Optimization of transmission factor of heat-reflective coating

The results of computer optimization of visual transmission factor Tv of the heat-reflective coating SnO2-Ag-SnO2 и TiO2-Ag-TiO2 with protective layers are presented. It is stated that for the condition Tv?80 % to be fulfilled the thickness of protective silver layers for SnO2-Ag-SnO2 should not exceed 1,5 nm, but for TiO2-Ag-TiO2 it is 2 nm. Heat reflective coatings with one protective layer are shown to possess better optical characteristics in comparison with two protective layers

Investigation of thin films MgAl2O4, deposited on the Si substrates by vacuum thermal evaporation

The article presents data on the study of X-ray structural and microstructural characteristics of thin films of aluminum-magnesium spinel MgAl2O4 deposited on Si substrates by vacuum thermal evaporation. MgAl2O4 films have a polycrystalline rhombic structure. The values of the unit cell parameters of MgAl2O4 are calculated. Scanning electron and atomic force microscopy showed that MgAl2O4 films have a densely packed structure without cracks. Physical characteristics and good adhesion of MgAl2O4 thin films to silicon substrates indicate their possibility of using in devices of opto- and microelectronics

A Criterion for Brittle Failure of Rocks Using the Theory of Critical Distances

This paper presents a new analytical criterion for brittle failure of rocks and heavily overconsolidated soils. Griffith’s model of a randomly oriented defect under a biaxial stress state is used to keep the criterion simple. The Griffith’s criterion is improved because the maximum tensile strength is not evaluated at the boundary of the defect but at a certain distance from the boundary, known as the critical distance. This fracture criterion is known as the Point Method, and is part of the Theory of Critical Distances, which is utilized in fracture mechanics. The proposed failure criterion has two parameters: the inherent tensile strength, ó0, and the ratio of the half-length of the initial crack/flaw to the critical distance, a/L. These parameters are difficult to measure but they may be correlated with the uniaxial compressive and tensile strengths, óc and ót. The proposed criterion is able to reproduce the common range of strength ratios for rocks and heavily overconsolidated soils (óc/ót=3-50) and the influence of several microstructural rock properties, such as texture and porosity. Good agreement with laboratory tests reported in the literature is found for tensile and low confining stresses.The work presented was initiated during a research project on “Structural integrity assessments of notch-type defects", for the Spanish Ministry of Science and Innovation (Ref.: MAT2010-15721)

Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load

This study was devoted to examine pile bearing capacity and to provide a reliable model to simulate pile load-settlement behaviour using a new artificial neural network (ANN) method. To achieve the planned aim, experimental pile load test were carried out on model open-ended steel piles, with pile aspect ratios of 12, 17, and 25. An optimised second-order Levenberg-Marquardt (LM) training algorithm has been used in this process. The piles were driven in three sand densities; dense, medium, and loose. A statistical analysis test was conducted to explore the relative importance and the statistical contribution (Beta and Sig) values of the independent variables on the model output. Pile effective length, pile flexural rigidity, applied load, sand-pile friction angle and pile aspect ratio have been identified to be the most effective parameters on model output. To demonstrate the effectiveness of the proposed algorithm, a graphical comparison was performed between the implemented algorithm and the most conventional pile capacity design approaches. The proficiency metric indicators demonstrated an outstanding agreement between the measured and predicted pile-load settlement, thus yielding a correlation coefficient (R) and root mean square error (RMSE) of 0.99, 0.043 respectively, with a relatively insignificant mean square error level (MSE) of 0.0019. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group

Computational Homogenization of Architectured Materials

Architectured materials involve geometrically engineered distributions of microstructural phases at a scale comparable to the scale of the component, thus calling for new models in order to determine the effective properties of materials. The present chapter aims at providing such models, in the case of mechanical properties. As a matter of fact, one engineering challenge is to predict the effective properties of such materials; computational homogenization using finite element analysis is a powerful tool to do so. Homogenized behavior of architectured materials can thus be used in large structural computations, hence enabling the dissemination of architectured materials in the industry. Furthermore, computational homogenization is the basis for computational topology optimization which will give rise to the next generation of architectured materials. This chapter covers the computational homogenization of periodic architectured materials in elasticity and plasticity, as well as the homogenization and representativity of random architectured materials