Fully discrete T‐ψ finite element method to solve a nonlinear induction hardening problem

We study an induction hardening model described by Maxwell's equations coupled with a heat equation. The magnetic induction field is assumed a nonlinear constitutional relation and the electric conductivity is temperature‐dependent. The T ‐ψ method is to transform Maxwell's equations to the vector–scalar potential formulations and to solve the potentials by means of the finite element method. In this article, we present a fully discrete T ‐ψ finite element scheme for this nonlinear coupled problem and discuss its solvability. We prove that the discrete solution converges to a weak solution of the continuous problem. Finally, we conclude with two numerical experiments for the coupled system.     

The selection of the optimal parameter in the modulus-based matrix splitting algorithm for linear complementarity problems

The modulus-based matrix splitting (MMS) algorithm is effective to solve linear complementarity problems (Bai in Numer Linear Algebra Appl 17: 917–933, 2010). This algorithm is parameter dependent, and previous studies mainly focus on giving the convergence interval of the iteration parameter. Yet the specific selection approach of the optimal parameter has not been systematically studied due to the nonlinearity of the algorithm. In this work, we first propose a novel and simple strategy for obtaining the optimal parameter of the MMS algorithm by merely solving two quadratic equations in each iteration. Further, we figure out the interval of optimal parameter which is iteration independent and give a practical choice of optimal parameter to avoid iteration-based computations. Compared with the experimental optimal parameter, the numerical results from three problems, including the Signorini problem of the Laplacian, show the feasibility, effectiveness and efficiency of the proposed strategy.

     

Chlorine dioxide catalytic oxidation and online FTIR spectroscopic analysis of simulated o-chlorophenol wastewater

An activated carbon-MnO₂ catalyst was prepared and used for chlorine dioxide catalytic oxidation of simulated o-chlorophenol wastewater. The COD removal efficiencies of chemical oxidation and catalytic oxidation are 28.6 and 93.5%, respectively. The COD removal efficiency of catalytic oxidation is greater than that of chemical oxidation at the same treatment condition. By using UV?CVis and online FTIR analysis technique, the intermediates during the degradation process were obtained. The benzene ring in o-chlorophenol was degraded into quinone and carboxylic acid, and finally changed into carbon dioxide and water during the catalytic oxidation. The degradation reaction mechanism of o-chlorophenol by chlorine dioxide catalytic oxidation was proposed based upon the experiment evidence.     

Study of inclusion complex formation between tropaeolin OO and beta-cyclodextrin by spectrophotometry and Infrared spectroscopy

The mechanism of the inclusion of tropaeolin OO (TPOO) and beta-cyclodextrin (beta-CD) has been studied by spectrophotometry. The inclusion depth of the guest molecule in the host molecule was demonstrated by infrared spectrometry. Effect of the pH, concentrations of beta-CD, solvents and ionic strength on the inclusion of TPOO and beta-CD were examined. The result showed that TPOO reacts with beta-CD to form a 1:1 host-guest complex with an apparent formation constant of 1.50 x 10(3) l mol(-1). The thermodynamic parameters of inclusion reaction, DeltaG degrees , DeltaH degrees and DeltaS degrees were obtained.     

Application of artificial neural network to predict the friction factor of open channel flow

The friction factor of an open channel flow is generally affected by the Reynolds number and the roughness conditions, and can be decided by laboratory or field measurements. During practical applications, researchers often find that a correct choice of the friction factor can be crucial to make a sound prediction of hydraulic problems. In this paper, a three-layer artificial neural network (ANN) was set up to predict the friction factors of an open channel flow, with the Reynolds number and the relative roughness as two input parameters. The Levenberg–Marquardt (LM) learning algorithm was employed to train the model by using

laboratory experimental data, and the trained network was tested by a single set separated from the rest of the data and a good correlation between the experimental and predicted results has been obtained. Finally, the ANN simulated results were compared with the calculated results obtained by the empirical formula and both comparisons showed that the ANN model can be used to predict the non-linear relationship between the friction factor and its influencing factors correctly once enough samples are provided. The successful application proved that ANN model can be used in engineering practice as a convenient and effective method, and those traditional hydraulic problems which are mostly based on laboratory tests can be analyzed by ANN modelling.     

Synthesis of porous titaniaspheres for HPLC by polymerization-induced colloid aggregation (PICA) using <Emphasis Type="Italic">tert-n</Emphasis>-butyl titanate

Porous titanic microspheres with a very narrow particle size distribution (PSD) which are useful as chromatographic packing materials for high performance liquid chromatography (HPLC), were synthesized by an improved process of polymerization-induced colloid aggregation method using tert-n -butyl titanate. Porous titania particles obtained after polymer combustion and sintering of the aggregates are 3.5 μm in diameter with a surface area of 9.6 m² g⁻¹ and an average pore diameter of 18.9 nm. The particles are strong enough to withstand the high packing pressure for a HPLC column. The article is published in the original.     

ChemInform Abstract: Investigation of Size‐Selective Zr2@Sin (n = 16—24) Caged Clusters.

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