1,721,007 research outputs found
Eulerian spectrum of finite-time Lyapunov exponents in compound channels
No full textFluid flows reveal a wealth of structures, such as vortices and barriers to transport. Usually, either an Eulerian or a Lagrangian frame of reference is employed in order to detect such features of the flow. However, the two frameworks detect structures that have different properties. Indeed, common Eulerian diagnostics (Hua-Klein and Okubo-Weiss criterion) employed in order to detect vortices do not always agree with Lagrangian diagnostics such as finite-time Lyapunov exponents. Besides, the former are Galilean-invariant whereas the latter is objective. However, both the Lagrangian and the Eulerian approaches to coherent structure detection must show some links under any inertial-frame. Compound channels flows have been accurately studied in the past, both from a Lagrangian and an Eulerian point of view. The features detected do not superimpose: Eulerian vortices do not coincide with barriers to transport. The missing link between the two approaches is here recovered thanks to a spectral analysis
Turbulent characteristics and Lagrangian Coherent Structures (LCS) in uniform Compound Channels
No full textSea waves transport of inertial micro-plastics: Mathematical model and applications
No full textPlastic pollution in seas and oceans has recently been recognized as one of the most impacting threats for the environment, and the increasing number of scientific studies proves that this is an issue of primary concern. Being able to predict plastic paths and concentrations within the sea is therefore fundamental to properly face this challenge. In the present work, we evaluated the effects of sea waves on inertial micro-plastics dynamics. We hypothesized a stationary input number of particles in a given control volume below the sea surface, solving their trajectories and distributions under a second-order regular wave. We developed an exhaustive group of datasets, spanning the most plausible values for particles densities and diameters and wave characteristics, with a specific focus on the Mediterranean Sea. Results show how the particles inertia significantly affects the total transport of such debris by waves
Settling velocity of microplastics exposed to wave action
No full textMicroplastic (MP) debris is recognized to be one of the most serious threats to marine environments. They are found in all seas and oceanic basins worldwide, even in the most remote areas. This is further proof that the transport of MPs is very efficient. In the present study, we focus our attention on MPs’ transport owing to the Stokes drift generated by sea waves. Recent studies have shown that the interaction between heavy particles and Stokes drift leads to unexpected phenomena mostly related to inertial effects. We perform a series of laboratory experiments with the aim to directly measure MPs’ trajectories under different wave conditions. The main objective is to quantify the inertial effect and, ultimately, suggest a new analytical formulation for the net settling velocity. The latter formula might be implemented in a larger scale transport model in order to account for inertial effects in a simplified approach
Fluidics of Single and Double Blade Guillotine Vitrectomy Probes in Balanced Salt Solution and Artificial Vitreous
No full textOn the selection of time-varying scenarios of wind and ocean waves: Methodologies and applications in the North Tyrrhenian Sea
No full textThis paper analyses a methodology to identify sub-series of waves parameters and wind speed able to explain the overall variability of the input dataset. To this end, the K-means clustering technique is applied to a 40-year long time series of hindcast data off the Genoa coastline (NW Italy). K-means aims to group the data in a reduced number of clusters, represented by as many modes of variability (or “model scenarios”). This work reviews and discusses a methodology to select time-varying model scenarios and assess the performances of K-means according to two indexes and increasing number of clusters. These indexes are used to compute the number of clusters best suited for the application at hand, testing different conditions as concerns the variables involved in the analysis and their temporal resolution. Results show that the indexes may not be consistent with each other, and that the number of scenarios to be reasonably employed strongly depends on how data are initially assembled. Finally, some of the model scenarios selected in front of Genoa are analysed and discussed in the framework of the local wind-wave climatology
Unraveling the Non-Homogeneous Dispersion Processes in Ocean and Coastal Circulations Using a Clustering Approach
No full textDispersion processes in environmental flows have been traditionally studied under the strong assumption of homogeneous, isotropic and stationary turbulence. To overcome this limitation, we propose a new approach that combines autocorrelation analysis of simulated Lagrangian trajectories together with unsupervised clustering. To test the approach, we consider several dynamic scenarios around a coastal gulf, subject to different forcing, in order to compare our method with other approaches. Lagrangian trajectories forced by the varying coastal circulation exhibited different behaviors, looping and non-looping paths, and produced a variety of Lagrangian autocorrelation functions. Our approach proves to be able to reveal spatio-temporal variations in ocean dispersion processes without any a priori knowledge of the character of the trajectories. Clusters based on the autocorrelation functions are associated to different inhomogeneous dispersion processes. Finally, we propose a new stochastic model capable of predicting the different forms of autocorrelations
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