have long been desired. This project will address this long-standing need with a two-phase plan. In the first phase, the team will investigate and utilize three novel 3D diagnostic techniques, including high resolution tomography, machine learning assisted diagnostics, and hybrid 3D diagnostics. In the second phase, the team will apply these new diagnostics to obtain 3D measurements of fundamental combustion properties in canonical turbulent flames. The target properties include 3D flame topography and 3D-3-component velocity. Both are crucial for understanding turbulent flame dynamics and improving model accuracy. This research is highly interdisciplinary and can contribute to fundamental combustion science, optical diagnostics, and computational modeling.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2019-03-01
Budget End
2022-02-28
Support Year
Fiscal Year
2018
Total Cost
$318,904
Indirect Cost
Name
University of Virginia
Department
Type
DUNS #
City
Charlottesville
State
VA
Country
United States
Zip Code
22904