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PL
Do wytworzenia cienkich warstw Ba0,6Sr0,4TiO3 (BST60/40), domieszkowanych MgO w ilości 1%, 3% i 5% molowych, na podłożach ze stali nierdzewnej zastosowano technologię zol-żel. Stwierdzono, że krystalizują one w symetrii tetragonalnej P4mm. Właściwości optyczne warstw badano z zastosowaniem spektroskopii ramanowskiej w zakresie zmian liczby falowej k = 40-1070 cm-1. Położenia maksimów na widmach ramanowskich określono, stosując metodę dopasowania krzywej pomiarowej funkcją Lorentza. Stwierdzono, że dominującą cechą na widmach oscylacyjnych warstw BST60/40-MgO jest występowanie charakterystycznego dla fazy tetragonalnej maksimum dla k ≈ 210 cm-1 i k ≈ 750 cm-1, a także odpowiadające drganiom oktaedrów TiO6 maksimum dla k ≈ 520 cm-1.
EN
In the present study, thin films of Ba0.6Sr0.4TiO3 (BST60/40) solid solution modified with 1, 3 and 5 mol.% MgO were prepared by the sol-gel-type deposition method. A multilayer spin–coating approach was utilized for the Ba0.6Sr0.4TiO3 – MgO thin film deposition on stainless steel substrates. Raman spectroscopy investigation of the MgO-doped Ba0.6Sr0.4TiO3 thin films grown on stainless steel substrates were performed within the wavenumber range k = 40-1070 cm-1. The measured Raman spectra were fitted using the Lorentzian peak type to determine positions of multiple overlapping peaks. It has been found that the dominant features in Raman spectra of BST60/40 – MgO thin films are as follows: a broad peak centered at k ≈ 210 cm-1, an asymmetric broad peak around k ≈ 520 cm-1, and a broad peak at around k ≈ 750 cm-1. The observed peaks are typical for the tetragonal structure. The middle frequency band can probably be assigned to vibrations of the TiO6 octahedra.
EN
In this paper an evolutionary algorithms (EA) application to the physically based approximation (PBA) of experimental and/or numerical data is considered. Such an approximation may simultaneously use the whole experimental, theoretical and heuristic knowledge about the analyzed problems. The PBA may be also applied for smoothing discrete data obtained from any rough numerical solution of the boundary value problem, and for solving inverse problems as well, like reconstruction of residual stresses based on experimental data. The PBA presents a very general approach formulated as a large non-linear constrained optimization problem. Its solution is usually complex and troublesome, especially in the case of non-convex problems. Here, considered is a solution approach of such problems based on the EA. However, the standard EA are rather slow methods, especially in the final stage of optimization process. In order to increase their solution efficiency, several acceleration techniques were introduced. Various benchmark problems were analyzed using the improved EA. The intended application of this research is reconstruction of residual stresses in railroads rails and vehicle wheels based on neutronography measurements.
EN
Efficient optimization algorithms are of great importance in many scientific and engineering applications. This paper considers development of dedicated Evolutionary Algorithms (EA) based approach for solving large, non-linear, constrained optimization problems. The EA are precisely understood here as decimal-coded Genetic Algorithms consisting of three basic operators: selection, crossover and mutation, followed by several newly developed calculation speed-up techniques. Efficiency increase of the EA computations may be obtained in several ways, including simple concepts proposed here like: solution smoothing and balancing, a posteriori solution error analysis, non-standard use of distributed and parallel calculations, and step-by-step mesh refinement. Efficiency of the proposed techniques has been evaluated using several benchmark tests. These preliminary tests indicate significant speed-up of the large optimization processes involved. Considered are applications of the EA to the sample problem of residual stresses analysis in elastic-plastic bodies being under cyclic loadings, and to a wide class of problems resulting from the Physically Based Approximation (PBA) of experimental data.
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