EPA is developing a model called the EPA Research Object-Oriented Oil Spill Model (ERO 3S) and associated databases to simulate the impacts of dispersants on oil slicks. Because there are features of oil slicks that align naturally with major concepts of object-oriented programming, this approach was chosen to implement the model. The two most obvious of these are the splitting of slicks into patches and the dispersal of oil into droplets. These have aspects of inheritance and polymorphism as individual slicks and droplets share some common behaviors (polymorphism) and share composition at the time of separation (inheritance). Other aspects of the object-oriented approach give the ability of the model to specify components of the graphical user interface and solution technique. This allows the computer code to contain models based upon varying conceptualization and/or suites of test problems. Each of these can have its own interface and selection of numerical solver that are generated automatically. When selected, each model can "tell" the interface what inputs are needed from the user, what numerical technique to use, and what outputs to display. The main model within ERO 3 S treats spreading of oil as a function of density, viscosity, interfacial tension, wind and current speeds. By imposing the implicit function theorem on the spreading equation, each of these parameters of the oil slick can vary with time. Thus change in physical properties driven by compositional change can be incorporated into the model. Compositional change is driven by primarily volatilization, but also dissolution. The data that drives the simulations is taken from four sources: chemical properties from the EPA/University of Georgia chemical property estimator called SPARC (SPARC-Performs Automated Reasoning in Chemistry), historical climate data from NOAA buoy data, a database of oil compositions and physical properties developed by Environment Canada, and a set of data on the effects of dispersants on oil slicks developed at the University of Cincinnati. The dispersant data were measured using a newly developed dispersant effectiveness testing protocol. This empirical data set includes the effects of oil composition, dispersant type, weathering, wave energy, temperature, and salinity. These properties are then matched to simulated conditions within the model. Temperature variation, for example, is treated by choosing a sequence of diurnal variations in temperature from the buoy database, generating temperature-dependent Henry's constants and solubilities from SPARC, and using the empirical dispersant data set to approximate dispersal of the oil slick. Given the time since spilling the oil, the simulated amount of weathering and the salinity, the transfer of oil into the water column due the dispersal of the oil is determined by the model. Simulation results with site-specific data from a tidal marsh in New York are presented as illustrative of planning exercises on dispersant usage.
|Original language||English (US)|
|Number of pages||1|
|Journal||American Chemical Society, Division of Petroleum Chemistry, Preprints|
|State||Published - Feb 1 2003|
All Science Journal Classification (ASJC) codes
- Fuel Technology