Dynamics of Multiphase Flows

Understand multiphase flows using multidisciplinary knowledge in physical principles, modelling theories, and engineering practices. This essential text methodically introduces the important concepts, governing mechanisms, and state-of-the-art theories, using numerous real-world applications, examples, and problems. Covers all major types of multiphase flows, including gas-solid, gas-liquid (sprays or bubbling), liquid-solid, and gas-solid-liquid flows. Introduces the volume-time-averaged transport theorems and associated Lagrangian-trajectory modelling and Eulerian-Eulerian multi-fluid modelling. Explains typical computational techniques, measurement methods and four representative subjects of multiphase flow systems. Suitable as a reference for engineering students, researchers, and practitioners, this text explores and applies fundamental theories to the analysis of system performance using a case-based approach.

A methodology for optimizing modeling configuration in the numerical modeling of oil concentrations in underwater blowouts: a North Sea case study

<p><strong>A METHODOLOGY FOR OPTIMIZING MODELING CONFIGURATION IN THE NUMERICAL MODELING OF OIL CONCENTRATIONS IN UNDERWATER BLOWOUTS: A NORTH SEA CASE STUDY</strong></p><p>Andrés Martínez<sup>a,*</sup>, Ana J. Abascal<sup>a</sup>, Andrés García<sup>a</sup>, Beatriz Pérez-Díaz<sup>a</sup>, Germán Aragón<sup>a</sup>, Raúl Medina<sup>a</sup></p><p><sup>a</sup>IHCantabria - Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Avda. Isabel Torres, 15, 39011 Santander, Spain</p><p><sup>* </sup>Corresponding author: [email protected]</p><p>Underwater oil and gas blowouts are not easy to repair. It may take months before the well is finally capped, releasing large amounts of oil into the marine environment. In addition, persistent oils (crude oil, fuel oil, etc.) break up and dissipate slowly, so they often reach the shore before the cleanup is completed, affecting vasts extension of seas-oceans, just as posing a major threat to marine organisms.</p><p>On account of the above, numerical modeling of underwater blowouts demands great computing power. High-resolution, long-term data bases of wind-ocean currents are needed to be able to properly model the trajectory of the spill at both regional (open sea) and local level (coastline), just as to account for temporal variability. Moreover, a large number of particles, just as a high-resolution grid, are unavoidable in order to ensure accurate modeling of oil concentrations, of utmost importance in risk assessment, so that threshold concentrations can be established (threshold concentrations tell you what level of exposure to a compound could harm marine organisms).</p><p>In this study, an innovative methodology has been accomplished for the purpose of optimizing modeling configuration: number of particles and grid resolution, in the modeling of an underwater blowout, with a view to accurately represent oil concentrations, especially when threshold concentrations are considered. In doing so, statistical analyses (dimensionality reduction and clustering techniques), just as numerical modeling, have been applied.</p><p>It is composed of the following partial steps: (i) classification of i representative clusters of forcing patterns (based on PCA and K-means algorithms) from long-term wind-ocean current hindcast data bases, so that forcing variability in the study area is accounted for; (ii) definition of j modeling scenarios, based on key blowout parameters (oil type, flow rate, etc.) and modeling configuration (number of particles and grid resolution); (iii) Lagrangian trajectory modeling of the combination of the i clusters of forcing patterns and the j modeling scenarios; (iv) sensitivity analysis of the Lagrangian trajectory model output: oil concentrations,  to modeling configuration; (v) finally, as a result, the optimal modeling configuration, given a certain underwater blowout (its key parameters), is provided.</p><p>It has been applied to a hypothetical underwater blowout in the North Sea, one of the world’s most active seas in terms of offshore oil and gas exploration and production. A 5,000 cubic meter per day-flow rate oil spill, flowing from the well over a 15-day period, has been modeled (assuming a 31-day period of subsequent drift for a 46-day modeling). Moreover, threshold concentrations of 0.1, 0.25, 1 and 10 grams per square meter have been applied in the sensitivity analysis. The findings of this study stress the importance of modeling configuration in accurate modeling of oil concentrations, in particular if lower threshold concentrations are considered.</p>

Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean

The detection of finite-time coherent particle sets in Lagrangian trajectory data, using data-clustering techniques, is an active research field at the moment. Yet, the clustering methods mostly employed so far have been based on graph partitioning, which assigns each trajectory to a cluster, i.e. there is no concept of noisy, incoherent trajectories. This is problematic for applications in the ocean, where many small, coherent eddies are present in a large, mostly noisy fluid flow. Here, for the first time in this context, we use the density-based clustering algorithm of OPTICS (ordering points to identify the clustering structure; Ankerst et al., 1999) to detect finite-time coherent particle sets in Lagrangian trajectory data. Different from partition-based clustering methods, derived clustering results contain a concept of noise, such that not every trajectory needs to be part of a cluster. OPTICS also has a major advantage compared to the previously used density-based spatial clustering of applications with noise (DBSCAN) method, as it can detect clusters of varying density. The resulting clusters have an intrinsically hierarchical structure, which allows one to detect coherent trajectory sets at different spatial scales at once. We apply OPTICS directly to Lagrangian trajectory data in the Bickley jet model flow and successfully detect the expected vortices and the jet. The resulting clustering separates the vortices and the jet from background noise, with an imprint of the hierarchical clustering structure of coherent, small-scale vortices in a coherent, large-scale background flow. We then apply our method to a set of virtual trajectories released in the eastern South Atlantic Ocean in an eddying ocean model and successfully detect Agulhas rings. We illustrate the difference between our approach and partition-based k-means clustering using a 2D embedding of the trajectories derived from classical multidimensional scaling. We also show how OPTICS can be applied to the spectral embedding of a trajectory-based network to overcome the problems of k-means spectral clustering in detecting Agulhas rings.

Wind-driven Advection Across Temperature Gradients Supports Phytoplankton Blooms in the Iron-limited Antarctic Polar Front

We demonstrate how the wind-driven Ekman transport enhances the advection and mixing of cells from the colder waters of the Surface Antarctic Waters from the south to the warmer waters of the northern Polar Front (PF) belt. This mechanism provides cells a mean ambient temperature near optimum levels for species-specific and, ultimately, community growth rates high enough to develop blooms under non-light limiting, macronutrients and iron conditions. A Lagrangian trajectory model was constructed for tracking plankton cells as tracers forced by winds and surface currents. We argue that wind-driven Ekman drift of surface currents can carry phytoplankton cells into warmer waters and thus increase their growth rates to potentially generate blooms, even under iron-limiting conditions. Depending on the region along the circumpolar front, increased winds can enhance this process, and further accelerate such temperature-controlled growth.

Ordering of trajectories reveals hierarchical finite-time coherent sets in Lagrangian particle data: detecting Agulhas rings in the South Atlantic Ocean

The detection of finite-time coherent particle sets in Lagrangian trajectory data using data clustering techniques is an active research field at the moment. Yet, the clustering methods mostly employed so far have been based on graph partitioning, which assigns each trajectory to a cluster, i.e. there is no concept of noisy, incoherent trajectories. This is problematic for applications to the ocean, where many small coherent eddies are present in a large fluid domain. In addition, to our knowledge none of the existing methods to detect finite-time coherent sets has an intrinsic notion of coherence hierarchy, i.e. the detection of finite-time coherent sets at different spatial scales. Such coherence hierarchies are present in the ocean, where basin scale coherence coexists with smaller coherent structures such as jets and mesoscale eddies. Here, for the first time in this context, we use the density-based clustering algorithm OPTICS (Ankerst et al., 1999) to detect finite-time coherent particle sets in Lagrangian trajectory data. Different from partition based clustering methods, OPTICS does not require to fix the number of clusters beforehand. Derived clustering results contain a concept of noise, such that not every trajectory needs to be part of a cluster. OPTICS also has a major advantage compared to the previously used DBSCAN method, as it can detect clusters of varying density. Further, clusters can also be detected based on density changes instead of absolute density. Finally, OPTICS based clusters have an intrinsically hierarchical structure, which allows to detect coherent trajectory sets at different spatial scales at once. We apply OPTICS directly to Lagrangian trajectory data in the Bickley jet model flow and successfully detect the expected vortices and the jet. The resulting clustering separates the vortices and the jet from background noise, with an imprint of the hierarchical clustering structure of coherent, small scale vortices in a coherent, large-scale, background flow. We then apply our method to a set of virtual trajectories released in the eastern South Atlantic Ocean in an eddying ocean model and successfully detect Agulhas rings. At larger scale, our method also separates the eastward and westward moving parts of the subtropical gyre. We illustrate the difference between our approach and partition based k-Means clustering using a 2-dimensional embedding of the trajectories derived from classical multidimensional scaling. We also show how OPTICS can be applied to the spectral embedding of a trajectory based network to overcome the problems of k-Means spectral clustering in detecting Agulhas rings.

Lagrangian Trajectory Simulation of Floating Objects in the State of São Paulo Coastal Region

In this work a computational model is presented in order to simulate the trajectory of objects near the Ilhabela island region, in São Paulo coastline, Brazil. The MOHID platform (MOdelagem HIDrodinâmica - Hydrodynamics Modelling) was used with the downscalling technique used to obtain local hydrodynamic currents at local scale. Two different applications are tested, the first is the hypothetical trajectory of a dead cetacean specimen drifting that could have happened in fact if it was not arrested to a more adequate spot near Ilhabela island in November, 2017, and the second is the simulation for the drift of floating objects that resulted from an accidental release of containers at the Port of Santos in August, 2017. The use of these technologies has great potential for researchers interested to simulate different drift occurrences near the Brazilian costal region.