arXiv subjects

Matthew X. Yao

Publications and source records attributed to Matthew X. Yao.

4 recordsLinked to original sources

arXiv2026

A Thermodynamically Consistent Manifold Model for Premixed Deflagrations & Detonations

Accurate modeling of compressible premixed flames, encompassing both deflagrations and detonations, remains a significant challenge for predictive Large Eddy Simulation (LES) due to the strong coupling between the thermochemical state and the local thermodynamic state. This work presents a manifold-based turbulent combustion model that ensures a fully consistent thermodynamic state between model and flow solver through an iterative procedure. The framework reproduces critical quantities including temperature, radical species, and source term profiles, addressing limitations of existing approaches that rely on low-Mach perturbations or tabulated ZND detonations without thermodynamic consistency. Validation is performed against one-dimensional and high-fidelity RDE-like data, demonstrating that the thermodynamically consistent model consistently outperforms existing approaches across a broad range of compressible flame regimes - including both deflagration and detonation. The results highlight the importance of fully accounting for the thermodynamic state to achieve accurate predictions. By capturing both deflagrative and detonative behavior within a single framework, the model provides a unified, versatile tool for LES of high-speed reacting flows and offers a foundation for future studies of compressible reacting flows, including applications to rotating detonation engines and other supersonic combustion systems.

physics.flu-dyn↗

arXiv2026

Neural-ISAM: A hybrid in-situ machine learning approach for complex manifold-based combustion models in LES of turbulent flames

Manifold-based combustion models decrease the cost of turbulent combustion simulations by projecting the thermochemical state onto a lower-dimensional manifold, allowing the thermochemical state to be computed separately from the flow solver. The solutions to the manifold equations have traditionally been precomputed and pretabulated, but this results in large memory requirements and significant precomputation cost even for simple models. One approach to alleviate the memory requirements is to use In-Situ Adaptive Manifolds (ISAM), which only stores solutions that are encountered during a simulation in a database built with In-Situ Adaptive Tabulation (ISAT). Even with ISAM, as the manifold complexity increases, the memory requirements can still grow too large. Another approach to reduce memory of these databases are machine learning methods, for they represent functions in a highly memory-compact manner. However, current implementations of these methods require the pregeneration of training datasets with little knowledge of the states present in a simulation. This work develops the Neural In-Situ Adaptive Manifolds (Neural-ISAM) method, which is designed to address the drawbacks of both adaptive tabulation and machine learning methods, and leverage their benefits by coupling neural networks to manifold databases on-the-fly. ISAM databases are built via ISAT, which stores the manifold solutions in a binary tree, and Neural-ISAM periodically searches this tree to identify regions that can be pruned. Neural networks are trained on the candidate regions, and these portions of the binary tree are then replaced by the trained neural network, reducing the memory requirements of the database. Neural-ISAM memory usage, computational performance, and accuracy is evaluated in LES of two turbulent flames with increasing manifold model complexity: Sandia Flame D and the Sandia Sooting flame.

physics.comp-ph↗

arXiv2023

Isolating effects of large and small scale turbulence on thermodiffusively unstable premixed hydrogen flames

Lean turbulent premixed hydrogen/air flames have substantially increased flame speeds, commonly attributed to differential diffusion effects. In this work, the effect of turbulence on lean hydrogen combustion is studied through Direct Numerical Simulation using detailed chemistry and detailed transport. Simulations are conducted at six Karlovitz numbers and three integral length scales. A general expression for the burning efficiency is proposed which depends on the conditional mean chemical source term and gradient of a progress variable. At a fixed Karlovitz number, the normalized turbulent flame speed and area both increase linearly with the integral length scale ratio. The effect on the mean source term profile is minimal, indicating that the increase in flame speed can solely be attributed to the increase in flame area. At a fixed integral length scale, both the flame speed and area first increase with Karlovitz number before decreasing. At higher Karlovitz numbers, the diffusivity is enhanced due to penetration of turbulence into the reaction zone, significantly dampening differential diffusion effects.

physics.flu-dyn↗

arXiv2018

On the detection of internal interfacial layers in turbulent flows

A novel approach to identify internal interfacial layers, or IILs, in wall-bounded turbulent flows is proposed. Using a Fuzzy Cluster Method (FCM) on the streamwise velocity component, a unique and unambiguous grouping of the Uniform Momentum Zones is achieved, thus allowing the identification of the IILs. The approach overcomes some of the key limitations of the histogram-based IIL identification methods. The method is insensitive to the streamwise domain length, can be used on inhomogeneous grids, uses all the available flow field data, is trivially extended to three dimensions, and does not need user-defined parameters (e.g. number of bins) other than the number of zones\add{. The number of zones can be automatically determined by an \emph{a priori} algorithm based on a Kernel Density Estimation algorithm, or KDE.} The clustering approach is applied to the turbulent boundary layer (experimental, planar PIV) and channel flow (numerical, DNS) \add{at varying Reynolds numbers}. \modify{The interfacial layers are characterized by a strong concentration of spanwise vorticity, with the outer-most layer located at the upper edge of the log-layer. The three-dimensional interface identification reveals a streak-like organization; we show that the organization of the IILs is correlated to the underlying wall-bounded turbulent structures. }

physics.flu-dyn↗