Most practical combustion phenomena are turbulent, but turbulent flames pose a challenging modeling problem due to the complex interactions between fluid flow, chemical kinetics, and thermodynamics. We are both studying the turbulence-chemistry interactions that occur in the combustion of practical fuels, and also developing better modeling techniques and best practices.
Turbulent mixing and biogeochemical processes in the ocean can interact when occurring at similar time scales; for example, carbonate formation occurs at similar time scales as small-scale turbulence in the upper ocean, while submesoscale eddies evolve over the same time scales as plankton blooms. This project seeks to understand how these processes interact with each other, which has implications for global biogeochemical cycles. It involves accurate large-eddy simulations of upper-ocean turbulence and detailed biogeochemical kinetic models.
Smoldering combustion plays an important role in wildfires, since it can contribute significantly to carbon emissions and also transition back to the flaming mode of combustion. However, the physical and chemical processes that control the ignition, propagation, and emissions of smoldering in woody fuels are not well understood, and this project is working to fix that.
The combustion community has generated many thousands of fundamental combustion data points using a variety of experimental apparatuses, which are extremely useful for validating and refining models for fuel oxidation. However, most such data are either represented via figures or tables, not openly accessible, or stored in a format not easily readable by people or machines. This project is designing human and machine-readable data standards for combustion measurements, and building an open, community database for them (along with tools to use the data).
Typical approaches to using supercomputing systems to solve large systems of partial differential equations, like those governing fluid flow, require significant amounts of memory transfer at every computational time step. This project is developing a method to reduce the number of communication steps compared with typical approaches, with the goal of improving overall performance.
Our group advocates for the open sharing of all research artifacts developed with public funds, including open access papers, open data, and open research software. We work on following best practices in software development, enabling reproducibility in computational research, and giving credit for software developments.
Software citation principles (2016)
This project is investigating a concept for efficient electrical power generation using a pulse detonation engine (PDE) coupled with direct power extraction via a magnetohydrodynamic (MHD) generator. The system is particularly well-suited for pure oxygen combustion (oxy-combustion) since it doesn't have any moving interior parts, so it could offer a viable approach to producing power efficiently and capturing the carbon from the exhaust.
Simulations of reacting fluid flows can be performed with accurate models for chemical kinetics by intelligently choosing and designing appropriate solvers. For this project, we are looking at choosing the best algorithm based on local conditions in a simulation and also the available computing hardware.
This project is looking at ways to evaluate fuels for advanced combustion engines that operate at lower combustion temperatures than conventional gasoline or diesel engines. Traditional fuel ratings such as octane number poorly predict the behavior of fuels in the newer engine modes, so we are working on ways to better quantify fuel performance there.
Simulations of combustion and chemically reacting flows use chemical kinetic models to describe how hydrocarbon fuels break down and react. But, these models can be extremely large and complex, particularly for fuel components relevant to practical transportation fuels. This project develops tools for reducing the size and complexity of these models.
Vacuum arc remelting (VAR) furnaces have been used for decades to produce high-quality metal ingots for demanding applications like in the aerospace and biomedical fields, but the extreme conditions inside a furnace make diagnostics and control of the process challenging. This project analyzed an approach for sensing the position of the electrical arc inside a VAR furnace using finite element simulations.