CAREER: Embedded Data Assimilation for Complex Turbulent Reacting Flows

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Addressing the challenges of climate change requires advanced, efficient, low-emission combustion technologies as well as an educated workforce to understand and solve these challenges. A key limiting factor is the inability of current simulation techniques to accurately predict turbulent combustion in the regimes needed for the design of low-emission combustors and sustainable fuels. Due to practical limits on computing resources, the computational simulations used for engineering design rely on simplified mathematical expressions for some aspects of turbulence and chemical physics; these models are almost always disconnected from each other and so do not capture key physical interactions. Recently, efficient numerical methods to calibrate complex models during flow simulations have been developed using techniques from machine learning and constrained optimization. While successful for simple nonreacting turbulent flows, these models have not been applied to highly nonlinear turbulent reacting flows. The principal objective of this project is to develop efficient methods to calibrate models for the missing physics in simulations of complex turbulent reacting flows, including flows in engineering geometries, which will enhance the predictive accuracy of practical calculations. The resulting methods will be useful across many areas of science and engineering and will be made publicly available in an open-source software package. The project will facilitate interdisciplinary partnerships and student education across traditional borders by developing an annual summer symposium on data and modeling for turbulent combustion. The project will also support the development of an education and research program for an underresourced high school, which will encourage broad understanding of energy science and participation in solutions to national and global energy challenges.This project will address the need for accurate, efficient turbulent combustion models by developing turbulence closures and optimization methods for both canonical and complex simulations of turbulent reacting flows. An adjoint-based optimization method will enable efficient optimization of closure models over the Navier–Stokes equations for canonical flows such as turbulent jet flames and wedge-shaped flameholders. The primary challenge in applying adjoint-based optimization is the need for intrusive access to a code’s data structures, which is practically impossible to achieve for general-purpose computational fluid dynamics (CFD) solvers. To address this, a novel co-optimization framework will be developed to leverage both adjoint-based optimization over canonical flows and ensemble Kalman-based (adjoint-free) optimization over geometrically complex flows and experimental data. This combined approach will train models for both the canonical and complex physics while alleviating the current limitations of embedded optimization for general-purpose CFD codes. More broadly, the scientific community is interested in developing methods to leverage large datasets; therefore, this project’s methods have potential to be adopted widely across disciplines. The resulting data, optimization framework, and trained models will be distributed as open-source software to facilitate replication, reuse, and extension by researchers in academia and industry.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.