Effect of surfactants on the instability of a two-layer film flow down an inclined plane

The effect of insoluble surfactants on the instability of a two-layer film flow down an inclined plane is investigated based on the Orr-Sommerfeld boundary value problem. The study, focusing on Stokes flow P. Gao and X.-Y. Lu, ``Effect of surfactants on the inertialess instability of a two-layer film flow,'' J. Fluid Mech. 591, 495-507 (2007)], is further extended by including the inertial effect. The surface mode is recognized along with the interface mode. The initial growth rate corresponding to the interface mode accelerates at sufficiently long-wave regime in the presence of surface surfactant. However, the maximum growth rate corresponding to both interface and surface modes decelerates in the presence of surface surfactant when the upper layer is more viscous than the lower layer. On the other hand, when the upper layer is less viscous than the lower layer, a new interfacial instability develops due to the inertial effect and becomes weaker in the presence of interfacial surfactant. In the limit of negligible surface and interfacial tensions, respectively, two successive peaks of temporal growth rate appear in the long-wave and short-wave regimes when the interface mode is analyzed. However, in the case of the surface mode, only the long-wave peak appears. (C) 2014 AIP Publishing LLC.

Effect of shrinkage induced flow on binary alloy dendrite growth: An equivalent undercooling model

This paper presents a theoretical model for studying the effects of shrinkage induced flow on the growth rate of binary alloy dendrites. An equivalent undercooling of the melt is defined in terms of ratio of the phase densities to represent the change in dendrite growth rate due to variation in solutal and thermal transport resulting from shrinkage induced flow. Subsequently, results for dendrite growth rate predicted by the equivalent undercooling model is compared with the corresponding predictions obtained using an enthalpy based numerical method for dendrite growth with shrinkage. The agreement is found to be good. Published by Elsevier Ltd.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the `feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

OPTICAL FLOW ESTIMATION USING APPROXIMATE NEAREST NEIGHBOR FIELD FUSION

This paper proposes an optical flow algorithm by adapting Approximate Nearest Neighbor Fields (ANNF) to obtain a pixel level optical flow between image sequence. Patch similarity based coherency is performed to refine the ANNF maps. Further improvement in mapping between the two images are obtained by fusing bidirectional ANNF maps between pair of images. Thus a highly accurate pixel level flow is obtained between the pair of images. Using pyramidal cost optimization, the pixel level optical flow is further optimized to a sub-pixel level. The proposed approach is evaluated on the middlebury dataset and the performance obtained is comparable with the state of the art approaches. Furthermore, the proposed approach can be used to compute large displacement optical flow as evaluated using MPI Sintel dataset.

STOPPING THE CCR FLOW AND ITS ISOMETRIC COCYCLES

It is shown how to use non-commutative stopping times in order to stop the CCR flow of arbitrary index and also its isometric cocycles, i.e. left operator Markovian cocycles on Boson Fock space. Stopping the CCR flow yields a homomorphism from the semigroup of stopping times, equipped with the convolution product, into the semigroup of unital endomorphisms of the von Neumann algebra of bounded operators on the ambient Fock space. The operators produced by stopping cocycles themselves satisfy a cocycle relation.

Ductility enhancement in nanoglass: role of interaction stress between flow defects

Using a thermodynamically consistent non-local plasticity model, the mechanistic origin of enhancement in ductility and suppression of dominant shear banding in nanoglasses (NGs) is analysed. It is revealed that the interaction stress between flow defects plays a central role in promoting global plasticity of NGs. Specifically, we find that the intrinsic length associated with this stress provides a scaling for the shear band width and its coupling with grain size governs the level of enhancement in the deformation behaviour of NGs. The present work may provide useful insights in developing highly ductile NGs for practical engineering applications.

Coded Illumination for Motion-blur Free Imaging of Cells on Cell-phone Based Imaging Flow Cytometer

Cell-phone based imaging flow cytometry can be realized by flowing cells through the microfluidic devices, and capturing their images with an optically enhanced camera of the cell-phone. Throughput in flow cytometers is usually enhanced by increasing the flow rate of cells. However, maximum frame rate of camera system limits the achievable flow rate. Beyond this, the images become highly blurred due to motion-smear. We propose to address this issue with coded illumination, which enables recovery of high-fidelity images of cells far beyond their motion-blur limit. This paper presents simulation results of deblurring the synthetically generated cell/bead images under such coded illumination.

Indigenous Development of an Imaging Flow Cytometer for Clinical and Biological Applications

Flow cytometry is a benchmark technique used for basic research and clinical diagnosis of various diseases. Despite being a high-throughput technique, it fails in capturing the morphology of cells being analyzed. Imaging flow cytometry is a combination of flow-cytometry and digital microscopy, which offers advantages of both the techniques. In this paper, we report on the development of an indigenous Imaging Flow Cytometer, realized with the combination of Optics, Microfluidics, and High-speed imaging. A custom-made bright-field transmission microscope is used to capture images of cells flowing across the microfluidic device. High-throughput morphological analysis on suspension of yeast cells is presented.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Penetration Characteristics of Air, Carbon Dioxide and Helium Transverse Sonic Jets in Mach 5 Cross Flow

An experimental investigation of sonic air, CO2 and Helium transverse jets in Mach 5 cross flow was carried out over a flat plate. The jet to freestream momentum flux ratio, J, was kept the same for all gases. The unsteady flow topology was examined using high speed schlieren visualisation and PIV. Schlieren visualisation provided information regarding oscillating jet shear layer structures and bow shock, Mach disc and barrel shocks. Two-component PIV measurements at the centreline, provided information regarding jet penetration trajectories. Barrel shocks and Mach disc forming the jet boundary were visualised/quantified also jet penetration boundaries were determined. Even though J is kept the same for all gases, the penetration patterns were found to be remarkably different both at the nearfield and the farfield. Air and CO2 jet resulted similar nearfield and farfield penetration pattern whereas Helium jet spread minimal in the nearfield.

Wall-mode instability in plane shear flow of viscoelastic fluid over a deformable solid

The linear stability analysis of a plane Couette flow of an Oldroyd-B viscoelastic fluid past a flexible solid medium is carried out to investigate the role of polymer addition in the stability behavior. The system consists of a viscoelastic fluid layer of thickness R, density rho, viscosity eta, relaxation time lambda, and retardation time beta lambda flowing past a linear elastic solid medium of thickness HR, density rho, and shear modulus G. The emphasis is on the high-Reynolds-number wall-mode instability, which has recently been shown in experiments to destabilize the laminar flow of Newtonian fluids in soft-walled tubes and channels at a significantly lower Reynolds number than that for flows in rigid conduits. For Newtonian fluids, the linear stability studies have shown that the wall modes become unstable when flow Reynolds number exceeds a certain critical value Re c which scales as Sigma(3/4), where Reynolds number Re = rho VR/eta, V is the top-plate velocity, and dimensionless parameter Sigma = rho GR(2)/eta(2) characterizes the fluid-solid system. For high-Reynolds-number flow, the addition of polymer tends to decrease the critical Reynolds number in comparison to that for the Newtonian fluid, indicating a destabilizing role for fluid viscoelasticity. Numerical calculations show that the critical Reynolds number could be decreased by up to a factor of 10 by the addition of small amount of polymer. The critical Reynolds number follows the same scaling Re-c similar to Sigma(3/4) as the wall modes for a Newtonian fluid for very high Reynolds number. However, for moderate Reynolds number, there exists a narrow region in beta-H parametric space, corresponding to very dilute polymer solution (0.9 less than or similar to beta < 1) and thin solids (H less than or similar to 1.1), in which the addition of polymer tends to increase the critical Reynolds number in comparison to the Newtonian fluid. Thus, Reynolds number and polymer properties can be tailored to either increase or decrease the critical Reynolds number for unstable modes, thus providing an additional degree of control over the laminar-turbulent transition.

Thermally developing combined electroosmotic and pressure-driven flow of nanofluids in a microchannel under the effect of magnetic field

In the present study, the heat transfer characteristics of thermally developing magnetohydroclynamic flow of nanofluid through microchannel are delineated by following a semi analytical approach. The combined influences of pressure driven flow, electroosmotic transport and magnetic field is taken into account for the analysis of the complex microscale thermal transport processes. Solutions for the normalized temperature distributions and the Nusselt number variations, considering the simultaneous interplay of electrokinetic effects (electroosmosis), magnetic effects, Joule heating and viscous dissipation are obtained, for constant wall temperature condition. Particular attention is paid to assess the role of nanolluids in altering the transport phenomena, through variations in the effective nanoparticle volume fractions, as well as the aggregate structure of the particulate phases. It is observed that magnetohydrodynamic effect reduces advective transport of the liquid resulting in gradual reduction of heat transfer. Increase in nanoparticle volume fraction shows decrease in heat transfer. Similar effects are observed with increase in aggregate sizes of the nanoparticles. The effect of the nanofluids on system irreversibility is also studied through entropy generation analysis due to flow and heat transfer in the microchannel. Total entropy generation is found to be dominant at the thermally developing region of the microchannel, whereas it drops sharply at the thermally developed region. Presence of nanoparticles in the base fluid reduces the total entropy generation in the microchannel, thereby indicating decrease in thermodynamic irreversibility with increasing nanoparticle volume fraction. (C) 2014 Elsevier Ltd. All rights reserved.

On the strain rate sensitivity of plastic flow in metallic glasses

The nanoindentation technique was employed to examine the strain rate sensitivity, m, and its dependence on the structural state of a Zr-based bulk metallic glass (BMG). The free volume content in the BMG was varied by examining samples in the as-cast (AC), shot-peened (SP), and structurally relaxed (SR) states. Hardness values measured at different loading rates and over a temperature range of 300-423 K as well as the strain-rate jump tests conducted in the quasi-static regime at room temperature, show that m is always negative. All the load-displacement (P-h) curves in this temperature regime exhibit serrated load-displacement responses, indicating that the shear band mediated inhomogeneous plastic flow governs deformation. Such localization of flow and associated softening is the raison d'etre for the negative m. Significant levels of pile-up around the indents were also noted. The order in the average values of hardness, pile-up heights, and the displacement bursts on the P-h curves was always such that SR > AC > SP, which is also the order of increasing free volume content. These observations were utilized to discuss the reasons for the negative strain rate sensitivity, and its dependence on the structural state of metallic glasses. It is suggested that the positive values of m reported in the literature for them are possibly experimental artefacts that arise due to large pile ups around the indents which lead to erroneous estimation in hardness values. (C) 2014 Elsevier B.V. All rights reserved.

Optical Flow based on Approximate Nearest Neighbor Field

In this work, we propose an algorithm for optical flow estimation using Approximate Nearest Neighbor Fields (ANNF). Proposed optical flow estimation algorithm consists of two steps, flow initialization using ANNF maps and cost filtering. Flow initialization is done by computing the ANNF map using FeatureMatch between two consecutive frames. The ANNF map obtained represents a noisy optical flow, which is refined by making use of superpixels. The best flow associated with each superpixel is computed by optimizing a cost function. The proposed approach is evaluated on Middlebury and MPI-Sintel optical flow dataset and is found to be comparable with the state of the art methods for optical flow estimation.

Crowd Flow Segmentation based on Motion Vectors in H.264 Compressed Domain

In this work, we have explored the prospect of segmenting crowd flow in H. 264 compressed videos by merely using motion vectors. The motion vectors are extracted by partially decoding the corresponding video sequence in the H. 264 compressed domain. The region of interest ie., crowd flow region is extracted and the motion vectors that spans the region of interest is preprocessed and a collective representation of the motion vectors for the entire video is obtained. The obtained motion vectors for the corresponding video is then clustered by using EM algorithm. Finally, the clusters which converges to a single flow are merged together based on the bhattacharya distance measure between the histogram of the of the orientation of the motion vectors at the boundaries of the clusters. We had implemented our proposed approach on the complex crowd flow dataset provided by 1] and compared our results by using Jaccard measure. Since we are performing crowd flow segmentation in the compressed domain using only motion vectors, our proposed approach performs much faster compared to other pixel domain counterparts still retaining better accuracy.