Quantum simulation using fidelity-profile optimization

Experimental quantum simulation of a Hamiltonian H requires unitary operator decomposition (UOD) of its evolution unitary U = exp(-iHt) in terms of native unitary operators of the experimental system. Here, using a genetic algorithm, we numerically evaluate the most generic UOD (valid over a continuous range of Hamiltonian parameters) of the unitary operator U, termed fidelity-profile optimization. The optimization is obtained by systematically evaluating the functional dependence of experimental unitary operators (such as single-qubit rotations and time-evolution unitaries of the system interactions) to the Hamiltonian (H) parameters. Using this technique, we have solved the experimental unitary decomposition of a controlled-phase gate (for any phase value), the evolution unitary of the Heisenberg XY interaction, and simulation of the Dzyaloshinskii-Moriya (DM) interaction in the presence of the Heisenberg XY interaction. Using these decompositions, we studied the entanglement dynamics of a Bell state in the DM interaction and experimentally verified the entanglement preservation procedure of Hou et al. Ann. Phys. (N.Y.) 327, 292 (2012)] in a nuclear magnetic resonance quantum information processor.

Freeform Skeletal Shape Optimization of Compliant Mechanisms

Compliant mechanisms are elastic continua used to transmit or transform force and motion mechanically. The topology optimization methods developed for compliant mechanisms also give the shape for a chosen parameterization of the design domain with a fixed mesh. However, in these methods, the shapes of the flexible segments in the resulting optimal solutions are restricted either by the type or the resolution of the design parameterization. This limitation is overcome in this paper by focusing on optimizing the skeletal shape of the compliant segments in a given topology. It is accomplished by identifying such segments in the topology and representing them using Bezier curves. The vertices of the Bezier control polygon are used to parameterize the shape-design space. Uniform parameter steps of the Bezier curves naturally enable adaptive finite element discretization of the segments as their shapes change. Practical constraints such as avoiding intersections with other segments, self-intersections, and restrictions on the available space and material, are incorporated into the formulation. A multi-criteria function from our prior work is used as the objective. Analytical sensitivity analysis for the objective and constraints is presented and is used in the numerical optimization. Examples are included to illustrate the shape optimization method.

Digestive ripening facilitated atomic diffusion at nanosize regime: Case of AuIn2 and Ag3In intermetallic nanoparticles

Monodisperse colloidal gold-indium (AuIn2) intermetallic nanoparticles have been synthesized from Au and In colloids using the digestive ripening process. Formation of the intermetallic proceeds via digestive ripening facilitated atomic diffusion of Au and In atoms from the Au and In nanoparticles followed simultaneously by their growth in the solution. Optimization of the reaction temperature was found to be crucial for the formation of AuIn2 intermetallic from gold and indium nanoparticles. Transmission electron microscopy revealed the presence of nearly monodisperse nanoparticles of Au and AuIn2 with particle size distribution of 3.7 +/- 1.0 nm and 5.0 +/- 1.6 nm, respectively. UV-visible spectral studies brought out the absence of SPR band in pure AuIn2 intermetallic nanoparticles. Optical study and electron microscopy, in combination with powder X-ray diffraction established phase pure AuIn2 intermetallic nanoparticles unambiguously. The potential of such an unprecedented approach has been further exploited in the synthesis of Ag3In intermetallic nanoparticles with the dimension of less than 10 nm. (C) 2014 Elsevier B.V. All rights reserved.

Optimization of degree of sphericity of primary phase during cooling slope casting of A356 Al alloy: Taguchi method and regression analysis

The present work presents the results of experimental investigation of semi-solid rheocasting of A356 Al alloy using a cooling slope. The experiments have been carried out following Taguchi method of parameter design (orthogonal array of L-9 experiments). Four key process variables (slope angle, pouring temperature, wall temperature, and length of travel of the melt) at three different levels have been considered for the present experimentation. Regression analysis and analysis of variance (ANOVA) has also been performed to develop a mathematical model for degree of sphericity evolution of primary alpha-Al phase and to find the significance and percentage contribution of each process variable towards the final outcome of degree of sphericity, respectively. The best processing condition has been identified for optimum degree of sphericity (0.83) as A(3), B-3, C-2, D-1 i.e., slope angle of 60 degrees, pouring temperature of 650 degrees C, wall temperature 60 degrees C, and 500 mm length of travel of the melt, based on mean response and signal to noise ratio (SNR). ANOVA results shows that the length of travel has maximum impact on degree of sphericity evolution. The predicted sphericity obtained from the developed regression model and the values obtained experimentally are found to be in good agreement with each other. The sphericity values obtained from confirmation experiment, performed at 95% confidence level, ensures that the optimum result is correct and also the confirmation experiment values are within permissible limits. (c) 2014 Elsevier Ltd. All rights reserved.

Smoothed Functional Algorithms for Stochastic Optimization Using q-Gaussian Distributions

Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, especially when the objective is to improve the performance of a stochastic system However, the performance of these methods depends on several parameters, such as the choice of a suitable smoothing kernel. Different kernels have been studied in the literature, which include Gaussian, Cauchy, and uniform distributions, among others. This article studies a new class of kernels based on the q-Gaussian distribution, which has gained popularity in statistical physics over the last decade. Though the importance of this family of distributions is attributed to its ability to generalize the Gaussian distribution, we observe that this class encompasses almost all existing smoothing kernels. This motivates us to study SF schemes for gradient estimation using the q-Gaussian distribution. Using the derived gradient estimates, we propose two-timescale algorithms for optimization of a stochastic objective function in a constrained setting with a projected gradient search approach. We prove the convergence of our algorithms to the set of stationary points of an associated ODE. We also demonstrate their performance numerically through simulations on a queuing model.

Adsorption study of silica gel particle for improvement in design of adsorption beds used in solar driven cooling units

Simulations using Ansys Fluent 6.3.26 have been performed to look into the adsorption characteristics of a single silica gel particle exposed to saturated humid air streams at Re=108 & 216 and temperature of 300K. The adsorption of the particle has been modeled as a source term in the species and the energy equations using a Linear Driving Force (LDF) equation. The interdependence of the thermal and the water vapor concentration field has been analysed. This work is intended to aid in understanding the adsorption effects in silica gel beds and in their efficient design. (C) 2013 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Photoluminescence, thermoluminescence and EPR studies of solvothermally derived Ni2+ doped Y(OH)(3) and Y2O3 multi-particle-chain microrods

Rod like structures of hexagonal Y(OH)(3):Ni2+ and cubic Y2O3:Ni2+ phosphors have been successfully synthesized by solvothermal method. X-ray diffraction studies of as-formed product shows hexagonal phase, whereas the product heat treated at 700 degrees C shows pure cubic phase. Scanning electron micrographs (SEM) of Y(OH)(3):Ni2+ show hexagonal rods while Y2O3:Ni2+ rods were found to consist of many nanoparticles stacked together forming multi-particle-chains. EPR studies suggest that the site symmetry around Ni2+ ions is predominantly octahedral. PL spectra show emission in blue, green and red regions due to the T-3(1)(P-3)->(3)A(2)(F-3), T-1(2)(D-1)->(3)A(2)(F-3) and T-1(2)(D-1)-> T-3(2)(F-3) transitions of Ni2+ ions, respectively. TL studies were carried out for Y(OH)(3):Ni2+ and Y2O3:Ni2+ phosphor upon gamma-dose for 1-6 kGy. A single well resolved glow peaks at 195 and 230 degrees C were recorded for Y(OH)(3):Ni2+ and Y2O3:Ni2+, respectively. The glow peak intensity increases linearly up to 4 kGy and 5 kGy for Y(OH)(3):Ni2+ and Y2O3:Ni2+, respectively. The kinetic parameters such as activation energy (E), frequency factor (s) and order of kinetics (b) were estimated by different methods. The phosphor follows simple glow peak structure, linear response with gamma dose, low fading and simple trap distribution, suggesting that it is quite suitable for radiation dosimetry. (C) 2014 Elsevier B.V. All rights reserved.

Nano-ZnO particle addition to monolithic magnesium for enhanced tensile and compressive response

In this study, the effects of nanoscale ZnO reinforcement on the room temperature tensile and compressive response of monolithic Mg were studied. Experimental observations indicated strength properties improvement due to nanoscale ZnO addition. A maximum increment in tensile yield strength by similar to 55% and compressive yield strength by 90% (with reduced tension-compression asymmetry) was achieved when 0.8 vol.% ZnO nanoparticles were added to Mg. While the fracture strain values under tensile loads were found to increase significantly (by similar to 95%, in case of Mg-0.48ZnO), it remained largely unaffected under compressive loads. The microstructural characteristics studied in order to comprehend the mechanical response showed significant grain refinement due to grain boundary pinning effect of nano-ZnO particles which resulted in strengthening of Mg. Texture analysis using X-ray and EBSD methods indicated weakening of basal fibre texture in Mg/ZnO nanocomposites which contributed towards the reduction in tension-compression yield asymmetry and enhancement in tensile ductility when compared to pure Mg. (C) 2014 Elsevier B.V. All rights reserved.

Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms

We present the first q-Gaussian smoothed functional (SF) estimator of the Hessian and the first Newton-based stochastic optimization algorithm that estimates both the Hessian and the gradient of the objective function using q-Gaussian perturbations. Our algorithm requires only two system simulations (regardless of the parameter dimension) and estimates both the gradient and the Hessian at each update epoch using these. We also present a proof of convergence of the proposed algorithm. In a related recent work (Ghoshdastidar, Dukkipati, & Bhatnagar, 2014), we presented gradient SF algorithms based on the q-Gaussian perturbations. Our work extends prior work on SF algorithms by generalizing the class of perturbation distributions as most distributions reported in the literature for which SF algorithms are known to work turn out to be special cases of the q-Gaussian distribution. Besides studying the convergence properties of our algorithm analytically, we also show the results of numerical simulations on a model of a queuing network, that illustrate the significance of the proposed method. In particular, we observe that our algorithm performs better in most cases, over a wide range of q-values, in comparison to Newton SF algorithms with the Gaussian and Cauchy perturbations, as well as the gradient q-Gaussian SF algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

A bottom-up approach for optimization of friction stir processing parameters; a study on aluminium 2024-T3 alloy

Friction stir processing (FSP) is emerging as one of the most competent severe plastic deformation (SPD) method for producing bulk ultra-fine grained materials with improved properties. Optimizing the process parameters for a defect free process is one of the challenging aspects of FSP to mark its commercial use. For the commercial aluminium alloy 2024-T3 plate of 6 mm thickness, a bottom-up approach has been attempted to optimize major independent parameters of the process such as plunge depth, tool rotation speed and traverse speed. Tensile properties of the optimum friction stir processed sample were correlated with the microstructural characterization done using Scanning Electron Microscope (SEM) and Electron Back-Scattered Diffraction (EBSD). Optimum parameters from the bottom-up approach have led to a defect free FSP having a maximum strength of 93% the base material strength. Micro tensile testing of the samples taken from the center of processed zone has shown an increased strength of 1.3 times the base material. Measured maximum longitudinal residual stress on the processed surface was only 30 MPa which was attributed to the solid state nature of FSP. Microstructural observation reveals significant grain refinement with less variation in the grain size across the thickness and a large amount of grain boundary precipitation compared to the base metal. The proposed experimental bottom-up approach can be applied as an effective method for optimizing parameters during FSP of aluminium alloys, which is otherwise difficult through analytical methods due to the complex interactions between work-piece, tool and process parameters. Precipitation mechanisms during FSP were responsible for the fine grained microstructure in the nugget zone that provided better mechanical properties than the base metal. (C) 2014 Elsevier Ltd. All rights reserved.

Simultaneous Perturbation Newton Algorithms for Simulation Optimization

We present a new Hessian estimator based on the simultaneous perturbation procedure, that requires three system simulations regardless of the parameter dimension. We then present two Newton-based simulation optimization algorithms that incorporate this Hessian estimator. The two algorithms differ primarily in the manner in which the Hessian estimate is used. Both our algorithms do not compute the inverse Hessian explicitly, thereby saving on computational effort. While our first algorithm directly obtains the product of the inverse Hessian with the gradient of the objective, our second algorithm makes use of the Sherman-Morrison matrix inversion lemma to recursively estimate the inverse Hessian. We provide proofs of convergence for both our algorithms. Next, we consider an interesting application of our algorithms on a problem of road traffic control. Our algorithms are seen to exhibit better performance than two Newton algorithms from a recent prior work.

Agglomeration front dynamics: Drying in sessile nano-particle laden droplets

The drying of sessile, nano-silica laden water droplet is studied under ambient conditions, in the absence of any convection. The drying process can be divided into two distinct regimes. During regime 1, the outer edge of the droplet remains pinned and particles agglomerate at the droplet periphery similar to the traditional coffee ring. However in regime 2, with further evaporation, both the liquid contact line and the agglomeration front starts moving radially inwards from the initial contact edge. The contact between the liquid and the agglomerate is maintained throughout regime 2 and the vaporisation driven liquid edge recession essentially drives the inward growth of the particle deposition. Fast kinetics of particle aggregation results in rapid growth of this agglomeration front as seen from the experiments. A theoretical formulation involving a simplistic model of the agglomeration front growth based on particle mass balance has been proposed. (C) 2014 Elsevier Ltd. All rights reserved,

Manipulating Crystallographic Texture of Sn Coatings by Optimization of Electrodeposition Process Conditions to Suppress Growth of Whiskers

The effects of two major electrodeposition process conditions, electrolyte bath temperature and current density, on the microstructure and crystallographic texture of pure tin coatings on brass and, ultimately, on the extent of whisker formation have been examined. The grain size of the deposited coatings increased with increasing electrolyte bath temperature and current density, which significantly affected the dominant texture: (211) or (420) was the dominant texture at low current densities whereas, depending on deposition temperature, (200) or (220) became the dominant texture at high current densities. After deposition, coatings were subjected to different environmental conditions, for example isothermal aging (room temperature, 50A degrees C, or 150A degrees C) for up to 90 days and thermal cycling between -25A degrees C and 85A degrees C for 100 cycles, and whisker growth was studied. The Sn coatings with low Miller index planes, for example (200) and (220), and with moderate aging temperature were more prone to whiskering than coating with high Miller index planes, for example (420), and high aging temperature. A processing route involving the optimum combination of current density and deposition temperature is proposed for suppressing whisker growth.

Quantification of the Particle Size and Stability of Graphene Oxide in a Variety of Solvents

The exceptional solution processing potential of graphene oxide (GO) is always one of its main advantages over graphene in terms of its industrial relevance in coatings, electronics, and energy storage. However, the presence of a variety of functional groups on the basal plane and edges of GO makes understanding suspension behavior in aqueous and organic solvents, a major challenge. Acoustic spectroscopy can also measure zeta potential to provide unique insight into flocculating, meta-stable, and stable suspensions of GO in deionized water and a variety of organic solvents (including ethanol, ethylene glycol, and mineral oil). As expected, a match between solvent polarity and the polar functional groups on the GO surface favors stable colloidal suspensions accompanied by a smaller aggregate size tending toward disperse individual flakes of GO. This work is significant since it describes the characteristics of GO in solution and its ability to act as a precursor for graphene-based materials.

Ni Nano-particle Encapsulated in Hollow Carbon Sphere Electrocatalyst in Polymer Electrolyte Membrane Water Electrolyzer

The present study evaluates the synthesis by solvo-thermal method and electrocatalytic activity of nickel nano-particles encapsulated in hollow carbon sphere, in hydrogen and oxygen evolution reaction in PEM water electrolyzer. The XRD patterns have ascertained the formation of nickel metal with different planes in face centered cubic (fcc) and hexagonal closed pack (hcp) form. SEM and TEM images have confirmed the nickel nano-particles with diameter of 10-50 nm inside the 0.2 mu m sized hollow carbon spheres. The BET surface area values gradually decreased with greater encapsulation of nickel; although the electrochemical active surface area (ECSA) values have been calculated as quite higher. It confirms the well dispersion of nickel in the materials and induces their electrocatalytic performance through the active surface sites. The cyclic voltammetric studies have evaluated hydrogen desorption peaks as five times more intense in nickel encapsulated materials, in comparison to the pure hollow carbon spheres. The anodic peak current density value has reached the highest level of 1.9 A cm(-2) for HCSNi10, which gradually decreases with lesser amount of nickel in the electrocatalysts. These electrocatalysts have been proved electrochemically stable during their usage for 48 h long duration under potentiostatic condition. (C) 2015 Elsevier Ltd. All rights reserved.