1,083 research outputs found
Development of a novel flamelet-based model to include preferential diffusion effects in autoignition of CH4/H2 flames
This study reports on the development of a flamelet-based reduction method for autoignition of hydrogen enriched methane-based fuels. The main focus is on the inclusion of preferential diffusion effects in the Flamelet Generated Manifolds (FGM) technique for autoigniting flames. Such a development of the FGM methodology is inevitable since investigations with detailed chemistry indicate that preferential diffusion strongly affects autoignition of these mixtures. First, a novel flamelet configuration based on Igniting Mixing Layer (IML) flamelets is proposed to accommodate preferential diffusion in a flamelet database. At the next stage, transport equations for controlling variables are derived with additional terms to account for preferential diffusion effects. The extended FGM model has been evaluated by comparing its predictions with those of detailed chemistry in both laminar and turbulent situations. In laminar situations, it is revealed that the model is able to predict accurately autoignition time scales of one-dimensional hydrogen enriched flames. The turbulent situations are studied by performing Direct Numerical Simulations (DNS) of a two-dimensional unsteady mixing layer. In this configuration, the proposed model yields a precise prediction of autoignition time scales as well. The model has also been assessed using the widely used Igniting Counter-Flow (ICF) flamelets instead of IML flamelets which leads to less accurate predictions especially at high hydrogen contents. The predictive power of the proposed model combined with simplicity of its implementation introduces an attractive reduced model for the computation of turbulent flames
Bayesian calibration of the nitrous oxide emission module of an agro-ecosystem model
Nitrous oxide (N2O) is the main biogenic greenhouse gas contributing to the global warming potential
(GWP) of agro-ecosystems. Evaluating the impact of agriculture on climate therefore requires a capacity
to predict N2O emissions in relation to environmental conditions and crop management. Biophysical
models simulating the dynamics of carbon and nitrogen in agro-ecosystems have a unique potential to
explore these relationships, but are fraught with high uncertainties in their parameters due to their
variations over time and space. Here, we used a Bayesian approach to calibrate the parameters of the N2O
submodel of the agro-ecosystem model CERES-EGC. The submodel simulates N2O emissions from the
nitrification and denitrification processes, which are modelled as the product of a potential rate with
three dimensionless factors related to soil water content, nitrogen content and temperature. These
equations involve a total set of 15 parameters, four of which are site-specific and should be measured on
site, while the other 11 are considered global, i.e. invariant over time and space. We first gathered prior
information on the model parameters based on the literature review, and assigned them uniform
probability distributions. A Bayesian method based on the Metropolis–Hastings algorithm was
subsequently developed to update the parameter distributions against a database of seven different
field-sites in France. Three parallel Markov chains were run to ensure a convergence of the algorithm.
This site-specific calibration significantly reduced the spread in parameter distribution, and the
uncertainty in the N2O simulations. The model’s root mean square error (RMSE) was also abated by 73%
across the field sites compared to the prior parameterization. The Bayesian calibration was subsequently
applied simultaneously to all data sets, to obtain better global estimates for the parameters initially
deemed universal. This made it possible to reduce the RMSE by 33% on average, compared to the
uncalibrated model. These global parameter values may be used to obtain more realistic estimates of
N2O emissions from arable soils at regional or continental scales
A computationally efficient approach for soot modeling with discrete sectional method and FGM chemistry
A novel approach for the prediction of soot formation in combustion simulations within the framework of discrete sectional method (DSM) based univariate soot model and Flamelet Generated Manifold (FGM) chemistry, referred to as FGM-CDSM, is proposed in this study. The FGM-CDSM considers the clustering of soot sections derived from the original soot particle size distribution function (PSDF) to minimize the computational cost. Unlike conventional DSM, in FGM-CDSM, governing equations for soot mass fractions are solved for the clusters, by using a pre-computed lookup table with tabulated soot source terms from the flamelet manifold, while the original soot PSDF is re-constructed in a post-processing stage. The flamelets employed for the manifold are computed with detailed chemistry and the complete sectional soot model. A comparative assessment of FGM-CDSM is conducted in laminar diffusion flames for its accuracy and computational performance against the detailed kinetics-based classical sectional model. Numerical results reveal that the FGM-CDSM can favorably reproduce the global soot quantities and capture their dynamic response predicted by detailed kinetics with a good qualitative agreement. Furthermore, compared to detailed kinetics, FGM-CDSM is shown to substantially reduce the computational cost of the complete reacting flow simulation with soot particle transport. Primarily, the use of FGM reduces the overall calculation by about two orders of magnitude compared to detailed kinetics, which is advanced further with the clustering of sections at a low memory footprint. Therefore, the present work demonstrates the promising capabilities of FGM-CDSM in the context of computationally efficient soot calculations and provides an excellent framework for extending its application to the simulations of turbulent sooting flames
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