Drag Reducing and Increasing Mechanism on Triangular Riblet Surface

Drag reducing and increasing mechanism on riblet surface has been studied through computational fluid dynamics(CFD).Drag reduction is achieved through the optimization of riblet geometry which would affect flow structure inside riblet grooves.Force and flow structure on riblet surface are analyzed and compared with those of smooth surface based on the k-εturbulence model.Drag reducing and increasing mechanism is proved to be related to microvortexes induced inside riblets which lead to Reynolds shear stress reduction significantly and is considered to be the dominant factor resulting in wall friction reduction.Simulation results also show that the pressure drag generating from the deviation of static pressure on the front and rear ends of riblets occurs and grows exponentially with Mach number,which can cause drag increasing.Furthermore,near-wall vortical structures,Reynolds shear stress and static pressure on riblet surfaces are also analyzed in detail.     

Very Long Baseline Interferometry: Dependencies on Frequency Stability

Very Long Baseline Interferometry (VLBI) is a differential technique observing radiation of compact extra-galactic radio sources with pairs of radio telescopes. For these observations, the frequency standards at the telescopes need to have very high stability. In this article we discuss why this is, and we investigate exactly how precise the frequency standards need to be. Four areas where good clock performance is needed are considered: coherence, geodetic parameter estimation, correlator synchronization, and UT1 determination. We show that in order to ensure the highest accuracy of VLBI, stability similar to that of a hydrogen maser is needed for time-scales up to a few hours. In the article, we are considering both traditional VLBI where extra-galactic radio sources are observed, as well as observation of man-made artificial radio sources emitted by satellites or spacecrafts.     

Investigations of the Mars Upper Atmosphere with ExoMars Trace Gas Orbiter

Space Science Reviews - Time measured by an ideal clock crucially depends on the gravitational potential and velocity of the clock according to general relativity. Technological advances in...     

An Artificial Neural Network based approach for estimation of rain intensity from spectral moments of a Doppler Weather Radar

By using a Doppler Weather Radar (DWR) at Shriharikota (13.66°N & 80.23°E), an Artificial Neural Network (ANN) based technique is proposed to improve the accuracy of rain intensity estimation. Three spectral moments of a Doppler spectra are utilized as an input data to an ANN. Rain intensity, as measured by the tipping bucket rain gauges around the DWR station, are considered as a target values for the given inputs. Rain intensity as estimated by the developed ANN model is validated by the rain gauges measurements. With the help of a developed technique, reasonable improvement in the estimation of rain intensity is observed. By using the developed technique, root mean square error and bias are reduced in the range of 34–18% and 17–3% respectively, compared to Z–R approach.     

Sunspot cycle 23 descent to an unusual minimum and forecasts for cycle 24 activity

The decay phase of the sunspot cycle 23 exhibited two unusual features. First, it lasted too long. Second, the interplanetary magnetic field intensity at earth orbit reached the lowest value since in situ measurements in space began in October 1963. These physical anomalies significantly altered the early forecasts for the sunspot activity parameters for cycle 24, made by several colleagues. We note that there was a significant change in the solar behavior during cycle 22. We discuss the observed trends and their effect on our empirical solar activity forecast technique, leading to our prediction for cycle 24 parameters; cycle 24 will be only half as active as cycle 23, reaching its peak in May 2013. We speculate on the possible implications of this outcome on future earth climate change and the ensuing socio-economic consequences.     

The Relativistic Proton Spectrometer (RPS) for the Radiation Belt Storm Probes Mission

The Relativistic Proton Spectrometer (RPS) on the Radiation Belt Storm Probes spacecraft is a particle spectrometer designed to measure the flux, angular distribution, and energy spectrum of protons from ～60 MeV to ～2000 MeV. RPS will investigate decades-old questions about the inner Van Allen belt proton environment: a nearby region of space that is relatively unexplored because of the hazards of spacecraft operation there and the difficulties in obtaining accurate proton measurements in an intense penetrating background. RPS is designed to provide the accuracy needed to answer questions about the sources and losses of the inner belt protons and to obtain the measurements required for the next-generation models of trapped protons in the magnetosphere. In addition to detailed information for individual protons, RPS features count rates at a 1-second timescale, internal radiation dosimetry, and information about electrostatic discharge events on the RBSP spacecraft that together will provide new information about space environmental hazards in the Earth’s magnetosphere.