Deep Water Pathways In The North Pacific Ocean Revealed By Lagrangian Particle Tracking

Lagrangian particle tracking experiments are conducted to investigate the pathways of deep water in the North Pacific Ocean. The flow field is taken from a state-of-the-art deep circulation simulation. An unprecedented number of particles are tracked to quantify the volume transport and residence time. Half of the North Pacific deep water returns to the Southern Ocean, and its principal pathway is along the western boundary current in the Southwest Pacific Basin in the deep layer. About 30 % is exported to the Indian Ocean after upwelling to the shallow layer in the western North Pacific Ocean. The rest is transported to the Arctic Ocean through the Bering Strait or evaporates within the Pacific Ocean. Upwelling of deep water is confined in the western North Pacific Ocean owing to the strong vertical mixing. The mean residence time of deep water in the North Pacific Ocean is estimated to be several hundred years, which is considerably shorter than the conventional understandings of the deep Pacific Ocean circulation.

CFD modelling of the condensation, evaporation, coagulation and crystallization processes in the cooling tower emissions

The aim of the paper is to develop the CFD model for the environmental impact assessment of the cooling tower. The methods applied for this problem are the single-phase turbulent multispecies flow modelling with the DPM Lagrangian particle tracking. The simulations have been carried out in the steady state SIMPLE solver using the ANSYS Fluent software. User Defined Functions have been defined to enhance the accuracy and versatility of the modelling approach in terms of turbulence, fog formation, evaporation, coagulation and crystallization modelling. The Chalk Point cooling tower experiment, laboratory tests with freezing droplets and analytical correlations are used to verify the customized parts of the new CFD model. The arbitrary small-town geometry is used to demonstrate the simulation capabilities of the fog and drift deposition as well as the temperature and relative humidity values near ground and buildings. The results indicate that the new CFD model is able to predict the cooling tower plume parameters, icing and salt contamination risks as well as drift deposition.

EXCHANGE CHARACTERISTICS OF AN ANTHROPOGENICALLY MODIFIED LAGOON: AN EULERIAN-LAGRANGIAN MODELING CASE STUDY WITH AN EMPHASIS ON THE NUMBER OF PARTICLES

The transport pathways and exchange characteristics of the Kamil Abdüş Lagoon in Istanbul, Turkey, are simulated using a finite element model with a Lagrangian particle tracking module. The lagoon is in the process of being reconfigured. The simulations are performed using a draft configuration. The effect of winds and the number of particles on the e-folding time is simulated. Results show that the lagoon is strongly dominated by winds with a correlation coefficient of 0.897 between the wind and residual current magnitudes. The lagoon e-folds in 9.1 days under realistic winds and in 14.3 days when there is no wind with confidence levels of 5%. The Lagrangian study uses six simulations with particle numbers ranging between 65073 and 2730486. A methodology based on confidence levels is proposed. It is observed that approximately 784 000 particles are necessary to obtain 5% level of confidence. With a problematic history and new planning options, the lagoon has a potential to be used as an example setting, all-field study ground for anthropogenically engineered coastal ecosystems.

Spatially and temporally resolved measurements of turbulent Rayleigh-Bénard convection by Lagrangian particle tracking of long-lived helium-filled soap bubbles

We present spatially and temporally resolved velocity and acceleration measurements of turbulent RayleighBénard convection spanning the whole volume (~ 1 m³) of a cylindrical sample with aspect ratio one. With the "Shake-The-Box" (STB) Lagrangian particle tracking (LPT) algorithm, we were able to instantaneously track up to 560,000 particles, corresponding to mean inter-particle distances down to 6 - 8 Kolmogorov lengths. We used the data assimilation scheme ‘FlowFit’, which involves continuity and Navier-Stokesconstraints, to map the scattered velocity and acceleration data on cubic grids, herewith recovering the smallest flow scales. Lagrangian and Eulerian visualizations reveal the dynamics of the large-scale circulation and its interplay with small scale structures, such as thermal plumes and turbulent background fluctuations. As a result, the complex time-dependent behavior of the LSC comprising azimuthal rotations, torsional oscillation and sloshing can be extracted from the data. Further, we found more seldom dynamic events, such as spontaneous reorientations of the LSC in the data from long-term measurements.

TrackFit: Uncertainty Quantification, Optimal Filtering and Interpolation of Tracks for Time-Resolved Lagrangian Particle Tracking

Advanced Lagrangian Particle Tracking methods (such as the STB algorithm (Schanz et al. 2016)) are a very useful tool for uncovering properties of flow. As a measurement technique, the results of such methods are perturbed by different sources of errors and noise. This work addresses the problem of optimal filtering of particle tracks as well as estimating uncertainties of derived quantities such as location, velocity and acceleration of observed particles. The behavior and performance of this new filtering method (“TrackFit”), first introduced at Gesemann et al. (2016) is analyzed and compared to the Savitzky–Golay filter (Savitzky and Golay (1964)) which is commonly used for these purposes. The optimal choice of parameters of this filtering method as well as the uncertainty quantification of the reconstructed tracks can be extracted from a spectral analysis of the recorded raw particle tracking data. This is in contrast to a Savitzky–Golay filter where the choice of parameters might often be driven by experience and gut feeling. Estimating the power spectral density (PSD) of the particle trajectory signals for the purpose of optimal filtering parameter selection represents a challenge due to possibly short trajectory signals. In the following work we will present a method for PSD estimation that is applicable in this scenario. In addition, we show that regardless of the choice of Savitzky–Golay filter parameters, the resulting filter will not approximate the ideal noise reduction filter well unlike the “TrackFit” described in this work.