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Results for “Computational Water, Energy, and Environmental Engineering”

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4 recordsLinked to original sources
USGS2014

Utilizing dimensional analysis with observed data to determine the significance of hydrodynamic solutions in coastal hydrology

In this paper, the authors present an analysis of the magnitude of the temporal and spatial acceleration (inertial) terms in the surface-water flow equations and determine the conditions under which these inertial terms have sufficient magnitude to be required in the computations. Data from two South Florida field sites are examined and the relative magnitudes of temporal acceleration, spatial acceleration, and the gravity and friction terms are compared. Parameters are derived by using dimensionless numbers and applied to quantify the significance of the hydrodynamic effects. The time series of the ratio of the inertial and gravity terms from field sites are presented and compared with both a simplified indicator parameter and a more complex parameter called the Hydrodynamic Significance Number (HSN). Two test-case models were developed by using the SWIFT2D hydrodynamic simulator to examine flow behavior with and without the inertial terms and compute the HSN. The first model represented one of the previously-mentioned field sites during gate operations of a structure-managed coastal canal. The second model was a synthetic test case illustrating the drainage of water down a sloped surface from an initial stage while under constant flow. The analyses indicate that the times of substantial hydrodynamic effects are sporadic but significant. The simplified indicator parameter correlates much better with the hydrodynamic effect magnitude for a constant width channel such as Miami Canal than at the non-uniform North River. Higher HSN values indicate flow situations where the inertial terms are large and need to be taken into account.

Florida↗
USGS2003

Stage-discharge relations for selected culverts and bridges in the Big Lost River flood plain at the Idaho National Engineering and Environmental Laboratory, Idaho

Information is needed by the U.S. Department of Energy at the Idaho National Engineering and Environmental Laboratory to determine the extent and severity of potential flooding at facilities along the Big Lost River. Two computer programs—the Culvert Analysis Program (CAP) and the HECRAS model—were used to define stage-discharge relations for 31 culverts and 2 bridge sites in a 10- mile reach of the river. These relations can be used to improve surface-water-flow models to evaluate potential flooding. Relations between headwater, tailwater, and discharge through each structure were unique. Discharge through the culverts as computed by the CAP ranged from about 0 cubic feet per second to as much discharge as could be conveyed, and tailwater elevations ranged from about 0 to 30 feet above the outlet elevation. Discharge through the bridges, as computed by the HEC-RAS model, ranged from nearly 0 to 7,000 cubic feet per second, and tailwater elevations ranged from nearly 0 to 30 feet above the streambed on the downstream cross section of each bridge. Stage-discharge relations provided in lookup tables in this report can be incorporated into numerical surface-water-flow models to simulate the effects of hydraulic structures on flood flows. One limitation of the CAP and HEC-RAS models is that changes in flow conditions, such as obstruction by sediment and debris, are not simulated. If flow through a hydraulic structure is obstructed by sediment or debris, then model-simulated discharges through the structure might be greater than would be experienced under actual conditions.

Idaho↗
USGS2022

Physics-guided recurrent neural networks for predicting lake water temperature

This chapter presents a physics-guided recurrent neural network model (PGRNN) for predicting water temperature in lake systems. Standard machine learning (ML) methods, especially deep learning models, often require a large amount of labeled training samples, which are often not available in scientific problems due to the substantial human labor and material costs associated with data collection. ML models have found tremendous success in several commercial applications, e.g., computer vision and natural language processing. The chapter presents PGRNN as a general framework for modeling physical processes in engineering and environmental systems. The proposed PGRNN explicitly incorporates physical laws such as energy conservation or mass conservation. In particular, researchers started pursing this direction by using residual modeling, where an ML model is learned to predict the errors, or residuals, made by a physics-based model. Advanced ML models, especially deep learning models, often require a large amount of training data for tuning model parameters.

Book chapter↗
USGS1998

Chlorofluorocarbons, sulfur hexafluoride, and dissolved permanent gases in ground water from selected sites in and near the Idaho National Engineering and Environmental Laboratory, Idaho, 1994-97

From July 1994 through May 1997, the U.S. Geological Survey in cooperation with the Department of Energy, sampled 86 wells completed in the Snake River Plain aquifer at and near the Idaho N ationa1 Engineering and Environmental Laboratory (INEEL). The wells were sampled for a variety of constituents including one- and two-carbon halocarbons. Concentrations of dichlorodifluoromethane (CFC-12), trichlorofluoromethane (CFC-11) and trichlorotrifluororoethane (CFC-113) were determined. The samples for halocarbon analysis were collected in 62-milliliter flame sealed borosilicate glass ampoules in the field. The data will be used to evaluate the ages of ground waters at INEEL. The ages of the ground water will be used to determine recharge rates, residence time, and travel time of water in the Snake River Plain aquifer in and near INEEL. The chromatograms of 139 ground waters are presented showing a large number of halomethanes, haloethanes, and haloethenes present in the ground waters underlying the INEEL. The chromatograms can be used to qualitatively evaluate a large number of contaminants at parts per trillion to parts per billion concentrations. The data can be used to study temporal and spatial distribution of contaminants in the Snake River Plain aquifer. Representative compressed chromatograms for all ground waters sampled in this study are available on two 3.5-inch high density computer disks. The data and the program required to decompress the data can be obtained from the U.S. Geological Survey office at Idaho Falls, Idaho. Sulfur hexafluoride (SF6) concentrations were measured in selected wells to determine the feasibility of using this environmental tracer as an age dating tool of ground water. Concentrations of dissolved nitrogen, argon, carbon dioxide, oxygen, and methane were measured in 79 ground waters. Concentrations of dissolved permanent gases are tabulated and will be used to evaluate the temperature of recharge of ground water in and near the INEEL.

Idaho↗