172
(Le Hénaff et al. 2012) or of oxygen anomalies indicating the presence of deep
intrusions (Paris et al. 2012). Numerical sensitivity experiments of meaningful
parameter ranges are a key tool for estimating the uncertainty associated with the
outcome of each variable and allow the gauging of the relative importance of each
variable in terms of outcome, such as large-scale surface expression (Le Hénaff
et al. 2012), sedimentation (North et al. 2015), and the formation of the deep
plume (Paris et al. 2012).
11.2 Laboratory Experiments and Observational Data
for Numerical Modeling Support
Knowledge of the equilibrium droplet size distribution (DSD) is likely one of the
most important characteristics of the jet plume to understand and to model the farfield oil trajectory and dispersal (Zhao et al. 2017). At present, several concepts and
existing models in the literature discuss the representative DSDs for the DWH blowout conditions (Socolofsky et al. 2011; Boxall et al. 2012; Paris et al. 2012, Adams
et al. 2013; Aman and Paris 2013; Aman et al. 2015; Zhao et al. 2014; Li et al. 2017;
Malone et al. 2018), in particular, the droplet mean diameter (d 50 ) and diameter
range, including the maximum droplet diameter. Most of the concepts seem to agree
that the DSDs expect to follow log-normal or Rosin-Rammler distribution shapes.
Some of the field studies of oil droplet formation under various hydrodynamic conditions and oil types do support the possibility of larger droplets (order of one to
several millimeters, e.g., Gros et al. 2017), although the diving experiments during
the DWH incident were more conservative in the droplet size estimates and reported
maximum droplet diameters near or slightly above 400 m (Davis and Loomis 2014;
Li et al. 2017 and Figs. 11.4 and 11.5 therein). The high-pressure and coldtemperature experiments reported in Aman et al. (2015) are specifically designed to
replicate the deep-sea DWH blowout conditions and provide support for the DSD
choices adapted for the modeling studies presented in this chapter.
11.2.1 Droplet Formation in Deep-Sea Conditions
Fundamentally, the dispersion of one fluid – oil or gas in this context – in a secondary fluid phase invokes a balance between turbulent shear stresses, which act to
rupture the interface and generate smaller dispersed particles and interfacial restorative forces, as illustrated in fundamental studies by Kolmogorov (1949). Hinze
(1955) characterized this balance in terms of the turbulence dissipation rate (TDR),
which is characterized by the rate at which turbulent energy is disseminated through
the cascade of eddies. Hinze (1955) also demonstrated that under simple mixing
conditions, the TDR contribution may be approximated through the use of Weber
number, where the constraints and required assumptions of this approach are
N. Perlin et al.
(Le Hénaff et al. 2012) or of oxygen anomalies indicating the presence of deep
intrusions (Paris et al. 2012). Numerical sensitivity experiments of meaningful
parameter ranges are a key tool for estimating the uncertainty associated with the
outcome of each variable and allow the gauging of the relative importance of each
variable in terms of outcome, such as large-scale surface expression (Le Hénaff
et al. 2012), sedimentation (North et al. 2015), and the formation of the deep
plume (Paris et al. 2012).
11.2 Laboratory Experiments and Observational Data
for Numerical Modeling Support
Knowledge of the equilibrium droplet size distribution (DSD) is likely one of the
most important characteristics of the jet plume to understand and to model the farfield oil trajectory and dispersal (Zhao et al. 2017). At present, several concepts and
existing models in the literature discuss the representative DSDs for the DWH blowout conditions (Socolofsky et al. 2011; Boxall et al. 2012; Paris et al. 2012, Adams
et al. 2013; Aman and Paris 2013; Aman et al. 2015; Zhao et al. 2014; Li et al. 2017;
Malone et al. 2018), in particular, the droplet mean diameter (d 50 ) and diameter
range, including the maximum droplet diameter. Most of the concepts seem to agree
that the DSDs expect to follow log-normal or Rosin-Rammler distribution shapes.
Some of the field studies of oil droplet formation under various hydrodynamic conditions and oil types do support the possibility of larger droplets (order of one to
several millimeters, e.g., Gros et al. 2017), although the diving experiments during
the DWH incident were more conservative in the droplet size estimates and reported
maximum droplet diameters near or slightly above 400 m (Davis and Loomis 2014;
Li et al. 2017 and Figs. 11.4 and 11.5 therein). The high-pressure and coldtemperature experiments reported in Aman et al. (2015) are specifically designed to
replicate the deep-sea DWH blowout conditions and provide support for the DSD
choices adapted for the modeling studies presented in this chapter.
11.2.1 Droplet Formation in Deep-Sea Conditions
Fundamentally, the dispersion of one fluid – oil or gas in this context – in a secondary fluid phase invokes a balance between turbulent shear stresses, which act to
rupture the interface and generate smaller dispersed particles and interfacial restorative forces, as illustrated in fundamental studies by Kolmogorov (1949). Hinze
(1955) characterized this balance in terms of the turbulence dissipation rate (TDR),
which is characterized by the rate at which turbulent energy is disseminated through
the cascade of eddies. Hinze (1955) also demonstrated that under simple mixing
conditions, the TDR contribution may be approximated through the use of Weber
number, where the constraints and required assumptions of this approach are
N. Perlin et al.