Mass-front velocity of dry granular flows influenced by the angle of the slope to the runout plane and particle size gradation

The mass-front velocities of granular flows results from the joint action of particle size gradations and the underlying surfaces.However,because of the complexity of friction during flow movement,details such as the slope-toe impedance effects and momentum-transfer mechanisms have not been completely explained by theoretical analyses,numerical simulations,or field investigations.To study the mass-front velocity of dry granular flows influenced by the angle of the slope to the runout plane and particle size gradations we conducted model experiments that recorded the motion of rapid and long-runout rockslides or avalanches.Flume tests were conducted using slope angles of 25°,35°,45°,and 55° and three particle size gradations.The resulting mass-front motions consisted of three stages:acceleration,velocity maintenance,and deceleration.The existing methods of velocity prediction could not explain the slowing effect of the slope toe or the momentum-transfer steady velocity stage.When the slope angle increased from 25° to 55°,the mass-front velocities dropped significantly to between 44.4% and59.6% of the peak velocities and energy lossesincreased from 69.1% to 83.7% of the initial,respectively.The velocity maintenance stages occurred after the slope-toe and mass-front velocity fluctuations.During this stage,travel distances increased as the angles increased,but the average velocity was greatest at 45°.At a slope angle of 45°,as the median particle size increased,energy loss around the slope toe decreased,the efficiency of momentum transfer increased,and the distance of the velocity maintenance stage increased.We presented an improved average velocity formula for granular flow and a geometrical model of the energy along the flow line.     

Proton flux and radiation dose from galactic cosmic rays in the lunar regolith and implications for organic synthesis at the poles of the Moon and Mercury

Galactic cosmic rays are a potential energy source to stimulate organic synthesis from simple ices. The recent detection of organic molecules at the polar regions of the Moon by LCROSS (Colaprete, A. et al. [2010]. Science 330, 463–468, http://dx.doi.org/10.1126/science.1186986), and possibly at the poles of Mercury (Paige, D.A. et al. [2013]. Science 339, 300–303, http://dx.doi.org/10.1126/science.1231106), introduces the question of whether the organics were delivered by impact or formed in situ. Laboratory experiments show that high energy particles can cause organic production from simple ices. We use a Monte Carlo particle scattering code (MCNPX) to model and report the flux of GCR protons at the surface of the Moon and report radiation dose rates and absorbed doses at the Moon’s surface and with depth as a result of GCR protons and secondary particles, and apply scaling factors to account for contributions to dose from heavier ions. We compare our results with dose rate measurements by the Cosmic Ray Telescope for the Effects of Radiation (CRaTER) experiment on Lunar Reconnaissance Orbiter (Schwadron, N.A. et al. [2012]. J. Geophys. Res. 117, E00H13, http://dx.doi.org/10.1029/2011JE003978) and find them in good agreement, indicating that MCNPX can be confidently applied to studies of radiation dose at and within the surface of the Moon. We use our dose rate calculations to conclude that organic synthesis is plausible well within the age of the lunar polar cold traps, and that organics detected at the poles of the Moon may have been produced in situ. Our dose rate calculations also indicate that galactic cosmic rays can induce organic synthesis within the estimated age of the dark deposits at the pole of Mercury that may contain organics.     

An Analytical Model For Threshold Crossing Rates Of Concentration Fluctuations In Dispersing Plumes

A simple analytical model is developed for the meanupcrossing rate of plume concentration fluctuations assuming that thisprocess can be well approximated by a lognormal process. The resultingexpression requires only the specification of the in-plume fluctuationintensity and in-plume Taylor micro-time scale and, hence, does notexplicitly involve the joint probability density function of theconcentration and its derivative. The analytical model provides agood fit to some field measurements of the mean upcrossing rate ina dispersing plume.     

Wiener optimal filtering of GRACE data

We present a spatial averaging method for Gravity Recovery and Climate Experiment (GRACE) gravity-field solutions based on the Wiener optimal filtering. The optimal filter is designed from the least-square minimization of the difference between the desired and filtered signals. It requires information about the power spectra of the desired gravitational signal and the contaminating noise, which is inferred from the average GRACE degree-power spectrum. We show that the signal decreases with increasing spherical harmonic degree j with approximately j^−b, where b = 1.5 for GRACE data investigations. This is termed the Second Kaula rule of thumb for temporal variations of the Earth’s gravity field. The degree power of the noise increases, in the logarithmic scale, linearly with increasing j. The Wiener optimal filter obtained for the signal model with b = 1.5 closely corresponds to a Gaussian filter with a spatial half width of 4° (∼440 km). We find that the filtered GRACE gravity signal is relatively insensitive to the exponent b of the signal model, which indicates the robustness of Wiener optimal filtering. This is demonstrated using the GFZ-GRACE gravity-field solution for April 2004.     

Groundwater Resources and Their Role in Water Supply to Crimea

The processes of formation and distribution of the resources of fresh groundwater and surface water in the territory of Crimea Peninsula are considered. Water availability in the natural–historic and administrative regions of the republic is characterized. The proportions between different categories of groundwater resources are shown with their role in water supply to Crimea taken into account. The presentday development of groundwater resources is analyzed and the possible increase in water consumption meeting geoecological requirements is substantiated.     

Spread <Emphasis Type="Italic">F</Emphasis> in the Midlatitude Ionosphere According to DPS-4 Ionosonde Data

The results of studying spread F obtained from the DPS-4 ionosonde data at the observatory of the Pushkov Institute of Terrestrial Magnetism, Ionosphere, and Radio Wave Propagation (Moscow) are presented. The methodical questions that arise during the study of a spread F phenomenon in the ionosphere are considered; the current results of terrestrial observations are compared with previously published data and the results of sounding onboard an Earth-satellite vehicle. The automated algorithm for estimation of the intensity of frequency spread F, which was developed by the authors and was successfully verified via comparison of the data of the digisonde DPS-4 and the results of manual processing, is described. The algorithm makes it possible to quantify the intensity of spread F in megahertz (the dFs parameter) and in the number of points (0, 1, 2, 3). The strongest spread (3 points) is shown to be most likely around midnight, while the weakest spread (0 points) is highly likely to occur during the daytime. The diurnal distribution of a 1–2 point spread F in the winter indicates the presence of additional maxima at 0300–0600 UT and 1400–1700 UT, which may appear due to the terminator. Despite the large volume of processed data, we can not definitively state that the appearance of spread F depends on the magnetic activity indices Kp, Dst, and AL, although the values of the dFs frequency spread interval strongly increased both at day and night during the magnetic storm of March 17–22, 2015, especially in the phase of storm recovery on March 20–22.     

Study on uncertainty of inversion and resolution limit in the presence of noise

This study analyzed the uncertainty of inversion and the resolution limit in the presence of noise by means of statistical experiments. The exhaustive method is adopted to obtain the global optimal solution in each experiment. We found that even with small level of noise, solutions fluctuate in a large range for the thin bed. The distribution of solutions in the presence of noise is closely related to the spread of the cost function in the absence of noise. As a result, the area of a certain neighborhood around the true solution on the spread of the cost function in the absence of noise is used to evaluate the uncertainty of inversion and the resolution limit in the presence of noise. In the case that the SNR (signal-to-noise ratio) is 5 in this study, solutions focus around the true solution with a very small uncertainty only when the bed thickness is greater than the reciprocal of the double predominant frequency of the convoluting wavelet.     

An inter-comparison of tidal solutions computed with a range of unstructured grid models of the Irish and Celtic Sea Regions

Three finite element codes, namely TELEMAC, ADCIRC and QUODDY, are used to compute the spatial distributions of the M₂, M₄ and M₆ components of the tide in the sea region off the west coast of Britain. This region is chosen because there is an accurate topographic dataset in the area and detailed open boundary M₂ tidal forcing for driving the model. In addition, accurate solutions (based upon comparisons with extensive observations) using uniform grid finite difference models forced with these open boundary data exist for comparison purposes. By using boundary forcing, bottom topography and bottom drag coefficients identical to those used in an earlier finite difference model, there is no danger of comparing finite element solutions for “untuned unoptimised solutions” with those from a “tuned optimised solution”. In addition, by placing the open boundary in all finite element calculations at the same location as that used in a previous finite difference model and using the same M₂ tidal boundary forcing and water depths, a like with like comparison of solutions derived with the various finite element models was possible. In addition, this open boundary was well removed from the shallow water region, namely the eastern Irish Sea where the higher harmonics were generated. Since these are not included in the open boundary, forcing their generation was determined by physical processes within the models. Consequently, an inter-comparison of these higher harmonics generated by the various finite element codes gives some indication of the degree of variability in the solution particularly in coastal regions from one finite element model to another. Initial calculations using high-resolution near-shore topography in the eastern Irish Sea and including “wetting and drying” showed that M₂ tidal amplitudes and phases in the region computed with TELEMAC were in good agreement with observations. The ADCIRC code gave amplitudes about 30 cm lower and phases about 8° higher. For the M₄ tide, in the eastern Irish Sea amplitudes computed with TELEMAC were about 4 cm higher than ADCIRC on average, with phase differences of order 5°. For the M₆ component, amplitudes and phases showed significant small-scale variability in the eastern Irish Sea, and no clear bias between the models could be found. Although setting a minimum water depth of 5 m in the near-shore region, hence removing wetting and drying, reduced the small-scale variability in the models, the differences in M₂ and M₄ tide between models remained. For M₆, a significant reduction in variability occurred in the eastern Irish Sea when a minimum 5-m water depth was specified. In this case, TELEMAC gave amplitudes that were 1 cm higher and phases 30° lower than ADCIRC on average. For QUODDY in the eastern Irish Sea, average M₂ tidal amplitudes were about 10 cm higher and phase 8° higher than those computed with TELEMAC. For M₄, amplitudes were approximately 2 cm higher with phases of order 15° higher in the northern part of the region and 15° lower in the southern part. For M₆ in the north of the region, amplitudes were 2 cm higher and about 2 cm lower in the south. Very rapid M₆ tidal-phase changes occurred in the near-shore regions. The lessons learned from this model inter-comparison study are summarised in the final section of the paper. In addition, the problems of performing a detailed model–model inter-comparison are discussed, as are the enormous difficulties of conducting a true model skill assessment that would require detailed measurements of tidal boundary forcing, near-shore topography and precise knowledge of bed types and bed forms. Such data are at present not available.