Storage Capacity

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Wan Choi - One of the best experts on this subject based on the ideXlab platform.

  • optimal Storage allocation for wireless cloud caching systems with a limited sum Storage Capacity
    IEEE Transactions on Wireless Communications, 2016
    Co-Authors: Bi Hong, Wan Choi
    Abstract:

    In wireless cloud Storage systems, the recovery failure probability depends on not only wireless channel conditions but also Storage size of each distributed Storage node. For an efficient utilization of limited Storage Capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud Storage system with a sum Storage Capacity constraint for both high signal-to-noise ratio (SNR) regime and low SNR regime. Then, we find the optimal Storage allocation strategy across distributed Storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor $ complete Storage nodes and an incomplete Storage node) is optimal for low SNR regime, where $T$ is the sum Storage Capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

  • optimal Storage allocation for wireless cloud caching systems with a limited sum Storage Capacity
    arXiv: Information Theory, 2016
    Co-Authors: Bi Hong, Wan Choi
    Abstract:

    In wireless cloud Storage systems, the recovery failure probability depends on not only wireless channel conditions but also Storage size of each distributed Storage node. For an efficient utilization of limited Storage Capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud Storage system with a sum Storage Capacity constraint for both high SNR regime and low SNR regime. Then, we find the optimal Storage allocation strategy across distributed Storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor$ complete Storage nodes and an incomplete Storage node) is optimal for low SNR regime, where $T$ is the sum Storage Capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

Albert I J M Van Dijk - One of the best experts on this subject based on the ideXlab platform.

  • global root zone Storage Capacity from satellite based evaporation
    Hydrology and Earth System Sciences, 2016
    Co-Authors: Lan Wangerlandsson, W G M Bastiaanssen, Hongkai Gao, Jonas Jagermeyr, Gabriel B Senay, Albert I J M Van Dijk
    Abstract:

    Abstract. This study presents an "Earth observation-based" method for estimating root zone Storage Capacity – a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone Storage Capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone Storage Capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale independent. In contrast to a traditional look-up table approach, our method captures the variability in root zone Storage Capacity within land cover types, including in rainforests where direct measurements of root depths otherwise are scarce. Implementing the estimated root zone Storage Capacity in the global hydrological model STEAM (Simple Terrestrial Evaporation to Atmosphere Model) improved evaporation simulation overall, and in particular during the least evaporating months in sub-humid to humid regions with moderate to high seasonality. Our results suggest that several forest types are able to create a large Storage to buffer for severe droughts (with a very long return period), in contrast to, for example, savannahs and woody savannahs (medium length return period), as well as grasslands, shrublands, and croplands (very short return period). The presented method to estimate root zone Storage Capacity eliminates the need for poor resolution soil and rooting depth data that form a limitation for achieving progress in the global land surface modelling community.

Bi Hong - One of the best experts on this subject based on the ideXlab platform.

  • optimal Storage allocation for wireless cloud caching systems with a limited sum Storage Capacity
    IEEE Transactions on Wireless Communications, 2016
    Co-Authors: Bi Hong, Wan Choi
    Abstract:

    In wireless cloud Storage systems, the recovery failure probability depends on not only wireless channel conditions but also Storage size of each distributed Storage node. For an efficient utilization of limited Storage Capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud Storage system with a sum Storage Capacity constraint for both high signal-to-noise ratio (SNR) regime and low SNR regime. Then, we find the optimal Storage allocation strategy across distributed Storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor $ complete Storage nodes and an incomplete Storage node) is optimal for low SNR regime, where $T$ is the sum Storage Capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

  • optimal Storage allocation for wireless cloud caching systems with a limited sum Storage Capacity
    arXiv: Information Theory, 2016
    Co-Authors: Bi Hong, Wan Choi
    Abstract:

    In wireless cloud Storage systems, the recovery failure probability depends on not only wireless channel conditions but also Storage size of each distributed Storage node. For an efficient utilization of limited Storage Capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud Storage system with a sum Storage Capacity constraint for both high SNR regime and low SNR regime. Then, we find the optimal Storage allocation strategy across distributed Storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor$ complete Storage nodes and an incomplete Storage node) is optimal for low SNR regime, where $T$ is the sum Storage Capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

Anthony C Withers - One of the best experts on this subject based on the ideXlab platform.

  • h2o Storage Capacity of olivine at 5 8 gpa and consequences for dehydration partial melting of the upper mantle
    Earth and Planetary Science Letters, 2012
    Co-Authors: Paola Ardia, Marc M Hirschmann, Anthony C Withers, T J Tenner
    Abstract:

    Abstract The H2O Storage capacities of peridotitic minerals place crucial constraints on the onset of hydrous partial melting in the mantle. The Storage capacities of minerals in equilibrium with a peridotite mineral assemblage (“peridotite-saturated” minerals) are lower than when the minerals coexist only with fluid because hydrous partial melt is stabilized at a lower activity of H2O. Here, we determine peridotite-saturated olivine H2O Storage capacities from 5 to 8 GPa and 1400–1500 °C in layered experiments designed to grow large (∼100–150 μm) olivine crystals in equilibrium with the full hydrous peridotite assemblage (melt+ol+opx+gar+cpx). The peridotite-saturated H2O Storage Capacity of olivine at 1450 °C rises from 57±26 ppm (by wt.) at 5 GPa to 254±60 ppm at 8 GPa. Combining these with results of a parallel study at 10–13 GPa ( Tenner et al., 2011 , CMP) yields a linear relation applicable from 5 to 13 GPa for peridotite-saturated H2O Storage Capacity of olivine at 1450 °C, C H 2 O olivine ( ppm ) = 57.6 ( ± 16 ) × P ( GPa ) − 169 ( ± 18 ) . Storage Capacity diminishes with increasing temperature, but is unaffected by variable total H2O concentration between 0.47 and 1.0 wt%. Both of these are as predicted for the condition in which the water activity in the melt is governed principally by the cryoscopic requirement of melt stability for a given temperature below the dry solidus. Measured olivine Storage capacities are in agreement or slightly greater than those predicted by a model that combines data from experimental freezing point depression and olivine/melt partition coefficients of H2O ( Hirschmann et al., 2009 ). Considering the temperature along the mantle geotherm, as well as available constraints on garnet/olivine and pyroxene/olivine partitioning of H2O ( D H 2 O gar / ol , D H 2 O px / ol ), we estimate the peridotite H2O Storage Capacity in the low velocity zone. The C H 2 O required to initiate melting between 150 and 250 km depth is between 270 and 855 ppm. We conclude that hydrous partial melting does not occur at these depths for H2O concentrations (50–200 ppm) typical of the convecting upper mantle sampled by mid-ocean ridge basalts.

  • Storage Capacity of h2o in nominally anhydrous minerals in the upper mantle
    Earth and Planetary Science Letters, 2005
    Co-Authors: Marc M Hirschmann, Cyril Aubaud, Anthony C Withers
    Abstract:

    Abstract The H 2 O Storage Capacity of nominally anhydrous minerals or rocks is the concentration of water that can be sequestered in the mineral(s) without stabilizing a hydrous fluid or melt. The Storage Capacity of the upper mantle is considerably greater than generally appreciated, as recent studies show that H 2 O uptake in olivine is ∼3 times that originally inferred by Kohlstedt et al. [D.L. Kohlstedt, H. Keppler, D.C. Rubie, Solubility of water in the α, β and γ phases of (Mg,Fe) 2 SiO 4 , Contrib. Mineral. Petrol. 123 1996 345–357.] and, at least at low pressure, pyroxene stores considerably more H 2 O than olivine. Consequently, H 2 O has smaller influence on small degree melting than inferred previously. Combining data on the Storage Capacity of olivine with constraints on partition coefficients between olivine, pyroxene, and garnet, we estimate that the Storage Capacity of the upper mantle just above the 410 km discontinuity is > 0.4 wt.%. Owing to the increasing mode of garnet at the expense of pyroxene, there is likely to be a local maximum in Storage Capacity between 350 and 400 km, and a local minimum just above the onset of wadsleyite stability. Although published data suggest that the Storage Capacity of wadsleyite diminishes with increasing temperature, the Storage Capacity of the transition zone likely is considerable because Fe-bearing wadsleyite has a larger Storage Capacity than Mg 2 SiO 4 . Peridotite upwelling from the transition zone will undergo partial melting above the 410 km discontinuity only if it has more H 2 O than the local Storage Capacity (i.e., > 0.4 wt.%), and the dehydrated residue cannot be drier than this unless it melts further under conditions where the Storage Capacity is less. Because residues of partial melting at 410 km have much more H 2 O than the 50–200 ppm H 2 O in the average upper mantle, they cannot be principal sources for the upper mantle. If hydrous melting occurs at 410 km, further upwelling of the residual peridotite will result in continued melting throughout the upper mantle, unless the Storage Capacity increases with decreasing depth. The partition coefficient of H 2 O between wadsleyite and olivine is ∼5, which is less extreme than previously assumed. Consequently, the effect of H 2 O on the depth and thickness of the 410 discontinuity may not be pronounced and typical (10 km) discontinuity thickness can be reconciled with up to ∼400 ppm H 2 O.

Wang Yifei - One of the best experts on this subject based on the ideXlab platform.

  • Comparative study of the influences of different water tank shapes on thermal energy Storage Capacity and thermal stratification
    Renewable Energy, 2016
    Co-Authors: Zheng Yang, Haisheng Chen, Liang Wang, Sheng Yong, Wang Yifei
    Abstract:

    Abstract The influences of different water tank shapes on thermal energy Storage Capacity and thermal stratification in the static mode of operation is investigated in this study under laminar natural convection. A new experimental apparatus is built, and a numerical model is developed to simulate the flow and heat transfer in the water tank. Computational results agree with the experimental data. Among the 10 different water tank shapes studied, the sphere and barrel water tanks are ideal for thermal energy Storage Capacity, whereas the cylinder water tank is the least favorable. The thermal energy Storage Capacity is closely related to the surface area of the water tank. According to the characteristics of the velocity and temperature fields, these shapes can be divided into three categories: shapes with sharp corners, those with hemispheres, and those with horizontal plane surface. Shapes with sharp corners have the highest degree of thermal stratification, whereas the shapes with horizontal plane surface possess the lowest. That of the shapes with hemispheres lies in between these two degrees. The thermal stratification of different shapes is determined by the flow at the bottom of the water tank and the heat transfer from the fluid to the environment.