Search

Electric discharge machining (EDM) is one of the viable techniques for machining of D2 steel, in which the material removal occurs by spark erosion. The poor selection of EDM parameters affect the machining quality and productivity. Considering this, the present work investigates the effect of current, charging and discharging time on the material removal rate (MRR) and the surface roughness (Ra) while machining of D2 steel using graphite electrode. The results were analysed using statistical methods to identify the effect of control parameters and to optimise the settings. The study shows that MRR and Ra was significantly influenced by current. At optimum settings, the MRR and the Ra obtained was 38.16 mg/min and 3.39 µm respectively. Morphological analysis of the machined surface show that the size of the globules formed decreased with increase in current.

The most important metallurgical variables that affect the structure and properties of components produced by powder bed fusion (PBF) are examined using a model, proposed and validated in part-I of this paper. These variables include the temperature and velocity fields, build shape and size, cooling rates, solidification parameters, dendrite arm spacing, hardness, distortion and lack of fusion defects for four common alloys used in additive manufacturing (AM), stainless steel 316 (SS 316), Ti-6Al-4V, Inconel 718 and AlSi10Mg. The process parameters examined include laser power, scanning speed, powder layer thickness, packing efficiency and hatch spacing. Among the four alloys, the largest molten pool of AlSi10Mg ensures good fusional bonding among layers and hatches but exhibits high solidification shrinkage. Therefore, AlSi10Mg is the most susceptible to distortion among the four alloys. SS 316 exhibits the opposite trend because of its smallest molten pool among the four alloys. For a particular alloy, lack of fusion and distortion can be minimized by careful selection of hatch spacing and scanning speed. For the dendritic growth of SS 316 and AlSi10Mg, refinement of the solidification microstructure through close spacing of the dendrite arms can be achieved using thinner layers and faster scanning. Asymmetry in liquid pool geometry because of the difference in the thermal properties of powder bed and solidified build can be minimized by reducing the scanning speed.

The microstructures and mechanical properties of friction stir processed 2507 super duplex stainless steel were examined. Experimental results revealed that there is an optimum traverse speed for a given rotational speed that gives minimum grain size. The individual and synergistic effects of FSP parameters such as heat input, strain rate and strain on the grain size of the material were evaluated. The results indicate that counteracting effect of heat input and the combined effect of strain rate and strain results in achieving minimum grain size at an intermediate traverse to rotation speed ratio. The twin boundaries (particularly Σ3 CSL boundaries) in the stir zone of friction stir processed material reduced considerably compared to that in the base material. Both the base material and the friction stir processed material with the smallest grain size achieved were subjected to tensile testing at ambient and elevated temperatures under different strain rates. The results obtained are presented and discussed here.

In order to theoretically analyze the Photoluminescence (PL) spectra and its dependence on temperature and power, we have calculated the electronic band structure of self-assembled InAs/GaAs Quantum Dots (QDs) with a temperature dependent 8 band k.p Hamiltonian by including the effects of strain. The transition energies for the ground state and the first excited state are calculated using a mathematical model, which takes into account the effect of both homogeneous and inhomogeneous broadening, caused due to carrier scattering processes and QD size variation, respectively. Our proposed analytical model describes the origin of bimodal peak in the PL spectra and its correlation with the transition energies for different temperatures by considering the temperature induced intrinsic carrier concentration and carrier relaxation time. The applicability of the derived expression is validated using the experimental data of single layer InAs/GaAs QDs grown using Stranski-Krastanov growth mode. In addition to this, the simplicity of the model and its various useful aspects including computation of temperature dependent electronic band profiles and complete PL spectra make it a potential tool to study the optoelectronic properties of QD heterostructures. Published by AIP Publishing.

Shear strength parameters such as cohesion are the most significant rock parameters which can be utilized for initial design of some geotechnical engineering applications. In this study, evaluation and prediction of rock material cohesion is presented using different approaches i.e., simple and multiple regression, artificial neural network (ANN) and genetic algorithm (GA)-ANN. For this purpose, a database including three model inputs i.e., p-wave velocity, uniaxial compressive strength and Brazilian tensile strength and one output which is cohesion of limestone samples was prepared. A meaningful relationship was found for all of the model inputs with suitable performance capacity for prediction of rock cohesion. Additionally, a high level of accuracy (coefficient of determination, R (2) of 0.925) was observed developing multiple regression equation. To obtain higher performance capacity, a series of ANN and GA-ANN models were built. As a result, hybrid GA-ANN network provides higher performance for prediction of rock cohesion compared to ANN technique. GA-ANN model results (R (2) = 0.976 and 0.967 for train and test) were better compared to ANN model results (R (2) = 0.949 and 0.948 for train and test). Therefore, this technique is introduced as a new one in estimating cohesion of limestone samples.

Delamination significantly affects the performance and reliability of photovoltaic (PV) modules. Recently, an active infrared thermography approach using step heating has been exploited for the detection and characterisation of delamination in PV modules. However, step heating takes longer observation time and causes overheating problems. This paper presents the effects of different thermal excitation waveforms namely rectangular, half-sine and short pulse, on the detection and characterisation of delamination in PV module by experiments and simulations. For simulation, a 3-dimensional electro-thermal model of heat conduction, based on resistance-capacitance network approach, has been exploited to study the variation in maximum thermal contrast and peak contrast time with the delamination thickness and heating parameters. Results show that the rectangular waveform provides better detection of delamination due to higher absolute contrast, while the half-sine waveform allows better characterisation of delamination in the PV modules with low-cost and low-power heat source. The high-energy short pulse enabled quick visualisation of delamination, but has limited practical implementation. The advantages and limitations of each waveform have been highlighted to assess the specific requirement for appropriate choice in the non-destructive thermographic inspection of delamination in PV modules at the manufacturing units or outdoor fields.

A precise control of the heat input is needed to restrict the development of Fe-Al brittle intermetallic compounds along the joint interface and the growth of the interface layer in mixed-material joints of aluminium and galvanized steel sheets. A novel study is presented here on the joining of aluminium alloys and galvanized steel sheets using a gas metal arc process with short-circuiting metal transfer and fast responsive control of current and voltage pulses. The influence of the processing conditions on the heat input, phase layer thickness, type of intermetallic compounds and joint strength is studied extensively. The permissible heat input is found to be in the range of 36-106J mm-(1) that has led to 0.68-6.10 mu m thick phase layers along the joint interface with the maximum joint strength of 208 MPa. (C) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

In this paper, the capabilities of hybrid polarimetric synthetic aperture radar are investigated to estimate soil moisture on bare and vegetated agricultural soils. A new methodology based on a compact polarimetric decomposition, together with a surface component inversion, is developed to retrieve surface soil moisture. A model-based compact decomposition technique is applied to obtain the surface scattering component under the assumption of a randomly oriented vegetation volume. After vegetation removal, the surface scattering component is inverted for soil moisture (under vegetation) by comparison with a surface component modeled by two physics-based scattering models: The integral equation method (IEM) and the extended Bragg model (X-Bragg). The developed algorithm, based on a two-layer (random volume over ground) scattering model, is applied on a time series of hybrid polarimetric C-band RISAT-1 right circular transmit linear receive data acquired from April to October 2014 over the Wallerfing test site in Lower Bavaria, Germany. The retrieved soil moisture is validated against in situ frequency-domain reflectometry measurements. Including the entire growing season (all acquired dates) and all crop types, the estimated soil moisture values indicate an overall rmse of 7 vol.% using the X-Bragg model and 10 vol.% using the IEM model. The proposed hybrid polarimetric soil-moisture inversion algorithm works well for bare soils ( $\mbox{rmse}=3.1$ –8.9 vol.%) with inversion rates of around 30–70%. The inversion rate for vegetation-covered soils ranges from 5% to 40%, including all phenological stages of the crops and different soil moisture conditions.

Al-Zn-Mg alloys are widely used as structural materials due to high strength-to-weight ratio and impact toughness. As fusion welds in these alloys commonly face hot cracking and macro porosity, friction stir welding is increasingly becoming the preferred recourse. We report here a detailed experimental study on friction stir welding of a specific Al-Zn-Mg alloy with its chemical compositions close to AA7039. The effect of tool rotational speed and welding speed on the weld profile, joint microstructure, and mechanical properties is studied extensively. The results show sound weld profiles and joint properties within the selected range of process conditions. Within the selected range of welding conditions, the welds made at a tool rotational speed of 350 rpm and welding speed of 3 mm/s have showed joint structure, tensile, and impact toughness properties fairly close to that of the base material.

This paper presents photovoltaic-oriented nodal analysis (PVONA), a general and flexible tool for efficient spatially resolved simulations for photovoltaic (PV) cells and modules. This approach overcomes the major problem of the conventional Simulation Program with Integrated Circuit Emphasis-based approaches for solving circuit network models, which is the limited number of nodes that can be simulated due to memory and computing time requirements. PVONA integrates a specifically designed sparse data structure and a graphics processing unit-based parallel conjugate gradient algorithm into a PV-oriented iterative Newton-Raphson solver. This first avoids the complicated and time-consuming netlist parsing, second saves memory space, and third accelerates the simulation procedure. In the tests, PVONA generated the local current and voltage maps of a model with 316 x 316 nodes with a thin-film PV cell in 15 s, i.e., using only 4.6% of the time required by the latest LTSpice package. The 2-D characterization is used as a case study and the potential application of PVONA toward quantitative analysis of electroluminescence are discussed.

Rockfall, like landslide is the disaster that could cause loss of life and property. Exact rockfall trajectories depends upon, slope geometry, slope roughness, static and dynamic friction, rolling resistance, density of rocks as well as the restitution characteristics of rocks. In all these parameters, coefficients of restitution play a vital role to control the trajectories of the rockfall. In this article, an attempt has been made to calculate the coefficient of restitution using laboratory experiments that include dropping of balls of different rock types on the same rock type slabs as well as same type of rock balls on different types of rock slab. Schmidt hardness has been also calculated for both, balls and slabs. Using these experimental results, an effort has been made to establish the relation between coefficient of restitution (COR) and Schmidt hardness. (C) 2015 Elsevier Ltd. All rights reserved.

Incidence of high fluoride (F−) in groundwater (>1.5 mg/L) in two tribal belts of eastern India, one around Chukru in the Palamau district of Jharkhand and the other around Karlakot in the Nuapada district of Odisha, has been studied. The maximum concentration of F− in groundwater from dug wells and tube wells is 10.30 mg/L in Chukru and 4.62 mg/L in Karlakot. The groundwaters are mildly alkaline with pH ranges of 7.52–8.22 and 7.33–8.20 in Chukru and Karlakot, respectively. The F− concentration is positively correlated with pH, electrical conductivity and SO4 2− in both areas. The high F− in groundwater resulted mainly from dissolution of biotite and fluorapatite in quartzofeldspathic gneiss. The ionic dominance pattern (in meq/L) is mostly in the order Ca2+ > Na+ > Mg2+ > K+ among cations and HCO3 − > SO4 2− > F− among anions in the Karlakot groundwater. Preliminary adsorption experiments were conducted on natural haematite iron ore and synthetic magnetite to evaluate their potential for F− removal from water. Effects of different parameters such as contact time, pH, adsorbent dose and initial F− concentration on the adsorption capacity of these materials were investigated. Strong dependence of F− removal on pH was observed for both the adsorbents. With natural haematite iron ore, the maximum F− removal of 66 % occurred at an initial pH of 3.2 for a solution with F− concentration of 3 mg/L, adsorbent dose of 7 g/L and overnight contact time. The haematite iron ore was observed to increase the pH of the F− solution. Adsorption equilibrium was not achieved with this adsorbent even after a contact time of 45.2 h. In the case of synthetic magnetite, 84 % F− removal was achieved after 2 min of contact time for a solution with F− concentration of 6 mg/L, adsorbent dose of 10 g/L and initial pH of 7. The results indicate high potential of both natural haematite iron ore and synthetic magnetite as adsorbents of F− in water.

Distributed electrical modelling and simulation plays an important role in investigating local operating points and the overall power generation of photovoltaic (PV) modules. A PV module is a three-dimensional device in which inhomogeneities can cause a non-uniform performance and hence, electrical mismatches which consequently reduce the overall power generation. Distributed modelling and simulation can be used to identify local electrical properties and their impacts on the power output. In this study, a flexible, distributed electrical network modelling approach is presented. The proposed approach introduces a hierarchical architecture built up from the diode model-based sub-cell level to the module level. A PV-oriented nodal analysis solver is developed to enable the spatially resolved quantitative analysis of electrical operating points by given local properties including irradiance, temperature, series resistance, shunt resistance and ideality factor. The approach has been verified by PSpice software. The case studies have shown that this modelling and simulation tool can be used to analyse spatially resolved characterisation results and to predict global and distributed operating points under different conditions.

Wind profiling radars operating at VHF and UHF bands are highly sensitive to radio-refractive index variation associated with small-scale atmospheric turbulence. The measurement of atmospheric turbulence at these radar stations may be used to estimate the range of the radar that can be achieved based on its sensitivity. Turbulence structure constant (C-n(2)) is one of the basic parameters to measure turbulence. In the present study, climatological (2000-2011) variation of C-n(2) from 1 to 20 km height over Mumbai (72 degrees 51'E, 19 degrees 6'N) and Guwahati (91 degrees 34'E, 26 degrees 6'N) regions using radiosonde data has been analysed. It has been observed that the mean a values over the two stations are in the range 10(-18) to 10(-12) m(-2/3). The C-n(2) values are higher in the lower troposphere where both temperature and humidity gradients contribute to refractive index variation, and lower in the middle and upper troposphere where moisture gradient is negligible. The lowest values of C-n(2) are observed just below the tropopause region between 10 and 16 km over Guwahati and 10 and 13 km over Mumbai region. As a coastal station, Mumbai shows higher values of C-n(2) (two orders of magnitude) at the surface and lower troposphere compared to Guwahati. It has been observed that over the two stations, in the lower troposphere the C-n(2) values of summer (March-May) and southwest monsoon period (June-September) are higher than post-monsoon (October-November) and winter (December-February) period.

Highlights? Levenberg-Marquardt (L-M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N)-based BPNN networks are equally efficient as adaptive learning (A-L) algorithm-based BPNN network. ? L-M algorithm has fastest network convergence rate, followed by BFGS update Q-N and A-L algorithm. ? A-L -based BPNN learns faster than BFGS update Q-N, and L-M takes maximum time for network training. ? A-L algorithm is relatively easy-to-understand and implement, as compared to L-M or BFGS update Q-N algorithm, for online process control. Monitoring and control of multiple process quality characteristics (responses) in grinding plays a critical role in precision parts manufacturing industries. Precise and accurate mathematical modelling of multiple response process behaviour holds the key for a better quality product with minimum variability in the process. Artificial neural network (ANN)-based nonlinear grinding process model using backpropagation weight adjustment algorithm (BPNN) is used extensively by researchers and practitioners. However, suitability and systematic approach to implement Levenberg-Marquardt (L-M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N) algorithm for modelling and control of grinding process is seldom explored. This paper provides L-M and BFGS algorithm-based BPNN models for grinding process, and verified their effectiveness by using a real life industrial situation. Based on the real life data, the performance of L-M and BFGS update Q-N are compared with an adaptive learning (A-L) and gradient descent algorithm-based BPNN model. The results clearly indicate that L-M and BFGS-based networks converge faster and can predict the nonlinear behaviour of multiple response grinding process with same level of accuracy as A-L based network.

This paper proposes a derivative-free two-stage extended Kalman filter (2-EKF) especially suited for state and parameter identification of mechanical oscillators under Gaussian white noise. Two sources of modeling uncertainties are considered: (1) errors in linearization, and (2) an inadequate system model. The state vector is presently composed of the original dynamical/parameter states plus the so-called bias states accounting for the unmodeled dynamics. An extended Kalman estimation concept is applied within a framework predicated on explicit and derivative-free local linearizations (DLL) of nonlinear drift terms in the governing stochastic differential equations (SDEs). The original and bias states are estimated by two separate filters; the bias filter improves the estimates of the original states. Measurements are artificially generated by corrupting the numerical solutions of the SDEs with noise through an implicit form of a higher-order linearization. Numerical illustrations are provided for a few single- and multidegree-of-freedom nonlinear oscillators, demonstrating the remarkable promise that 2-EKF holds over its more conventional EKF-based counterparts. DOI: 10.1061/(ASCE)EM.1943-7889.0000255. (C) 2011 American Society of Civil Engineers.

Neural networks are being used as tools for data analysis in a variety of applications. Neural network technique is cited in the literature as a 'Black Box' approach and criticized most for the lack of interpretability of the network weights obtained during the model building process. Some attempts have been made in the past in this direction to interpret the contributions of explanatory variables in prediction problem using the weights of neural network. In the present study, a new approach is proposed to interpret the relative importance of independent variables in neural networks and a comparison with the connection weight approach is presented. The performance of this approach is studied for various data characteristics and is found to be a better method in comparison to a well known method existing in the literature. An example from behavioral science is also considered to illustrate how the performance of the proposed approach translates to a real life situation. (C) 2011 Elsevier B.V. All rights reserved.

Dry electrical discharge machining is one of the novel EDM variants, which uses gas as dielectric fluid. Experimental characterization of material removal in dry electrical discharge drilling technique is presented in this paper. It is based on six-factor, three-level experiment using L 27 orthogonal array. All the experiments were performed in a ‘quasi-explosion’ mode by controlling pulse ‘off-time’ so as to maximize the material removal rate (MRR). Furthermore, an enclosure was provided around the electrodes with the aim to create a back pressure thereby restricting expansion of the plasma in the dry EDM process. The main response variables analyzed in this work were MRR, tool wear rate (TWR), oversize and compositional variation across the machined cross-sections. Statistical analysis of the results show that discharge current ( I ), gap voltage ( V ) and rotational speed ( N ) significantly influence MRR. TWR was found close to zero in most of the experiments. A predominant deposition of melted and eroded work material on the electrode surface instead of tool wear was evident. Compositional variation in the machined surface has been analyzed using EDAX; it showed migration of tool and shielding material into the work material. The study also analyzed erosion characteristics of a single-discharge in the dry EDM process vis-a-vis the conventional liquid dielectric EDM. It was observed that at low discharge energies, single-discharge in dry EDM could give larger MRR and crater radius as compared to that of the conventional liquid dielectric EDM.

Two wire tandem submerged arc welding (SAW-T) involves application of two electrode wires to form a single weld pool with one wire leading the other along the weld interface. Realisation of the influences of the lead and trail wire currents on the weld bead morphology and the weld strength are important in SAW-T. The authors here present an experimental and computational investigation on SAW-T process. The concept of central composite rotatable design is followed for designing the experimental trials. The final weld width and the reinforcement height showed greater sensitivity to trail current while the penetration was influenced primarily by lead current. A three-dimensional heat transfer analysis is carried out using finite element method. The computed weld profiles were validated with the corresponding measured results. The computed cooling rates showed a decline with higher welding current and, the corresponding sample welds showed reduced acicular ferrite percentage and lower weld strength.

Feasibility of electromagnetic welding of flat sheets is ascertained by comparing the minimum impact velocities determined by using software ANSYS/EMAG and ABAQUS with the required minimum velocity obtained from analytical considerations. Magnetic forces acting on the sheets are computed from ANSYS/EMAG simulations. The velocity of impact and the pressure profile acting on the sheets are found from ABAQUS simulations. A criterion for weld formation is subsequently arrived at based on the simulations, which is further validated using experimental results. The reasons for no-weld zone in Al-to-Al EM weld and complete metal continuity (absence of no-weld zone) in Al-to-SS EM weld have been analyzed based on the numerical and experimental results.

Numerical models of fusion welding traditionally compute temperature field for a given set of welding conditions in a forward manner. The reliability of computed temperature profile depends on the accuracy of a number of model input parameters, values of which are uncertain in nature. Here, the authors show that a genetic algorithm (GA) assisted integrated numerical model, following either convection or conduction based calculations, can identify the suitable values of the uncertain model input parameters and in turn provide reliable computed results. Powered with GA, the integrated model is used further in a reverse manner to predict multiple sets of welding conditions for a target weld geometry. The convection based calculations have been able to provide more reliable multiple welding variables in reverse calculations.

Although numerical heat transfer models based on conduction mode of heat transfer have become a strong basis for the quantitative analysis of fusion welding, they still find limited use in actual design for three primary reasons. First, these traditional models consider a volumetric heat source term, which ironically requires a-priori knowledge of the final weld pool dimensions. Second, the numerical models need confident values of a few parameters, e. g. arc efficiency and arc radius, which are usually uncertain and requires many trial and error simulations to realise their suitable values. Third, these models are rarely attempted for the prediction of possible weld conditions for a requisite or target weld dimensions, which is of paramount interest in design for welding. The present work attempts to circumvent these issues by linking a genetic algorithm (GA) based global optimisation scheme with a finite element based three-dimensional numerical heat transfer model. The numerical model includes a volumetric heat source that adapts itself to the computed weld pool geometry at any instant. The GA module identifies the optimum values of a set of uncertain parameters needed for the reliable modelling calculations and next, identifies the suitable values of the process variables, e. g. weld current, for a target weld dimension. In each case, the GA module guides the numerical model to compute weld dimensions for a given set of inputs, traces the sensitivity of the error in prediction on the inputs being optimised, updates them accordingly and reuses the numerical model to finally obtain their optimised values. The complete integrated model is validated with a number of experimental results in gas tungsten arc spot welding processes.

The reliable operation of advanced integrated circuits (ICs) is impacted by the presence of randomly generated heat-flux transients. These transients can induce severe nonlinearities in the device response and can result in the degradation of ohmic contacts for p-n diodes as well as gate dielectric for metal-oxide field-effect transistors. In this paper, we focus on the impact and mitigation of steady-state and transient heat flux, with levels ranging from 0 to 250 W/cm(2). The impact analysis exercise focused on the heat-flux-induced nonlinear variation of the p-n diode power exponent factor (alpha). For the steady-state heat-flux scenario, the alpha-V(FB) (forward bias voltage) characteristics yield a maxima point (alpha(m)) which is observed to decrease monotonically with an increase in diode temperature. The am value is further useful for deriving other parameters, such as saturation current (I(s)), series resistance (R(s)), and the diode ideality factor (beta). The transient heat-flux scenario yields a transient power exponent factor (alpha(T), maxima-alpha(mT)) which is distinct from the steady-state case. The alpha(mT) shows an inverse dependence on instantaneous diode temperature. Transient mitigation is evident when the diode power exponent parameter is recovered under the application of single-phase as well as two-phase on-chip fluid flows. Finally, while our primary focus has been on transient mitigation, we have also looked at the feasibility of localizing transient heat sources based on temperature profiles generated using an on-chip distributed resistance temperature detector sensor array. In real-life ICs, the systematic localization and characterization of heat sources will be of interest in order to provide information on the origin of transients, thus leading to modifications in circuit design or process integration steps.

Although heat transfer and fluid flow models have provided significant insight about the welding processes and welded materials, currently they are not widely used, mainly because of two difficulties. First, the model predictions do not always agree with experiments because the values of energy absorption efficiency and other parameters cannot be prescribed from scientific principles. Second, the available models are unidirectional and cannot currently predict welding variables necessary to attain a target weld attribute. Here we provide a rigorous proof that the heat transfer and fluid flow models can be combined with an appropriate genetic algorithm (GA) to enhance reliability of computational results and achieve inverse modeling capability. The new capability enables systematic tailoring of weld attributes based on scientific principles. In particular, the GA-based optimization of arc efficiency, arc radius, effective thermal conductivity, and effective viscosity using a limited volume of experimental data led to superior weld geometry computations for a wide variety of welding conditions. Furthermore, the inverse model's ability to calculate multiple combinations of arc current, voltage, and welding speed needed to achieve a target weld geometry was developed and rigorously tested by welding experiments.

Recent failures of waterfront retaining walls under seismic conditions have made the topic important to the researchers to obtain an alternative design solution for those structures. In the present paper, a simple design methodology for waterfront reinforced soil-retaining wall by considering both the hydrodynamic pressure and seismic forces acting simultaneously on the wall has been proposed. The seismic forces are calculated using the pseudo-dynamic approach, which considers the velocities of the shear and primary waves propagating through the soil. Limit equilibrium analysis by considering the horizontal slice method has been adopted. Two types of failure surfaces, viz., linear and polylinear have been used and the results have been reported in terms of normalized value of the geosynthetic reinforcement strength required to counter the hydrostatic pressure, hydrodynamic pressure, and the seismic inertia forces acting on the waterfront wall. Effects of different parameters on this normalized value of the geosynthetic reinforcement strength are also assessed. It was observed that the requirement of geosynthetic reinforcement increases with increase in the horizontal seismic acceleration, vertical seismic acceleration, and the hydrodynamic pressure acting on the waterfront reinforced soil-wall system. Comparing the results of the present methodology with an existing methodology but for the dry backfill case shows that the present results are on the economical side. Merits of the proposed simple design methodology are also highlighted.

Bovine seminal parameters (motility, forward progression, and acrosome reaction) were tested using three different collection methods. To represent a traditional collection method, one method collected into a dry, standard 15 mL conical tube. The other two collection methods utilized the revolutionary technology patented under the trade name BreedMaXtm; one dry BreedMaXtm (designated as BreedMaXtm-) tube was used, and one BreedMaXtm tube as designed was used (designated as BreedMaXtm+). BreedMaXtm was developed with the intention of allowing the breeder to breed with less semen to obtain more breedings from a single ejaculate. The collected data suggest that presence or absence of media in the BreedMaXtm appeared to have little effect on motility and forward progression; yet there was a trend toward delayed acrosome reaction in the BreedMaXtm+. The results of this study suggest that the BreedMaXtm+ maintains semen parameters for longer periods of time as compared to the conventional collection techniques.

The operation of a three-phase induction motor from a single-phase supply with a capacitor in circuit is well known. While detailed investigations on the performance of the induction motor under such operating conditions are well reported in the literature, no such work is reported for the synchronous motor. The line-start permanent-magnet synchronous motor (LSPMSM) is actively being considered as an energy efficient alternative to the induction motor in general purpose, heavy duty applications. The paper presents an analytical and experimental evaluation on the performance of a three-phase LSPMSM fed from a single-phase supply. This analysis is carried out using symmetrical component theory. A capacitor is connected across two stator phases during starting and running. Estimation of the capacitor value to achieve the desired objectives, such as minimum unbalance, maximum power factor, maximum torque or maximum efficiency, is carried out. Load tests are conducted to validate the simulation results.

Weld joint dimensions and weld metal mechanical properties are important quality characteristics of any welded joint. The success of building these characteristics in any welding situation depends on proper selection-cum-optimisation of welding process parameters. Such optimisation is critical in the pulsed current gas metal arc welding process (GMAW-P), as the heat input here is closely dictated by a host of additional pulse parameters in comparison to the conventional gas metal are welding process. Neural network based models are excellent alternatives in such situations where a large number of input conditions govern certain outputs in a manner that is often difficult to adjudge a priori. Six individual prediction models dei;eloped using neural network methodology are presented here to estimate ultimate tensile strength, elongation, impact toughness, weld bead width, weld reinforcement height and penetration of the final weld joint as a function of four pulse parameters, e.g. peak current, base current, pulse on time and pulse frequency. The experimental data employed here are for GMA W-P welding of extruded sections of high strength Al-Zn-Mg alloy (7005). In each case, a committee of different possible network architectures is used, including the final optimum network, to assess the uncertainty in estimation. The neural network models developed here could estimate all the outputs except penetration fairly accurately.

We summarize the constraints on three flavor neutrino mixing coming from data. We first map out the allowed region in the three neutrino parameter space using solar and atmospheric neutrino data. We then incorporate the results of reactor and long baseline experiments in our analysis and show that the parameter space is drastically reduced. We conclude by pointing out that the results of Borexino and SNO will further help in constraining the parameter space.

We present a scheme based on the total least squares estimator to track the motion of a point source, by observing a sequence of images of an object illuminated by it. The initial shape of the object is estimated from any one of the existing shape and source from shading schemes from a single image frame. The total least squares formulation is used that considers this estimate of shape as being a noisy measurement of the actual shape, The method tracks the illuminant motion between successive frames from the flow field. Using the data only from two successive frames, an approach is developed to estimate the underlying illuminant motion between those two frames. This approach is then extended and a recursive method which uses the data from all previous image frames is developed to track the illuminant motion. An incremental shape recovery scheme using the proposed illuminant motion estimator is also presented. The method is then extended to deal with the presence of ambient light and the motion of multiple illuminants. We present typical simulation results to validate the proposed illuminant motion and shape update schemes. No feature point correspondence is required between the successive frames. Also the method is applicable to any general reflectance map. (C) 1998

A group of stable new ruthenium (II) mixed tris-chelated complexes of the type [Ru(bpy)(2)(L)]ClO4 . 2H(2)O, [bpy:2,2'-bipyridine; L:anionic form of KSSCOR, R = Me, Et, (n)pr, (n)Bu] have been prepared by the reaction of Ru(bpy)(2)CO3 with KSSCOR in methanol. The complexes are essentially diamagnetic and behave as 1:1 electrolytes in CH3CN. All the complexes show metal to ligand charge transfer transitions in the visible region (495-450 nm). The complexes do not exhibit any emission spectra at room temperature. The geometry of the complexes has been assessed by high-resolution H-1 NMR spectroscopy. Reductions of coordinated bpy units are observed in the range -1.5 and -1.8 V, respectively. All the complexes exhibit one reversible ruthenium (II)-ruthenium(III) oxidation couple near 0.7 V (versus SCE) in acetonitrile solution at platinum electrode. Coulometric oxidation of [Ru-II(bpy)(2)(L)](+) at 0.9 V.(vs. SCE) as well as chemical oxidation by Ce(IV) in 0.1M aqueous HClO4 produces unstable green coloured ruthenium(III) congener of [Ru-II(bpy)(2)(L)(+) i.e. [Ru-III(bpy)(2)(L)](+2). The presence of trivalent ruthenium in the oxidized solution is evidenced by the rhombic EPR spectrum with g values (g(1) = 2.720, g(2) = 2.370 and g(3) = 1.700).

Chill block melt spinning has been employed to produce rapidly solidified (RS) ribbons of D2 steel (in wt% 1.7 C, 12.0 Cr, 0.69 V, balance Fe) and the effect of melt-spinning parameters on ribbon geometry has been studied. Microstructure and phases have been examined using SEM, XRD and magnetic measurements. In addition to a substantial refinement of grain size and of carbide precipitation, RS suppresses martensitic transformation and produces a supersaturated austenite phase. The effect of isochronal (1 h) heat treatment (623 to 1073 KA) has been evaluated using Vickers microhardness measurements. It shows that RS imparts to D2 steel the ability to age-harden. The peak ageing in 1 h occurs at 833 KA with an apparent activation energy of 225 kJ mol-1 estimated by differential thermal analysis. The aged ribbons are found to contain M7C3, M23C6 and Fe3C in a ferritic matrix.

Thin-film SnO2 gas sensors are fabricated using an electron-beam evaporation method at pre-optimized substrate temperatures and post-deposition heat-treatment temperature. The response of these sensors is studied for various operating temperatures and reducing gas ambients. Reducing gases used for testing the response are hydrogen, carbon monoxide and methane, as they belong to three distinct chemical families. It is observed that the response. of these sensors follows a power-law relationship, with the exponent value always remaining less than one at higher concentrations of reducing gas. Films deposited at 25 and 350-degrees-C and heat treated at 650-degrees-C in O2 ambient for 2 h are found to be more suitable for sensor application compared to films deposited at intermediate temperatures and heat treated under identical conditions. Further, the response of the sensor is found to deteriorate with a decrease in operating temperature. It is also concluded from the results that a thin-film SnO2 sensor can be used as a selective sensor for H-2 at either low operating temperature or under very low background concentrations of other reducing gases. Finally, the above-mentioned results are explained using existing physical models.

MIT Artificial Intelligence Laboratory Vision Group