Adaptive Physics-informed Machine Learning Strategies for Turbulent Combustion Modeling

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The design and optimization of combustion devices is crucial in the mission to combat climate change and achieve national security goals. Simulations can play a role in this mission by enabling rapid virtual testing of combustors at various design configurations, so that promising designs can be selected for physical prototyping. However, turbulent combustion simulations involve solving for a large number of molecular species that are produced and consumed as part of the combustion process. Due to this, combustion simulations require the use of many computer processors for several hours on supercomputers or compute clusters. This limits the usefulness of promising computer models for practical design and optimization endeavors. This work contributes to the ongoing quest to develop reduced combustion models that decrease the simulation times and required computing resources, yet preserve accuracy.In response to the excessive computational costs of turbulent combustion, physics-based reduced-order models have been introduced. These models often solve chemistry in an “offline” phase, store the solution in a table, and then interpolate the table’s entries to retrieve the chemical state during the “online” phase. The use of these lookup tables, however, suffers from the curse of dimensionality, wherein the size of the table and the interpolation complexities increase exponentially with the number of control variables. As a result, these lookup tables are limited to situations that employ a few control variables, thus preventing their application to many practical combustion devices. This work aims to address this problem by developing machine learning strategies to efficiently learn combustion physics within physically derived low-dimensional manifolds. This will be achieved by introducing machine learning models that are adaptive, consistent with the underlying physical laws, and suitable for high-dimensional combustion state spaces. This work will enable simulations at levels of fidelity that are currently impossible to perform using traditional tabulation techniques, and therefore, will aid in the design and development of clean and efficient combustion technologies. Furthermore, the tools developed in this study will be vital to the broad area of scientific machine learning, which continues to have an increasingly important impact on the modeling of many physical systems.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.