Application of hybrid binomial Langevin-multiple mapping conditioning method to reacting jet flow

The hybrid binomial Langevin-MMC (Multiple Mapping Conditioning) method combines the advantages of the binomial Langevin and MMC models in a consistent manner to overcome difficulties in each. The binomial Langevin method provides joint velocity-scalar statistics, but the treatment of scalars is complex since specification of the bounds is not trivial. The MMC method is capable of dealing with the mixing of any number of scalars, but it can be difficult to specify coefficients involving averages of the scalars and the introduced reference space. The difficulties are overcome by using the velocity statistics from the binomial Langevin model to obtain the reference variable for MMC and, subsequently, the mixing of MMC scalars is performed in a manner that minimises the difference between the mixture fractions for each submodel. The current work expands past studies of NO conversion in a mixing layer to include a study of the Sandia D Flame in preparation for the application to more complex combustion phenomena. Results compare favourably with experimental data and other models

Hybrid binomial Langevin-multiple mapping conditioning modeling of a reacting mixing layer

A novel, stochastic, hybrid binomial Langevin-multiple mapping conditioning (MMC) model—that utilizes the strengths of each component—has been developed for inhomogeneous flows. The implementation has the advantage of naturally incorporating velocity-scalar interactions through the binomial Langevin model and using this joint probability density function (PDF) to define a reference variable for the MMC part of the model. The approach has the advantage that the difficulties encountered with the binomial Langevin model in modeling scalars with nonelementary bounds are removed. The formulation of the closure leads to locality in scalar space and permits the use of simple approaches (e.g., the modified Curl’s model) for transport in the reference space. The overall closure was evaluated through application to a chemically reacting mixing layer. The results show encouraging comparisons with experimental data for the first two moments of the PDF and plausible results for higher moments at a relatively modest computational cost

Mixture-fraction based hybrid binomial-langevin–MMC modelling applied to auto-ignition in vitiated flow

A new hybrid binomial Langevin–MMC (Multiple Mapping Conditioning) modelling approach is proposed. The mixture fraction derived from the binomial Langevin model is used to specify the reference variable for MMC. The modified Curl’s model is used to close the stochastic MMC mixing term. The new model is applied to a jet burner with a vitiated co-flow (the 'Cabra burner') with methane as the fuel. The first- and second-order statistics show good agreement with experimental data

The NILE Project — Advances in the Conversion of Lignocellulosic Materials into Ethanol

NILE ("New Improvements for Lignocellulosic Ethanol") was an integrated European project (2005-2010) devoted to the conversion of lignocellulosic raw materials to ethanol. The main objectives were to design novel enzymes suitable for the hydrolysis of cellulose to glucose and new yeast strains able to efficiently converting all the sugars present in lignocellulose into ethanol. The project also included testing these new developments in an integrated pilot plant and evaluating the environmental and socio-economic impacts of implementing lignocellulosic ethanol on a large scale. Two model raw materials – spruce and wheat straw – both preconditioned with similar pretreatments, were used. Several approaches were explored to improve the saccharification of these pretreated raw materials such as searching for new efficient enzymes and enzyme engineering. Various genetic engineering methods were applied to obtain stable xylose- and arabinose-fermenting Saccharomyces cerevisiae strains that tolerate the toxic compounds present in lignocellulosic hydrolysates. The pilot plant was able to treat 2 tons of dry matter per day, and hydrolysis and fermentation could be run successively or simultaneously. A global model integrating the supply chain was used to assess the performance of lignocellulosic ethanol from an economical and environmental perspective. It was found that directed evolution of a specific enzyme of the cellulolytic cocktail produced by the industrial fungus, Trichoderma reesei, and modification of the composition of this cocktail led to improvements of the enzymatic hydrolysis of pretreated raw material. These results, however, were difficult to reproduce at a large scale. A substantial increase in the ethanol conversion yield and in specific ethanol productivity was obtained through a combination of metabolic engineering of yeast strains and fermentation process development. Pilot trials confirmed the good behaviour of the yeast strains in industrial conditions as well as the suitability of lignin residues as fuels. The ethanol cost and the greenhouse gas emissions were highly dependent on the supply chain but the best performing supply chains showed environmental and economic benefits. From a global standpoint, the results showed the necessity for an optimal integration of the process to co-develop all the steps of the process and to test the improvements in a flexible pilot plant, thus allowing the comparison of various configurations and their economic and environmental impacts to be determined. <br> Le projet NILE, acronyme de "New Improvements for Lignocellulosic Ethanol", était un projet européen (2005-2010) consacré à la conversion des matières premières lignocellulosiques en éthanol. Ses principaux objectifs étaient de concevoir de nouvelles enzymes adaptées à l’hydrolyse de la cellulose en glucose et de nouvelles souches de levure capables de convertir efficacement tous les sucres présents dans la lignocellulose en éthanol. Une autre partie du projet consistait à tester ces nouveaux systèmes dans une installation pilote et à évaluer les impacts environnementaux et socio-économiques de la production et utilisation à grande échelle d’éthanol lignocellulosique. Deux matières premières modèles (l’épicéa et la paille de blé) prétraitées de façon semblable, ont été étudiées. Différentes approches ont été tentées pour améliorer la saccharification de ces matières premières, par exemple, la recherche de nouvelles enzymes efficaces ou l’ingénierie d’enzymes. Plusieurs stratégies d’ingénierie génétique ont été utilisées pour obtenir des souches stables de Saccharomyces cerevisiae capables de fermenter le xylose et l’arabinose, et de tolérer les composés toxiques présents dans les hydrolysats lignocellulosiques. L’installation pilote pouvait traiter 2 tonnes de matières sèches par jour, et l’hydrolyse et la fermentation pouvaient être menées successivement ou simultanément. Un modèle global intégrant la chaîne d’approvisionnement en matière première a servi à évaluer les performances économiques et environnementales de la production d’éthanol lignocellulosique. L’évolution dirigée d’une enzyme du cocktail cellulolytique produit par le champignon Trichoderma reesei, et la modification de la composition de ce cocktail améliorent l’hydrolyse enzymatique des matières premières prétraitées. Cependant, ces résultats n’ont pu être reproduits à grande échelle. Le rendement de conversion et la productivité spécifique en éthanol ont été sensiblement augmentés grâce à l’ingénierie métabolique des souches de levure et au développement d’un procédé optimal de fermentation. Les essais en pilote ont confirmé le bon comportement de ces souches de levure en conditions industrielles ainsi que la possibilité d’utiliser les résidus riches en lignine comme combustible. Le coût de production de l’éthanol et le bilan des émissions de gaz à effet de serre étaient très dépendants des sources d’énergie utilisées. D’un point de vue plus global, les résultats ont montré que l’optimisation du procédé nécessite de codévelopper toutes les étapes de façon intégrée et de valider les améliorations dans une installation pilote, afin notamment de pouvoir comparer différentes configurations et d’en déterminer les effets sur l’économie du procédé et ses impacts environnementaux

A mixture-fraction-based hybrid binomial Langevin-multiple mapping conditioning model

Generalized Multiple Mapping Conditioning (MMC) allows for the use of any physical quantity to represent the required reference variable provided that it delivers the desired behavior. The binomial Langevin model (BLM) has been shown to predict higher statistical moments with good accuracy. However, joint-scalar modeling for many scalars becomes problematic because scalar bounds must be specified as conditional on other scalars to preserve elemental balances. The resulting volumes in state space become exceptionally complex for realistic problem sizes. In the current work, this central difficulty is avoided by using only velocity and mixture fraction statistics from the BLM with the latter used as the MMC reference variable. The principal advantage of this method is that the implementation of the binomial Langevin mixture fraction is relatively straightforward and provides a direct physical link to MMC. The MMC model is closed using an augmented modified Curl's model where the selection of particle pairs for (turbulent) mixing ensures proximity in reference space and a corresponding closeness in physical space. The method is evaluated for a lifted methane jet flame undergoing auto-ignition in a vitiated coflow. Most of the major features of the flow are well reproduced and found to generally outperform other modeling approaches, including Large Eddy Simulations using simplified treatments of turbulence--chemistry interactions such as unsteady flamelet/progress variable descriptions

Rich methane laminar flames doped with light unsaturated hydrocarbons. Part II: 1,3butadiene

In line with the study presented in the part I of this paper, the structure of a laminar rich premixed methane flame doped with 1,3-butadiene has been investigated. The flame contains 20.7% (molar) of methane, 31.4% of oxygen and 3.3% of 1,3-butadiene, corresponding to an equivalence ratio of 1.8, and a ratio C4H6 / CH4 of 16 %. The flame has been stabilized on a burner at a pressure of 6.7 kPa using argon as dilutant, with a gas velocity at the burner of 36 cm/s at 333 K. The temperature ranged from 600 K close to the burner up to 2150 K. Quantified species included usual methane C0-C2 combustion products and 1,3-butadiene, but also propyne, allene, propene, propane, 1,2-butadiene, butynes, vinylacetylene, diacetylene, 1,3-pentadiene, 2-methyl-1,3-butadiene (isoprene), 1-pentene, 3-methyl-1-butene, benzene and toluene. In order to model these new results, some improvements have been made to a mechanism previously developed in our laboratory for the reactions of C3-C4 unsaturated hydrocarbons. The main reaction pathways of consumption of 1,3-butadiene and of formation of C6 aromatic species have been derived from flow rate analyses. In this case, the C4 route to benzene formation plays an important role in comparison to the C3 pathway

MLVA polymorphism of Salmonella enterica subspecies isolated from humans, animals, and food in Cambodia

<p>Abstract</p> <p>Background</p> <p><it>Salmonella </it>(<it>S</it>.) <it>enterica </it>is the main cause of salmonellosis in humans and animals. The epidemiology of this infection involves large geographical distances, and strains related to an episode of salmonellosis therefore need to be reliably discriminated. Due to the limitations of serotyping, molecular genotyping methods have been developed, including multiple loci variable number of tandem repeats (VNTR) analysis (MLVA). In our study, 11 variable number tandem-repeats markers were selected from the <it>S. enterica </it>Typhimurium LT2 genome to evaluate the genetic diversity of 206 <it>S. enterica </it>strains collected in Cambodia between 2001 and 2007.</p> <p>Findings</p> <p>Thirty one serovars were identified from three sources: humans, animals and food. The markers were able to discriminate all strains from 2 to 17 alleles. Using the genotype phylogeny repartition, MLVA distinguished 107 genotypes clustered into two main groups: <it>S. enterica </it>Typhi and other serovars. Four serovars (Derby, Schwarzengrund, Stanley, and Weltevreden) were dispersed in 2 to 5 phylogenic branches. Allelic variations within <it>S. enterica </it>serovars was represented using the minimum spanning tree. For several genotypes, we identified clonal complexes within the serovars. This finding supports the notion of endemo-epidemic diffusion within animals, food, or humans. Furthermore, a clonal transmission from one source to another was reported. Four markers (STTR3, STTR5, STTR8, and Sal20) presented a high diversity index (DI > 0.80).</p> <p>Conclusions</p> <p>In summary, MLVA can be used in the typing and genetic profiling of a large diversity of <it>S. enterica </it>serovars, as well as determining the epidemiological relationships of the strains with the geography of the area.</p