Coupled Mechanisms of Precipitation and Atomization in Burning Nanofluid Fuel Droplets

Understanding the combustion characteristics of fuel droplets laden with energetic nanoparticles (NP) is pivotal for lowering ignition delay, reducing pollutant emissions and increasing the combustion efficiency in next generation combustors. In this study, first we elucidate the feedback coupling between two key interacting mechanisms, namely, secondary atomization and particle agglomeration; that govern the effective mass fraction of NPs within the droplet. Second, we show how the initial NP concentration modulates their relative dominance leading to a masterslave configuration. Secondary atomization of novel nanofuels is a crucial process since it enables an effective transport of dispersed NPs to the flame (a pre-requisite condition for NPs to burn). Contrarily, NP agglomeration at the droplet surface leads to shell formation thereby retaining NPs inside the droplet. In particular, we show that at dense concentrations shell formation (master process) dominates over secondary atomization (slave) while at dilute particle loading it is the high frequency bubble ejections (master) that disrupt shell formation (slave) through its rupture and continuous outflux of NPs. This results in distinct combustion residues at dilute and dense concentrations, thereby providing a method of manufacturing flame synthesized microstructures with distinct morphologies.

Nickel substitution induced effects on gas sensing properties of cobalt ferrite nanoparticles

Ni2+ ion induced unusual conductivity reversal and an enhancement in the gas sensing properties of ferrites based gas sensors, is reported. The Co1-xNixFe2O4 (for x = 0, 0.5 and 1) nanoparticles were synthesized by wet chemical co-precipitation method and gas sensing properties were studied as a function of composition and temperature. The structural, morphological and microstructural characterization revealed crystallite size of in the range 10-20 nm with porous morphology consisting of nano-sized grains. The Energy Dispersive X-ray (EDX) mapping confirms homogeneous distribution of Co, Ni, Fe and O elements in the ferrites. The non-stoichiometry of the inverse spinel type ferrites and the relative concentration of Ni3+/Co3+ defects were studied using X-ray photoelectron spectroscopy. It is found that the addition of Ni2+ ions into cobalt ferrite shows preferred selectivity towards CO gas at high temperature (325 degrees C) and ethanol gas at low temperature (250 degrees C), unlike undoped cobalt ferrite or undoped nickel ferrite, which show similar response for both these gases. Moreover, an unusual conductivity reversal is observed, except cobalt ferrite due to the difference in reactivity of the gases as well as characteristic non-stoichiometry of ferrites. This behavior is highly gas ambient dependent and hence can be well-exploited for selective detection of gases. (C) 2015 Elsevier B.V. All rights reserved.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1 degrees x 1 degrees gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller ``missing'' values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

Room temperature ferromagnetism and white light emissive CdS:Cr nanoparticles synthesized by chemical co-precipitation method

Undoped and Cr (3% and 5%) doped CdS nanoparticles were synthesized by chemical co-precipitation method. The synthesized nanocrystalline particles are characterized by energy dispersive X-ray analysis (EDAX), scanning electron microscope (SEM), X-ray Diffraction (XRD), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), photoluminescence (PL), Electron paramagnetic resonance (EPR), vibrating sample magnetometer (VSM) and Raman spectroscopy. XRD studies indicate that Cr doping in host CdS result a structural change from Cubic phase to mixed (cubic + hexagonal) phase. Due to quantum confinement effect, widening of the band gap is observed for undoped and Cr doped CdS nanoparticles compared to bulk CdS. The average particle size calculated from band gap values is in good agreement with the TEM study calculation and it is around 4-5 nm. A strong violet emission band consisting of two emission peaks is observed for undoped CdS nanoparticles, whereas for CdS:Cr nanoparticles, a broad emission band ranging from 420 nm to 730 nm with a maximum at similar to 587 nm is observed. The broad emission band is due to the overlapped emissions from variety of defects. EPR spectra of CdS:Cr samples reveal resonance signal at g = 2.143 corresponding to interacting Cr3+ ions. VSM studies indicate that the diamagnetic CdS nanoparticles are transform to ferromagnetic for 3% Cr3+ doping and the ferromagnetic nature is diminished with increasing the doping concentration to 5%. (C) 2015 Elsevier B.V. All rights reserved.

An Exponential Lower Bound for Homogeneous Depth Four Arithmetic Formulas

We show here a 2(Omega(root d.log N)) size lower bound for homogeneous depth four arithmetic formulas. That is, we give an explicit family of polynomials of degree d on N variables (with N = d(3) in our case) with 0, 1-coefficients such that for any representation of a polynomial f in this family of the form f = Sigma(i) Pi(j) Q(ij), where the Q(ij)'s are homogeneous polynomials (recall that a polynomial is said to be homogeneous if all its monomials have the same degree), it must hold that Sigma(i,j) (Number of monomials of Q(ij)) >= 2(Omega(root d.log N)). The above mentioned family, which we refer to as the Nisan-Wigderson design-based family of polynomials, is in the complexity class VNP. Our work builds on the recent lower bound results 1], 2], 3], 4], 5] and yields an improved quantitative bound as compared to the quasi-polynomial lower bound of 6] and the N-Omega(log log (N)) lower bound in the independent work of 7].

Delineation of homogeneous temperature regions: a two-stage clustering approach

Homogeneous temperature regions are necessary for use in hydrometeorological studies. The regions are often delineated by analysing statistics derived from time series of maximum, minimum or mean temperature, rather than attributes influencing temperature. This practice cannot yield meaningful regions in data-sparse areas. Further, independent validation of the delineated regions for homogeneity in temperature is not possible, as temperature records form the basis to arrive at the regions. To address these issues, a two-stage clustering approach is proposed in this study to delineate homogeneous temperature regions. First stage of the approach involves (1) determining correlation structure between observed temperature over the study area and possible predictors (large-scale atmospheric variables) influencing the temperature and (2) using the correlation structure as the basis to delineate sites in the study area into clusters. Second stage of the approach involves analysis on each of the clusters to (1) identify potential predictors (large-scale atmospheric variables) influencing temperature at sites in the cluster and (2) partition the cluster into homogeneous fuzzy temperature regions using the identified potential predictors. Application of the proposed approach to India yielded 28 homogeneous regions that were demonstrated to be effective when compared to an alternate set of 6 regions that were previously delineated over the study area. Intersite cross-correlations of monthly maximum and minimum temperatures in the existing regions were found to be weak and negative for several months, which is undesirable. This problem was not found in the case of regions delineated using the proposed approach. Utility of the proposed regions in arriving at estimates of potential evapotranspiration for ungauged locations in the study area is demonstrated.

Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators

There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies.

Homogeneous Hermitian holomorphic vector bundles and the Cowen-Douglas class over bounded symmetric domains

It is known that all the vector bundles of the title can be obtained by holomorphic induction from representations of a certain parabolic group on finite-dimensional inner product spaces. The representations, and the induced bundles, have composition series with irreducible factors. We write down an equivariant constant coefficient differential operator that intertwines the bundle with the direct sum of its irreducible factors. As an application, we show that in the case of the closed unit ball in C-n all homogeneous n-tuples of Cowen-Douglas operators are similar to direct sums of certain basic n-tuples. (c) 2015 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.

Effect of quasicrystalline phase on the deformation behavior of Zr62Al9.5Ni9.5Cu14Nb5 bulk metallic glass

Quasicrystalline phase with different volume fraction were formed by isothermally annealing the as-castZr(62)Al(9.5)Ni(9.5)Cu(14)Nb(5) bulk metallic glass at 723 K for different times. The effects of quasicrystals on the deformation behavior of the materials were studied by nanoindentation and compression test. It revealed that the alloys with homogeneous amorphous structure exhibit pronounced flow serrations during the nanoindentation loading, while no obvious flow serration is observed for the sample with quasicrystals more than 10 vol.%. However, further compression tests confirm that the no-serrated flows are formed due to different reasons. For annealed samples containing quasicrystals less than 35 vol.%, continuous plastic deformation occurs due to propagation of multiple shear bands. While the disappearance of serrated flow cannot be explained by the generation of multiple shear bands for samples containing quasicrystals more than 35 vol.%, which will fracture with a totally different fracture mode, namely, dimple fracture mode under loading instead of shear fracture mode. (c) 2005 Published by Elsevier B.V.