NO emission prediction using virtual optimized chemistry

A reduced order kinetic model for NO (Nitric Oxide) prediction, based on the recently developed virtual chemistry methodology, is proposed in this article. Temperature and heat release are resolved through the optimization of a virtual main mechanism whereas NO emissions are reproduced through the optimization of a dedicated sub-mechanism. The proposed NO sub-mechanism is optimized over a learning database made of 1D-premixed and 1D opposed jet diusion flames. The approach is further assessed in a premixed 2D CFD laminar flame computation proposing a direct comparison against fully detailed chemistry

The role of thermophoretic effect in the formation of soot from liquid fuels

In order to rationalize soot formation in two-phase combustion, the related dynamics can be conveniently studied in simpler systems. In the latest twenty years, experimental activity in drop towers and in the outer space have allowed to investigate the combustion of isolated droplets in microgravity conditions, i.e. spherically symmetric systems where buoyancy effects and slip velocity are absent, yet still containing the major phenomena affecting real combustion (unsteady evolution, convection, gas and soot radiation, heterogeneous properties and so on). In such conditions, it had been speculated [1] that a key role in soot formation is played by thermophoretic effect, because of which solid particles are transported towards the droplet surface, thus increasing their residence times in the fuel-rich area, where soot growth is kinetically favoured. The spherical symmetry also allows to numerically study these systems with a relatively low computational weight. The importance of thermophoresis in the dynamics of soot formation can thus be investigated in a variety of operating conditions (droplet size, pressure, composition, etc.), which is the subject of this work. Starting from a description of the constitutive parts of the isolated-droplet model, the transient dynamics of soot formation in n-heptane droplets is analysed. The impact of the submodel describing thermophoresis is considered in detail, and indications about its possible refinements are provided

Predictive-Quality Surface Reaction Chemistry in Real Reactor Models: Integrating First-Principles Kinetic Monte Carlo Simulations into Computational Fluid Dynamics

We present a numerical framework to integrate first-principles kinetic Monte Carlo (1p-kMC) based microkinetic models into the powerful computational fluid dynamics (CFD) package CatalyticFoam. This allows for the simultaneous account of a predictive-quality surface reaction kinetics inside an explicitly described catalytic reactor geometry. Crucial means toward an efficient and stable implementation are the exploitation of the disparate time scales of surface chemistry and gas-phase transport, as well as the reliable interpolation of irregularly gridded 1p-kMC data by means of an error-based modified Shepard approach. We illustrate the capabilities of the framework using the CO oxidation at Pd(100) and RuO2(110) model catalysts in different reactor configurations and fluid dynamic conditions as showcases. These showcases underscore both the necessity and value of having reliable treatments of the surface chemistry and flow inside integrated multiscale catalysis simulations when aiming at an atomic-scale understanding of the catalytic function in near-ambient environments. Our examples highlight how intricately this function is affected by specifics of the reactor geometry and heat dissipation channels on the one end, and on the other end by characteristics of the intrinsic catalytic activity that are only captured by treatments beyond prevalent mean-field rate equations

A CFD model for biomass flame-combustion analysis

The present work addresses the study of the combustion of individual biomass particle surrounded by a gas stream of N2/O2 under the operating conditions encountered in a drop tube reactor. The aim of this analysis is to give a better insight into the chemical and physical processes that occur both at particle and reactor scale where the volatiles, generated by the biomass pyrolysis, burn in a fuel particle enveloped flame. A comprehensive CFD model was developed within the open-source OpenFOAM® framework in order to properly handle the computational mesh and the discretization of the characteristic governing equations. At the reactor scale, the reactive flow was described by the equations for continuous, multicomponent, compressible and thermally-perfect mixtures of gases. At the particle scale, instead, the solid particle was considered as a porous media with isotropic and uniform morphological properties

DropletSMOKE++: A comprehensive multiphase CFD framework for the evaporation of multidimensional fuel droplets

This paper aims at presenting the DropletSMOKE++ solver, a comprehensive multidimensional computational framework for the evaporation of fuel droplets, under the influence of a gravity field and an external fluid flow. The Volume Of Fluid (VOF) methodology is adopted to dynamically track the interface, coupled with the solution of energy and species equations. The evaporation rate is directly evaluated based on the vapor concentration gradient at the phase boundary, with no need of semi-empirical evaporation sub-models. The strong surface tension forces often prevent to model small droplets evaporation, because of the presence of parasitic currents. In this work we by-pass the problem, eliminating surface tension and introducing a centripetal force toward the center of the droplet. This expedient represents a major novelty of this work, which allows to numerically hang a droplet on a fiber in normal gravity conditions without modeling surface tension. Parasitic currents are completely suppressed, allowing to accurately model the evaporation process whatever the droplet size. DropletSMOKE++ shows an excellent agreement with the experimental data in a wide range of operating conditions, for various fuels and initial droplet diameters, both in natural and forced convection. The comparison with the same cases modeled in microgravity conditions highlights the impact of an external fluid flow on the evaporation mechanism, especially at high pressures. Non-ideal thermodynamics for phase-equilibrium is included to correctly capture evaporation rates at high pressures, otherwise not well predicted by an ideal gas assumption. Finally, the presence of flow circulation in the liquid phase is discussed, as well as its influence on the internal temperature field. DropletSMOKE++ will be released as an open-source code, open to contributions from the scientific community

Prediction of flammable range for pure fuels and mixtures using detailed kinetics

In this work, the flammable range of several hydrocarbons was predicted using a freely-propagating flame method for pure hydrocarbons and their mixtures, investigating the effects of operating conditions, in terms of temperature, pressure, fuel/oxidizer composition. The model showed accurate agreement with a wide set of experimental data. The average deviation between the experiments and the model was reduced to ∼20% for the UFL of methanol, methane, ethane, propane, n-butane, n-heptane, ethylene, benzene and two different mixtures, methane/ethylene and methanol/benzene. Model performance was improved for the upper flammability limit by including the effect of soot radiation, modeled using an optically-thin approximation. A comprehensive kinetic mechanism was adopted, and a skeletal kinetic mechanism including a soot sectional model was used to predict soot formation in rich flames. Comparison with Calculated Adiabatic Flame Temperature (CAFT) and Le Chatelier models was also carried out, discussing the advantages of a model including the effects of chemical kinetics. Sensitivity analysis was performed to point out the major role of chemical kinetics especially at the UFL, where chemistry drives the process. This methodology showed that the chemical interaction between two different fuels at the rich limit is the reason for the deviation from the thermally controlled behavior. Finally, chemistry was found to be relevant even for the lean flammability limits predictions of lower alkanes, when pure N2O is used as oxidizer

Numerical Studies of Premixed and Diffusion Meso/Micro-Scale Flames

This work reports a numerical investigation of premixed and diffusion meso/micro-scale flames, with homogeneous reactions, for two different cases involving CH4/air premixed dynamics flames and co-flow diffusion flame with H2/N2 mixture. In the first case, the numerical predictions are able to capture the flame characteristics behaviour for different fuel equivalent ratio and geometries, showing the accuracy of the reactions model, which uses a very detailed kinetics. In the second case, the analysis concerns the effect of a porous bluff body in pollutants emissions, especially the NOx formation, at meso scale, considering different porous temperatures and geometrical configurations

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the context of the Sample- Partitioning Adaptive Reduced Chemistry approach was investigated in this work, to increase the on-the-fly classification accuracy for very large thermochemical states. The proposed methodology was firstly compared with an on-the-fly classifier based on the Principal Component Analysis reconstruction error, as well as with a standard ANN (s-ANN) classifier, operating on the full thermochemical space, for the adaptive simulation of a steady laminar flame fed with a nitrogen-diluted stream of n-heptane in air. The numerical simulations were carried out with a kinetic mechanism accounting for 172 species and 6,067 reactions, which includes the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to C. Among all the aforementioned classifiers, the one exploiting the combination of an FE step with ANN proved to be more efficient for the classification of high-dimensional spaces, leading to a higher speed-up factor and a higher accuracy of the adaptive simulation in the description of the PAH and soot-precursor chemistry. Finally, the investigation of the classifier's performances was also extended to flames with different boundary conditions with respect to the training one, obtained imposing a higher Reynolds number or time-dependent sinusoidal perturbations. Satisfying results were observed on all the test flames

An a priori DNS analysis of scale similarity based combustion models for LES of non-premixed jet flames

In this work, recently developed finite-rate dynamic scale similarity (SS) sub-grid scale (SGS) combustion models have been a priori assessed and compared with the Eddy Dissipation Concept (EDC) and “no model” approaches based on a Direct Numerical Simulation (DNS) database of a temporally evolving non-premixed jet flame. Two different filter widths, one placed in the inertial range and the other in the near dissipation range, have been used. The analyses were carried out in two time instants corresponding to instants of maximum local extinction and re-ignition. Conditional averaged filtered chemical source terms, conditioned on different parameters in the composition space, have been presented. Improvements are observed using the dynamic SS models compared to the two other approaches in the prediction of filtered chemical source terms of individual species while using larger filter widths. However, discrepancies still exists using the dynamic SS model on the turbulent/non-turbulent interfaces of the jet, mainly in the prediction of the oxidizer consumption rate