50
measurements in the far-field plume or intrusion layer but less for determination of
the initial DSD next to the discharge site due to the larger drop diameters and high
oil concentration that are to be expected there.
As an alternative, video imaging systems can be used for droplet size measurements. A promising approach by Davies et al. (2017) uses a holographic camera
system to size particles in sample volume with 3–50 mm path length. The large path
length and adjustable magnification enable sizing in the range of 28 μm to several
millimetres. Based on backlighted images of small sample volumes, oil concentration is again a limiting factor in this approach as images should ideally contain no
overlapping particles in the view plane to enable automated image analysis and sizing. In addition, a narrow path length and small sample volume poses the risk of
under-sampling large particles (Davies et al. 2017).
At the cost of generating images that might not be automatically evaluable, endoscopic systems with incident lighting can handle very high oil concentrations over a
large diameter range in the order of magnitude from 10
1
to 10
3
μm (Maaß et al.
2011; Malone et al. 2018).
4.2.5 Critical Review of Datasets
The confident translation of laboratory or pilot-scale experiments to the field
remains an outstanding research objective. Multiple authors have proposed correlations with the claim of accurately representing the field scale, yet in all cases the
empirical or semiempirical correlations employed require extrapolation.
Fundamentally, the scientific method imposes an upper limit on the confidence of
any extrapolation, particularly in the case it includes any empirical contribution.
While several scaling quantities may be considered to unite laboratory and pilotscale data, including the Reynolds and Weber numbers, it should be recognized that
these quantities are fundamentally approximations that balance contributions in
simple turbulent systems. To be clear, the Weber and Reynolds numbers are not
defined for a turbulent plume and are not defined for a flow system containing dispersed phases (oil, gas and potentially hydrate in this case). As a consequence, the
attempts to unify the datasets reported in Table 4.1 within a single correlation have
failed to date. One attempt has shown limited success – as discussed in Sect. 4.3.3 –
where turbulence is evaluated at a more fundamental level than the Weber and
Reynolds numbers provide.
4.3 Modelling Approaches
Based on the experimental and field data presented in the previous section and
Table 4.1, different models were developed to predict the initial droplet size distribution of an accidental subsea oil discharge. These models can be divided into two
K. Malone et al.
measurements in the far-field plume or intrusion layer but less for determination of
the initial DSD next to the discharge site due to the larger drop diameters and high
oil concentration that are to be expected there.
As an alternative, video imaging systems can be used for droplet size measurements. A promising approach by Davies et al. (2017) uses a holographic camera
system to size particles in sample volume with 3–50 mm path length. The large path
length and adjustable magnification enable sizing in the range of 28 μm to several
millimetres. Based on backlighted images of small sample volumes, oil concentration is again a limiting factor in this approach as images should ideally contain no
overlapping particles in the view plane to enable automated image analysis and sizing. In addition, a narrow path length and small sample volume poses the risk of
under-sampling large particles (Davies et al. 2017).
At the cost of generating images that might not be automatically evaluable, endoscopic systems with incident lighting can handle very high oil concentrations over a
large diameter range in the order of magnitude from 10
1
to 10
3
μm (Maaß et al.
2011; Malone et al. 2018).
4.2.5 Critical Review of Datasets
The confident translation of laboratory or pilot-scale experiments to the field
remains an outstanding research objective. Multiple authors have proposed correlations with the claim of accurately representing the field scale, yet in all cases the
empirical or semiempirical correlations employed require extrapolation.
Fundamentally, the scientific method imposes an upper limit on the confidence of
any extrapolation, particularly in the case it includes any empirical contribution.
While several scaling quantities may be considered to unite laboratory and pilotscale data, including the Reynolds and Weber numbers, it should be recognized that
these quantities are fundamentally approximations that balance contributions in
simple turbulent systems. To be clear, the Weber and Reynolds numbers are not
defined for a turbulent plume and are not defined for a flow system containing dispersed phases (oil, gas and potentially hydrate in this case). As a consequence, the
attempts to unify the datasets reported in Table 4.1 within a single correlation have
failed to date. One attempt has shown limited success – as discussed in Sect. 4.3.3 –
where turbulence is evaluated at a more fundamental level than the Weber and
Reynolds numbers provide.
4.3 Modelling Approaches
Based on the experimental and field data presented in the previous section and
Table 4.1, different models were developed to predict the initial droplet size distribution of an accidental subsea oil discharge. These models can be divided into two
K. Malone et al.