204 resultados para PPS-Radial


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The exact process(es) that generate(s) dense filaments which then form prestellar cores within them is unclear. Here we study the formation of a dense filament using a relatively simple set-up of a pressure-confined, uniform-density cylinder. We examine if its propensity to form a dense filament and further, to the formation of prestellar cores along this filament, bears on the gravitational state of the initial volume of gas. We report a radial collapse leading to the formation of a dense filamentary cloud is likely when the initial volume of gas is at least critically stable (characterised by the approximate equality between the mass line-density for this volume and its maximum value). Though self-gravitating, this volume of gas, however, is not seen to be in free-fall. This post-collapse filament then fragments along its length due to the growth of a Jeans-like instability to form prestellar cores. We suggest dense filaments in typical star-forming clouds classified as gravitationally super-critical under the assumption of: (i) isothermality when in fact, they are not, and (ii) extended radial profiles as against pressure-truncated, that significantly over-estimates their mass line-density, are unlikely to experience gravitational free-fall. The radial density and temperature profile derived for this post-collapse filament is consistent with that deduced for typical filamentary clouds mapped in recent surveys of nearby star-forming regions.

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Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

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In this paper, attempt is made to solve a few problems using the Polynomial Point Collocation Method (PPCM), the Radial Point Collocation Method (RPCM), Smoothed Particle Hydrodynamics (SPH), and the Finite Point Method (FPM). A few observations on the accuracy of these methods are recorded. All the simulations in this paper are three dimensional linear elastostatic simulations, without accounting for body forces.

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This work deals with an experimental study of the breakup characteristics of liquids with different surface tension and viscosity from a hollow cone hydraulic injector nozzle induced by pressure-swirl. The experiments were conducted at Reynolds numbers Re-p=9500-23,000. The surface tension and viscosity of the surrogate fuels were altered from 72 to 30 mN/m and 1.1 to 1.6 mN s/m(2), respectively. High speed photography and Phase Doppler Particle Anemometry were utilized to study the atomization process. Velocity and drop size measurements of the spray using PDPA in both axial and radial directions indicate a dependency on surface tension. However, these effects are dominant only at low Reynolds numbers and are negligible at high Reynolds number. Downstream of the nozzle, coalescence of droplets due to collision was also found to be significant and the diameters were compared for different liquids. For viscous fluids up to 1.6 cP, the independent effects of viscosity and injection pressure are studied. In general, the spray cone angle increases with increase in pressure. At high pressures, an increase in viscosity leads to higher drop sizes following primary and secondary breakup compared to water. This study will extend our understanding of surrogate fuel film breakup and highlight the importance of long and short wavelength instabilities. (C) 2013 Elsevier Ltd. All rights reserved

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Current paper reports synthesis of chemical free graphene by unzipping of the carbon nanotubes (CNTs) using high strain rate deformation at 150K. A specially designed cryomill operating at 150 K was used for the experiments. The mechanism of unzipping was further explored using molecular dynamics (MD) simulations. Both experimental and simulation results reveal two modes of unzipping through radial and shear loading. (C) 2015 Elsevier Ltd. All rights reserved.

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The present work aims to investigate the phase transition, dispersion and diffusion behavior of nanocomposites of carbon nanotube (CNT) and straight chain alkanes. These materials are potential candidates for organic phase change materials(PCMs) and have attracted flurry of research recently. Accurate experimental evaluation of the mass, thermal and transport properties of such composites is both difficult as well as economically taxing. Additionally it is crucial to understand the factors that results in modification or enhancement of their characteristic at atomic or molecular level. Classical molecular dynamics approach has been extended to elucidate the same. Bulk atomistic models have been generated and subjected to rigorous multistage equilibration. To reaffirm the approach, both canonical and constant-temperature, constant-pressure ensembles were employed to simulate the models under consideration. Explicit determination of kinetic, potential, non-bond and total energy assisted in understanding the enhanced thermal and transport property of the nanocomposites from molecular point of view. Crucial parameters including mean square displacement and simulated self diffusion coefficient precisely define the balance of the thermodynamic and hydrodynamic interactions. Radial distribution function also reflected the density variation, strength and mobility of the nanocomposites. It is expected that CNT functionalization could improve the dispersion within n-alkane matrix. This would further ameliorate the mass and thermal properties of the composite. Additionally, the determined density was in good agreement with experimental data. Thus, molecular dynamics can be utilized as a high throughput technique for theoretical investigation of nanocomposites PCMs. (C) 2015 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.

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The present study reports a noninvasive technique for the measurement of the pulse transit time differential (PTTD) from the pulse pressure waveforms obtained at the carotid artery and radial artery using fiber Bragg grating pulse recorders (FBGPR). PTTD is defined as the time difference between the arrivals of a pulse pressure waveform at the carotid and radial arterial sites. The PTTD is investigated as an indicator of variation in the systolic blood pressure. The results are validated against blood pressure variation obtained from a Mindray Patient Monitor. Furthermore, the pulse wave velocity computed from the obtained PTTD is compared with the pulse wave velocity obtained from the color Doppler ultrasound system and is found to be in good agreement. The major advantage of the PTTD measurement via FBGPRs is that the data acquisition system employed can simultaneously acquire pulse pressure waveforms from both FBGPRs placed at carotid and radial arterial sites with a single time scale, which eliminates time synchronization complexity. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)

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This paper presents two methods of star camera calibration to determine camera calibrating parameters (like principal point, focal length etc) along with lens distortions (radial and decentering). First method works autonomously utilizing star coordinates in three consecutive image frames thus independent of star identification or biased attitude information. The parameters obtained in autonomous self-calibration technique helps to identify the imaged stars with the cataloged stars. Least Square based second method utilizes inertial star coordinates to determine satellite attitude and star camera parameters with lens radial distortion, both independent of each other. Camera parameters determined by the second method are more accurate than the first method of camera self calibration. Moreover, unlike most of the attitude determination algorithms where attitude of the satellite depend on the camera calibrating parameters, the second method has the advantage of computing spacecraft attitude independent of camera calibrating parameters except lens distortions (radial). Finally Kalman filter based sequential estimation scheme is employed to filter out the noise of the LS based estimation.

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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.

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Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

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Wrist pulse signal contains more important information about the health status of a person and pulse signal diagnosis has been employed in oriental medicine since very long time. In this paper we have used signal processing techniques to extract information from wrist pulse signals. For this purpose we have acquired radial artery pulse signals at wrist position noninvasively for different cases of interest. The wrist pulse waveforms have been analyzed using spatial features. Results have been obtained for the case of wrist pulse signals recorded for several subjects before exercise and after exercise. It is shown that the spatial features show statistically significant changes for the two cases and hence they are effective in distinguishing the changes taking place due to exercise. Support vector machine classifier is used to classify between the groups, and a high classification accuracy of 99.71% is achieved. Thus this paper demonstrates the utility of the spatial features in studying wrist pulse signals obtained under various recording conditions. The ability of the model to distinguish changes occurring under two different recording conditions can be potentially used for health care applications.

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Heat exchanger design plays a significant role in the performance of solid state hydrogen storage device. In the present study, a cylindrical hydrogen storage device with an embedded annular heat exchanger tube with radial circular copper fins, is considered. A 3-D mathematical model of the storage device is developed to investigate the sorption performance of metal hydride (MH). A prototype of the device is fabricated for 1 kg of MH alloy, LaNi5, and tested at constant supply pressure of hydrogen, validating the simulation results. Absorption characteristics of storage device have been examined by varying different operating parameters such as hydrogen supply pressure and cooling fluid temperature and velocity. Absorption process is completed in 18 min when these parameters are 15 bar, 298 K and 1 m/s respectively. A study of geometric parameters of copper fins (such as perforation, number and thickness of fin) has been carried out to investigate their effects on absorption process. Copyright (C) 2015, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

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We report molecular dynamics (MD) simulations to explore the influence of a counterion on the structure and dynamics of cationic and anionic solvation shells for various ions in methanol at 298 K. We show that the variation in ionic size of either the cation or the anion in an ion pair influences the solvation structure of the other ion as well as the diffusivity in an electrolyte solution of methanol. The extent of ionic association between the cation and its counteranion of different ionic sizes has been investigated by analyzing the radial distribution functions (RDFs) and the orientation of methanol molecules in the first solvation shell (FSS) of ions. It is shown that the methanol in the FSS of the anion as well the cation exhibit quite different radial and orientational structures as compared to methanol which lie in the FSS of either the anion or the cation but not both. We find that the coordination number (CN) of F-, Cr-, and I- ions decreases with increasing size of the anion which is contrary to the trend reported for the anions in H2O. The mean residence time (MRT) of methanol molecules in the FSS of ions has been calculated using the stable states picture (SSP) approach. It is seen that the ion-counterion interaction has a considerable influence on the MRT of methanol molecules in the FSS of ions. We also discuss the stability order of the ion-counterion using the potentials of mean force (PMFs) for ion pairs with ions of different sizes. The PMF plots reveal that the Li+-F- pair (small-small) is highly stable and the Li+-I- pair is least stable (small-large) in electrolyte solutions.

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Pressure-swirl nozzles (simplex nozzles) are used in various field applications such as aero-engines, power generation, spray painting and agricultural irrigation. For this particular nozzle, research in the past decade has dealt with the development of numerical models for predicting droplet distribution profiles. Although these results have been valuable, the experimental results have been contradictory, therefore fundamental understanding of the influence of properties in nozzle is important. This paper experimentally investigates the effect of surfactants on breakup and coalescence. Since most of the fuels and biofuels have low surface tension compared to water, a comparative analysis between a surfactant solution and a liquid fuel is imperative. For this experimental study, a simplex nozzle characterized as flow number 0.4 will be utilized. The injection pressures will range from 0.3 - 4Mpa while altering the surface tension from 72 to 28mN/m. By applying Phase Doppler Particle Anemometry (PDPA) which is a non-intrusive laser diagnostic technique, the differences in spray characteristics due to spray surface tension can be highlighted. The average droplet diameter decreases for a low surface tension fluid in the axial direction in comparison to pure water. The average velocity of droplets is surprisingly lower in the same spray zone. Measurements made in the radial direction show no significant changes, but at the locations close to the nozzle, water droplets have larger diameter and velocity. The results indicate the breakup and coalescence regimes have been altered when surface tension is lowered. A decrease in surface tension alters the breakup length while increasing the spray angle. Moreover, higher injection pressure shortens the breakup length and decrease in overall diameter of the droplets. By performing this experimental study the fundamentals of spray dynamics, such as spray formation, liquid breakup length, and droplet breakup regimes can be observed as a function of surface tension and how a surrogate fuel compares with a real fuel for experimental purposes. This knowledge potentially will lead to designing a better atomizer or new biofuels.

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The standard procedure of groundwater resource estimation in India till date is based on the specific yield parameters of each rock type (lithology) derived through pumping test analysis. Using the change in groundwater level, specific yield, and area of influence, groundwater storage change could be estimated. However, terrain conditions in the form of geomorphological variations have an important bearing on the net groundwater recharge. In this study, an attempt was made to use both lithology and geomorphology as input variables to estimate the recharge from different sources in each lithology unit influenced by the geomorphic conditions (lith-geom), season wise separately. The study provided a methodological approach for an evaluation of groundwater in a semi-arid hard rock terrain in Tirunelveli, Tamil Nadu, India. While characterizing the gneissic rock, it was found that the geomorphologic variations in the gneissic rock due to weathering and deposition behaved differently with respect to aquifer recharge. The three different geomorphic units identified in gneissic rock (pediplain shallow weathered (PPS), pediplain moderate weathered (PPM), and buried pediplain moderate (BPM)) showed a significant variation in recharge conditions among themselves. It was found from the study that Peninsular gneiss gives a net recharge value of 0.13 m/year/unit area when considered as a single unit w.r.t. lithology, whereas the same area considered with lith-geom classes gives recharge values between 0.1 and 0.41 m/year presenting a different assessment. It is also found from this study that the stage of development (SOD) for each lith-geom unit in Peninsular gneiss varies from 168 to 230 %, whereas the SOD is 223 % for the lithology as a single unit.