Interaction of the vortex systems detached from a propeller with a rudder installed in its wake is investigated by CFD. The correct prediction of this phenomenon is of great interest in naval hydrodynamics research, it being the source of irradiated noise and vibratory loads. The phenomenology is addressed by simulating a single bladed propeller (INSEAN E779A) and a rudder characterized by a rectangular plane area and symmetric sectional shape (NACA0020 profiles). The main focus is on the hydro-loads developed by the rudder and their correlation with the different phases of the interaction of the tip vortex with the rudder. The phenomenon is also investigated, through a preliminary computation on a coarser mesh, on the actual propeller geometry (4-bladed).

The generation of complex vorticity pattern in aft-finocyl solid rocket motors is inves- tigated in this paper by means of full-3D ILES CFD simulations with a high-order/low- dissipation class of centered numerical schemes with oscillation control and an immersed boundary treatment of the propellant grain surface, treated with a level-set approach. The development of vortical/shear structures is observed both at the motor axis, immediately downstream the igniter and across the finocyl region and in the submergence region. The first ones are rather a relevant finding which characterizes the flowfield structures that develops inside the combustion chamber of an aft-finocyl geometry, that find confirmation in both small scale cold-flow tests and theoretical justification in fundamental works. The second one are instead more classical vortical structures that belong to the class of angle shear-layers, due to the turning flow characteristic of solid rocket motors. These vortical structures are found to induce very low-level, but present, pressure oscillations as due to the coupling of the vorticity pattern and pressure waves. These pressure oscillations result into oscillations of the thrust delivered by the SRM, involving both a longitudinal cham- ber mode excitation (corresponding to the first chamber longitudinal mode) and a lateral chamber mode excitation. The level of such thrust component oscillations is of the order of one percent of the delivered motor thrust, with the uncertainty assessed by both grid convergence analyses and sub-grid model of the ILES approach. These flowfield character- istics are a little dependent upon the motor configuration, and in particular, the angle of gimbaling imposed to the nozzle and a bias-offset of the propellant grain with respect to the motor assembly.

Magnetoencephalopgraphy (MEG) is a non-invasive functional imaging modality for mapping cerebral electromagnetic activity from measurements of the weak magnetic field that it generates. It is well known that the MEG inverse problem, i.e. the problem of identifying electric currents from the induced magnetic fields, is a severely underdetermined problem and, without complementary prior information, no unique solution can be found. In the literature, many regularization techniques were proposed. In particular, optimization-based methods usually explain the data by superficial sources even when the activity is deep in the brain. A way to make easier the identification of deep focal sources is the use of depth weighting. We revisit the MEG inverse problem, regularization and depth weighting from a Bayesian point of view by hierarchical models: The primary unknown is the discretized current density inside the head, and we postulate a conditionally Gaussian anatomical prior model. In this model, each current element, or dipole, has a preferred, albeit not fixed, direction that is extracted from the anatomical data of the subject. The variance of each dipole is not fixed a priori, but modeled itself as a random variable described by its hyperprior density. The hypermodel is then used to build a fast iterative algorithm with the novel feature that their parameters are determined using an empirical Bayes approach. The hypermodel provides a very natural Bayesian interpretation for sensitivity weighting, and the parameters in the hyperprior provide a tool for controlling the focality of the solution, thus leading to a flexible algorithm that can handle both sparse and distributed sources. To demonstrate the effects of different parameter selections under optimal conditions, we test the algorithm on synthetic but realistic data. The tests show that the hierarchical Bayesian models combined with linear algebraic methods provide a versatile framework to develop robust and flexible numerical methods, and are able to overcome some of the limitations of standard regularization techniques, for instance deep source localization. The proposed algorithm is computationally efficient, gives a direct control of how well the computed estimates satisfy the data and is designed to easily accommodate different types of prior information.

Electrocorticography (ECoG) is a neurophysiological modality that measures the distribution of electrical potentials, associated with either spontaneous or evoked neural activity, by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data, however, requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. This study addresses the ECoG source modelling using a beamformer method. First, we compute the lead-field matrix which maps the neural currents onto the sensors space: a novel routine for the computation of the lead-field matrix, based on the tools provided by the OpenMEEG framework, was used [2]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability; thus, we perform an analysis of the condition number of the lead-field matrix which provides quantitative information on the numerical instability of the problem, independently of the kind of inversion algorithm applied. Finally, we provide quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer. The validation of the effectiveness of beamforming in ECoG is performed both with synthetic data and with experimental data recorded during a rapid visual categorization task.

The brain is a connected network, requiring complex-system measures to describe its organization principles. The normalized compression distance (NCD) [1] is a parameter -free, quasi universal similarity measure that estimates the information shared by two signals comparing the compression length of one signal given the other. Here, we aim at testing whether this new measure is a suitable quantifier of the functional connectivity between cortical regions. In particular, we tested whether NCD between homologous hemispheric regions is smaller (higher connectivity) in the same person than across different people, if it is smaller in the dominant hemisphere and if it depends on age. We used the Functional Source Separation (FSS) [2] algorithm on magnetoencephalographic (MEG) data in order to identify functionally homologous areas in the two hemispheres devoted to the somatosensory contra-lateral hand representation (FS_S1) in 28 healthy people. Therefore, we calculated NCD between the left and right FS_S1s activities at rest. We found that NCD 1) between left and right FS_S1s of the same person was smaller than across different people (p<10-7consistently) 2) was smaller within the left dominant hemisphere than within the non dominant right one (p=3*10 7) and 3) became more variable in older than younger people (p=.01). This preliminary work shows that NCD, which measures the similarity of neuronal source activities via their compression sizes, displays an excellent ability in quantifying the similarity among neuronal activities, catching the maximal similarity expected for functionally homologous cortical areas of the two hemispheres. Thus, NCD seems a good candidate for two-nodes functional connectivity measure in resting state, able to overcome the limitations intrinsic to the classical Fourier or autoregressive estimates in assessing dynamics properties of the brain connectivity.

We present a lattice Boltzmann realization of Grad's extended hydrodynamic approach to nonequilibrium flows. This is achieved by using higher-order isotropic lattices coupled with a higher-order regularization procedure. The method is assessed for flow across parallel plates and three-dimensional flows in porous media, showing excellent agreement of the mass flow with analytical and numerical solutions of the Boltzmann equation across the full range of Knudsen numbers, from the hydrodynamic regime to ballistic motion.

It is shown that lattice kinetic theory based on short-lived quasiparticles proves very effective in simulating the complex dynamics of strongly interacting fluids (SIF). In particular, it is pointed out that the shear viscosity of lattice fluids is the sum of two contributions, one due to the usual interactions between particles (collision viscosity) and the other due to the interaction with the discrete lattice (propagation viscosity). Since the latter is negative, the sum may turn out to be orders of magnitude smaller than each of the two contributions separately, thus providing a mechanism to access SIF regimes at ordinary values of the collisional viscosity. This concept, as applied to quantum superfluids in one-dimensional optical lattices, is shown to reproduce shear viscosities consistent with the AdS-CFT holographic bound on the viscosity/entropy ratio. This shows that lattice kinetic theory continues to hold for strongly coupled hydrodynamic regimes where continuum kinetic theory may no longer be applicable.

In this paper, we develop a lattice Boltzmann model for relativistic magnetohydrodynamics (MHD). Even though the model is derived for resistive MHD, it is shown that it is numerically robust even in the high conductivity (ideal MHD) limit. In order to validate the numerical method, test simulations are carried out for both ideal and resistive limits, namely the propagation of Alfven waves in the ideal MHD and the evolution of current sheets in the resistive regime, where very good agreement is observed comparing to the analytical results. Additionally, two-dimensional magnetic reconnection driven by Kelvin-Helmholtz instability is studied and the effects of different parameters on the reconnection rate are investigated. It is shown that the density ratio has a negligible effect on themagnetic reconnection rate, while an increase in shear velocity decreases the reconnection rate. Additionally, it is found that the reconnection rate is proportional to sigma(-1/2), sigma being the conductivity, which is in agreement with the scaling law of the Sweet-Parker model. Finally, the numerical model is used to study the magnetic reconnection in a stellar flare. Three-dimensional simulation suggests that the reconnection between the background and flux rope magnetic lines in a stellar flare can take place as a result of a shear velocity in the photosphere.

Transport phenomena still stand as one of the most challenging problems in computational physics. By exploiting the analogies between Dirac and lattice Boltzmann equations, we develop a quantum simulator based on pseudospin-boson quantum systems, which is suitable for encoding fluid dynamics transport phenomena within a lattice kinetic formalism. It is shown that both the streaming and collision processes of lattice Boltzmann dynamics can be implemented with controlled quantum operations, using a heralded quantum protocol to encode non-unitary scattering processes. The proposed simulator is amenable to realization in controlled quantum platforms, such as ion-trap quantum computers or circuit quantum electrodynamics processors.

By tailoring the geometry of the upper boundary in turbulent Rayleigh-Benard convection we manipulate the boundary layer-interior flow interaction, and examine the heat transport using the lattice Boltzmann method. For fixed amplitude and varying boundary wavelength., we find that the exponent beta in the Nusselt-Rayleigh scaling relation, Nu - 1 proportional to Ra-beta, is maximized at lambda =lambda(max) approximate to ( 2 pi)(-1), but decays to the planar value in both the large (lambda >> lambda(max)) and small (lambda << lambda(max)) wavelength limits. The changes in the exponent originate in the nature of the coupling between the boundary layer and the interior flow. We present a simple scaling argument embodying this coupling, which describes the maximal convective heat flux. editor's choice Copyright (C) EPLA, 2015

In this study, we compare different diffuse and sharp interface schemes of direct-forcing immersed boundary - thermal lattice Boltzmann method (IB-TLBM) for non-Newtonian flow over a heated circular cylinder. Both effects of the discrete lattice and the body force on the momentum and energy equations are considered, by applying the split-forcing Lattice Boltzmann equations. A new technique based on predetermined parameters of direct forcing IB-TLBM is presented for computing the Nusselt number. The study covers both steady and unsteady regimes (20<Re<80) in the power-law index range of 0.6< n <1.4, encompassing both shear-thinning and shear-thickening non-Newtonian fluids. The numerical scheme, hydrodynamic approach and thermal parameters of different interface schemes are compared in both steady and unsteady cases. It is found that the sharp interface scheme is a suitable and possibly competitive method for thermal-IBM in terms of accuracy and computational cost.

A discrete Boltzmann model (DBM) is developed to investigate the hydrodynamic and thermodynamic non-equilibrium (TNE) effects in phase separation processes. The interparticle force drives changes and the gradient force, induced by gradients of macroscopic quantities, opposes them. In this paper, we investigate the interplay between them by providing a detailed inspection of various non-equilibrium observables. Based on the TNE features, we define TNE strength which roughly estimates the deviation amplitude from the thermodynamic equilibrium. The time evolution of the TNE intensity provides a convenient and efficient physical criterion to discriminate the stages of the spinodal decomposition and domain growth. Via the DBM simulation and this criterion, we quantitatively study the effects of latent heat and surface tension on phase separation. It is found that the TNE strength attains its maximum at the end of the spinodal decomposition stage, and it decreases when the latent heat increases from zero. The surface tension effects are threefold, prolong the duration of the spinodal decomposition stage, decrease the maximum TNE intensity, and accelerate the speed of the domain growth stage.

This paper deals with the issue of forecasting energy production of a Photo-Voltaic (PV) plant, needed by the Distribution System Operator (DSO) for grid planning. As the energy production of a PV plant is strongly dependent on the environmental conditions, the DSO has difficulties to manage an electrical system with stochastic generation. This implies the need to have a reliable forecasting of the irradiance level for the next day in order to setup the whole distribution network. To this aim, this paper proposes the use of transfer function models. The assessment of quality and accuracy of the proposed method has been conducted on a set of scenarios based on real data.

Nowadays, Magnetic Resonance Spectroscopy (MRS) represents a powerful nuclear magnetic resonance (NMR) technique in oncology since it provides information on the biochemical profile of tissues, thereby allowing clinicians and radiologists to identify in a non-invasive way the different tissue types characterising the sample under investigation. The main purpose of the pre-sent chapter is to provide a review of the most recent and significant applica-tions of non-negative matrix factorization (NMF) to MRS data in the field of tissue typing methods for tumour diagnosis. Specifically, NMF-based methods for the recovery of constituent spectra in ex vivo and in vivo brain MRS data, for brain tissue pattern differentiation using Magnetic Resonance Spectro-scopic Imaging (MRSI) data, and for automatic detection and visualisation of prostate tumours will be described. Furthermore, since several NMF imple-mentations are available in the literature, a comparison in terms of pattern de-tection accuracy of some NMF algorithms will be reported and discussed, and the NMF performance for MRS data analysis will be compared with that of other blind source separation (BSS) techniques.

In this work, we find and test a new approximated formula (based on the thin plate approximation), for recovering small, unknown damages on the inaccessible surface of a thin conducting (aluminium) plate. We solve this inverse problem from a controlled heat flux and a sequence of temperature maps on the accessible front boundary of our sample. We heat the front boundary by means of a sinusoidal flux. In the meanwhile, we take a sequence of temperature maps of the same side by means of an infrared camera. This procedure is called active infrared thermography. The solution of the heat equation on the accessible boundary of the damaged sample simulates the collection of data. We use domain derivative to linearize the boundary value problem for heat equation. Then, Fourier analysis on the periodic component of solutions leads us to an elliptic BVP. Finally, we apply perturbation theory in order to find out an approximation of the damage. Numerical tests obtained with synthetic data are encouraging. The solution of the heat equation on the accessible boundary of the damaged sample simulates the collection of data by means of the infrared camera. We use domain derivative to linearize the BVP for heat equation. Then, Fourier analysis on the periodic component of solutions leads us to an elliptic BVP. Finally, we apply the perturbation theory in (Formula presented.) (a is the thickness of our sample) in order to find out an approximation of the damage. Numerical tests obtained with synthetic data are encouraging.

We address the problem of supply chain management for a set of fresh and highly perishable products. Our activity mainly concerns forecasting sales. The study involves 19 retailers (small and medium size stores) and a set of 156 different fresh products. The available data is made of three year sales for each store from 2011 to 2013. The forecasting activity started from a pre-processing analysis to identify seasonality, cycle and trend components, and data filtering to remove noise. Moreover, we performed a statistical analysis to estimate the impact of prices and promotions on sales and customers' behaviour. The filtered data is used as input for a forecasting algorithm which is designed to be interactive for the user. The latter is asked to specify ID store, items, training set and planning horizon, and the algorithm provides sales forecasting. We used ARIMA, ARIMAX and transfer function models in which the value of parameters ranges in predefined intervals. The best setting of these parameters is chosen via a two-step analysis, the first based on well-known indicators of information entropy and parsimony, and the second based on standard statistical indicators. The exogenous components of the forecasting models take the impact of prices into account. Quality and accuracy of forecasting are evaluated and compared on a set of real data and some examples are reported.

A stochastic model for describing the firing activity of a couple of interacting neurons subject to time-dependent stimuli is proposed. Two stochastic differential equations suitably coupled and including periodic terms to represent stimuli imposed to one or both neurons are considered to describe the problem. We investigate the first passage time densities through specified firing thresholds for the involved time non-homogeneous Gauss-Markov processes. We provide simulation results and numerical approximations of the firing densities. Asymptotic behaviors of the first passage times are also given.

In this work, we exploit the integration of an advanced synthetic aperture radar (SAR) interferometry technique and the application of the finite-element method for the assessment and the interpretation of a localized subsidence phenomenon that took place within a specific area of Lisbon, Portugal. SAR images over the Lisbon city, covering different time intervals in the period of 1995-2010, were acquired and processed by means of the persistent scatterers (PSs) technique. Results clearly reveals a localized subsidence, limited to an area 2 km × 1.5 km wide, which has been confirmed by the leveling performed in 1976, 1996, and 2010. A physical interpretation of the observed ground deformations is provided based on the results of a finite-element model using stratigraphic data, \textit{in situ} piezometric measurements, and geotechnical properties of the involved soils. The ground subsidence is interpreted as the consequence of a consolidation process affecting the central fine-grained soil layer, which in turn has been driven by water withdrawal from the existing aquifers. The change of the hydraulic boundary conditions was generated by the excavation works for the construction of underground lines and also by the reduction of rainfall water infiltration as an effect of the increase in ground surface impermeable areas due to urbanization. The consequent consolidation process of the compressible fine-grained soil layer is supposed to provide a reasonable explanation of the observed time series of ground displacement in the examined area.

The work mainly discusses the use of the Ground-Based Synthetic Aperture Radar (GBSAR) interferometry technique to observe and control the behavior of earthfill or rockfill embankments for dam impoundments. This non-invasive technique provides overall displacements patterns measured with a sub-millimeter accuracy. The need of reliable monitoring of old embankment dams is rapidly increasing since a large number of these structures are still equipped with old monitoring devices, usually installed some decades ago, which can give only information on localized areas of the embankment. A case study regarding the monitoring of an earthfill dam embankment in Southern Italy by means of GBSAR interferometry is presented.

Solutions to the n-Laplace equation with a right-hand side f are considered. We exhibit the largest rearrangement-invariant space to which f has to belong for every local weak solution to be continuous. Moreover, we find the optimal modulus of continuity of solutions when f ranges in classes of rearrangement-invariant spaces, including Lorentz, Lorentz-Zygmund and various standard Orlicz spaces.