Overview of Existing Modeling Platforms. Scott Socolofsky Coastal and Ocean Engineering Texas A&M University Berkeley, California November 27, 2012

Size: px

Start display at page:

Download "Overview of Existing Modeling Platforms. Scott Socolofsky Coastal and Ocean Engineering Texas A&M University Berkeley, California November 27, 2012"

Kristin Beasley
6 years ago
Views:

1 Overview of Existing Modeling Platforms Scott Socolofsky Coastal and Ocean Engineering Texas A&M University Berkeley, California November 27, 2012

2 Purposes of Models Response. Where to go to find the spilled oil How to make decisions about response actions (e.g., to apply subsurface dispersants or not) Planning. Designing mitigation strategies before an accident and understanding the risks of an accident Biological Effects Modeling. To quantify exposure and predict effects of an event, either in real time or forensically. NRDA. Hindcasting and quantifying impacts for an historic event Science. To explore the physics of buoyanct, multi-component, multiphase flows. Usually for the purpose of improving models ability to address the other model purposes.

3 Elements of a Modeling Platform

4 Existing Modeling Platforms GNOME General NOAA Operational Modeling Environment, NOAA OSCAR Oil Spill Contingency and Response, SINTEF SIMAP & OILMAPDEEP Integrated Oil Spill Impact Model System & Deepwater Oil Spill Model, ASA OSIS Oil Spill Information System, BMT-ARGOSS ERO3S EPA Research Object-Oriented Oil Spill, U.S. EPA OSRA Oil Spill Risk Analysis, BOEM CDOG Comprehensive Deepwater Oil and Gas Model, Yapa et al., Clarkson University Other Literature Models (e.g., TAMU, MIT)

5 Initial Conditions Initial Droplet Size Distribution Prescribed Empirical based on correlations with Weber number (Hinze 1955) and modified Weber number (Wang & Calabrese 1986) Population balance models accounting for breakup and coalescence dynamically (e.g., Bandara & Yapa 2011) Zone of Flow Establishment (ZFE) Start plume models with finite width and velocity Froude number conditions (e.g., Wuest et al. 1992)

Plume Models Integral (one-dimensional) approached based on self-similarity and the entrainment hypothesis Two main types: Lagrangian or Eulerian Well-documented in the literature: CDOG Papers by

6 Plume Models Integral (one-dimensional) approached based on self-similarity and the entrainment hypothesis Two main types: Lagrangian or Eulerian Well-documented in the literature: CDOG Papers by Yapa et al. DeepBlow Papers by Johansen et al. MIT/SIMP Papers by Adams et al. and Socolofsky et al. Must account for possibility of separation between bubbles and droplets due to crossflow and/or stratification Track oil from the ZFE to an intrusion or surface expression Must provide initial conditions to Lagrangian particle tracking models

7 Separation by Crossflow Employed in Lagrangian models (DeepBlow and CDOG) Eject bubbles when their trajectory due to their slip velocity is too steep to stay within the plume

8 Separation by Stratification Employed in Eulerian models (MIT, TAMU, lake aeration models) Eject entrained fluid when its momentum flux dies to zero

9 Boundary Conditions Currents, temperature, and salinity profiles Provided by general circulation models (e.g., Gulf of Mexico) SABGOM South Atlantic Bight and Gulf of Mexico Nowcast/ Forecase System (He et al. at NCSU) HYCOM + NCODA Hybrid Coordinate Ocean Model and Navy Coupled Ocean Data Assimilation Model (National Ocean Partnership Program) Other ROMS Regional Ocean Modeling System Usually linked by data assimilation to satellite data to get loop current correct Better predictions as surface (via data assimilation) than at depth Typical grid resolution is of order kilometers Typical temporal resolution is of order seconds (calculated) and hours / days (reported)

Lagrangian Particle Tracking Models Track fluid elements that may contain bubbles, drops, dissolved hydrocarbons Tracking equation includes advection (needs CFD model output) and diffusion

10 Lagrangian Particle Tracking Models Track fluid elements that may contain bubbles, drops, dissolved hydrocarbons Tracking equation includes advection (needs CFD model output) and diffusion (stochastic random walk) Advantage is the subgrid nature of the model that avoids excessive numerical dispersion Can track millions of particles in multiple CFD model domains Challenge to define initial conditions of fluid element and to convert properties along a trajectory to concentration Expensive to compute complex chemistry LTRANS, CMS or built-in routine

11 Fate Modeling Predict rise velocity, dissolution, biodegradation, emulsion formation, hydrates, surface weathering, etc. Include non-ideal effects (pressure) and mixture chemistry Variations of the Ranz-Marshall Equation Mass transfer coefficient depends on hydrodynamics and diffusion coefficient. See e.g., Clift et al. (1979). Solubility depends on non-ideal equation of state. Use industry software such as MultiFlash by InfoChem.

Surface Oil Models Predict evolution of surface and near-surface oil Dilution and mixing of surfacing oil (and gas) Advection by currents (with significant wind forcing) and spreading Evolution

12 Surface Oil Models Predict evolution of surface and near-surface oil Dilution and mixing of surfacing oil (and gas) Advection by currents (with significant wind forcing) and spreading Evolution processes include Evaporation and volatilization Emulsification Wave mixing and effect of dispersant application Effects of burning and clean-up Easier problem to study experimentally (can use large atmospheric tanks) More data available during a spill Therefore, better understood than subsurface oil transport

Exposure and Effects Convert oil presence (gas, liquid and dissolved) into exposure Predict region above a threshold toxicological endpoint Predict dose by integrating concentration over time Relate

13 Exposure and Effects Convert oil presence (gas, liquid and dissolved) into exposure Predict region above a threshold toxicological endpoint Predict dose by integrating concentration over time Relate exposure to effects Track populations of species of interest Predict mortality due to acute toxic effects Predict movement of hydrocarbons through the food chain Track regions of direct contact (usually for birds, mammals, and other wildlife) Usually stochastic and risk based Of high economic (e.g., fisheries) and health (e.g., responders) importance; therefore, key aspect of response (ASA SIMAP Documentation)

14 Mapping Sample output from OSCAR Wirtz, et al. 2007

15 Status of Existing Platforms Initial droplet size prescribed or poorly validated equations Plume model account for gas dissolution, some oil dissolution, arresting by stratification (but not peeling) and crossflow separation Circulation model couple to some general circulation model(s) using various time-steps of model coupling Lagrangian particle tracking track oil, gas, and dissolved hydrocarbons; oil usually degrades in first-order die-off Dissolution wide variability of dissolution models; predictive quality is linked to 1.) validation data and 2.) performance of equation of state Surface Models most validated to good datasets Effects see biological community for details

16 Improvements for Existing Models Initial droplet size ongoing experiments; need for large-scale experimental validation data Plume model include detrainment and subsequent plume stage formation; need data on downdraught plume in crossflow Circulation model ability to link to multiple models Lagrangian particle tracking show strong sensitivity to CFD model spatial and temporal scale; need data on chaotic mixing Dissolution oil solubility is important for biological effects modeling; need reliable and fast equations of state Surface models most models are mature Effects As computing power increases, biological community modeling can expand; currently follows box-model approach

Offshore Oil Exploration: Probabilistic Simulations of Plume Dispersion and Potential Impacts on Beaches

Offshore Oil Exploration: Probabilistic Simulations of Plume Dispersion and Potential Impacts on Beaches Lindino Benedet, Msc, Ph. Oc. Rafael Bonanata, Msc, Ph. Oc. Thomas P. Pierro, Msc, P.E. Thomas Campbell,