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The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 72 h delivered by the ECMWF EPS. The ensemble consists of 50 ensemble covering the entire globe with a horizontal resolution of 18 km. For verification we use nacelle wind measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts is performed by heteroscedastic Ensemble Model Output Statistics (EMOS) at individual sites. We show a remarkable improvement of ensemble wind forecasts from ECMWF EPS for energy applications. |
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Complex fluids are binary mixtures that have a coexistence between two phases: solid–liquid (suspensions or solutions of macromolecules such as polymers), solid–gas (granular), liquid–gas (foams) or liquid–liquid (emulsions). They exhibit unusual mechanical responses to applied stress or strain due to the geometrical constraints that the phase coexistence imposes. A relevant example of complex fluid is a that of a viscoelastic solution. Viscoelasticity is the property of materials that exhibit both viscous and elastic characteristics when undergoing deformation. A spectacular consequence of viscoelasticity is the drag reduction effect: addition of minute amounts (a few tenths of p.p.m. in weight) of long-chain soluble polymers to water leads to a strong reduction (up to 80%) of the power necessary to maintain a given throughput in a channel. We have investigated this issue in different contexts, ranging from unbounded flow, the so-called Kolmogorov flow, to convective systems with emphasis to the Rayleigh-Taylor (RT) turbulence. The figure on the left refers to such a situation: a viscoelastic fluid (right panel) is subject to RT instability and turbulence and is compared to a similar regime but relative to a Newtonian fluid (left panel). Different mixing properties and drag reduction are observed. |
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Energy harvesting (also known as power harvesting or energy scavenging) is the process by which energy is derived from external sources (e.g. solar power, thermal energy and wind energy), captured, and stored for small, wireless autonomous devices, like those used in wearable electronics and wireless sensor networks. Energy harvesters provide a very small amount of power for low-energy electronics. We proposed an energy harvesting device, based on a wing elastically bounded to a fixed support (see figure). Large amplitude and periodic oscillations can be induced when this system is subject to wind, if a few parameters are carefully set. A linear stability analysis as well as two-dimensional numerical simulations confirms the existence of instability regions in the parameter space. In order to harvest energy by using this system, we considered two different methods: an electromagnetic coupling and and electromechanical coupling via elastomeric capacitors. |
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Plants and animals use plumes, barbs, tails, feathers, hairs and fins to aid locomotion. Many of these appendages are not actively controlled, instead they have to interact passively with the surrounding fluid to generate motion. We use theory, experiments, and numerical simulations to show that an object with a protrusion in a separated flow drifts sideways by exploiting a symmetry-breaking instability similar to the instability of an inverted pendulum. Our model explains why the straight position of an appendage in a fluid flow is unstable and how it stabilizes either to the left or right of the incoming flow direction. It is plausible that organisms with appendages in a separated flow use this newly discovered mechanism for locomotion; examples include the drift of plumed seeds without wind and the passive reorientation of motile animals. |
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Geophysical fluid dynamics is the study of naturally occurring, large-scale flows, on Earth and other planets. It is applied here to the motion of fluids in the ocean and to gases in the atmosphere of Earth. Different strategies are used in this context: i) Direct Numerical Simulations (DNS) to study the role of turbulence in the cloud-microphysics phenomena; ii) Large-Eddy Simulations (LES) to investigate the structure of temperature fluctuations in the Atmospheric Boundary Layer; iii) Reynolds-Averaged Navier-Stokes equations (RANS) to study the spatio-temporal evolution of weather systems in the Earth atmosphere (see figure on the left). We use the Weather Research and Forecasting (WRF) Model for this purpose. It is a next-generation mesoscale numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. Both activities are carried out in our group. |
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Turbulent Transport and Mixing is concerned with the fundamental physics, mathematical modeling, analysis, and computation of the enhanced advection and diffusion of heat, mass, and momentum that often characterizes turbulence. Turbulent mixing is a familiar phenomenon but one which still presents many interesting open questions. For example, how can turbulent mixing of, say, passive scalars be quantitatively characterized? What aspects of turbulent diffusion can be rigorously deduced from the fundamental equations of motion? What are the most effective approaches to reduce and/or close models of turbulent mixing? How can insights from rigorous mathematical studies and experimental investigations aid numerical analysis and simulations? All these questions are addressed in our activity. Specifically, multiple-scale expansions together with Direct Numerical Simulations are exploited to answer the above questions. The focus is the passive transport of particles with and without inertia. Attention to the so-called anomalous diffusion regime is also paid in these contexts. |
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One of the main focuses of modern works on turbulence is on trying to understand the reasons of partial failure of the famous 1941 Kolmogorov's theory. This is the famous "intermittency" problem which received considerable attention by Kolmogorov himself in 1961. A similar problem more recently appeared for a linear problem known as the "advection-diffusion system". Despite the linearity of the problem, intermittency appears also in this case for sufficiently large Peclet numbers. A relevant step forward for the understanding of basic mechanisms at the very origin of intermittency in this problem has been achieved starting from the pioneering work of R.H. Kraichnan entitled "Anomalous scaling of a randomly advected passive scalar" published in Physical Review Letters in February 1994. Our activity in the field started from this seminal paper and it is still in progress. |