Numerical iterative methods and repetitive processes

Implicit schemes are a popular approach to the discretization of linear partial differential equations by finite differences. They require to solve a set of linear equations in each time step. Since finite difference discretizations lead to a local coupling, these systems of equations are sparse and can be effectively solved by iterative methods. Numerical procedures of this type are known in control theory as repetitive processes. They have mostly been used to solve problems in control like processes where passes of finite length are repeated over and over. This paper shows how the implicit discretization of partial differential equations can be cast into the framework of repetitive processes. Thus it establishes a link between yet unrelated results in numerical mathematics and control theory.     

Identification of the crystalline-phases in thin pentacene layers by Raman spectroscopy

We investigated the different crystallographic phases in thin pentacene layers by the micro-Raman spectroscopy. For the first time we have determined the presence of orthorhombic, thin film and triclinic bulk phase in thin pentacene layers by the micro-Raman polarization spectroscopy, which were confirmed by XRD measurements.     

Remote monitoring of buildings using a ground-based SAR: Application to cultural heritage survey

In this Letter, the authors describe an innovative application of radar interferometric techniques aimed to monitor structural deformations of buildings. The proposed application is based on the use of ground-based instrumentation able to operate as Interferometric Synthetic Aperture Radar (InSAR). Preliminary experimental results on a model of an historical building encourage the development of this technique for use in architectural heritage survey.     

Speciation of zinc in municipal solid waste incineration fly ash after heat treatment: an X-ray absorption spectroscopy study

Fly ash is commonly deposited in special landfills as it contains toxic concentrations of heavy metals, such as Zn, Pb, Cd, and Cu. This study was inspired by our efforts to detoxify fly ash from municipal solid waste incineration by thermal treatment to produce secondary raw materials suited for reprocessing. The potential of the thermal treatment was studied by monitoring the evaporation rate of zinc from a certified fly ash (BCR176) during heating between 300 and 950 degrees C under different carrier gas compositions. Samples were quenched at different temperatures for subsequent investigation with X-ray absorption spectroscopy (XAS). The XAS spectra were analyzed using principal component analysis (PCA), target transformation (TT), and linear combination fitting (LCF) to analyze the major Zn compounds in the fly ash as a function of the temperature. The original fly ash comprised about 60% zinc oxides mainly in the form of hydrozincite (Zn5(OH)6(CO3)2) and 40% inerts like willemite (Zn2SiO4) and gahnite (ZnAl2O4) in a weight ratio of about 3:1. At intermediate temperatures (550-750 degrees C) the speciation underlines the competition between indigenous S and Cl with solid zinc oxides to form either volatile ZnCl2 or solid ZnS. ZnS then transformed into volatile species at about 200 degrees C higher temperatures. The inhibiting influence of S was found absent when oxygen was introduced to the inert carrier gas stream or chloride-donating alkali salt was added to the fly ash.     

Use of NC kernel data for surface roughness monitoring in milling operations

This work focuses on developing an application based on the information contained in the numerical control (NC) kernel for surface roughness monitoring of the part in process. A human?Cmachine interface (HMI) was developed in order to facilitate the interaction between the operator and the NC kernel with a graphical user interface working in the computer numerically controlled (CNC) screen. Experimentation was carried out in order to obtain the data to be modeled with artificial neural networks for surface roughness average parameter (Ra) predictions. Finally, a compact solution was implemented through global user data (GUD). Data from the HMI and from the kernel are collected in the GUD and analyzed with the artificial neural network. The application provides the surface roughness average parameter of the part in process and gives optimized parameters to the operator. Verification tests were carried out, showing accurate results. The use of the application developed in this research ensures the surface roughness Ra requirement, improves cutting parameters, reduces manual finishing operations and unacceptable parts at the end of the manufacturing process, and provides a solution implemented in the machine tool CNC screen without the need of any other external sensors.