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This paper presents a u‐p (displacement‐pressure) semi‐Lagrangian reproducing kernel (RK) formulation to effectively analyze landslide processes. The semi‐Lagrangian RK approximation is constructed based on Lagrangian discretization points with fixed kernel supports in the current configuration. As a result, it tracks state variables at discretization points while allowing extreme deformation and material separation that is beyond the capability of Lagrangian formulations. The u‐p formulation following Biot theory is incorporated into the formulation to describe poromechanics of saturated geomaterials. In addition, a stabilized nodal integration method to ensure stability of the domain integration and kernel contact algorithms to model contact between bodies are introduced in the u‐p semi‐Lagrangian RK formulation. The proposed method is verified with several numerical examples and validated with an experimental result and the field data of an actual landslide. 相似文献
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W. Ngan L. Van Waerbeke A. Mahdavi C. Heymans H. Hoekstra † 《Monthly notices of the Royal Astronomical Society》2009,396(2):1211-1216
The precision study of dark matter using weak lensing by large-scale structure is strongly constrained by the accuracy with which one can measure galaxy shapes. Several methods have been devised but none has demonstrated the ability to reach the level of precision required by future weak lensing surveys. In this paper, we explore new avenues to the existing 'Shapelets' approach, combining a priori knowledge of the galaxy profile with the power of orthogonal basis function decomposition. This paper discusses the new issues raised by this matched filter approach and proposes promising alternatives to shape measurement techniques. In particular, it appears that the use of a matched filter (e.g. Sérsic profile) restricted to elliptical radial fitting functions resolves several well-known Shapelet issues. 相似文献
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The potential mineralization and immobilization of soil nitrogen (N), phosphorus (P) and sulfur (S) are relatively high in natural ecosystems. This study was conducted to investigate the changes in essential plant macronutrients; N, P, and S status in response to different soil depth in rangeland ecosystems in vitro. The net nutrient mineralization was measured during 90 days at different depths (0–15, 15–30, 30–45 and 45–60 cm), using kinetic models to estimate the release rate. The net ammonification and mineralization of P and S were described using parabolic diffusion equation, while the power function equation was used to describe the net nitrification. The results indicated that the amount of released ammonium (NH4 +) decreased with time and depth and the rates of net ammonification were negative in all samples. Conversely, nitrification increased with time and depth and the rates were all positive. The net mineralization for both P and S reduced with time. The concentration of mineralized SO4 2? increased with depth like nitrate (NO3 ?). Accumulation of SO4 2? and NO3 ? in subsurface soils and NH4 + and P at surface horizons can increase the potential of their loss by leaching or volatilization. 相似文献
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The Permian–Triassic boundary (PTB) is a world‐wide event characterized by the most extensive mass extinction in the history of life. In the Persian Gulf, the rock record of this time interval host one of the most important hydrocarbon reserves in the world: the South Pars Gas Field and its southern extension, the North Dome (or North Field). These carbonate and evaporite successions were sampled in eight wells for petrographic, geochemical and porosity–permeability studies. An important characteristic of the Dalan and Kangan formations is the centimetre‐scale lithological heterogeneities caused by facies changes and diagenetic imprints that led to the compartmentalization of these reservoirs. These Permian–Triassic (P‐T) sediments were deposited in a shallow marine homoclinal ramp. The PTB in this hydrocarbon field is represented by a reworked coarse‐grained intraclastic/bioclastic grainstone facies deposited during a marine transgression. Prolonged subaerial exposure in the P‐T transition caused hypersaline and meteoric diagenesis, including extensive cementation, dolomitization and some dissolution, influencing reservoir characteristics of bordering units. Both δ18O and δ13C values in this succession mirror worldwide excursions typical of other P‐T sections, with some variations due to diagenetic alterations. A pronounced decline in 87Sr/86Sr values, reflective of global seawater geochemistry for most of the Permian is evident in our data. Reservoir quality declines through the late Permian, as a result of facies change and diagenesis. The Late Permian is succeeded by a Triassic transgressive facies and decline in reservoir quality. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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This study challenges the use of three nature‐inspired algorithms as learning frameworks of the adaptive‐neuro‐fuzzy inference system (ANFIS) machine learning model for short‐term modeling of dissolved oxygen (DO) concentrations. Particle swarm optimization (PSO), butterfly optimization algorithm (BOA), and biogeography‐based optimization (BBO) are employed for developing predictive ANFIS models using seasonal 15 min data collected from the Rock Creek River in Washington, DC. Four independent variables are used as model inputs including water temperature (T), river discharge (Q), specific conductance (SC), and pH. The Mallow's Cp and R2 parameters are used for choosing the best input parameters for the models. The models are assessed by several statistics such as the coefficient of determination (R2), root‐mean‐square error (RMSE), Nash–Sutcliffe efficiency, mean absolute error, and the percent bias. The results indicate that the performance of all‐nature‐inspired algorithms is close to each other. However, based on the calculated RMSE, they enhance the accuracy of standard ANFIS in the spring, summer, fall, and winter around 13.79%, 15.94%, 6.25%, and 12.74%, respectively. Overall, the ANFIS‐PSO and ANFIS‐BOA provide slightly better results than the other ANFIS models. 相似文献
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