Characterisation of different one-stage blower configurations using 3D unsteady numerical flow simulations

This paper deals with the CFD investigation of the flow in a one-stage radial flow blower-aggregate. The main aim of this numerical study is to compute the relevant operating characteristics of the blower-aggregate and to determine detailed information about the flow characteristics inside it. The distributions of these flow characteristics in the blower determined by the commercial code ANSYS-FLUENT [1] are available to judge whether the elements of the blower are working properly, or not. The calculated characteristics of operating parameters are compared in this paper with measured data given by experimental tests of the blower-aggregate for their validation [2]. The blower-aggregates investigated numerically are noted by BA₁, BA₂ and BA₃ in this paper

Phenotypic and Genotypic Characterization of Escherichia coli Isolated from Untreated Surface Waters

A common member of the intestinal microbiota in humans and animals is Escherichia coli. Based on the presence of virulence factors, E. coli can be potentially pathogenic. The focus of this study was to isolate E. coli from untreated surface waters (37 sites) in Illinois and Missouri and determine phenotypic and genotypic diversity among isolates. Water samples positive for fecal coliforms based on the Colisure® test were streaked directly onto Eosin Methylene Blue (EMB) agar (37°C) or transferred to EC broth (44.5°C). EC broth cultures producing gas were then streaked onto EMB agar. Forty-five isolates were identified as E. coli using API 20E and Enterotube II identification systems, and some phenotypic variation was observed in metabolism and fermentation. Antibiotic susceptibility of each isolate was also determined using the Kirby-Bauer Method. Differential responses to 10 antimicrobial agents were seen with 7, 16, 2, and 9 of the isolates resistant to ampicillin, cephalothin, tetracycline, and triple sulfonamide, respectively. All of the isolates were susceptible or intermediate to amoxicillin, ciprofloxacin, polymyxin B, gentamicin, imipenem, and nalidixic acid. Genotypic variation was assessed through multiplex Polymerase Chain Reaction for four virulence genes (stx1 and stx2 [shiga toxin], eaeA [intimin]; and hlyA [enterohemolysin]) and one housekeeping gene (uidA [-D-glucuronidase]). Genotypic variation was observed with two of the isolates possessing the virulence gene (eaeA) for intimin. These findings increase our understanding of the diversity of E. coli in the environment which will ultimately help in the assessment of this organism and its role in public health

Evaluation of acidogenesis products’ effect on biogas production performed with metagenomics and isotopic approaches

Background: During the acetogenic step of anaerobic digestion, the products of acidogenesis are oxidized to substrates for methanogenesis: hydrogen, carbon dioxide and acetate. Acetogenesis and methanogenesis are highly interconnected processes due to the syntrophic associations between acetogenic bacteria and hydrogenotrophic methanogens, allowing the whole process to become thermodynamically favorable. The aim of this study is to determine the influence of the dominant acidic products on the metabolic pathways of methane formation and to find a core microbiome and substrate-specific species in a mixed biogas-producing system. Results: Four methane-producing microbial communities were fed with artificial media having one dominant component, respectively, lactate, butyrate, propionate and acetate, for 896 days in 3.5-L Up-flow Anaerobic Sludge Blanket (UASB) bioreactors. All the microbial communities showed moderately different methane production and utilization of the substrates. Analyses of stable carbon isotope composition of the fermentation gas and the substrates showed differences in average values of δ13C(CH4) and δ13C(CO2) revealing that acetate and lactate strongly favored the acetotrophic pathway, while butyrate and propionate favored the hydrogenotrophic pathway of methane formation. Genome-centric metagenomic analysis recovered 234 Metagenome Assembled Genomes (MAGs), including 31 archaeal and 203 bacterial species, mostly unknown and uncultivable. MAGs accounted for 54%–67% of the entire microbial community (depending on the bioreactor) and evidenced that the microbiome is extremely complex in terms of the number of species. The core microbiome was composed of Methanothrix soehngenii (the most abundant), Methanoculleus sp., unknown Bacteroidales and Spirochaetaceae. Relative abundance analysis of all the samples revealed microbes having substrate preferences. Substrate-specific species were mostly unknown and not predominant in the microbial communities. Conclusions: In this experimental system, the dominant fermentation products subjected to methanogenesis moderately modified the final effect of bioreactor performance. At the molecular level, a different contribution of acetotrophic and hydrogenotrophic pathways for methane production, a very high level of new species recovered, and a moderate variability in microbial composition depending on substrate availability were evidenced. Propionate was not a factor ceasing methane production. All these findings are relevant because lactate, acetate, propionate and butyrate are the universal products of acidogenesis, regardless of feedstock

The Hoopoe's Uropygial Gland Hosts a Bacterial Community Influenced by the Living Conditions of the Bird

Molecular methods have revealed that symbiotic systems involving bacteria are mostly based on whole bacterial communities. Bacterial diversity in hoopoe uropygial gland secretion is known to be mainly composed of certain strains of enterococci, but this conclusion is based solely on culture-dependent techniques. This study, by using culture-independent techniques (based on the 16S rDNA and the ribosomal intergenic spacer region) shows that the bacterial community in the uropygial gland secretion is more complex than previously thought and its composition is affected by the living conditions of the bird. Besides the known enterococci, the uropygial gland hosts other facultative anaerobic species and several obligated anaerobic species (mostly clostridia). The bacterial assemblage of this community was largely invariable among study individuals, although differences were detected between captive and wild female hoopoes, with some strains showing significantly higher prevalence in wild birds. These results alter previous views on the hoopoe-bacteria symbiosis and open a new window to further explore this system, delving into the possible sources of symbiotic bacteria (e.g. nest environments, digestive tract, winter quarters) or the possible functions of different bacterial groups in different contexts of parasitism or predation of their hoopoe host.This work was supported by the Ministerio de Ciencia y Tecnología (projects CGL2005-06975/BOSFEDER; CGL2007-61251/BOSFEDER), the Ministerio de Ciencia e Innovación (projects CGL2009-14006/BOSFEDER; CGL2010-19233-C03-01/BOSFEDER; CGL2010-19233-C03-03/BOSFEDER), the Ministerio de Economía y Competitividad (projects CGL2013-48193-C3-1-P/BOSFEDER; CGL2013-48193-C3-2-P/BOSFEDER), and the Junta de Andalucía (RNM 345, P09-RNM-4557). SMRR received a grant from the Ministerio de Ciencia e Innovación (FPI program, BES-2011-047677)

Characterization of different one-stage blower designs using three-dimensional unsteady numerical flow simulation

This paper deals with the computational fluid dynamics investigation of the flow in a one-stage radial flow blower-aggregate. The main aim of this numerical study is to compute the relevant operating characteristics of the blower and to determine detailed information about the flow characteristics inside it. The distributions of these flow characteristics in the blower determined by the commercial code are available to judge whether the elements of the blower are working properly, or not. The calculated characteristics of operating parameters are compared with measured data given by experimental tests of the blower-aggregate for their validation

Optimization of Savonius Turbines after improving the k-ε Turbulence Model in modeFRONTIER

The accurate prediction of turbulent flows is a fundamental issue to improve existing devices and develop new configurations. A detailed level of prediction can be obtained with Direct Numerical Simulation (DNS), but limitations in computer power restrict its application to simple configurations and low Reynolds number. Large Eddy Simulation (LES) is now feasible for many situations but it is still an expensive solution for industrial applications. Therefore, numerical simulations based on Reynolds Averaged Navier-Stokes (RANS) are still widely used today for engineering problems. In the RANS models, closure constants are introduced in order to replace higher order correlations originated during the process of averaging Navier-Stokes equation. These constants are usually determined semi-empirically based on simple flows. Nevertheless, these models are applied in quite different and complex configurations. For a particular flow, it is likely that the prediction can be improved with the adjustment of the constants and therefore a large span of values can be found in literature with values calibrated based on the experience of the user, theoretical considerations and single objective numerical optimization. The determination of the model constants for engineering turbulence models is indeed a difficult task. The values are often considered as some ad-hoc values. Changing one parameter in order to observe consequences concerning, for instance, the time-averaged turbulent velocity distribution or the shear stress distribution, is easy. But the simultaneous modification of several parameters of a turbulence model in order to increase accuracy rapidly becomes a formidable issue. If all the model parameters were changed in small steps, then the number of possible combinations would yield an enormous – and probably unnecessary – computational effort to explore the whole domain. In that case, numerical optimization techniques may help to speed-up the search procedure to find the best possible combination of the model constants with a minimum computational load, since optimization is much more efficient than a simple trial and error manual procedure