In this paper we are interested in the polynomial Krylov approximations for the computation of phi(A)upsilon, where A is a square matrix, v represents a given vector, and. is a suitable function which can be employed in modern integrators for differential problems. Our aim consists of proposing and analyzing innovative a posteriori error estimates which allow a good control of the approximation procedure. The effectiveness of the results we provide is tested on some numerical examples of interest.

In this paper we propose a numerical method for the simulation of acousticfields, in presence of an obstacle in the physical domain, by finite difference solution of a two-dimensional differential model on boundary-fittedgrids. The computational process gathers the elliptic grid generation, the linear Euler equations in curvilinear coordinates, the associated bicharacteristic equation and a fully explicit finite difference approximation. We detail the models, the algorithms and a series of both literature and new test problems. We present computed velocity and pressure fields by the help of figures. The method results to be able to reconstruct field evolutions and near obstacle perturbations caused by one or more, fixed or moving, pressure sources.

We present a detailed description of biopolymer translocation through a nanopore in the presence of a solvent using an innovative multiscale methodology that treats the biopolymer at the microscopic scale as combined with a self-consistent mesoscopic description for the solvent fluid dynamics. We report evidence for quantized current blockade depending on the folding configuration and offer detailed information on the role of hydrodynamic correlations in speeding up the translocation process.

We present results from recent direct numerical simulations of heavy particle transport in homogeneous, isotropic, fully developed turbulence, with grid resolution up to 5123 and R? ? 185. By following the trajectories of millions of particles with different Stokes numbers, St ? [0.16 : 3.5], we are able to characterize in full detail the statistics of particle acceleration. We focus on the probability density function of the normalised acceleration a/arms and on the behaviour of their rootmean-squared acceleration arms as a function of both St and R?. We explain our findings in terms of two concurrent mechanisms: particle clustering, very effective for small St, and filtering induced by finite particle response time, taking over at larger St.

Cloud detection from geostationary satellite multispectral images through statistical methodologies is investigated. Discriminant analysis methods are considered to this purpose, endowed with a nonparametric density estimation and a linear transform into principal and independent components. The whole methodology is applied to the MSG-SEVIRI sensor through a set of test images covering the central and southern part of Europe. "Truth" data for the learning phase of discriminant analysis are taken from the cloud mask product MOD35 in correspondence of passages of MODIS close to the SEVIRI images. Performance of the discriminant analysis methods is estimated over sea/land, daytime/nighttime both when training and test datasets coincide and when they are different. Discriminant analysis shows very good performance in detecting clouds, especially over land. PCA and ICA are effective in improving detection.

Sharp constants are exhibited in exponential inequalities corresponding to the limiting case of the Sobolev inequalities in Lorentz-Sobolev spaces of arbitrary order.

We present a collection of eight data sets from state-of-the-art experiments and numerical simulations on turbulent velocity statistics along particle trajectories obtained in different flows with Reynolds numbers in the range Rlambda[is-an-element-of][120:740]. Lagrangian structure functions from all data sets are found to collapse onto each other on a wide range of time lags, pointing towards the existence of a universal behavior, within present statistical convergence, and calling for a unified theoretical description. Parisi-Frisch multifractal theory, suitably extended to the dissipative scales and to the Lagrangian domain, is found to capture the intermittency of velocity statistics over the whole three decades of temporal scales investigated here.

A detailed comparison between data from experimental measurements and numerical simulations of Lagrangian velocity structure functions in turbulence is presented. Experimental data, at Reynolds number ranging from R? = 350 to R? = 815, are obtained in a swirling water flow between counter-rotating baffled disks. Direct numerical simulations (DNS) data, up to R? = 284, are obtained from a statistically homogeneous and isotropic turbulent flow. By integrating information from experiments and numerics, a quantitative understanding of the velocity scaling properties over a wide range of time scales and Reynolds numbers is achieved. To this purpose, we discuss in detail the importance of statistical errors, anisotropy effects, and finite volume and filter effects, finite trajectory lengths. The local scaling properties of the Lagrangian velocity increments in the two data sets are in good quantitative agreement for all time lags, showing a degree of intermittency that changes if measured close to the Kolmogorov time scales or at larger time lags. This systematic study resolves apparent disagreement between observed experimental and numerical scaling properties.

Distributions of heavy particles suspended in incompressible turbulent flows are investigated by means of high-resolution direct numerical simulations. It is shown that particles form fractal clusters in the dissipative range, with properties independent of the Reynolds number. Conversely, in the inertial range, the particle distribution is not scale-invariant. It is however shown that deviations from uniformity depends only on a rescaled contraction rate, and not on the local Stokes number given by dimensional analysis. Particle distribution is characterized by voids spanning all scales of the turbulent flow; their signature on the coarse-grained mass probability distribution is an algebraic behavior at small densities.

Particles with different density from the advecting turbulent fluids cluster due to the different response of light and heavy particles to turbulent fluctuations. This study focuses on the quantitative characterization of the segregation of dilute polydisperse inertial particles evolving in turbulent flow, as obtained from direct numerical simulation of homogeneous isotropic turbulence. We introduce an indicator of segregation amongst particles of different inertia and/or size, from which a length scale r(seg), quantifying the segregation degree between two particle types, is deduced.

We propose a thermal lattice Boltzmann model to study gaseous flow in microcavities. The model relies on the use of a finite difference scheme to solve the set of evolution equations. By adopting diffuse reflection boundary conditions to deal with flows in the slip regime, we study the micro-Couette flow in order to select the best numerical scheme in terms of accuracy. The scheme based on flux limiters is then used to simulate a microlid-driven cavity flow by using an efficient and parallel implementation. The numerical results are in very good agreement with the available results recovered with different methods.

To study naive and memory CD8 T cell turnover, we performed BrdU incorporation experiments in adult thymectomized C57BL/6 mice and analyzed data in a mathematical framework. The following aspects were novel: 1) we examined the bone marrow, in addition to spleen and lymph nodes, and took into account the sum of cells contained in the three organs; 2) to describe both BrdU-labeling and -delabeling phase, we designed a general mathematical model, in which cell populations were distinguished based on the number of divisions; 3) to find parameters, we used the experimentally determined numbers of total and BrdU(+) cells and the BrdU-labeling coefficient. We treated mice with BrdU continuously via drinking water for up to 42 days, measured by flow cytometry BrdU incorporation at different times, and calculated the numbers of BrdU(+) naive (CD44(int/low)) and memory (CD44(high)) CD8 T cells. By fitting the model to data, we determined proliferation and death rates of both subsets. Rates were confirmed using independent sets of data, including the numbers of BrdU(+) cells at different times after BrdU withdrawal. We found that both doubling time and half-life of the memory population were approximately 9 wk, whereas for the naive subset the doubling time was almost 1 year and the half-life was roughly 7 wk. Our findings suggest that the higher turnover of memory CD8 T cells as compared with naive CD8 T cells is mostly attributable to a higher proliferation rate. Our results have implications for interpreting physiological and abnormal T cell kinetics in humans.