979 resultados para Fast test


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A supercritical CO2 test facility is currently being developed at Indian Institute of Science, Bangalore, India to analyze the performance of a closed loop Brayton cycle for concentrated solar power (CSP) generation. The loop has been designed for an external heat input of 20 kW a pressure range of 75-135 bar, flow rate of 11 kg/min, and a maximum cycle temperature of 525 degrees C. The operation of the loop and the various parametric tests planned to be performed are discussed in this paper The paper addresses various aspects of the loop design with emphasis on design of various components such as regenerator and expansion device. The regenerator design is critical due to sharp property variations in CO2 occurring during the heat exchange process between the hot and cold streams. Two types of heat exchanger configurations 1) tube-in-tube (TITHE) and 2) printed circuit heat exchanger (PCHE) are analyzed and compared. A PCHE is found to be similar to 5 times compact compared to a TITHE for identical heat transfer and pressure drops. The expansion device is being custom designed to achieve the desired pressure drop for a range of operating temperatures. It is found that capillary of 5.5 mm inner diameter and similar to 2 meter length is sufficient to achieve a pressure drop from 130 to 75 bar at a maximum cycle temperature of 525 degrees C.

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Characterized not just by high Mach numbers, but also high flow total enthalpies-often accompanied by dissociation and ionization of flowing gas itself-the experimental simulation of hypersonic flows requires impulse facilities like shock tunnels. However, shock tunnel simulation imposes challenges and restrictions on the flow diagnostics, not just because of the possible extreme flow conditions, but also the short run times-typically around 1 ms. The development, calibration and application of fast response MEMS sensors for surface pressure measurements in IISc hypersonic shock tunnel HST-2, with a typical test time of 600 mu s, for the complex flow field of strong (impinging) shock boundary layer interaction with separation close to the leading edge, is delineated in this paper. For Mach numbers 5.96 (total enthalpy 1.3 MJ kg(-1)) and 8.67 (total enthalpy 1.6 MJ kg(-1)), surface pressures ranging from around 200 Pa to 50 000 Pa, in various regions of the flow field, are measured using the MEMS sensors. The measurements are found to compare well with the measurements using commercial sensors. It was possible to resolve important regions of the flow field involving significant spatial gradients of pressure, with a resolution of 5 data points within 12 mm in each MEMS array, which cannot be achieved with the other commercial sensors. In particular, MEMS sensors enabled the measurement of separation pressure (at Mach 8.67) near the leading edge and the sharply varying pressure in the reattachment zone.

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Speech polarity detection is a crucial first step in many speech processing techniques. In this paper, an algorithm is proposed that improvises the existing technique using the skewness of the voice source (VS) signal. Here, the integrated linear prediction residual (ILPR) is used as the VS estimate, which is obtained using linear prediction on long-term frames of the low-pass filtered speech signal. This excludes the unvoiced regions from analysis and also reduces the computation. Further, a modified skewness measure is proposed for decision, which also considers the magnitude of the skewness of the ILPR along with its sign. With the detection error rate (DER) as the performance metric, the algorithm is tested on 8 large databases and its performance (DER=0.20%) is found to be comparable to that of the best technique (DER=0.06%) on both clean and noisy speech. Further, the proposed method is found to be ten times faster than the best technique.

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We develop an approximate analytical technique for evaluating the performance of multi-hop networks based on beaconless IEEE 802.15.4 ( the ``ZigBee'' PHY and MAC), a popular standard for wireless sensor networks. The network comprises sensor nodes, which generate measurement packets, relay nodes which only forward packets, and a data sink (base station). We consider a detailed stochastic process at each node, and analyse this process taking into account the interaction with neighbouring nodes via certain time averaged unknown variables (e.g., channel sensing rates, collision probabilities, etc.). By coupling the analyses at various nodes, we obtain fixed point equations that can be solved numerically to obtain the unknown variables, thereby yielding approximations of time average performance measures, such as packet discard probabilities and average queueing delays. The model incorporates packet generation at the sensor nodes and queues at the sensor nodes and relay nodes. We demonstrate the accuracy of our model by an extensive comparison with simulations. As an additional assessment of the accuracy of the model, we utilize it in an algorithm for sensor network design with quality-of-service (QoS) objectives, and show that designs obtained using our model actually satisfy the QoS constraints (as validated by simulating the networks), and the predictions are accurate to well within 10% as compared to the simulation results in a regime where the packet discard probability is low. (C) 2015 Elsevier B.V. All rights reserved.

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Three-dimensional (3-D) full-wave electromagnetic simulation using method of moments (MoM) under the framework of fast solver algorithms like fast multipole method (FMM) is often bottlenecked by the speed of convergence of the Krylov-subspace-based iterative process. This is primarily because the electric field integral equation (EFIE) matrix, even with cutting-edge preconditioning techniques, often exhibits bad spectral properties arising from frequency or geometry-based ill-conditioning, which render iterative solvers slow to converge or stagnate occasionally. In this communication, a novel technique to expedite the convergence of MoMmatrix solution at a specific frequency is proposed, by extracting and applying Eigen-vectors from a previously solved neighboring frequency in an augmented generalized minimum residual (AGMRES) iterative framework. This technique can be applied in unison with any preconditioner. Numerical results demonstrate up to 40% speed-up in convergence using the proposed Eigen-AGMRES method.

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Climate change in response to a change in external forcing can be understood in terms of fast response to the imposed forcing and slow feedback associated with surface temperature change. Previous studies have investigated the characteristics of fast response and slow feedback for different forcing agents. Here we examine to what extent that fast response and slow feedback derived from time-mean results of climate model simulations can be used to infer total climate change. To achieve this goal, we develop a multivariate regression model of climate change, in which the change in a climate variable is represented by a linear combination of its sensitivity to CO2 forcing, solar forcing, and change in global mean surface temperature. We derive the parameters of the regression model using time-mean results from a set of HadCM3L climate model step-forcing simulations, and then use the regression model to emulate HadCM3L-simulated transient climate change. Our results show that the regression model emulates well HadCM3L-simulated temporal evolution and spatial distribution of climate change, including surface temperature, precipitation, runoff, soil moisture, cloudiness, and radiative fluxes under transient CO2 and/or solar forcing scenarios. Our findings suggest that temporal and spatial patterns of total change for the climate variables considered here can be represented well by the sum of fast response and slow feedback. Furthermore, by using a simple 1-D heat-diffusion climate model, we show that the temporal and spatial characteristics of climate change under transient forcing scenarios can be emulated well using information from step-forcing simulations alone.

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Development of computationally efficient and accurate attitude rate estimation algorithm using low-cost commercially available star sensor arrays and processing unit for micro-satellite mission is presented. Our design reduces the computational load of least square (LS)-based rate estimation method while maintaining the same accuracy compared to other rate estimation approaches. Furthermore, rate estimation accuracy is improved by using recently developed fast and accurate second-order sliding mode observer (SOSMO) scheme. It also gives robust estimation in the presence of modeling uncertainties, unknown disturbances, and measurement noise. Simulation study shows that rate estimation accuracy achieved by our LS-based method is comparable with other methods for a typical commercially available star sensor array. The robustness analysis of SOSMO with respect to measurement noise is also presented in this paper. Simulation test bench for a practical scenario of satellite rate estimation uses moment-of-inertia variation and environmental disturbances affecting a typical micro-satellite at 500km circular orbit. Comparison studies of SOSMO with 1-SMO and pseudo-linear Kalman filter show that satisfactory estimation accuracy is achieved by SOSMO.

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Ultra-fast two-step anodization method is developed for obtaining ordered nano-pores on aluminium (Al) foil. First anodization was carried out for 10 min, followed by 3 min of second anodization at high voltage (150 V) compared to previous reports of anodization times of 12 h (40-60 V). The pore dimensions on anodized alumina are 180 nm for pore diameter and 130 nm for inter-pore distance. It was evident that by increasing the anodization voltage to 150 V, the diameter of the pores formed was above 150 nm. The electrolyte and its temperature affect the shape and size of the pore formation. At lower anodization temperature, controlled pore formation was observed. The anodized samples were characterized using the field emission scanning electron microscope (FE-SEM) to determine the pore diameter and inter-pore distance. Using UV-Visible spectroscopy, the reflectance spectra of anodized samples were measured. The alumina (Al2O3) peaks were identified by x-ray diffraction (XRD) technique. The x-ray photo electron spectroscopy (XPS) analysis confirmed the Al 2p peak at 73.1 eV along with the oxygen O 1s at 530.9 eV and carbon traces C 1s at 283.6 eV.

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A heterostructure of graphene and zinc oxide (ZnO) nanowires (NWs) is fabricated by sandwiching an array of ZnO NWs between two graphene layers for an ultraviolet (UV) photodetector. This unique structure allows NWs to be in direct contact with the graphene layers, minimizing the effect of the substrate or metal electrodes. In this device, graphene layers act as highly conducting electrodes with a high mobility of the generated charge carriers. An excellent sensitivity is demonstrated towards UV illumination, with a reversible photoresponse even for a short period of UV illumination. Response and recovery times of a few milliseconds demonstrated a much faster photoresponse than most of the conventional ZnO nanostructure-based photodetectors. It is shown that the generation of a built-in electric field between the interface of graphene and ZnO NWs effectively contributes to the separation of photogenerated electron-hole pairs for photocurrent generation without applying any external bias. Upon application of external bias voltage, the electric field further increases the drift velocity of photogenerated electrons by reducing the charge recombination rates, and results in an enhancement of the photocurrent. Therefore, the graphene-based heterostructure (G/ZnO NW/G) opens avenues to constructing a novel heterostructure with a combination of two functionally dissimilar materials.

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A heterostructure of graphene and zinc oxide (ZnO) nanowires (NWs) is fabricated by sandwiching an array of ZnO NWs between two graphene layers for an ultraviolet (UV) photodetector. This unique structure allows NWs to be in direct contact with the graphene layers, minimizing the effect of the substrate or metal electrodes. In this device, graphene layers act as highly conducting electrodes with a high mobility of the generated charge carriers. An excellent sensitivity is demonstrated towards UV illumination, with a reversible photoresponse even for a short period of UV illumination. Response and recovery times of a few milliseconds demonstrated a much faster photoresponse than most of the conventional ZnO nanostructure-based photodetectors. It is shown that the generation of a built-in electric field between the interface of graphene and ZnO NWs effectively contributes to the separation of photogenerated electron-hole pairs for photocurrent generation without applying any external bias. Upon application of external bias voltage, the electric field further increases the drift velocity of photogenerated electrons by reducing the charge recombination rates, and results in an enhancement of the photocurrent. Therefore, the graphene-based heterostructure (G/ZnO NW/G) opens avenues to constructing a novel heterostructure with a combination of two functionally dissimilar materials.

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It was demonstrated in earlier work that, by approximating its range kernel using shiftable functions, the nonlinear bilateral filter can be computed using a series of fast convolutions. Previous approaches based on shiftable approximation have, however, been restricted to Gaussian range kernels. In this work, we propose a novel approximation that can be applied to any range kernel, provided it has a pointwise-convergent Fourier series. More specifically, we propose to approximate the Gaussian range kernel of the bilateral filter using a Fourier basis, where the coefficients of the basis are obtained by solving a series of least-squares problems. The coefficients can be efficiently computed using a recursive form of the QR decomposition. By controlling the cardinality of the Fourier basis, we can obtain a good tradeoff between the run-time and the filtering accuracy. In particular, we are able to guarantee subpixel accuracy for the overall filtering, which is not provided by the most existing methods for fast bilateral filtering. We present simulation results to demonstrate the speed and accuracy of the proposed algorithm.

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This study reports the synthesis and photophysical properties of a star-shaped, novel, fluoranthene-tetraphenylethene (TFPE) conjugated luminogen, which exhibits aggregation-induced blue-shifted emission (AIBSE). The bulky fluoranthene units at the periphery prevent intramolecular rotation (IMR) of phenyl rings and induces a blueshift with enhanced emission. The AIBSE phenomenon was investigated by solvatochromic and temperature-dependent emission studies. Nanoaggregates of TFPE, formed by varying the water/THF ratio, were investigated by SEM and TEM and correlated with optical properties. The TFPE conjugate was found to be a promising fluorescent probe towards the detection of nitroaromatic compounds (NACs), especially for 2,4,6-trinitrophenol (PA) with high sensitivity and a high Stern-Volmer quenching constant. The study reveals that nanoaggregates of TFPE formed at 30 and 70% water in THF showed unprecedented sensitivity with detection limits of 0.8 and 0.5ppb, respectively. The nanoaggregates formed at water fractions of 30 and 70% exhibit high Stern-Volmer constants (K-sv=79998 and 51120m(-1), respectively) towards PA. Fluorescence quenching is ascribed to photoinduced electron transfer between TFPE and NACs with a static quenching mechanism. Test strips coated with TFPE luminogen demonstrate fast and ultra-low-level detection of PA for real-time field analysis.

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Hippocampal pyramidal neurons express an intraneuronal map of spectral tuning mediated by hyperpolarization-activated cyclic-nucleotide-gated nonspecific-cation channels. Modeling studies have predicted a critical regulatory role for A-type potassium (KA) channels towards augmenting functional robustness of this map. To test this, we performed patch-clamp recordings from soma and dendrites of rat hippocampal pyramidal neurons, and measured spectral tuning before and after blocking KA channels using two structurally distinct pharmacological agents. Consistent with computational predictions, we found that blocking KA channels resulted in a significant reduction in resonance frequency and significant increases in input resistance, impedance amplitude and action-potential firing frequency across the somato-apical trunk. Furthermore, across all measured locations, blocking KA channels enhanced temporal summation of postsynaptic potentials and critically altered the impedance phase profile, resulting in a significant reduction in total inductive phase. Finally, pair-wise correlations between intraneuronal percentage changes (after blocking KA channels) in different measurements were mostly weak, suggesting differential regulation of different physiological properties by KA channels. Our results unveil a pivotal role for fast transient channels in regulating theta-frequency spectral tuning and intrinsic phase response, and suggest that degeneracy with reference to several coexisting functional maps is mediated by cross-channel interactions across the active dendritic arbor.

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The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.

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In the present paper the rarefied gas how caused by the sudden change of the wall temperature and the Rayleigh problem are simulated by the DSMC method which has been validated by experiments both in global flour field and velocity distribution function level. The comparison of the simulated results with the accurate numerical solutions of the B-G-K model equation shows that near equilibrium the BG-K equation with corrected collision frequency can give accurate result but as farther away from equilibrium the B-G-K equation is not accurate. This is for the first time that the error caused by the B-G-K model equation has been revealed.