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We present three-dimensional, time-dependent simulations of the flowfield of a laboratory-scale slot burner. The simulations are performed using an adaptive time-dependent low Mach number combustion algorithm based on a second-order projection formulation that conserves both species mass and total enthalpy. The methodology incorporates detailed chemical kinetics and a mixture model for differential species diffusion. Methane chemistry and transport are modeled using the DRM-19 mechanism along with its associated thermodynamics and transport databases. Adaptive mesh refinement dynamically resolves the flame and turbulent structures. Detailed comparisons with experimental measurements show that the computational results provide a good prediction of the flame height, the shape of the time-averaged parabolic flame surface area, and the global consumption speed (the volume per second of reactants consumed divided by the area of the time-averaged flame). The thickness of the computed flame brush increases in the streamwise direction, and the flame surface density profiles display the same general shapes as the experiment. The structure of the simulated flame also matches the experiment; reaction layers are thin (typically thinner than 1 mm) and the wavelengths of large wrinkles are 5-10 mm. Wrinkles amplify to become long fingers of reactants which burn through at a neck region, forming isolated pockets of reactants. Thus both the simulated flame and the experiment are in the "corrugated flamelet regime."

This report documents the SPIN Fortran computer program that computes species, temperature and velocity profiles, and deposition rates in a steady-state one-dimensional rotating disk or stagnation-point flow chemical vapor deposition (CVD) reactor. The program accounts for finite-rate gas-phase and surface chemical kinetic and multicomponent molecular transport. The governing differential equations form a two-* This document describes the features in version 3.83. We expect that this software package will continue to evolve, and thus later versions may render portions of this document out of date.

We present a three-dimensional, time-dependent simulation of a laboratory-scale rod-stabilized premixed turbulent V-flame. The experimental parameters correspond to a turbulent Reynolds number, Re t ‫؍‬ 40, and to a Damkö hler number, Da ‫؍‬ 6. The simulations are performed using an adaptive time-dependent low-Machnumber model with detailed chemical kinetics and a mixture model for differential species diffusion. The algorithm is based on a second-order projection formulation and does not require an explicit subgrid model for turbulence or turbulence͞chemistry interaction. Adaptive mesh refinement is used to dynamically resolve the flame and turbulent structures. Here, we briefly discuss the numerical procedure and present detailed comparisons with experimental measurements showing that the computation is able to accurately capture the basic flame morphology and associated mean velocity field. Finally, we discuss key issues that arise in performing these types of simulations and the implications of these issues for using computation to form a bridge between turbulent flame experiments and basic combustion chemistry. (6)] have made substantial progress in understanding basic flame physics and developing models that can be used for engineering design. However, the inability of theory to deal with the complexity of realistic chemical kinetics in a turbulent flowfield, and the present limitations in experimental diagnostics to resolve 3D flame properties, represent major obstacles to continued progress.Numerical simulation offers the potential to augment theory and experiment and overcome the limitations of standard approaches in analyzing laboratory-scale flames. The excessive computational costs of incorporating detailed transport and chemical kinetics have necessitated compromises in the fidelity or scope of simulations for premixed turbulent combustion. Simulation of laboratory-scale systems typically involves models for subgrid-scale turbulent fluctuations. Approaches based on large eddy simulation or Reynolds-averaged Navier-Stokes fall into this class. In addition to the turbulence model, these approaches require a model for the speed of flame propagation in a turbulent field or some other model for turbulencechemistry interaction.The goal of the present work is to simulate a laboratory-scale flame, Ϸ10-12 cm in length, without a turbulence model, a burning velocity model, or the introduction of some other type of turbulence closure hypothesis. Standard computational tools for this type of simulation are based on high-order (more than four) explicit integration methods for the compressible NavierStokes equations and are typically referred to as direct numerical simulations. The computational requirements of direct numerical simulations have limited most simulations to small-scale 2D models. Recent work by Vervisch et al. (7) presents the simulation of a laboratory-scale ''turbulent'' premixed V-flame in two dimensions and represents the current state-of-the-art in 2D direct numerical simulations. Tanahashi e...

This paper reports on a combined experimental and modeling investigation of NO x formation in nitrogen-diluted laminar methane diffusion flames seeded with ammonia. The methane-ammonia mixture is a surrogate for biomass fuels which contain significant fuel-bound nitrogen. The experiments use flue-gas sampling to measure the concentration of stable species in the exhaust gas, including NO, O 2 , CO, and CO 2 . The computations evolve a two-dimensional low Mach number model using a solution-adaptive projection algorithm to capture fine-scale features of the flame. The model includes detailed thermodynamics and chemical kinetics, differential diffusion, buoyancy, and radiative losses. The models shows good agreement with the measurements over the full range of experimental NH 3 seeding amounts. As more NH 3 is added, a greater percentage is converted to N 2 rather than to NO. The simulation results are further analyzed to trace the changes in NO formation mechanisms with increasing amounts of ammonia in the fuel.

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