Modelling effects of subgrid-scale mixture fraction variance in LES of a piloted diffusion flame

A posteriori analysis of the statistics of two large-eddy simulation (LES) solutions describing a piloted methane–air (Sandia D) flame is performed on a series of grids with progressively increased resolution reaching about 10.5 million cells. Chemical compositions, density and temperature fields are modelled with a steady flamelet approach and parametrised by the mixture fraction. The difference between the LES solutions arises from a different numerical treatment of the subgrid scale (SGS) mixture fraction variance – an important quantity of interest in non-premixed combustion modelling. In the first case (model I), the variance transport equation is solved directly, while in the second (model II), an equation for the square of the mixture fraction is solved, and the variance is computed from its definition. The comparison of the LES solutions is based on the convergence properties of their statistics with respect to the turbulence resolution length scale. The dependence of the LES statistics is analysed for velocity and the mixture fraction fields, and tested for convergence. For the most part, the statistics converge for the finest grids, but the variance of the mixture fraction shows some residual grid dependence in the high-gradient regions of the jet near field. The SGS variance given by model I exhibits realisability everywhere, whereas in regions of the flame model II is non-realisable, predicting negative variances. Furthermore, the LES statistics of model I exhibit superior convergence behaviour.     

受限空间油气爆燃火焰形态

通过受限空间油气爆燃可视化实验发现，在不同初始油气体积分数下，爆燃火焰呈现出不同的表观特征，据此提出了受限空间油气爆燃的3种火焰形态，即光滑球形火焰、褶皱球形火焰和卷曲絮状火焰。分析了3种火焰形态的形成机理，并通过实验观测与理论分析，给出了区分3种火焰形态的临界条件。结合实验中采集到的关键参数，总结了不同的火焰形态下受限空间油气爆燃的反应产物、最大压力、升压速率、反应时间、火焰强度等关键参数的特征与变化规律。     

Molecular signal suppression by in situ microextraction in nuclear magnetic resonance spectroscopy

The detailed characterization of complex mixtures by NMR is often hampered by the presence of signals from uninformative compounds, the resonances of which overlap with those of the molecules of interest. We provide here a proof of principle for an approach to NMR signal suppression in complex samples using Molecularly Imprinted Polymers (MIPS). Addition of a few milligrams of polymer to a solution traps the target molecule in typical micromolar to millimolar concentration, thus achieving in situ signal suppression, without altering any other spectral features. This method minimized any manipulation or perturbation of the spectrum and was applied to a complex mixture of known compounds and to a plant extract, in both cases spiked with a compound (bisphenol A), which was subsequently removed by selective binding to a complementary MIP. What is described in this report is comparable with microextraction and may in due course be applied to a large number of analytical challenges. Copyright © 2014 John Wiley & Sons, Ltd.     

Vesicle formation in aqueous mixture of the cetyltrimetylammonium bromide and an anionic chitosan derivative

The present work aimed at research the physic-chemical properties of the interaction of N-decyl-O-chitosan sulfate (an amphiphilic chitosan derivative, C₁₀-OCHS) with cetyltrimetylammonium bromide (CTAB) by the steady-state fluorescent, static/dynamic surface tension, turbidity and transmission electron microscopy (TEM). The results showed that the complex of C₁₀-OCHS/CTAB had high surface activity and lower critical aggregation concentration. Besides, the C₁₀-OCHS/CTAB could self-assemble into various aggregates like irregular spherical aggregates, vesicles or polydisperse aggregates from lower to higher concentrations of CTAB with a mixed C₁₀-OCHS concentration of 200?mg/L.     

Hierarchical Model-Based Clustering for Large Datasets

In recent years, hierarchical model-based clustering has provided promising results in a variety of applications. However, its use with large datasets has been hindered by a time and memory complexity that are at least quadratic in the number of observations. To overcome this difficulty, this article proposes to start the hierarchical agglomeration from an efficient classification of the data in many classes rather than from the usual set of singleton clusters. This initial partition is derived from a subgraph of the minimum spanning tree associated with the data. To this end, we develop graphical tools that assess the presence of clusters in the data and uncover observations difficult to classify. We use this approach to analyze two large, real datasets: a multiband MRI image of the human brain and data on global precipitation climatology. We use the real datasets to discuss ways of integrating the spatial information in the clustering analysis. We focus on two-stage methods, in which a second stage of processing using established methods is applied to the output from the algorithm presented in this article, viewed as a first stage.     

Composite Likelihood Inference in a Discrete Latent Variable Model for Two-Way “Clustering-by-Segmentation” Problems

We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g., exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g., consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. Therefore, we introduce a composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data.     

The solubility of bosentan in aqueous-2-propanol mixtures at several temperatures,measurement and data correlation

ABSTRACT

The solubilities of bosentan (BST) in binary aqueous mixtures of 2-propanol at temperatures ranging from 293.15 to 313.15 K were determined using a shake-flask method. The produced data were modelled with the Jouyban-Acree-van’t Hoff model and difference between the predicted data and experimental ones were illustrated by percent average relative deviations (%ARD). Moreover, the thermodynamic functions of dissolution for BST in the aqueous 2-propanol solutions were computed which suggest that the dissolution process is endothermic and not spontaneous.     

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

The nonlinear, nonnegative single‐mixture blind source separation problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined blind source separation problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi‐mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi‐mixture into a high‐dimensional reproducible kernel Hilbert space. Under sparse probabilistic conditions that were previously imposed on sources, the single‐mixture nonlinear problem is converted into an equivalent linear, multiple‐mixture problem that consists of the original sources and their higher‐order monomials. These monomials are suppressed by robust principal component analysis and hard, soft, and trimmed thresholding. Sparseness‐constrained nonnegative matrix factorizations in reproducible kernel Hilbert space yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of eight dependent components from one nonlinear mixture. The method is further demonstrated on three nonlinear chemical reactions of peptide synthesis in which 25, 19, and 28 dependent analytes are extracted from one nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry‐based non‐targeted metabolic profiling, such as biomarker identification studies. Copyright © 2015 John Wiley & Sons, Ltd.     

Volumetric behaviour of the (2,2,4-trimethylpentane + methylbenzene + butan-1-ol) ternary system and its binary sub-systems within the temperature range (298.15–328.15) K

Densities and speeds of sound of the (2,2,4-trimethylpentane + methylbenzene + butan-1-ol) ternary system as well as all its binary sub-systems were measured at four temperatures, namely 298.15 K, 308.15 K, 318.15 K, and 328.15 K at atmospheric pressure by a vibrating-tube densimeter DSA 5000. The binary (isooctane + toluene) system was studied previously. Excess quantities (molar volume, adiabatic compressibility, and isobaric thermal expansivity) of the mixtures studied were calculated from the experimental densities and speed of sounds. The excess molar volume data were correlated using the Redlich–Kister equation. Both the positive and S-shaped excess molar volume curves were found for the systems studied. The excess molar volumes versus concentration of binary systems differed in the shape and temperature dependence. The experimental binary data were compared with literature data. The experimental excess molar volumes were analyzed by means of the Extended Real Associated Solution (ERAS) model. The experimental data and the ERAS model can help to estimate real behaviour of the systems studied.     

Dynamic Surface Elasticity of Polyelectrolyte/Surfactant Adsorption Films at the Air/Water Interface: Carboxylmethylchitosan and Cetyltrimethylammonium Bromide

The dynamic surface elasticity of the solution of carboxymethylchitosan (CMCH) and cetyltrimethylammonium bromide (C₁₆TAB) was measured as a function of the surfactant concentration and the surface age by the oscillating drop method. The results showed that the surface layer of the CMCH/C₁₆TAB solution transformed from loose to structured with the increase in the surfactant concentration. The surface elasticity versus the surface pressure curve during the adsorption showed different features at different surfactant concentration ranges, which was connected with the structural transformation of the surface layer. Our results show that the measurement of dynamic surface dilational properties is a powerful tool for studying the surface kinetics, which is helpful for dealing non-equilibrium systems in industry.