Simultaneous measurement of flow pattern and mass transfer coefficient in bubble columns

Mass transfer studies were carried out in a bubble column using the chemical method. Catalytic oxidation of sodium sulfite was chosen for the studies and the corresponding specific rates of oxidation were obtained using a stirred cell. Laser Doppler anemometer (LDA) was used to measure the instantaneous velocities in the same stirred cell as well as in bubble columns (100 and i.d.). An efficient algorithm based on the multiresolution analysis of the velocity-time data using wavelets was used for the isolation of data belonging to the gas and liquid phases. Eddy isolation model was used for the characterization of the eddy motion including the estimation of the energy dissipation rate. Using the knowledge of eddy motion, a methodology was developed for the prediction of true mass transfer coefficient (k_L) in a stirred cell as well as in bubble columns. The predicted values of k_L have been compared with the experimental values obtained by the chemical method.     

A hardware-based algorithm for virtual machine provisioning in a private cloud

Cloud computing is becoming a key factor in the market day by day. Therefore, many companies are investing or going to invest in this sector for development of large data centers. These data centers not only consume more energy but also produce greenhouse gases. Because of large amount of power consumption, data center providers go for different types of power generator to increase the profit margin which indirectly affects the environment. Several studies are carried out to reduce the power consumption of a data center. One of the techniques to reduce power consumption is virtualization. After several studies, it is stated that hardware plays a very important role. As the load increases, the power consumption of the CPU is also increased. Therefore, by extending the study of virtualization to reduce the power consumption, a hardware-based algorithm for virtual machine provisioning in a private cloud can significantly improve the performance by considering hardware as one of the important factors.     

Efficiently adapting graphical models for selectivity estimation

Query optimizers rely on statistical models that succinctly describe the underlying data. Models are used to derive cardinality estimates for intermediate relations, which in turn guide the optimizer to choose the best query execution plan. The quality of the resulting plan is highly dependent on the accuracy of the statistical model that represents the data. It is well known that small errors in the model estimates propagate exponentially through joins, and may result in the choice of a highly sub-optimal query execution plan. Most commercial query optimizers make the attribute value independence assumption: all attributes are assumed to be statistically independent. This reduces the statistical model of the data to a collection of one-dimensional synopses (typically in the form of histograms), and it permits the optimizer to estimate the selectivity of a predicate conjunction as the product of the selectivities of the constituent predicates. However, this independence assumption is more often than not wrong, and is considered to be the most common cause of sub-optimal query execution plans chosen by modern query optimizers. We take a step towards a principled and practical approach to performing cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate that estimation errors can be greatly reduced, leading to orders of magnitude more efficient query execution plans in many cases. Optimization time is kept in the range of tens of milliseconds, making this a practical approach for industrial-strength query optimizers.     

Identification of statistical patterns in complex systems via symbolic time series analysis

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.     

Broadband dielectric spectroscopy studies of glassy‐state relaxations in annealed poly(2,5‐benzimidazole)

A broadband dielectric spectroscopic (BDS) study of poly(2,5‐benzimidazole) revealed three sub‐glass relaxations and the manner in which the time scales of these local molecular motions shift with annealing temperature and time. Also issuing from the BDS studies was a trend in the degree of connectivity of charge migration pathways and conductivity with annealing temperature and time. These studies were complemented with dynamic mechanical analysis, which showed the same relaxations, and solid state ¹H and ¹³C NMR studies that showed hydrogen bonding group mobility versus temperature. Wide angle X‐ray diffraction investigations indicated an increase in chain‐packing efficiency that was used to rationalize the BDS and NMR results. Copyright © 2011 Society of Chemical Industry     

Wavelength conversion using rare earth doped oxides in polyolefin based nanocomposite films

Nanocomposites are a new class of polymeric materials which include organic or inorganic nano‐phase materials. These nanocomposite films can show interesting properties which have many applications for wavelength conversion and energy saving. In this paper, we present polyolefin based nanocomposite films prepared using wavelength‐converting red ‘phosphors’ such as Y₂O₃: Eu³⁺ and LiAl₅O₈:Fe³⁺ and blue ‘phosphor’ CaMgSi₂O₆: Eu²⁺. The durability of the polymer chain is unaffected by incorporation of inorganic nanoparticles as there is no direct interaction between them. The addition of surfactant in the nanocomposite film helps to improve the dispersion ability of the nanoparticles and increase the flexibility of the polymer film. Wavelength‐converting ‘nanophosphors’ incorporated in a polymer matrix help to increase the tensile strength of the film. The films show excellent wavelength conversion ability of UV light into the visible and near IR range. These nanocomposite films have great application in energy saving devices. Copyright © 2012 Society of Chemical Industry     

Conductivity and atmospheric aging studies of polypyrrole-coated cotton fabrics

Polypyrrole (PPy) is one of the preferred alternatives among the intrinsically conductive polymers (ICPs). In this study, PPy-coated cotton (PPy-CT) fabrics were synthesized by two step in situ chemical polymerization. The reaction parameters, such as monomer concentration and temperature, were studied in detail. The surface resistivity of PPy-CT fabrics ranged ∼ 15–5000 Ω⁻². To assess long-term usage potential, the atmospheric aging of conductivity characteristics of treated fabrics was monitored over a period of 6 months. It was found that the synthesis temperature had a significant impact on conductivity and atmospheric aging of PPy-CT fabrics. Furthermore, various sulfonic acid sodium salts added as external doping agents during polymerization also had a positive effect. The scanning electron microscopy revealed smoother morphology of sulfonic acid salt doped PPy coatings. The overall study addresses the durability aspect of PPy-CT fabrics in potential applications areas. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Carbon Doping in Boron Suboxide: Structure,Energetics, and Elastic Properties

The structural, electronic, and elastic properties of pristine and carbon‐doped boron suboxide (B₆O) are calculated using density functional theory. The results indicate that it is energetically preferable for a single carbon atom to substitute into an oxygen site rather than a boron site. The lattice parameters and cell volume increase to relieve the residual stress created by the carbon substitution. The interstitial position is not favorable for a single atom substitution. However, if two carbon atoms substitute for two neighboring oxygen atoms, then it becomes energetically favorable to dope an interstitial oxygen, boron, or carbon atom along the C–C chain. If the interstitial dopant is either boron or carbon, a local B₄C‐like structure with either a C–B–C or C–C–C chain is created within the boron suboxide unit cell. The resulting structure shows improvements in the bulk modulus at the expense of the shear and Young's moduli. The moduli further improve if an additional carbon is substituted within a polar or equatorial site of the neighboring B₁₂ icosahedron. Based on these calculations, we conclude that carbon doping can either harden or soften B₆O depending on the manner in which the substitutions are populated. Furthermore, as B₆O samples are often oxygen deficient, C doping can occupy such sites and improve the elastic properties.     

Evaluation of Multipoint Dosing Strategy in a Miniaturized Tubular Reactor: Nitration of Salicylic Acid

A 2D non‐isothermal reaction engineering model for a tubular reactor was developed for a complex reaction network of salicylic acid nitration. The influence of different operating and design parameters was studied to minimize the amount of secondary nitration products and limit the maximum temperature inside the reactor. Critical temperature effects were observed for larger reaction tubes, whereas close to isothermal conditions were perceived in smaller tubes. With single‐point dosing of both reactants, complete conversion can be achieved but formation of secondary nitration products cannot be avoided. For a given number of dosing points, a suitable combination of the operating parameters allowed to achieve complete conversion and better yield of the desired product.