Turbulent spray combustion is a common phenomenon occurring in a number of practical devices such as aircraft engines, gas turbines, and chemical reactors. A predictive model for spray combustion is vital in estimating an operational envelope for the safe and efficient use of these systems. Development of such models, however, is a tremendous challenge given the multiscale multiphysics nature of the spray combustion process.
In the recent past, large-eddy simulation (LES) has emerged as a viable alternative to conventional Reynolds-Averaged Navier-Stokes (RANS) approach to combustion modeling. In LES, the large-scale motions are directly computed while the small-scale processes are modeled. While LES has shown considerable accuracy in predicting single-phase flames far from extinction, modeling more complex processes such as spray combustion or flame extinction faces the same insurmountable difficulties as RANS-based approaches. The main reason for this impasse is that LES combustion models are nearly unaltered versions of the corresponding RANS models. The objective of this work is to develop a new generation of combustion models specifically for LES with the goal of predicting all spatial statistics of the flow configuration accurately. The new framework, termed conditionally-filtered probability density function (FPDF) approach, will provide a statistically consistent approach for understanding modeling and numerical errors in combustion modeling. The PI will closely interact with experimentalists from the University of Sydney and researchers from Dow Chemical Company to rigorously test the models developed here.
To ensure timely dissemination of research advances, a multi-level educational program is planned. A web-based learning platform that provides access to models, computational databases, and research codes will be developed. Interactive tools for classroom learning will also be developed under this framework. These tools will be used to train both industrial researchers and students in the latest advances in computational combustion science. Further, an internship program for high-school students will be supported through this project. Motivated students, especially from underrepresented groups, will have an opportunity to participate in the research activities and interact with UT students.
