Computationally Efficient Data-Enhanced Manifold Modeling of Multi-Modal Turbulent Combustion

Abstract

The design of improved energy conversion devices may be facilitated by Large Eddy Simulation (LES) – a computationally efficient modeling approach for simulating turbulent flows. Brute-force combustion modeling approaches that directly transport up to hundreds or even thousands of chemical species are generally applicable but intractable in simulations of realistic systems. Projecting the high-dimensional thermochemical state onto a reduced-order manifold provides an efficient alternative for modeling the unresolved combustion processes but does not traditionally generalize to the multi-modal combustion regimes present in practical engineering devices, introducing a fundamental modeling trade-off between computational cost and model generality. Though more general, higher-dimensional manifold models capable of breaking this trade-off exist in theory, their implementation is impeded by large computational cost and memory requirements associated with pretabulating the thermochemical state as well as unclosed terms that appear in the manifold equations. A novel algorithm termed In-Situ Adaptive Manifolds (ISAM) is developed to enable LES implementations of more general, higher-dimensional manifold models by computing manifold solutions 'on-the-fly' and reusing them with In-Situ Adaptive Tabulation (ISAT). ISAM is verified and evaluated via LES of two canonical turbulent nonpremixed jet flames and extended to two higher-dimensional manifold models capable of capturing multiple and/or inhomogeneous stream mixing and multi-modal combustion. The computational cost of ISAM rapidly reaches parity with traditional pretabulation approaches independent of the chemical mechanism size and model complexity while requiring up to seven orders of magnitude less memory. Then, data-based approaches are leveraged to augment physics-based manifold models – namely, to provide closure for unclosed dissipation rates that parameterize the solutions to the manifold equations. The instantaneous dissipation rate profiles in both premixed and multi-modal turbulent combustion are extracted from Direct Numerical Simulation (DNS) databases, and deep neural networks (DNNs) are trained to accurately capture the previously unconsidered spatiotemporal variation of the profile shapes. Quantitative predictions of flame stabilization, ignition, and pollutant formation are shown to be particularly sensitive to the shape of the dissipation rate profiles. In conjunction with ISAM, the hybrid physics- and data-based models developed in this dissertation represent a critical advancement in multi-modal turbulent combustion simulations – tools essential for developing cleaner, more efficient power generation technology.