Effect of holmium substitution on structural and electrical properties of barium zirconate titanate ferroelectric ceramics

The holmium substituted Ba_1−3x/2Ho_xZr_0.025Ti_0.975O₃ (x=0.01, 0.02, 0.025, 0.05) compositions were synthesized by the solid state reaction technique. The synthesized specimens were characterized for their structural and electrical properties using X-ray diffractometer, scanning electron microscopy, impedance analyzer and loop tracer. Phase analysis shows the formation of secondary phase Ho₂Ti₂O₇ for Ho≥2.5 mol% substitution. The microstructural investigation shows that the holmium substitution significantly reduces the grain size. The substitution of holmium increases the Curie temperature for x≤0.02 whereas Curie temperature decreases for x≥0.025. The maximum dielectric constant at transition temperature is observed for x=0.02. The solubility limit is 2 mol% and for x≥0.025 some of the holmium atoms enter B-sites and forms the secondary phase. An increase is observed in the coercive field of the specimens with the increasing holmium content.     

Electric Fields Obviate Constrained Sintering

The phenomenon of constrained sintering, where large rigid inclusions of alumina have been shown to significantly reduce the rate of sintering of titania [Bordia and Raj (1988) J. Am. Ceram. Soc. 71, 302–310], is shown to subside nearly completely during flash sintering carried out under modest electrical fields. The result is explained by a different mechanism for volumetric and shear deformation under electric fields. It is proposed that vacancy and interstitials generated within the grains migrate to grain boundaries and pores to produce both volumetric and shear strain at equal rates, since, in this way, the diffusion distance for both modes of deformation becomes the same. In conventional sintering, where transport occurs from one interface to another, the diffusion distance for shear is twice as far as for densification, which retards sintering should it become controlled by shear deformation, as seen in constrained sintering.     

Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India

Forecasting of groundwater levels is very useful for planning integrated management of groundwater and surface water resources in a basin. In the present study, artificial neural network models have been developed for groundwater level forecasting in a river island of tropical humid region, eastern India. ANN modeling was carried out to predict groundwater levels 1 week ahead at 18 sites over the study area. The inputs to the ANN models consisted of weekly rainfall, pan evaporation, river stage, water level in the drain, pumping rate and groundwater level in the previous week, which led to 40 input nodes and 18 output nodes. Three different ANN training algorithms, viz., gradient descent with momentum and adaptive learning rate backpropagation (GDX) algorithm, Levenberg–Marquardt (LM) algorithm and Bayesian regularization (BR) algorithm were employed and their performance was evaluated. As the neural network became very large with 40 input nodes and 18 output nodes, the LM and BR algorithms took too much time to complete a single iteration. Consequently, the study area was divided into three clusters and the performance evaluation of the three ANN training algorithms was done separately for all the clusters. The performance of all the three ANN training algorithms in predicting groundwater levels over the study area was found to be almost equally good. However, the performance of the BR algorithm was found slightly superior to that of the GDX and LM algorithms. The ANN model trained with BR algorithm was further used for predicting groundwater levels 2, 3 and 4 weeks ahead in the tubewells of one cluster using the same inputs. It was found that though the accuracy of predicted groundwater levels generally decreases with an increase in the lead time, the predicted groundwater levels are reasonable for the larger lead times as well.     

A finite element analysis of fracture initiation in ductile/brittle periodically layered composites

A near-tip plane strain finite element analysis of a crack terminating at and normal to the interface in a laminate consisting of alternate brittle and ductile layers is conducted under mode-I loading. The studies are carried out for a system representing steel/alumina composite laminate. The Gurson constitutive model, which accounts for the ductile failure mechanisms of microvoid nucleation, growth and coalescence, is employed within the framework of small deformation plasticity theory. Evolution of plastic zone and damage in the ductile layer is monitored with increasing load. High plastic strain localization and microvoid damage accumulation are found to occur along the brittle/ductile interface at the crack-tip. Fracture initiation in the ductile phase is predicted and the conditions for crack renucleation in the brittle layer ahead of the crack are established for the system under consideration. Ductile fracture initiation has been found to occur before plasticity spreads in multiple ductile layers. Effects of material mismatch and yield strength on the plastic zone evolution are briefly discussed. This revised version was published online in August 2006 with corrections to the Cover Date.     

Hydrogen-induced intergranular stress corrosion cracking (HI-IGSCC) of 0.35C–3.5Ni–1.5Cr–0.5Mo steel fastener

This paper brings a failure case study of high strength 0.35C–3.5Ni–1.5Cr–0.5Mo steel fastener, which failed due to hydrogen-induced intergranular stress corrosion cracking (HI-IGSCC). 0.35C–3.5Ni–1.5Cr–0.5Mo steel in hardened and tempered condition, meeting the specified axial tensile stress rating of 1250 MPa is widely used as fasteners in space programmes.In the course of assembly of the structural parts of a satellite launch vehicle, 10 nos of fasteners developed cracks on tightening using a torque wrench set to 6 N m torque surprisingly.Also some fasteners, which were under assembly load of more than 6 months in the same vehicle assembly, were found to be cracked.The failure was attributed to hydrogen-induced intergranular stress corrosion cracking (HI-IGSCC). The details of the analysis and mechanism involved in the HI-IGSCC are presented in detail.Detailed metallurgical analyses of the cracked fasteners support the successive steps of the corrosion enhanced plasticity model, which is based on a local softening in the SCC crack region. The mechanism of a dislocation pileup ahead of a crack under corrosion and stress due to diffusing hydrogen promotes stress concentration against micro-obstacle and caused failure.     

Different glazing systems and their impact on human thermal comfort—Indian scenario

In the present communication, fifteen different glazing systems ranging from 3 mm single glazed clear glass to double glazed with low-e and solar control coating, have been analysed in terms of their human thermal comfort impact. Thermal comfort is measured in term of PMV (predicted mean vote) and PPD (predicted percentage of dissatisfied). Study encompasses all the six climatic zones of India. By using OPTICS 5.0 and WINDOW 5.0, U-values, solar heat gain coefficient, inside glazing surface temperatures and inside solar radiation have been computed. Depending upon different climatic zones, six sets of different design conditions, in terms of ambient temperatures, solar radiation and wind velocity, have been chosen. Typical values of metabolic rate and clothing insulation taken are 1.2 met and 0.5 clo for summer and 1.0 met and 1.0 clo for winter, respectively. Inside room air velocity is taken as 0.15 m s⁻¹ round the year. Room temperature is taken as 20 °C in winter and 25 °C in summer. It is found that for cold station (e.g. Leh) all glazings except solar control glazings, ensure thermal comfort and total PPD is less than 10% (|PMV|?0.5). For warm and hot climates, solar control glazings are thermally suitable. Results for winter night of Delhi shows that all the 15 glazings are inadequate for thermal comfort and PPD, due to cold feeling, varies between 27% and 33% approximately.     

Laboratory investigation of bearing capacity behaviour of strip footing on reinforced flyash slope

The paper presents the results of laboratory model tests on bearing capacity behaviour of a strip footing resting on the top of a geogrid reinforced flyash slope. A series of model footing tests covering a wide range of boundary conditions, including unreinforced cases were conducted by varying parameters such as location and depth of embedment of single geogrid layer, number of geogrid layers, location of footing relative to the slope crest, slope angles and width of footing. The results of the investigation indicate that both the pressure–settlement behaviour and the ultimate bearing capacity of footing resting on the top of a flyash slope can be enhanced by the presence of reinforcing layers. However the efficiency of flyash geogrid system increases with the increasing number of geogrid layers and edge distance of footing from the slope. Based on experimental results critical values of geogrid parameters for maximum reinforcing effects are established. Experimental results obtained from a series of model tests have been presented and discussed in the paper.     

Self-organizing maps for pattern recognition in design of alloys

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.