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.

Optical diagnostics of fuel-air mixing and vortex formation in a cavity combustor

The current work reports optical diagnostic measurements of fuel-air mixing and vortex structure in a single cavity trapped vortex combustor (TVC). Specifically, the mixture fraction using acetone PLIF technique in the non-reacting flow, and PIV measurements in the reacting flow are reported for the first time in trapped vortex combustors. The fuel-air momentum flux ratio, where the air momentum corresponds to that entering the cavity through a specially-incorporated flow guide vane, is used to characterize the mixing. The acetone PLIF experiments show that at high momentum flux ratios, the fuel-air mixing in the cavity is very minimal and is enhanced as the momentum flux ratio reduces, due to a favourable vortex formation in the cavity. Stoichiometric mixture fraction surfaces show that the mixing causes the reaction surfaces to shift from non-premixed to partially-premixed stratified mixtures. PIV measurements conducted in the non-reacting flow in the cavity further reinforce this observation. The scalar dissipation rates of mixture fraction were compared with the contours of RMS of fluctuating velocity and showed very good agreement. The regions of maximum mixing are observed to be along the fuel air interface. Reacting flow Ply measurements which differ substantially from the non-reacting cases primarily because of the heat release from combustion and the resulting gas expansion show that the vortex is displaced from the centre of the cavity towards the guide vane. Overall, the measurements show interesting features of the flow including the presence of the dual cavity structure and lead to a clear understanding of the underlying physics of the cavity flow highlighting the importance of the fuel-air momentum ratio parameter. (C) 2014 Elsevier Inc. All rights reserved.

Morphological Evolution in Air-Stable Metallic Iron Nanostructures and Their Magnetic Study

Iron nanostructures with morphology ranging from discrete nanoparticles to nearly monodisperse hierarchical nanostructures have been successfully synthesized using solvated metal atom dispersion (SMAD) method. Such a morphological evolution was realized by tuning the molar ratio of ligand to metal. Surface energy minimization in confluence with strong magnetic interactions and ligand-based stabilization results in the formation of nanospheres of iron. The as-prepared amorphous iron nanostructures exhibit remarkably high coercivity in comparison to the discrete nanoparticles and bulk counterpart. Annealing the as-prepared amorphous Fe nanostructures under anaerobic conditions affords air-stable carbon-encapsulated Fe(0) and Fe3C nanostructures with retention of the morphology. The resulting nanostructures were thoroughly analyzed by powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM), and Raman spectroscopy. TGA brought out that Fe3C nanostructures are more robust toward oxidation than those of a-Fe. Finally, detailed magnetic studies were carried out by superconducting quantum interference device (SQUID) magnetometer and it was found that the magnetic properties remain conserved even upon exposure of the annealed samples to ambient conditions for months.

How does spacer length of imidazolium gemini surfactants control the fabrication of 2D-Langmuir films of silver-nanoparticles at the air-water interface?

A series of gemini surfactants based on cationic imidazolium ring as polar headgroup, abbreviated as Im-n-Im], 2Br(-) (n = 2, 5,6 and 12), was synthesized. Their ability to stabilize silver nanoparticles in aqueous media was investigated. The resulting suspensions were characterized by UV-Vis spectroscopy and transmission electron microscopy (TEM). They exhibit specific morphologies by adopting different supramolecular assemblies in aqueous media depending on the internal packing arrangements and on the number of spacer methylene units -(CH2)(n)-]. Individual colloids were extracted from the aqueous to chloroform layer and spread at the air/water interface to allow the formation of well-defined Langmuir films. By analysis of the surface pressure-area isotherms, the details about the packing behavior and orientation of the imidazolium gemini surfactant capped silver nanoparticles were obtained. Morphological features of the dynamic process of monolayer compression at the air-water interface were elucidated using Brewster angle microscopy (BAM). These monolayers were further transferred on mica sheets by the Langmuir-Blodgett technique at their associated collapse pressure and the morphology of these monolayers was investigated by atomic force microscopy (AFM). The number of spacer methylene units (CH2)(n)-] of the gemini surfactants exerted critical influence in modulating the characteristics of the resulting Langmuir films. (C) 2014 Elsevier Inc. All rights reserved.

Diurnal and seasonal variation of mixing ratio and delta C-13 of air CO2 observed at an urban station Bangalore, India

We present here observations on diurnal and seasonal variation of mixing ratio and delta C-13 of air CO2, from an urban station-Bangalore (BLR), India, monitored between October 2008 and December 2011. On a diurnal scale, higher mixing ratio with depleted delta C-13 of air CO2 was found for the samples collected during early morning compared to the samples collected during late afternoon. On a seasonal scale, mixing ratio was found to be higher for dry summer months (April-May) and lower for southwest monsoon months (June-July). The maximum enrichment in delta C-13 of air CO2 (-8.04 +/- 0.02aEuro degrees) was seen in October, then delta C-13 started depleting and maximum depletion (-9.31 +/- 0.07aEuro degrees) was observed during dry summer months. Immediately after that an increasing trend in delta C-13 was monitored coincidental with the advancement of southwest monsoon months and maximum enrichment was seen again in October. Although a similar pattern in seasonal variation was observed for the three consecutive years, the dry summer months of 2011 captured distinctly lower amplitude in both the mixing ratio and delta C-13 of air CO2 compared to the dry summer months of 2009 and 2010. This was explained with reduced biomass burning and increased productivity associated with prominent La Nina condition. While compared with the observations from the nearest coastal and open ocean stations-Cabo de Rama (CRI) and Seychelles (SEY), BLR being located within an urban region captured higher amplitude of seasonal variation. The average delta C-13 value of the end member source CO2 was identified based on both diurnal and seasonal scale variation. The delta C-13 value of source CO2 (-24.9 +/- 3aEuro degrees) determined based on diurnal variation was found to differ drastically from the source value (-14.6 +/- 0.7aEuro degrees) identified based on seasonal scale variation. The source CO2 identified based on diurnal variation incorporated both early morning and late afternoon sample; whereas, the source CO2 identified based on seasonal variation included only afternoon samples. Thus, it is evident from the study that sampling timing is one of the important factors while characterizing the composition of end member source CO2 for a particular station. The difference in delta C-13 value of source CO2 obtained based on both diurnal and seasonal variation might be due to possible contribution from cement industry along with fossil fuel / biomass burning as predominant sources for the station along with differential meteorological conditions prevailed.

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.

Efficient Solid-State Light-Emitting CuCdS Nanocrystals Synthesized in Air

Semiconductor nanocrystals (NCs) possess high photoluminescence (PL) typically in the solution phase. In contrary, PL rapidly quenches in the solid state. Efficient solid state luminescence can be achieved by inducing a large Stokes shift. Here we report on a novel synthesis of compositionally controlled CuCdS NCs in air avoiding the usual complexity of using inert atmosphere. These NCs show long-range color tunability over the entire visible range with a remarkable Stokes shift up to about 1.25eV. Overcoating the NCs leads to a high solid-state PL quantum yield (QY) of ca. 55% measured by using an integrating sphere. Unique charge carrier recombination mechanisms have been recognized from the NCs, which are correlated to the internal NC structure probed by using extended X-ray absorption fine structure (EXAFS) spectroscopy. EXAFS measurements show a Cu-rich surface and Cd-rich interior with 46% Cu-I being randomly distributed within 84% of the NC volume creating additional transition states for PL. Color-tunable solid-state luminescence remains stable in air enabling fabrication of light-emitting diodes (LEDs).

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

EXPERIMENTAL STUDIES ON AIR-ASSISTED ATOMIZATION OF JATROPHA PURE PLANT OIL

The present study focuses on exploring air-assisted atomization strategies for effective atomization of high-viscosity biofuels, such as pure plant oils (PPOs). The first part of the study concerns application of a novel air-assisted impinging jet atomization for continuous spray applications, and the second part concerns transient spray applications. The particle/droplet imaging analysis (PDIA) technique along with direct imaging methods are used for the purpose of spray characterization. In the first part, effective atomization of Jatropha PPO is demonstrated at gas-to-liquid ratios (GLRs) on the order 0.1. The effect of liquid and gas flow rates on the spray characteristics is evaluated, and results indicate a Sauter mean diameter (SMD) of 50 mu m is achieved with GLRs as low as 0.05. In the second part of the study, a commercially available air-assisted transient atomizer is evaluated using Jatropha PPO. The effect of the pressure difference across the air injector and ambient gas pressure on liquid spray characteristics is studied. The results indicate that it is possible to achieve the same level of atomization of Jatropha as diesel fuel by operating the atomizer at a higher pressure difference. Specifically, a SMD of 44 mu m is obtained for the Jatropha oil using injection pressures of <1 MPa. A further interesting observation associated with this injector is the near constancy of a nondimensional spray penetration rate for the Jatropha oil spray.

Nonlinear control of an air-breathing engine including its validation with vehicle guidance

An implementable nonlinear control design approach is presented for a supersonic air-breathing ramjet engine. The primary objective is to ensure that the thrust generated by the engine tracks the commanded thrust without violating the operational constraints. An important constraint is to manage the shock wave location in the intake so that it neither gets detached nor gets too much inside the intake. Both the objectives are achieved by regulating the fuel flow to the combustion chamber and by varying the throat area of the nozzle simultaneously. The design approach accounts for the nonlinear cross-coupling effects and nullifies those. Also, an extended Kalman filter has been used to filter out the sensor and process noises as well as to make the states available for feedback. Furthermore, independent control design has been carried out for the actuators. To test the performance of the engine for a realistic flight trajectory, a representative trajectory is generated through a trajectory optimization process, which is augmented with a newly-developed finite-time state dependent Riccati equation technique for nullifying the perturbations online. Satisfactory overall performance has been obtained during both climb and cruise phases. (C) 2015 Elsevier Masson SAS. All rights reserved.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

Sparse Representation based Anomaly Detection using HOMV in H.264 Compressed Videos

In this paper, we have proposed an anomaly detection algorithm based on Histogram of Oriented Motion Vectors (HOMV) 1] in sparse representation framework. Usual behavior is learned at each location by sparsely representing the HOMVs over learnt normal feature bases obtained using an online dictionary learning algorithm. In the end, anomaly is detected based on the likelihood of the occurrence of sparse coefficients at that location. The proposed approach is found to be robust compared to existing methods as demonstrated in the experiments on UCSD Ped1 and UCSD Ped2 datasets.

Anomaly detection in compressed H.264/AVC video

Real time anomaly detection is the need of the hour for any security applications. In this article, we have proposed a real time anomaly detection for H.264 compressed video streams utilizing pre-encoded motion vectors (MVs). The proposed work is principally motivated by the observation that MVs have distinct characteristics during anomaly than usual. Our observation shows that H.264 MV magnitude and orientation contain relevant information which can be used to model the usual behavior (UB) effectively. This is subsequently extended to detect abnormality/anomaly based on the probability of occurrence of a behavior. The performance of the proposed algorithm was evaluated and bench-marked on UMN and Ped anomaly detection video datasets, with a detection rate of 70 frames per sec resulting in 90x and 250x speedup, along with on-par detection accuracy compared to the state-of-the-art algorithms.

Air microjet system for non-contact force application and the actuation of micro-structures

We demonstrate a non-contact technique to apply calibrated and localized forces in the micro-Newton to milli-Newton range using an air microjet. An electromagnetically actuated diaphragm controlled by a signal generator is used to generate the air microjet. With a nozzle diameter of 150 mu m, the microjet diameter was maintained to a maximum of 1 mm at a distance of 5 mm from the nozzle. The force generated by the microjet was measured using a commercial force sensor to determine the velocity profile of the jet. Axial flow velocities of up to 25 m s(-1) were obtained at distances as long as 6 mm. The microjet exerted a force up to 1 mu N on a poly dimethyl siloxane (PDMS) micropillar (50 mu m in diameter, 157 mu m in height) and 415 mu N on a PDMS membrane (3 mm in diameter, 28 mu m thick). We also demonstrate that from a distance of 6 mm our microjet can exert a peak pressure of 187 Pa with a total force of about 84 mu N on a flat surface with 8 V operating voltage. Out of the cleanroom fabrication and robust design make this system cost effective and durable.