Research activities at CTRFL encompass computational modeling of multi-physics turbulent flows. The ultimate goal of our research is to develop predictive computational tools for practical systems relevant to energy and propulsion and their interactions with the environment. Such tools would shorten design cycles, allow for aggressive (high efficiency, low emissions, etc.) energy and propulsion system designs, and minimize environmental impacts to provide the energy conversion for a future, net-zero economy. To enable this goal, we must develop a deep understanding of the physics and chemistry of interest, develop truly predictive models based on this understanding, and develop computational tools that scale on current and future high performance computing platforms.

Our technical approach utilizes two fidelities: full-fidelity simulations (Direct Numerical Simulation) to probe fundamental physics and chemistry and inform the development of predictive models and high-fidelity engineering simulations (Large Eddy Simulation). Currently, we are interested in several specific areas and utilize both fidelities to explore these areas. In addition, we are actively developing techniques for assessing the uncertainties in our models and predictions and utilizing techniques from data science to develop models and accelerate simulations. Additional details about each of these areas are provided on this page below.

**Large Eddy Simulation of Turbulent Reacting Flows**

Investigators: John Boerchers, Israel Bonilla, Trevor Fush, Hernando Maldonado Colman, Sydney Rzepka, Katie VanderKam, Michael Walker

Large Eddy Simulation (LES) is a high-fidelity approach for the simulation of turbulent reacting (or non-reacting) flows. In LES, the governing equations are spatially filtered, and only the large scales of the flow are resolved by the calculations. Therefore, the unresolved small scales, which include small scale turbulence, molecular transport, and chemical reactions, must be modeled. The majority of our work in LES is in modeling these unresolved phenomena with current emphases on modeling non-adiabatic multi-modal combustion and unification of the modeling of combustion and turbulence. Computationally, our capabilities range from geometrically simple laboratory-scale configurations to full-scale practical devices such as gas turbine combustors.

**Direct Numerical Simulation of Turbulent Reacting Flows**

Investigators: John Boerchers, Sydney Rzepka, Katie VanderKam, Michael Walker

Direct Numerical Simulation (DNS) is a full-fidelity approach for the simulation of turbulent reacting (or non-reacting) flows. In DNS, all of the scales of the flow are resolved, and no models are required. However, due to the large range of scales in turbulent combustion (microns to meters), such calculations are not feasible for full-scale device simulations. Nonetheless, DNS is a valuable tool to explore the fundamental physics and chemistry, albeit over limited parameter ranges, since no modeling is required. The major challenge with DNS is the sheer size of the calculations, which are performed on up to tens of thousands of processors on some of the largest supercomputers in the world. As a result, in addition to probing fundamental physics and chemistry, our work in this area also involves the development of numerical methods and software algorithms that can scale to these very large computers.

**Fundamental Turbulence**

Investigators:

The structure and mixing characteristics of turbulence are fundamentally important in developing predictive models for turbulent reacting flows. In LES, the small-scale turbulence is unresolved and must be modeled. These models are critically important, for this small-scale turbulence is responsible for mixing of fuel and air, wrinkling of flames, and transferring heat and mass toward or away from walls. Our main interests involve the understanding the effects of different physical phenomena on turbulence. In particular, a major effort involves understanding the influence of combustion heat release on the small-scale structure of turbulence. Most of our understanding and models for turbulence come from the non-reacting flow community, so this investigation will critically assess the applicability of these models to reacting flows. Our longer-term goal is the development of a unified framework for modeling turbulence including the effects of combustion heat release on turbulence.

**Turbulent Combustion Modeling**

Investigators: John Boerchers, Israel Bonilla, Efe Eroz, Trevor Fush, Hernando Maldonado Colman, Agnes Robang, Sydney Rzepka, Katie VanderKam, Michael Walker

In LES, the small flame length scales are not resolved and must be modeled. Our approach utilizes the notion of reduced-order manifolds to describe unresolved combustion processes. In a manifold-based approach, the high-dimensional thermochemical state is projected onto a low-dimensional manifold parameterized by a few variables. When history effects are unimportant, these manifolds can be computed *a priori*, tabulated, and accessed during LES calculations, leading to a substantial reduction in computational cost. When history effects are important, manifold-based approaches significantly simplify closure of the LES governing equations. Conventional manifold-based approaches have been confined a single asymptotic model of (adiabatic) combustion: premixed combustion, nonpremixed combustion, and homogeneous autoignition. Our primary efforts involve the development of more general manifolds for non-adiabatic multi-modal combustion.

**Emissions: Soot and NOx**

Investigators: Israel Bonilla, Efe Eroz, Hernando Maldonado Colman, Sydney Rzepka

Soot and nitrogen oxides (NOx) are two undesirable by-products of combustion due to their detrimental effects on our environment and health. Unlike the oxidation of fuel to form (primarily) carbon dioxide and water, the formation of soot and NOx are kinetically-controlled processes (i.e., “slow”), and our usual turbulent combustion models do not account for kinetically controlled processes (i.e., chemistry is assumed to be sufficiently “fast”). Furthermore, the chemical kinetics governing the formation of these pollutants, the effects of fuel structure on their formation, and their evolution in turbulent reacting flows remain not well understand at a fundamental level. Our principal efforts in this area involve a combination of LES and DNS to understand these effects and subsequently develop predictive models for LES. In addition, for soot, novel mathematical models are required to describe the evolution of the particle population, and we are currently developing new mathematical models that can predict not only gross amounts of soot but also its size distribution in practical combustion devices.

**Uncertainty Quantification in Turbulent Reacting Flows**

Investigators:

While our approaches utilize state-of-the-art, high-fidelity models, we would be foolish to believe that our simulations are exact predictions of reality. All simulations are polluted by some degree of uncertainty, and quantification of this uncertainty is required to make informed decisions based on the simulation results. For simulations of turbulent reacting flows, this uncertainty may be parametric uncertainty in operating conditions, boundary conditions, etc. or parametric uncertainty in kinetic rates, thermodynamic properties, transport properties, etc. In addition, this uncertainty can be structural uncertainties due to improper model form due to model assumptions or model extrapolation. Quantification of this uncertainty is a daunting task requiring a characterization of these input uncertainties and the development of efficient methods for propagating these uncertainties through our expensive calculations. Our current efforts in Uncertainty Quantification (UQ) include the development of new approaches to model form uncertainty quantification for LES using inherently physics-based approaches.

**Data Science for Turbulent Reacting Flows**

Investigators: Trevor Fush

Data science is rapidly expanding into many domains of engineering and science. Our group is asking a relatively simple question: how can approaches and algorithms from the broader data science community be used for developing physics-based models for turbulent reacting flows or accelerating simulations of turbulent reacting flows? Our current efforts are focused on integrating data-based approaches into our physics-based modeling approaches. In particular, our group is currently pursing hybrid physics-based and data-based models, using data science tools to directly isolate and validate individual physical assumptions in our physics-based models, and using data science tools to enable the use of more sophisticated and general physics-based models (such as turbulent combustion models for non-adiabatic multi-modal combustion).

**Other Energy Conversion Processes**

Investigators: Aditya Aiyer (Offshore Wind), Hannah Williams (Offshore Wind)

Many of the computational modeling approaches that we develop for turbulent combustion can be applied to other energy conversion processes. Particular applications of interest include wind power, concentrating solar power, nuclear fission reactors, and nuclear fusion reactors.