Overview of Existing Modeling Platforms

Scott Socolofsky Coastal and Ocean Engineering

Texas A&M University

Berkeley, California November 27, 2012

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.

Elements of a Modeling Platform

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)

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 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

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

Separation by Stratification   Employed in Eulerian models (MIT, TAMU, lake aeration models)

  Eject entrained fluid when its momentum flux dies to zero

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 (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

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 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 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)

Mapping Sample output from OSCAR

Wirtz, et al. 2007

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

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