981 resultados para Wind forecasting
Resumo:
This paper presents the Treadport Active Wind Tunnel (TPAWT)-a full-body immersive virtual environment for the Treadport locomotion interface designed for generating wind on a user from any frontal direction at speeds up to 20 kph. The goal is to simulate the experience of realistic wind while walking in an outdoor virtual environment. A recirculating-type wind tunnel was created around the pre-existing Treadport installation by adding a large fan, ducting, and enclosure walls. Two sheets of air in a non-intrusive design flow along the side screens of the back-projection CAVE-like visual display, where they impinge and mix at the front screen to redirect towards the user in a full-body cross-section. By varying the flow conditions of the air sheets, the direction and speed of wind at the user are controlled. Design challenges to fit the wind tunnel in the pre-existing facility, and to manage turbulence to achieve stable and steerable flow, were overcome. The controller performance for wind speed and direction is demonstrated experimentally.
Resumo:
Using hydrodynamical simulations, we show for the first time that an episode of star formation in the centre of the Milky Way, with a star formation rate (SFR) similar to 0.5 M-circle dot yr(-1) for similar to 30 Myr, can produce bubbles that resemble the Fermi bubbles (FBs), when viewed from the solar position. The morphology, extent and multiwavelength observations of FBs, especially X-rays, constrain various physical parameters such as SFR, age, and the circumgalactic medium (CGM) density. We show that the interaction of the CGM with the Galactic wind driven by star formation in the central region can explain the observed surface brightness and morphological features of X-rays associated with the FBs. Furthermore, assuming that cosmic ray electrons are accelerated in situ by shocks and/or turbulence, the brightness and morphology of gamma-ray emission and the microwave haze can be explained. The kinematics of the cold and warm clumps in our model also matches with recent observations of absorption lines through the bubbles.
Resumo:
Northeast India and its adjoining areas are characterized by very high seismic activity. According to the Indian seismic code, the region falls under seismic zone V, which represents the highest seismic-hazard level in the country. This region has experienced a number of great earthquakes, such as the Assam (1950) and Shillong (1897) earthquakes, that caused huge devastation in the entire northeast and adjacent areas by flooding, landslides, liquefaction, and damage to roads and buildings. In this study, an attempt has been made to find the probability of occurrence of a major earthquake (M-w > 6) in this region using an updated earthquake catalog collected from different sources. Thereafter, dividing the catalog into six different seismic regions based on different tectonic features and seismogenic factors, the probability of occurrences was estimated using three models: the lognormal, Weibull, and gamma distributions. We calculated the logarithmic probability of the likelihood function (ln L) for all six regions and the entire northeast for all three stochastic models. A higher value of ln L suggests a better model, and a lower value shows a worse model. The results show different model suits for different seismic zones, but the majority follows lognormal, which is better for forecasting magnitude size. According to the results, Weibull shows the highest conditional probabilities among the three models for small as well as large elapsed time T and time intervals t, whereas the lognormal model shows the lowest and the gamma model shows intermediate probabilities. Only for elapsed time T = 0, the lognormal model shows the highest conditional probabilities among the three models at a smaller time interval (t = 3-15 yrs). The opposite result is observed at larger time intervals (t = 15-25 yrs), which show the highest probabilities for the Weibull model. However, based on this study, the IndoBurma Range and Eastern Himalaya show a high probability of occurrence in the 5 yr period 2012-2017 with >90% probability.
Resumo:
The Boltzmann equation of the sand particle velocity distribution function in wind-blown sand two-phase flow is established based on the motion equation of single particle in air. And then, the generalized balance law of particle property in single phase granular flow is extended to gas-particle two-phase flow. The velocity distribution function of particle phase is expanded into an infinite series by means of Grad's method and the Gauss distribution is used to replace Maxwell distribution. In the case of truncation at the third-order terms, a closed third-order moment dynamical equation system is constructed. The theory is further simplified according to the measurement results obtained by stroboscopic photography in wind tunnel tests.
Resumo:
Sensor networks can be naturally represented as graphical models, where the edge set encodes the presence of sparsity in the correlation structure between sensors. Such graphical representations can be valuable for information mining purposes as well as for optimizing bandwidth and battery usage with minimal loss of estimation accuracy. We use a computationally efficient technique for estimating sparse graphical models which fits a sparse linear regression locally at each node of the graph via the Lasso estimator. Using a recently suggested online, temporally adaptive implementation of the Lasso, we propose an algorithm for streaming graphical model selection over sensor networks. With battery consumption minimization applications in mind, we use this algorithm as the basis of an adaptive querying scheme. We discuss implementation issues in the context of environmental monitoring using sensor networks, where the objective is short-term forecasting of local wind direction. The algorithm is tested against real UK weather data and conclusions are drawn about certain tradeoffs inherent in decentralized sensor networks data analysis. © 2010 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.
Resumo:
Turbulent air flows over developing wind waves in the air-sea boundary layer are numerically simulated without considering wave breaking. Influences of wind waves on air flows are considered using a model of significant wave and surface roughness, with a formula proposed for calculating the surface roughness, k - epsilon model is adopted to simulate turbulent flows. The results of the drag coefficient and turbulence characteristics agree well with the observations.
Resumo:
Sand velocity in aeolian sand transport was measured using the laser Doppler technique of PDPA (Phase Doppler Particle Analyzer) in a wind tunnel. The sand velocity profile, probability distribution of particle velocity, particle velocity fluctuation and particle turbulence were analyzed in detail. The experimental results verified that the sand horizontal velocity profile can be expressed by a logarithmic function above 0.01 in, while a deviation occurs below 0.01 m. The mean vertical velocity of grains generally ranges from -0.2 m/s to 0.2 m/s, and is downward at the lower height, upward at the higher height. The probability distributions of the horizontal velocity of ascending and descending particles have a typical peak and are right-skewed at a height of 4 turn in the lower part of saltation layer. The vertical profile of the horizontal RMS velocity fluctuation of particles shows a single peak. The horizontal RMS velocity fluctuation of sand particles is generally larger than the vertical RMS velocity fluctuation. The RMS velocity fluctuations of grains in both horizontal and vertical directions increase with wind velocity. The particle turbulence intensity decreases with height. The present investigation is helpful in understanding the sand movement mechanism in windblown sand transport and also provides a reference for the study of blowing sand velocity. (C) 2007 Elsevier B.V All rights reserved.