Real-Time and Adaptive Chemical Kinetic Model Reduction Coupled with Turbulence

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Combustion of fossil fuels remains the primary means of providing power needs of high energy density applications in this country and all industrial nations. The chemistry of combustion involves a very large number of molecular species. To fully understand the physics of combustion, one must know how these species behave and evolve in a given operating condition. This is a dauting task, especially if the operation involves turbulent mixing. The interaction between chemical reaction and turbulence is very complex and has been a subject of intense study for over many decades. In recent years, easier access to supercomputing resources has enabled the research community to develop sophisticated and efficient computational tools to model complex chemical reactions. However, the computational power remains far less than what is needed for including turbulent transport of chemical species. This project will systematically address these limitations in order to enable modeling of processes involving chemical kinetics and unsteady dynamics. Such problems are also common in biology, atmospherics, climatology, and medicine. This project will also help train a new generation of researchers with the most modern tools in applied mathematics and computer science for modeling and simulation of engineering problems.The goal of this research project is to develop an on-the-fly reduction scheme that enables modeling and simulation of reactive turbulent flows involving a very for a large number of species. The thesis of this research is predicated on the fact that the compositional elements in turbulent reactive flow are highly correlated, and these correlations can be exploited to reduce the computational cost of solving species transport equations. The methodology presents a fundamentally different workflow from current practice in chemistry reduction schemes, in that the correlated structures are extracted on the fly. For that reason, it offers the following advantages: (i) The reduction is not done a priori; therefore, it is general and not restricted to a particular operating condition. (ii) It accounts for the full turbulent chemistry interactions with inclusion of all of the important reactions. (iii) The resulting manifolds are suitable for turbulent reactive predictions in all flame regimes. (iv) The method facilitates a significant reduction of the computational cost and storage as compared to those currently in use. This research crosses several disciplines: turbulent combustion, data driven modeling, reduced order modeling, and high-performance computing.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.