Application and Verification of a three-dimensional Hydrodynamic Model to Hamilton Harbour, Canada

A multi-layered three-dimensional hydrodynamic model has been developed to provide flow fields and water level changes in Hamilton Harbour. The field data collected in Hamilton Harbour during 1990 & 1991 field seasons was used for model verification. The simulated currents were compared with current meter data. Results from the trajectory model are in good agreement with the drogue experimental data. A quantitative criterion to evaluate the trajectory comparison was established with the help of the trajectory model using the random-walk approach. By using the water level changes in the Burlington Ship Canal, the model predictions were validated with the measurements at three water level stations in the Harbour. These comparisons demonstrate that the models can simulate the major features of the water current and level changes in Hamilton Harbour.

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Numerical Modelling of Circulation in Lake Sperillen, Norway

Hydrology Research ◽

10.2166/nh.2000.0005 ◽

2000 ◽

Vol 31 (1) ◽

pp. 57-72 ◽

Cited By ~ 5

Author(s):

N. R. B. Olsen ◽

D. K. Lysne

Keyword(s):

Numerical Model ◽

Stokes Equations ◽

Three Dimensional ◽

Field Measurements ◽

Navier Stokes ◽

Water Current ◽

Simple Method ◽

Vertical Temperature Profile ◽

Navier Stokes Equations ◽

Good Agreement

A three-dimensional numerical model was used to model water circulation and spatial variation of temperature in Lake Sperillen in Norway. A winter situation was simulated, with thermal stratification and ice cover. The numerical model solved the Navier-Stokes equations on a 3D unstructured non-orthogonal grid with hexahedral cells. The SIMPLE method was used for the pressure coupling and the k-ε model was used to model turbulence, with a modification for density stratification due to the vertical temperature profile. The results were compared with field measurements of the temperature in the lake, indicating the location of the water current. Reasonably good agreement was found.

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Inundation of the Ziltendorfer Lowland during the Odra flood 1997 and its impact on the main river – a numerical study

Journal of Hydroinformatics ◽

10.2166/hydro.2006.011b ◽

2006 ◽

Vol 8 (3) ◽

pp. 181-192 ◽

Cited By ~ 2

Author(s):

Hilmar Messal ◽

Heinz-Theo Mengelkamp

Keyword(s):

Water Level ◽

Hydrodynamic Model ◽

Numerical Study ◽

Complex Flow ◽

Dam Breach ◽

Main River ◽

Flooding Event ◽

Odra River ◽

Flooding Period ◽

Good Agreement

The inundation of the Ziltendorfer Lowland, a farmland polder with some villages, during the major flooding event in July 1997 in the Odra watershed, is simulated with the two-dimensional hydrodynamic model TRIM2D. Inflow and outflow through three consecutive embankment breaches make up a complex flow regime which governs the inundation and the water level in the Odra river. With reasonable assumptions for the dam breach genesis the inundation and depletion are simulated in good agreement with the observed flooding front positions. Simulations with a one-dimensional hydrodynamic model for “dam breach” and “no dam breach” scenarios confirm analytical considerations of an increase of the water level downstream in the main river for the “dam breach” scenario because of an outflow from the Ziltendorfer Lowland into the Odra river during the peak flooding period.

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Predicting the Water Level Fluctuation in an Alpine Lake Using Physically Based, Artificial Neural Network, and Time Series Forecasting Models

Mathematical Problems in Engineering ◽

10.1155/2015/708204 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 16

Author(s):

Chih-Chieh Young ◽

Wen-Cheng Liu ◽

Wan-Lin Hsieh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Water Level ◽

Hydrodynamic Model ◽

Three Dimensional ◽

Water Level Fluctuation ◽

Level Fluctuation ◽

Ann Model ◽

Physically Based ◽

Armax Model

Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL) in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN) model (back propagation neural network, BPNN), a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX) model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations) was collected for model calibration (training) and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation) were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.

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A Free Energy Perturbation Approach to Estimate the Intrinsic Solubilities of Drug-like Small Molecules

10.26434/chemrxiv.10263077.v1 ◽

2019 ◽

Author(s):

Sayan Mondal ◽

Gary Tresadern ◽

Jeremy Greenwood ◽

Byungchan Kim ◽

Joe Kaus ◽

...

Keyword(s):

Solid State ◽

Small Molecules ◽

Three Dimensional ◽

Aqueous Solubility ◽

Computational Approach ◽

Free Energy Perturbation ◽

Perturbation Approach ◽

Energy Perturbation ◽

State Form ◽

Good Agreement

<p>Optimizing the solubility of small molecules is important in a wide variety of contexts, including in drug discovery where the optimization of aqueous solubility is often crucial to achieve oral bioavailability. In such a context, solubility optimization cannot be successfully pursued by indiscriminate increases in polarity, which would likely reduce permeability and potency. Moreover, increasing polarity may not even improve solubility itself in many cases, if it stabilizes the solid-state form. Here we present a novel physics-based approach to predict the solubility of small molecules, that takes into account three-dimensional solid-state characteristics in addition to polarity. The calculated solubilities are in good agreement with experimental solubilities taken both from the literature as well as from several active pharmaceutical discovery projects. This computational approach enables strategies to optimize solubility by disrupting the three-dimensional solid-state packing of novel chemical matter, illustrated here for an active medicinal chemistry campaign.</p>

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