Tsunami generation by paddle motion and its interaction with a beach: Lagrangian modelling and experiment

A 2D Lagrangian numerical wave model is presented and validated against a set of physical wave-flume experiments on interaction of tsunami waves with a sloping beach. An iterative methodology is proposed and applied for experimental generation of tsunami-like waves using a piston-type wavemaker with spectral control. Three distinct types of wave interaction with the beach are observed with forming of plunging or collapsing breaking waves. The Lagrangian model demonstrates good agreement with experiments. It proves to be efficient in modelling both wave propagation along the flume and initial stages of strongly non-linear wave interaction with a beach involving plunging breaking. Predictions of wave runup are in agreement with both experimental results and the theoretical runup law

Foreword to the special section on the Spring Conference on Computer Graphics 2015 (SCCG'2015)

[Excerpt] It is our pleasure to present this special section of Computers & Graphics (C&G), featuring the selected best papers presented at the 31st Spring Conference on Computer Graphics 2015 (www. sccg.sk), which was held April 22–24, 2015 in Smolenice, Slovakia. The venue is probably the oldest regular annual meeting of computer graphics in Central Europe, covering all relevant innovative ideas in computer graphics, image processing and their applications. The philosophy of SCCG is to bring together top experts and young researchers in CG in order to support a good and sustained communication channel for East–West European exchange of prospective ideas. [...]info:eu-repo/semantics/publishedVersio

Statistical characterization of spatio-temporal sediment dynamics in the Venice lagoon

Characterizing the dynamics of suspended sediment is crucial when investigating the long-term evolution of tidal landscapes. Here we apply a widely tested mathematical model which describes the dynamics of cohesive and noncohesive sediments, driven by the combined effect of tidal currents and wind waves, using 1 year long time series of observed water levels and wind data from the Venice lagoon. The spatiotemporal evolution of the computed suspended sediment concentration (SSC) is analyzed on the basis of the \u201cpeak over threshold\u201d theory. Our analysis suggests that events characterized by high SSC can be modeled as a marked Poisson process over most of the lagoon. The interarrival time between two consecutive over threshold events, the intensity of peak excesses, and the duration are found to be exponentially distributed random variables over most of tidal flats. Our study suggests that intensity and duration of over threshold events are temporally correlated, while almost no correlation exists between interarrival times and both durations and intensities. The benthic vegetation colonizing the central southern part of the Venice lagoon is found to exert a crucial role on sediment dynamics: vegetation locally decreases the frequency of significant resuspension events by affecting patiotemporal patterns of SSCs also in adjacent areas. Spatial patterns of the mean interarrival of over threshold SSC events are found to be less heterogeneous than the corresponding patterns of mean interarrivals of over threshold bottom shear stress events because of the role of advection/dispersion processes in mixing suspended sediments within the lagoon. Implications for long-term morphodynamic modeling of tidal environments are discussed

Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data

For oceanographic applications, probabilistic forecasts typically have to deal with i) high-dimensional complex models, and ii) very sparse spatial observations. In search-and-rescue operations at sea, for instance, the short-term predictions of drift trajectories are essential to efficiently define search areas, but in-situ buoy observations provide only very sparse point measurements, while the mission is ongoing. Statistically optimal forecasts, including consistent uncertainty statements, rely on Bayesian methods for data assimilation to make the best out of both the complex mathematical modeling and the sparse spatial data. To identify suitable approaches for data assimilation in this context, we discuss localisation strategies and compare two state-of-the-art ensemble-based methods for applications with spatially sparse observations. The first method is a version of the ensemble-transform Kalman filter, where we tailor a localisation scheme for sparse point data. The second method is the implicit equal-weights particle filter which has recently been tested for related oceanographic applications. First, we study a linear spatio-temporal model for contaminant advection and diffusion, where the analytical Kalman filter provides a reference. Next, we consider a simplified ocean model for sea currents, where we conduct state estimation and predict drift. Insight is gained by comparing ensemble-based methods on a number of skill scores including prediction bias and accuracy, distribution coverage, rank histograms, spatial connectivity and drift trajectory forecasts

High-resolution imaging beneath the Santorini volcano

Volcanoes are surface expressions of much deeper magmatic systems, inaccessible to direct observation. Constraining the geometry and physical properties of these systems, in particular detecting high melt fraction (magma) reservoirs, is key to managing a volcanic hazard and understanding fundamental processes that lead to the formation of continents. Unfortunately, unambiguous evidence of magma reservoirs has not yet been provided due to the limited resolving power of the geophysical methods used so far. Here, a high-resolution imaging technique called full-waveform inversion was applied to study the magmatic system beneath the Santorini volcanic field, one of the most volcanically and seismically active regions of Europe. Quality-controlled inversion of 3d wide-angle, multi-azimuth ocean-bottom seismic data revealed a previously undetected high melt fraction reservoir 3 km beneath the Kolumbo volcano, a centre of microseismic and hydrothermal activity of the field. To enable the above method to handle land data, two major algorithmic improvements were added to the high-performance inversion code. First, to simulate instrument response of land seismometers, a pressure-velocity conversion has been implemented in a way that ensures reciprocity of the discretised 2nd-order acoustic wave equation. Second, the immersed-boundary method, originally developed for computational fluid dynamics, was implemented to simulate the wave-scattering off the irregular topography of the Santorini caldera. These advancements can be readily used to provide a higher-resolution image of the melt reservoir beneath the Santorini caldera already detected by means of travel-time tomography.Open Acces

Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Forecasting of ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using the recently proposed two-stage implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components – including the model, model errors, and particle filter – take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyze the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast

Data-driven deep-learning methods for the accelerated simulation of Eulerian fluid dynamics

Deep-learning (DL) methods for the fast inference of the temporal evolution of fluid-dynamics systems, based on the previous recognition of features underlying large sets of fluid-dynamics data, have been studied. Specifically, models based on convolution neural networks (CNNs) and graph neural networks (GNNs) were proposed and discussed. A U-Net, a popular fully-convolutional architecture, was trained to infer wave dynamics on liquid surfaces surrounded by walls, given as input the system state at previous time-points. A term for penalising the error of the spatial derivatives was added to the loss function, which resulted in a suppression of spurious oscillations and a more accurate location and length of the predicted wavefronts. This model proved to accurately generalise to complex wall geometries not seen during training. As opposed to the image data-structures processed by CNNs, graphs offer higher freedom on how data is organised and processed. This motivated the use of graphs to represent the state of fluid-dynamic systems discretised by unstructured sets of nodes, and GNNs to process such graphs. Graphs have enabled more accurate representations of curvilinear geometries and higher resolution placement exclusively in areas where physics is more challenging to resolve. Two novel GNN architectures were designed for fluid-dynamics inference: the MuS-GNN, a multi-scale GNN, and the REMuS-GNN, a rotation-equivariant multi-scale GNN. Both architectures work by repeatedly passing messages from each node to its nearest nodes in the graph. Additionally, lower-resolutions graphs, with a reduced number of nodes, are defined from the original graph, and messages are also passed from finer to coarser graphs and vice-versa. The low-resolution graphs allowed for efficiently capturing physics encompassing a range of lengthscales. Advection and fluid flow, modelled by the incompressible Navier-Stokes equations, were the two types of problems used to assess the proposed GNNs. Whereas a single-scale GNN was sufficient to achieve high generalisation accuracy in advection simulations, flow simulation highly benefited from an increasing number of low-resolution graphs. The generalisation and long-term accuracy of these simulations were further improved by the REMuS-GNN architecture, which processes the system state independently of the orientation of the coordinate system thanks to a rotation-invariant representation and carefully designed components. To the best of the author’s knowledge, the REMuS-GNN architecture was the first rotation-equivariant and multi-scale GNN. The simulations were accelerated between one (in a CPU) and three (in a GPU) orders of magnitude with respect to a CPU-based numerical solver. Additionally, the parallelisation of multi-scale GNNs resulted in a close-to-linear speedup with the number of CPU cores or GPUs.Open Acces

Lattice Boltzmann modeling for shallow water equations using high performance computing

The aim of this dissertation project is to extend the standard Lattice Boltzmann method (LBM) for shallow water flows in order to deal with three dimensional flow fields. The shallow water and mass transport equations have wide applications in ocean, coastal, and hydraulic engineering, which can benefit from the advantages of the LBM. The LBM has recently become an attractive numerical method to solve various fluid dynamics phenomena; however, it has not been extensively applied to modeling shallow water flow and mass transport. Only a few works can be found on improving the LBM for mass transport in shallow water flows and even fewer on extending it to model three dimensional shallow water flow fields. The application of the LBM to modeling the shallow water and mass transport equations has been limited because it is not clearly understood how the LBM solves the shallow water and mass transport equations. The project first focuses on studying the importance of choosing enhanced collision operators such as the multiple-relaxation-time (MRT) and two-relaxation-time (TRT) over the standard single-relaxation-time (SRT) in LBM. A (MRT) collision operator is chosen for the shallow water equations, while a (TRT) method is used for the advection-dispersion equation. Furthermore, two speed-of-sound techniques are introduced to account for heterogeneous and anisotropic dispersion coefficients. By selecting appropriate equilibrium distribution functions, the standard LBM is extended to solve three-dimensional wind-driven and density-driven circulation by introducing a multi-layer LB model. A MRT-LBM model is used to solve for each layer coupled by the vertical viscosity forcing term. To increase solution stability, an implicit step is suggested to obtain stratified flow velocities. Numerical examples are presented to verify the multi-layer LB model against analytical solutions. The model’s capability of calculating lateral and vertical distributions of the horizontal velocities is demonstrated for wind- and density- driven circulation over non-uniform bathymetry. The parallel performance of the LBM on central processing unit (CPU) based and graphics processing unit (GPU) based high performance computing (HPC) architectures is investigated showing attractive performance in relation to speedup and scalability