Comparison of Different Turbulence Models on Sediment Transport in the Open Channel Test Case
EFDC+ Explorer Modeling System
The complete modeling package for hydrodynamics, sediment transport, chemical fate and transport, and eutrophication.
Sediment transport in open channel flows is influenced by turbulence, which governs the distribution of momentum and sediment throughout the water column. EFDC 12 was employed to simulate the effects of different turbulence models on sediment transport. Starting from EEMS version 12, users have the flexibility to construct models using turbulence closures sourced from GOTM (General Ocean Turbulence Model) for vertical turbulence, in addition to the original EFDC+ options. This flexibility enables users to choose turbulence closure models (k-ε, k-ω, MY25) within a consistent numerical framework. This blog investigates the performance of three turbulence closure models—k-ε, k-ω, and MY2.5—using a three-dimensional numerical simulation.
Mass Balance Calculation for Waterbodies using EEMS
A waterbody typically has multiple tributaries contributing flow and other constituents to the domain. Modeling the waterbody requires the estimation of mass balance of all the constituents that enter and exit the modeling domain. Mass balance is an essential aspect of weight-of-evidence based model calibration.
Once you develop a model using EEMS, you can use the EE interface to quickly estimate the mass balance. This blog describes the process of doing this estimation for the Caloosahatchee Estuary model as an example.
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Lake Washington Real-time Temperature Model
DSI has developed the Lake Washington Real-time Temperature Model as an example of a real-time data and modeling facility to serve the scientific community in Seattle, Washington, US. The major goals of the model are to:
The model uses measured atmospheric temperature such as wind speed, wind direction and air temperature. It also uses measured inflows and water temperatures. Outflows from the Ballard Locks are based on water balance calculations in the real-time model.
In the News: Identification of pollutant sources and evaluation of water quality improvement alternatives of a large river
The performance of water quality models depends on both data from the external inputs and the internal processes of a water body. Limited field data can often be the major cause of errors in water quality prediction when modeling, especially in large environments. The aim of this study by Professor Dongil Seo and colleagues was to improve the prediction accuracy of water quality in a large lake using the combined application of an artificial neural network (ANN) method and a numerical model.