The lack of fundamental knowledge of the interaction between an acoustic wave and a turbulent boundary layer grazing an acoustically treated surface, such as an acoustic liner, is the cause of unexpected and unphysical results found when performing the acoustic characterization of the sound absorbing surface with inverse eduction methods. This is because, in this field, acoustic and aerodynamic have never been fully coupled. To fill this knowledge gap, the acoustic and hydrodynamic velocities near an acoustically treated surface must be measured. Since it cannot be done only with state-of-the-art experiments, because of hardware and field-of-view limitations, I propose to complement experiments with scale-resolved high-fidelity numerical simulations based on the lattice-Boltzmann very-large-eddy simulation method. Numerical results will be used to explain the physics of the acoustic-flow interaction. Advanced data analysis methodologies will be developed and applied to separate the acoustic-induced velocity near the wall from the hydrodynamic one. At the same time, the numerical database will be used to compare inverse methods, employed to acoustically characterize the sound absorbing surfaces, in order to explain the physical reasons behind the unexpected results, and propose physics-based corrections. Furthermore, by describing the flow-acoustic interaction, it will be possible to model and predict the drag increase caused by the coupling between the acoustic-induced velocity and the free-stream one. My description of the flow-acoustic interaction will solve the scientific debate about the unexpected results and pave the way towards future broadband low-noise low-drag acoustic meta-surfaces to increase propulsion efficiency and reduce noise of future, more sustainable, aircraft engines.

We intend to set up a new globalized perspective to tackle water and food security in the 21st century. This issue is intrinsically global as the international trade of massive amounts of food makes societies less reliant on locally available water, and entails large-scale transfers of virtual water (defined as the water needed to produce a given amount of a food commodity). The network of virtual water trade connects a large portion of the global population, with 2800 km3 of virtual water moved around the globe in a year. We provide here definitive indications on the effects of the globalization of (virtual) water on the vulnerability to a water crisis of the global water system. More specifically, we formulate the following research hypotheses: 1) The globalization of (virtual) water resources is a short-term solution to malnourishment, famine, and conflicts, but it also has relevant negative implications for human societies. 2) The virtual water dynamics provide the suitable framework in order to quantitatively relate water-crises occurrence to environmental and socio-economic factors. 3) The risk of catastrophic, global-scale, water crises will increase in the next decades. To test these hypotheses, we will capitalize on the tremendous amount of information embedded in nearly 50 years of available food and virtual water trade data. We will adopt an innovative research approach based on the use of: advanced statistical tools for data verification and uncertainty modeling; methods borrowed from the complex network theory, aimed at analyzing the propagation of failures through the network; multivariate nonlinear analyses, to reproduce the dependence of virtual water on time and on external drivers; multi-state stochastic modeling, to study the effect on the global water system of random fluctuations of the external drivers; and scenario analysis, to predict the future probability of occurrence of water crises.

Electroencephalography (EEG) from scalp potentials is a crucial non-invasive technique for imaging the electric brain activity. EEG is of primary importance in several scenarios of substantial societal impact ranging from electric characterization of epileptic seizures to the development of brain computer interfaces. One of the key technologies in advanced EEG imaging is the source imaging/localization procedure where the volume electric current responsible for the scalp potential is obtained solving an inverse source problem, in turn requiring several solutions of the EEG forward problem. The imaging of the brain currents is what makes the standard EEG readings into high resolution EEG brain imaging. Lamentably, the standard EEG forward problem has cubical computational complexity resulting in an eight times increase in computer memory and computational time every time the problem doubles in size. This substantially limits the impact of EEG imaging, especially in low power computational systems and in social settings that cannot afford expensive computing machines. The object of the “TurboEEG” project is to radically change this paradigm, we will translate the outcomes of the ERC Consolidator Grant project “321” into the field of EEG neuroimaging obtaining and distributing the first existing EEG open source package that will solve the EEG imaging problem in linear complexity, speeding up standard EEG imaging of orders of magnitude and making high resolution EEGs widely available and cheap. Since EEG source imaging is what translate a standard EEG device into an high resolution one, the new open source tool will have a substantial impact on society by making available high resolution EEG technology in environments where top computational power is not available, such as small hospitals, ambulatories, schools and universities. The new open-source tool will in fact also have a substantial impact on education, professional training, and brain research.