A method of computing the gravitational field of an axially symmetric flat galaxy

A method has been developed for computing the gravitational force field of an axially symmetric flat galaxy from its surface mass density. The method is simple to program, fast, and accurate. An inversion formula is derived that allows computation of surface density from rotation curves by use of any method that converts density to force. The method is compared with a method of Clutton-Brock that utilizes Hankel transforms of Laguerre functions.     

Predicting conductance due to upconing using neural networks

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables. In this study, the ANN input predictor variables were initial conductance, total precipitation, mean daily temperature, and total pumping extraction. The ANNs were used to predict salinity (specific conductance) at a single monitoring well located near a high-capacity municipal-supply well over time periods ranging from 30 d to several years. Model accuracy was compared against both measured/interpolated values and predictions were made with linear regression, and in general, excellent prediction accuracy was achieved. For example, although the average percent change of conductance over 90-d periods was 39%, the absolute mean prediction error achieved with the ANN was only 1.1%. The ANNs were also used to conduct a sensitivity analysis that quantified the importance of each of the four predictor variables on final conductance values, providing valuable insights into the dynamics of the system. The results demonstrate that the ANN technology can serve as a powerful and accurate prediction and management tool, minimizing degradation of ground water quality to the extent possible by identifying appropriate pumping policies under variable and/or changing climate conditions.     

A neural network model for predicting aquifer water level elevations

Artificial neural networks (ANNs) were developed for accurately predicting potentiometric surface elevations (monitoring well water level elevations) in a semiconfined glacial sand and gravel aquifer under variable state, pumping extraction, and climate conditions. ANNs "learn" the system behavior of interest by processing representative data patterns through a mathematical structure analogous to the human brain. In this study, the ANNs used the initial water level measurements, production well extractions, and climate conditions to predict the final water level elevations 30 d into the future at two monitoring wells. A sensitivity analysis was conducted with the ANNs that quantified the importance of the various input predictor variables on final water level elevations. Unlike traditional physical-based models, ANNs do not require explicit characterization of the physical system and related physical data. Accordingly, ANN predictions were made on the basis of more easily quantifiable, measured variables, rather than physical model input parameters and conditions. This study demonstrates that ANNs can provide both excellent prediction capability and valuable sensitivity analyses, which can result in more appropriate ground water management strategies.     

Airborne detection of ecosystem responses to an extreme event: Phytoplankton displacement and abundance after hurricane induced flooding in the Pamlico-Albemarle Sound system,North Carolina

Airborne laser-induced fluorescence measurements were used to detect and monitor ecosystem wide changes in the distribution and concentration of chlorophyll biomass and colored dissolved organic matter in the Pamlico-Albemarle Sound system, North Carolina, U.S., following massive flooding caused by a series of three hurricanes in the late summer of 1999. These high-resolution data provided a significantly more detailed representation of the overall changes occurring in the system than could have been achieved by synoptic sampling from any other platform. The response time for the distribution of chlorophyll biomass to resume pre-flood conditions was used as a measure of ecosystem stability. Chlorophyll biomass patterns were reestablished within four mo of the flooding, whereas higher chlorophylla biomass concentrations persisted for approximately 6 mo. The primary trophic level in the Pamlico-Albemarle Sound system returned to equilibrium in less than a year of a major perturbation.     

CLASSIFICATION OF TWENTY-SIX CHONDRITES FROM ROOSEVELT COUNTY,NEW MEXICO

Twenty-six of the fifty-seven stone meteorites listed in Huss (1979) from Roosevelt County, New Mexico, have been classified in the present study. Microprobe analyses indicate 15 H type, 9 L type and 2 LL type chondrites. Based on compositional, textural, and locational comparisons, as many as 10 chondrites may be paired to three distinct falls.     

Numerical simulation of fracture flow with a mixed-hybrid FEM stochastic discrete fracture network model

We introduce a discrete fracture network model of stationary Darcy flow in fractured rocks. We approximate the fractures by a network of planar circle disks, which is generated on the basis of statistical data obtained from field measurements. We then discretize this network into a mesh consisting of triangular elements placed in three-dimensional space. We use geometrical approximations in fracture planes, which allow for a significant simplification of the final triangular meshes. We consider two-dimensional Darcy flow in each fracture. In order to accurately simulate the channeling effect, we assign to each triangle an aperture defining its hydraulic permeability. For the discretization we use the lowest order Raviart-Thomas mixed finite element method. This method gives quite an accurate velocity field, which is computed directly and which satisfies the mass balance on each triangular element. We demonstrate the use of this method on a model problem with a known analytical solution and describe the generation and triangulation of the fracture network and the computation of fracture flow for a particular real situation.