Well-balanced schemes were introduced to numerically enforce consistency with longtime behavior of the underlying continuous PDE. When applied to linear kinetic models, like the Goldstein-Taylor system, this construction generates discretizations which are inconsistent with the hydrodynamic stiff limit (despite it captures diffusive limits quite well).

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.

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 likelihood of a subglacial lake beneath Amundsenisen Plateau at Southern Spitzbergen, Svalbard, pointed out by the flat signal within the Ground Penetrating Radar (GPR) remote survey of the area, is justified, here, via numerical simulation. This investigation has been developed under the assumption that the icefield thickness does not change on average, as it is confirmed by recently published physical measurements taken over the past forty years.

In the present work the numerical simulation of breaking wave processes is discussed. A detailed analysis is performed using Smoothing Particle Hydrodynamics (SPH) models as well as a mesh-based Level-Set Finite Volume Method (LS-FVM). Considerations on the numerical dissipation involved in such models are discussed within the frameworks of weakly compressible and incompressible ssumptions. The breaking wave processes are simulated using both mono- and two-phases models. Due to the extensive test-cases discussed, the present analysis is limited to a bi-dimensional framework.

We show that causality violation in a Kerr naked singularity spacetime is constrained by the existence of (radial) potential barriers. We extend to the class of vortical non-equatorial null geodesics confined to $$\theta $$? $$=$$= constant hyperboloids (boreal orbits) previous results concerning timelike ones (Calvani et al. in Gen Rel Gravit 9:155, 1978), showing that within this class of orbits, the causality principle is rigorously satisfied.

In the present work the simulation of water impacts is discussed. The investigation is mainly focused on the energy dissipation involved in liquid impacts in both the frameworks of the weakly compressible and incompressible models. A detailed analysis is performed using a weakly compressible Smoothed Particle Hydrodynamics (SPH) solver and the results are compared with the solutions computed by an incompressible mesh-based Level-Set Finite Volume Method (LS-FVM). Impacts are numerically studied using single-phase models through prototypical problems in 1D and 2D frameworks.

Magnetic resonance spectroscopy (MRS) has been shown to be a potentially important medical diagnostic tool. The success of MRS depends on the quantitative data analysis, i.e., the interpretation of the signal in terms of relevant physical parameters, such as frequencies, decay constants, and amplitudes. A variety of time-domain algorithms to extract parameters have been developed. On the one hand, there are so-called blackbox methods. Minimal user interaction and limited incorporation of prior knowledge are inherent to this type of method.

Gene expression data from high-throughput assays, such as microarray, are often used to predict cancer survival. Available datasets consist of a small number of samples (n patients) and a large number of genes (p predictors). Therefore, the main challenge is to cope with the high-dimensionality. Moreover, genes are co-regulated and their expression levels are expected to be highly correlated. In order to face these two issues, network based approaches can be applied.