arXiv subjects

Michael E. Mueller

Publications and source records attributed to Michael E. Mueller.

10 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

Observations and empirical functions for the ocean surface wave spectrum

Accurate parameterizations of ocean wave spectra are necessary in a wide array of disciplines including coastal, ocean, and naval engineering as well as in the study of wave interactions and ocean-atmosphere momentum flux. Many such applications use spectrum parameterizations based on temporal data collected well over a half century ago. The development of spatial wave measurement techniques that can accurately capture a larger range of scales allows us to revisit the question of how best to represent an ocean wave spectrum in a variety of ocean wave conditions. We discuss two commonly used wave spectrum parameterizations through a comparison to data collected in field campaigns studying fetch-limited, fully-developed, and mixed sea conditions. We discuss a spectrum parameterization for fully-developed seas that has a $k^{-2.5}$ (or $ω^{-4}$) dependence on the wavenumber (or angular frequency) in the tail as opposed to the $k^{-3}$ (or $ω^{-5}$) dependence seen in other frequently-used parameterizations. With knowledge of the peak wavenumber $k_p$ and significant wave height $H_s$, alongside the wind speed, fully-developed conditions can be well-represented. We then compare the impact of using different wave spectrum parameterizations through a Large Eddy Simulation (LES) study of Marine Atmospheric Boundary Layers (MABLs) over the sea surface and find that changing the parameterization used results in variations in the equivalent roughness akin to significant changes in wave conditions.

physics.ao-ph↗

arXiv2026

Sea Surface Roughness Dependence on Ocean Wave Parameters through Large Eddy Simulation with Local Subfilter Wave Drag

Characterizing the Marine Atmospheric Boundary Layer (MABL) requires understanding the coupling between ocean waves and the turbulent atmospheric boundary layer above them. This coupling controls momentum exchange between the atmosphere and the ocean; it is of practical importance in the global climate, flow of ocean currents, ocean engineering, and offshore wind energy. Computational study of the MABL is complex because it must resolve the coupled physics of waves and turbulence over a wide range of spatial and temporal scales. This study expands on approaches for representing dynamic, local waves in Large Eddy Simulations (LES) of the MABL by developing a subfilter wave drag model to be local and scale-invariant. It explores the effects of different wave parameters (significant wave height and peak frequency of the wave energy spectrum) on the resulting momentum flux beyond monotonic relationships between surface stress through friction velocity $u_\ast$ and wind velocity above the surface $U_{10}$. Results are compared to field data and in a discussion on how representation of the MABL and associated momentum flux need to account for both wind and wave effects.

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↗

arXiv2025

Rush-to-equilibrium concept for minimizing reactive nitrogen emissions in ammonia combustion

Ammonia (NH3) is a zero-carbon fuel that has been receiving increasing attention for power generation and even transportation. Compared to H2, NH3's volumetric energy density is higher, is not as explosive, and has well established transport and storage technologies. Yet, NH3 has poor flammability and flame stability characteristics and more reactive nitrogen (RN: NOx, N2O) emissions than hydrocarbon fuels, at least with traditional combustion processes. Partially cracking NH3 (into a NH3-H2-N2 mixture, AHN) addresses its flammability and stability issues. RN emissions remain a challenge, and mechanisms of their emissions are fundamentally different in NH3 and hydrocarbon combustion. While rich-quench-lean NH3 combustion strategies have shown promise, the largest contributions to RN emissions are the unrelaxed emissions in the fuel-rich stage due to overshoot of thermodynamic equilibrium within the reaction zone of premixed flames coupled with finite residence times available for relaxation to equilibrium. This work introduces a rush-to-equilibrium concept for AHN combustion, which aims to reduce the unrelaxed RN emissions in finite residence times by accelerating the approach to equilibrium. In the concept, a flow particle is subjected to a decaying mixing rate as it transits the premixed flame. This mitigates the mixing effects that prevents the particle approach to equilibrium, and promotes the chemistry effects to push the particle toward equilibrium, all while considering finite residence times. Evaluated with a state-of-the-art combustion model at gas turbine conditions, the concept shows the potential to reduce RN emissions by an order of magnitude, and that works irrespective of cracking extent, pressure, temperature, etc. A brief discussion of possible practical implementation reveals reasonable geometric and flow parameters characteristic of modern gas turbine combustors.

physics.flu-dyn↗

arXiv2024

Large Eddy Simulation of the evolution of the soot size distribution in turbulent nonpremixed flames using the Bivariate Multi-Moment Sectional Method

A joint volume-surface formalism of the Multi-Moment Sectional Method (MMSM) is developed to describe the evolution of soot size distribution in turbulent reacting flows. The bivariate MMSM (or BMMSM) considers three statistical moments per section, including the total soot number density, total soot volume, and total soot surface area per section. A linear profile along the volume coordinate is considered to reconstruct the size distribution within each section, which weights a delta function along the surface coordinate. The closure for the surface considers that the primary particle diameter is constant so the surface/volume ratio constant within each section. The inclusion of the new variable in BMMSM allows for the description of soot's fractal aggregate morphology compared to the strictly spherical assumption of its univariate predecessor. BMMSM is shown to reproduce bimodal soot size distributions in simulations of one-dimensional laminar sooting flames as in experimental measurements. To demonstrate its performance in turbulent reacting flows, BMMSM is coupled to a Large Eddy Simulation framework to simulate a laboratory-scale turbulent nonpremixed jet flame. Computational results are validated against available experimental measurements of soot size distribution, showing the ability of BMMSM to reproduce the evolution of the size distribution from unimodal to bimodal moving downstream in the flame. In general, varying the number of sections has limited influence on results, and accurate results are obtained with as few as eight sections so 24 total degrees of freedom. The impact of using a different statistical model for soot (HMOM) is also investigated. The total computational cost of using BMMSM as low as approximately 44% more than the cost of HMOM. The new formulation results in a computationally efficient approach for the soot size distribution in turbulent reacting flows.

physics.flu-dyn↗

arXiv2024

Large Eddy Simulation of the evolution of the soot size distribution in turbulent nonpremixed bluff body flames

Large Eddy Simulation (LES) was used to investigate the evolution of the soot size distribution in a series of turbulent nonpremixed bluff body flames, with different bluff body diameters. The new Bivariate Multi-Moment Sectional Method (BMMSM) is employed to characterize the size distribution. BMMSM combines elements of sectional methods and methods of moments and is capable of reproducing fractal aggregate morphology, thanks to its joint volume-surface formulation, all at relatively low computational costs with fewer transported soot scalars compared to traditional sectional methods. LES results show soot volume fraction profiles agreeing correctly with the experimental measurements, exhibiting significant improvement compared to previous work using the HMOM. The evolution of the particle size distribution function (PSDF) was examined across the flame series and show that the size distribution is less sensitive to the bluff body diameter than the overall soot volume fraction, which increases with increasing bluff body diameter. The PSDF across the flame exhibit different features compared to turbulent nonpremixed jet flames. The long residence times in the recirculation zone leads to a nearly bimodal size distribution, which eventually becomes bimodal in the downstream jet-like region. Further analysis indicates that a size distribution model is needed to correctly predict the soot evolution. Remarkably, due to improved descriptions of oxidation with BMMSM compared to HMOM, significant nucleation and condensation rates in both the recirculation zone and jet-like region were found using BMMSM, on the same order of magnitude as surface growth and oxidation, leading to the improved prediction of mean soot volume fraction compared to HMOM. This work reveals that the need of size distribution is crucial to both predict global soot quantities accurately and reproduce fundamental mechanisms.

physics.flu-dyn↗

arXiv2023

A dynamic wall modeling approach for Large Eddy Simulation of offshore wind farms in realistic oceanic conditions

Due to the multitude of scales present in realistic oceanic conditions, resolving the surface stress is computationally intensive, motivating modeling approaches. In this work, a dynamic wave drag model is developed for Large Eddy Simulation to quantify the effects of multiscale dynamically rough surfaces on the atmospheric boundary layer. The waves are vertically unresolved, and the total drag due to the horizontally resolved portion of the wave spectrum is computed through a superposition of the force from each mode. As LES can only resolve the horizontal wind-wave interactions to the filter scale $Δ$, the effects of the horizontally unresolved, subfilter waves are modeled by specifying a roughness length scale characterizing the unresolved wave energy spectrum. This subfilter roughness is set proportional to the subfilter root-mean-square of the height distribution, and the constant of proportionality is evaluated dynamically during the simulation based on the assumption that the total drag force at the wave surface is independent of the filter scale. The dynamic approach is used to simulate the airflow over a spectrum of moving waves and the results are validated against high-fidelity phase-resolved simulations. The dynamic approach combined with the wave spectrum drag model is then used to study flow through a fixed-bottom offshore wind farm. The dynamic model accurately adapts to the changing velocity field and accurately predicts the mean velocity profiles and power produced from the offshore wind farm. Further, the effect of the wind-wave interactions on the mean velocity profiles, power production, and kinetic energy budget are quantified.

physics.flu-dyn↗

arXiv2023

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that is routinely generated. In applications that are constrained by memory and computational intensity, excessively large datasets may hinder scientific discovery, making data reduction a critical component of data-driven methods. Datasets are growing in two directions: the number of data points and their dimensionality. Whereas dimension reduction typically aims at describing each data sample on lower-dimensional space, the focus here is on reducing the number of data points. A strategy is proposed to select data points such that they uniformly span the phase-space of the data. The algorithm proposed relies on estimating the probability map of the data and using it to construct an acceptance probability. An iterative method is used to accurately estimate the probability of the rare data points when only a small subset of the dataset is used to construct the probability map. Instead of binning the phase-space to estimate the probability map, its functional form is approximated with a normalizing flow. Therefore, the method naturally extends to high-dimensional datasets. The proposed framework is demonstrated as a viable pathway to enable data-efficient machine learning when abundant data is available. An implementation of the method is available in a companion repository (https://github.com/NREL/Phase-space-sampling).

cs.LG↗

arXiv2022

A sea surface-based drag model for Large Eddy Simulation of wind-wave interaction

Monin-Obukhov similarity theory (MOST) is a well-tested approach for specifying the fluxes when the roughness surfaces are homogeneous. For flow over waves (inhomogeneous surfaces), phase-averaged roughness length scales are often prescribed through models based on the wave characteristics and the wind speed. However, such approaches lack generalizability over different wave ages and steepnesses due to the reliance on model coefficients tuned to specific datasets. In this paper, a sea surface-based hydrodynamic drag model applicable to moving surfaces is developed to model the pressure-based surface drag felt by the wind due to the waves. The model is based on the surface gradient approach of \citet{Anderson2010} applicable to stationary obstacles and extended here to the wind-wave problem. The wave drag model proposed specifies the hydrodynamic force based on the incoming momentum flux, wave phase speed, and the surface frontal area. The drag coefficient associated with the wind-wave momentum exchange is determined based on the wave steepness. The wave drag model is used to simulate turbulent airflow above a monochromatic wave train with different wave ages and wave steepnesses. The mean velocity profiles and model form stresses are validated with available lab-scale experimental data and show good agreement across a wide range of wave steepnesses and wave ages. The drag force is correlated with the wave surface gradient and out-of-phase with the wave height distribution by a factor $π/2$ for the sinusoidal wave-train considered. These results demonstrate that the current approach is sufficiently general over a wide parameter space compared to wave phase-averaged models with a minimal increase in computational cost.

physics.flu-dyn↗