Hybrid treatment of small droplets in atomized jet

International audienceL'atomisation de combustible a un impact direct sur l'émission de polluants dans l'atmosphère. Face au besoin de caractériser l'atomisation primaire, l'étude numérique de l'intéraction liquide-gaz croît dans le but de maîtriser la création de particules polluantes et de la réduire. Elle est effectuée sur l'ensemble du spray, de son injection dans la chambre de combustion jusqu'à l'évaporation des gouttes créées suite au secondary breakup. Notre but est d'augmenter la précision du transport des gouttes au sein des jets atomisés, typiquement, une goutte est 100 fois plus petite que le diamètre d'injection. Cette différence d'échelle rend la définition de l'interface liquide-gaz complexe et crééer des zones sous résolues. Pour résoudre ce probleme d'échelle, un coupling entre un suivi Eulérien et un suivi Lagrangian a été proposé, voir Hermann, [1]. Cette communication se concentre sur les critères de transformation d'une goutte eulérienne en particule lagrangienne et les modifications physiques et numériques entourant cette transformation. Cette communication se concentre sur l'implémentation d'une méthode de suivi de particule polydisperse basée sur des critères géometriques. Ils sont finalement appliqués sur l'étude d'un jet atomisé. Abstract : Atomization of liquid fuel has a direct impact on the production of pollutant emission in engineering propulsion devices. Due to the multiple challenges in experimental investigations, motivation for numerical study is increasing on liquid/gas interaction from injection till dispersed spray zone. Our purpose is to increase the accuracy of the treatment of droplets in atomized jet, which are typically 100 times smaller than the injection size. As the size of the droplets reduces with the primary breakup of liquid fuel, it is increasingly challenging to track the interface of the droplets accurately. To solve this multis-cale issue, a coupled tracking Eulerian-Lagrangian Method is proposed, see Hermann, [1]. This communication focuses on the criteria of transformation of this coupling from interface captured droplets to Lagrangian particles and numerical/physical reconstruction during this process. From the literature, interaction criteria of transformation are all geometric, implementation of physical parameter is made in this communication. Those criteria are finally applied on a liquid jet atomization

From droplets to particles: Transformation criteria

International audienceAtomization of liquid fuel has a direct impact on the production of pollutant emission in engineering propulsion devices. Due to the multiple challenges in experimental investigations, motivation for numerical study is increasing on liquid-gas interaction from injection till dispersed spray zone. Our purpose is to increase the accuracy of the treatment of droplets in atomized jet, which are typically 100 times smaller than the characteristic injection length size. As the characteristic length reduces downstream to the jet, it is increasingly challenging to track the interface of the droplets accurately. To solve this multiscale issue, a coupled tracking Eulerian-Lagrangian Method exists [1]. It consists in transforming the small droplets to Lagrangian droplets that are transported with drag models. In addition to the size transformation criteria, one can consider geometric parameters to determine if a droplet has to be transformed. Indeed, the geometric criteria are there for two reasons. The first one is the case where the droplets can break if there are not spherical. The second one is about the drag models that are based on the assumption that the droplet is spherical. In this paper we make a review of the geometric criteria used in the literature. New geometric criteria are also proposed. Those criteria are validated and then discussed in academic cases and a 3D airblast atomizer simulation. Following the analysis of the results the authors advise the use of the deformation combined with surface criteria as the geometric transformation criteria. Introduction Atomization is a phenomenon encountered in many applications such as sprays in cosmetic engineering or aerospace engineering for jet propulsion [2]. In the combustion chamber, the total surface of the interface separating the two phases is a key parameter. Primary and secondary breakup have been extensively investigated in the literature. However, in order to fully describe the complete process, one has to capture droplets in dispersed zone 100 times smaller than jet diameter. Atomization is then a multiphase and a multiscale flow phenomenon which is still far from being understood. Due to this wide range of scale, the Direct Numerical Simulation (DNS) of such process requires robust and efficient codes. DNS is an important tool to analyse the experimental results and go further into the atomization understanding. In the past few years, numerical schemes of Interface Capturing Method (ICM) have been improved but faced numerical limitation. For instance, the treatment of the small droplets is the most challenging part when the entire process is treated in DNS. When dealing with unresolved structures we face different problems such as the dilution or the creation of numerical instabilities. To avoir them, a strategy is to remove small structures during the simulation, see Shinjo et al. [3]. But, those methods do not collect information on smallest droplets in atomization application. Introduction of Adaptive Mesh Refinement (AMR) in DNS is a first answer to this issue, it consists in refining unresolved area under numerical concept and focus on the interface between two phases instead of refining the entire domain. In dense spray, AMR tends to refine the entire zone and becomes as expensive as a full domain refinement. A solution is to transform the smallest droplets into point particles and remove AMR in this area. This strategy is called Eulerian-Lagrangian coupling [1], it assumes that small droplets will no longer break during the simulation and that the Lagrangian models reproduce correctly the droplet transport. These physical assumptions are implemented to answer numerical issue and improve the computational cost. This Eulerian-Lagrangian coupling is based on transformation criteria that defines when an ICM structure has to be transformed into Lagrangian particle and when a Lagrangian particle has to be transformed back into ICM. The main purpose of the present communication is to provide a detailed analysis of the ICM to Lagrangian transformation criteria. The geometri

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Advanced optical imaging in living embryos

Developmental biology investigations have evolved from static studies of embryo anatomy and into dynamic studies of the genetic and cellular mechanisms responsible for shaping the embryo anatomy. With the advancement of fluorescent protein fusions, the ability to visualize and comprehend how thousands to millions of cells interact with one another to form tissues and organs in three dimensions (xyz) over time (t) is just beginning to be realized and exploited. In this review, we explore recent advances utilizing confocal and multi-photon time-lapse microscopy to capture gene expression, cell behavior, and embryo development. From choosing the appropriate fluorophore, to labeling strategy, to experimental set-up, and data pipeline handling, this review covers the various aspects related to acquiring and analyzing multi-dimensional data sets. These innovative techniques in multi-dimensional imaging and analysis can be applied across a number of fields in time and space including protein dynamics to cell biology to morphogenesis

Routine Modeling with Time Series Metric Learning

version éditeur : https://rd.springer.com/chapter/10.1007/978-3-030-30484-3_47International audienceTraditionally, the automatic recognition of human activities is performed with supervised learning algorithms on limited sets of specific activities. This work proposes to recognize recurrent activity patterns, called routines, instead of precisely defined activities. The modeling of routines is defined as a metric learning problem, and an architecture, called SS2S, based on sequence-to-sequence models is proposed to learn a distance between time series. This approach only relies on inertial data and is thus non intrusive and preserves privacy. Experimental results show that a clustering algorithm provided with the learned distance is able to recover daily routines

Guns, germs, and trees determine density and distribution of gorillas and chimpanzees in Western Equatorial Africa

We present a range-wide assessment of sympatric western lowland gorillas Gorilla gorilla gorilla and central chimpanzees Pan troglodytes troglodytes using the largest survey data set ever assembled for these taxa: 59 sites in five countries surveyed between 2003 and 2013, totaling 61,000 person-days of fieldwork. We used spatial modeling to investigate major drivers of great ape distribution and population trends. We predicted density across each taxon’s geographic range, allowing us to estimate overall abundance: 361,900 gorillas and 128,700 chimpanzees in Western Equatorial Africa—substantially higher than previous estimates. These two subspecies represent close to 99% of all gorillas and one-third of all chimpanzees. Annual population decline of gorillas was estimated at 2.7%, maintaining them as Critically Endangered on the International Union for Conservation of Nature and Natural Resources (IUCN) Red List. We quantified the threats to each taxon, of which the three greatest were poaching, disease, and habitat degradation. Gorillas and chimpanzees are found at higher densities where forest is intact, wildlife laws are enforced, human influence is low, and disease impacts have been low. Strategic use of the results of these analyses could conserve the majority of gorillas and chimpanzees. With around 80% of both subspecies occurring outside protected areas, their conservation requires reinforcement of anti-poaching efforts both inside and outside protected areas (particularly where habitat quality is high and human impact is low), diligent disease control measures (including training, advocacy, and research into Ebola virus disease), and the preservation of high-quality habitat through integrated land-use planning and implementation of best practices by the extractive and agricultural industries.Additional co-authors: Nicolas Bout, Thomas Breuer, Genevieve Campbell, Pauwel De Wachter, Marc Ella Akou, Fidel Esono Mba, Anna T. C. Feistner, Bernard Fosso, Roger Fotso, David Greer, Clement Inkamba-Nkulu, Calixte F. Iyenguet, Max Kokangoye, Hjalmar S. Kühl, Stephanie Latour, Bola Madzoke, Calixte Makoumbou, Guy-Aimé F. Malanda, Richard Malonga, Victor Mbolo, David B. Morgan, Prosper Motsaba, Gabin Moukala, Brice S. Mowawa, Mizuki Murai, Christian Ndzai, Tomoaki Nishihara, Zacharie Nzooh, Lilian Pintea, Amy Pokempner, Hugo J. Rainey, Tim Rayden, Heidi Ruffler, Crickette M. Sanz, Angelique Todd, Hilde Vanleeuwe, Ashley Vosper, Ymke Warren, and David S. Wilki

Targeted metatranscriptomics of compost derived consortia reveals a GH11 exerting an unusual exo-1,4-β-xylanase activity

Background: Using globally abundant crop residues as a carbon source for energy generation and renewable chemicals production stands out as a promising solution to reduce current dependency on fossil fuels. In nature, such as in compost habitats, microbial communities efficiently degrade the available plant biomass using a diverse set of synergistic enzymes. However, deconstruction of lignocellulose remains a challenge for industry due to recalcitrant nature of the substrate and the inefficiency of the enzyme systems available, making the economic production of lignocellulosic biofuels difficult. Metatranscriptomic studies of microbial communities can unveil the metabolic functions employed by lignocellulolytic consortia and identify new biocatalysts that could improve industrial lignocellulose conversion. Results: In this study, a microbial community from compost was grown in minimal medium with sugarcane bagasse sugarcane bagasse as the sole carbon source. Solid-state nuclear magnetic resonance was used to monitor lignocellulose degradation; analysis of metatranscriptomic data led to the selection and functional characterization of several target genes, revealing the first glycoside hydrolase from Carbohydrate Active Enzyme family 11 with exo-1,4-β-xylanase activity. The xylanase crystal structure was resolved at 1.76 Å revealing the structural basis of exo-xylanase activity. Supplementation of a commercial cellulolytic enzyme cocktail with the xylanase showed improvement in Avicel hydrolysis in the presence of inhibitory xylooligomers. Conclusions: This study demonstrated that composting microbiomes continue to be an excellent source of biotechnologically important enzymes by unveiling the diversity of enzymes involved in in situ lignocellulose degradation

Dissimilar responses of fungal and bacterial communities to soil transplantation simulating abrupt climate changes.

Both fungi and bacteria play essential roles in regulating soil carbon cycling. To predict future carbon stability, it is imperative to understand their responses to environmental changes, which is subject to large uncertainty. As current global warming is causing range shifts toward higher latitudes, we conducted three reciprocal soil transplantation experiments over large transects in 2005 to simulate abrupt climate changes. Six years after soil transplantation, fungal biomass of transplanted soils showed a general pattern of changes from donor sites to destination, which were more obvious in bare fallow soils than in maize cropped soils. Strikingly, fungal community compositions were clustered by sites, demonstrating that fungi of transplanted soils acclimatized to the destination environment. Several fungal taxa displayed sharp changes in relative abundance, including Podospora, Chaetomium, Mortierella and Phialemonium. In contrast, bacterial communities remained largely unchanged. Consistent with the important role of fungi in affecting soil carbon cycling, 8.1%-10.0% of fungal genes encoding carbon-decomposing enzymes were significantly (p < 0.01) increased as compared with those from bacteria (5.7%-8.4%). To explain these observations, we found that fungal occupancy across samples was mainly determined by annual average air temperature and rainfall, whereas bacterial occupancy was more closely related to soil conditions, which remained stable 6 years after soil transplantation. Together, these results demonstrate dissimilar response patterns and resource partitioning between fungi and bacteria, which may have considerable consequences for ecosystem-scale carbon cycling