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Numerical avalanche dynamics models have become an essential part of snow engineering. Coupled with field observations and historical records, they are especially helpful in understanding avalanche flow in complex terrain. However, their application poses several new challenges to avalanche engineers. A detailed understanding of the avalanche phenomena is required to construct hazard scenarios which involve the careful specification of initial conditions (release zone location and dimensions) and definition of appropriate friction parameters. The interpretation of simulation results requires an understanding of the numerical solution schemes and easy to use visualization tools. We discuss these problems by presenting the computer model RAMMS, which was specially designed by the SLF as a practical tool for avalanche engineers. RAMMS solves the depth-averaged equations governing avalanche flow with accurate second-order numerical solution schemes. The model allows the specification of multiple release zones in three-dimensional terrain. Snow cover entrainment is considered. Furthermore, two different flow rheologies can be applied: the standard Voellmy–Salm (VS) approach or a random kinetic energy (RKE) model, which accounts for the random motion and inelastic interaction between snow granules. We present the governing differential equations, highlight some of the input and output features of RAMMS and then apply the models with entrainment to simulate two well-documented avalanche events recorded at the Vallée de la Sionne test site.
Ein 34-jähriger männlicher Patient stellte sich zur Abklärung einer seit dem 9. Lebensjahr bestehenden und im letzten Jahr rasch progredienten Visusminderung beider Augen bei uns vor. Er beschrieb eine subjektiv zunehmende, im Spiegel für ihn selbst sichtbare, weißliche Trübung in der Pupille beidseits und eine starke Blendempfindlichkeit. Nebenbefundlich gab er rezidivierende Konjunktivitiden und morgens verklebte Lider an. Eine Allergie auf Gräserpollen und eine Unverträglichkeit auf Alkohol sowie mehrere Lebensmittel seien ebenfalls bekannt.
Zusätzlich leidet der Patient an stark ausgeprägtem atopischem Ekzem. Dieses wurde nie systemisch, sondern nur bei Bedarf mit kortisonhaltiger Salbe therapiert.
Oxorhenium(V) complexes [ReOX3(PPh3)2] (X = Cl, Br) react with phenylacetylene under formation of complexes with ylide-type ligands. Compounds of the compositions [ReOCl3(PPh3){C(Ph)C(H)(PPh3)}] (1), [ReOBr3(OPPh3){C(Ph)C(H)(PPh3)}] (2), and [ReOBr3(OPPh3){C(H)C(Ph)(PPh3)}] (3) were isolated and characterized by X-ray diffraction. They contain a ligand, which was formed by a nucleophilic attack of released PPh3 at coordinated phenylacetylene. The structures of the products show that there is no preferable position for this attack. Cleavage of the Re–C bond in 3 and dimerization of the organic ligand resulted in the formation of the [{(PPh3)(H)CC(Ph)}2]2+ cation, which crystallized as its [(ReOBr4)(OReO3)]2– salt.
Realization of a calorimetric gas sensor on polyimide foil for applications in aseptic food industry
(2010)
Quantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining post-processing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and—performing a corresponding forward simulation—state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.