Vertical mixing of gravel over a long flood series

Vertical sediment exchange is a fundamental component of bedload transport in gravel‐bed channels. This paper describes the characteristic depth of exchange achieved over a long flood series. Analysis is based on 11 recoveries of magnetically tagged gravels deployed in Carnation Creek, Canada, completed between 1990 and 2008. Vertical grain exchange mixes gravels throughout the streambed relatively rapidly. Within one to eight floods the mean burial depth approaches two times the surface layer thickness, quantified by the 90th percentile of the size distribution. Finer gravels are mixed more rapidly into the bed than coarser gravels. Both active and passive grain exchanges throughout most of the bed produce the overall vertical distribution of marked grains. Gravel exchanges exhibit fairly consistent patterns once tracers are well mixed by large floods. Results highlight the role of flood sequence in determining exchange depths, support the notion of an upper limit to exchange, and underscore the importance of passive grain exchange. Copyright © 2011 John Wiley & Sons, Ltd.     

Curating NASA's extraterrestrial samples—Past, present, and future

The NASA Johnson Space Center Astromaterials Acquisition and Curation Office has the unique responsibility to curate NASA's extraterrestrial samples – from past and forthcoming missions – into the indefinite future. Presently curation includes documentation, preservation, preparation, and distribution of samples from the Moon, asteroids, comets, the solar wind, and the planet Mars. Each of these sample sets has a unique history and comes from a unique environment. The curation laboratories and procedures developed over forty years have proven both necessary and sufficient to serve the evolving needs of a worldwide research community. A new generation of sample return missions is being planned and proposed to destinations across the solar system. Curation must evolve to meet the increased challenges of these new samples.     

Numerical investigation of wave propagation in the Liverpool Bay, NW England

The computer model for near shore wave propagation,SWAN,was used to study wave climates in Liverpool Bay,northwest England with various input parameters,including bottom friction factor,white capping,wind drag formulation and effects of tidal modulations.Results were compared with in-situ measurements and reveal the impacts from these inputs on the predictions of wave height and propagation distributions.In particular,the model results were found very sensitive to different input formulations,and tend to underestimate the wave parameters under storm conditions in comparison with the observations.It is therefore important to further validate the model against detailed field measurements,particularly under large storms that are often of the primary concern.     

SCUBA observations of NGC 1275

Deep SCUBA observations of NGC 1275 at 450 and 850 μm along with the application of deconvolution algorithms have permitted us to separate the strong core emission in this galaxy from the fainter extended emission around it. The core has a steep spectral index and is likely caused primarily by the active galactic nucleus. The faint emission has a positive spectral index and is clearly caused by extended dust in a patchy distribution out to a radius of ∼20 kpc from the nucleus. These observations have now revealed that a large quantity of dust, ∼     (two orders of magnitude larger than that inferred from previous optical absorption measurements), exists in this galaxy. We estimate the temperature of this dust to be ∼20 K (using an emissivity index of     and the gas/dust ratio to be 360. These values are typical of spiral galaxies. The dust emission correlates spatially with the hot X-ray emitting gas, which may be a result of collisional heating of broadly distributed dust by electrons. As the destruction time-scale is short, the dust cannot be replenished by stellar mass loss and must be externally supplied, via either the infalling galaxy or the cooling flow itself.     

Large-scale stochastic linear inversion using hierarchical matrices

Stochastic inverse modeling deals with the estimation of functions from sparse data, which is a problem with a nonunique solution, with the objective to evaluate best estimates, measures of uncertainty, and sets of solutions that are consistent with the data. As finer resolutions become desirable, the computational requirements increase dramatically when using conventional solvers. A method is developed in this paper to solve large-scale stochastic linear inverse problems, based on the hierarchical matrix (or ? ² matrix) approach. The proposed approach can also exploit the sparsity of the underlying measurement operator, which relates observations to unknowns. Conventional direct algorithms for solving large-scale linear inverse problems, using stochastic linear inversion techniques, typically scale as ??(n ² m+nm ²), where n is the number of measurements and m is the number of unknowns. We typically have n ? m. In contrast, the algorithm presented here scales as ??(n ² m), i.e., it scales linearly with the larger problem dimension m. The algorithm also allows quantification of uncertainty in the solution at a computational cost that also grows only linearly in the number of unknowns. The speedup gained is significant since the number of unknowns m is often large. The effectiveness of the algorithm is demonstrated by solving a realistic crosswell tomography problem by formulating it as a stochastic linear inverse problem. In the case of the crosswell tomography problem, the sparsity of the measurement operator allows us to further reduce the cost of our proposed algorithm from ??(n ² m) to $\mathcal {O}(n^{2} \sqrt {m} + nm)$ . The computational speedup gained by using the new algorithm makes it easier, among other things, to optimize the location of sources and receivers, by minimizing the mean square error of the estimation. Without this fast algorithm, this optimization would be computationally impractical using conventional methods.     

The deep geothermal potential of the Berlin area

In this study, we model the geothermal potential of deep geological formations located in the Berlin region in Germany. Berlin is situated in a sedimentary geological setting (northeastern German basin), comprising low-enthalpic aquifers at horizons down to 4–5 km depth. In the Berlin region, the temperature increases almost linearly with depth by about 30 K per kilometer, thus allowing for direct heating from deep aquifer reservoirs in principle. Our model incorporates eight major sedimentary units (Jurassic, Keuper, Muschelkalk, Upper/Middle/Lower Buntsandstein, Zechstein Salt and Sedimentary Rotliegend). Owing to lack of available petro-physical rock data for the Berlin region, we have evaluated literature data for the larger northeastern German basin to develop a thermodynamic field model which regards depth-corrected equations of state within statistical intervals of confidence. Integration over the thicknesses of the respective structural units yields their “heat in place”—energy densities associated with the pore fluid and the rock matrix under local conditions in Joule per unit area at the surface. The model predicts that aquifers in the Middle Buntsandstein and in the Sedimentary Rotliegend may well exhibit energy densities about 10 GJ m^?2 for the pore fluids and 20 GJ m^?2 to 40 GJ m^?2 for the rock matrices on average. Referring these figures to the city area of Berlin (about 892 km²), a significant hydrothermal potential results, which however remained undeveloped until today for the reason of present development risks. The model accounts for these risks through statistical intervals of confidence which are in the order of ±60 to ±80 % of the trend figures. To minimize these uncertainties, scientific field explorations were required in order to assess the petro-physical aquifer properties locally.