20,197 research outputs found
Segmentation of Fault Networks Determined from Spatial Clustering of Earthquakes
We present a new method of data clustering applied to earthquake catalogs,
with the goal of reconstructing the seismically active part of fault networks.
We first use an original method to separate clustered events from uncorrelated
seismicity using the distribution of volumes of tetrahedra defined by closest
neighbor events in the original and randomized seismic catalogs. The spatial
disorder of the complex geometry of fault networks is then taken into account
by defining faults as probabilistic anisotropic kernels, whose structures are
motivated by properties of discontinuous tectonic deformation and previous
empirical observations of the geometry of faults and of earthquake clusters at
many spatial and temporal scales. Combining this a priori knowledge with
information theoretical arguments, we propose the Gaussian mixture approach
implemented in an Expectation-Maximization (EM) procedure. A cross-validation
scheme is then used and allows the determination of the number of kernels that
should be used to provide an optimal data clustering of the catalog. This
three-steps approach is applied to a high quality relocated catalog of the
seismicity following the 1986 Mount Lewis () event in California and
reveals that events cluster along planar patches of about 2 km, i.e.
comparable to the size of the main event. The finite thickness of those
clusters (about 290 m) suggests that events do not occur on well-defined
euclidean fault core surfaces, but rather that the damage zone surrounding
faults may be seismically active at depth. Finally, we propose a connection
between our methodology and multi-scale spatial analysis, based on the
derivation of spatial fractal dimension of about 1.8 for the set of hypocenters
in the Mnt Lewis area, consistent with recent observations on relocated
catalogs
Stochastic turbulence modeling in RANS simulations via Multilevel Monte Carlo
A multilevel Monte Carlo (MLMC) method for quantifying model-form
uncertainties associated with the Reynolds-Averaged Navier-Stokes (RANS)
simulations is presented. Two, high-dimensional, stochastic extensions of the
RANS equations are considered to demonstrate the applicability of the MLMC
method. The first approach is based on global perturbation of the baseline eddy
viscosity field using a lognormal random field. A more general second extension
is considered based on the work of [Xiao et al.(2017)], where the entire
Reynolds Stress Tensor (RST) is perturbed while maintaining realizability. For
two fundamental flows, we show that the MLMC method based on a hierarchy of
meshes is asymptotically faster than plain Monte Carlo. Additionally, we
demonstrate that for some flows an optimal multilevel estimator can be obtained
for which the cost scales with the same order as a single CFD solve on the
finest grid level.Comment: 40 page
The Fractal Geometry of the Cosmic Web and its Formation
The cosmic web structure is studied with the concepts and methods of fractal
geometry, employing the adhesion model of cosmological dynamics as a basic
reference. The structures of matter clusters and cosmic voids in cosmological
N-body simulations or the Sloan Digital Sky Survey are elucidated by means of
multifractal geometry. A non-lacunar multifractal geometry can encompass three
fundamental descriptions of the cosmic structure, namely, the web structure,
hierarchical clustering, and halo distributions. Furthermore, it explains our
present knowledge of cosmic voids. In this way, a unified theory of the
large-scale structure of the universe seems to emerge. The multifractal
spectrum that we obtain significantly differs from the one of the adhesion
model and conforms better to the laws of gravity. The formation of the cosmic
web is best modeled as a type of turbulent dynamics, generalizing the known
methods of Burgers turbulence.Comment: 35 pages, 8 figures; corrected typos, added references; further
discussion of cosmic voids; accepted by Advances in Astronom
SiSeRHMap v1.0: A simulator for mapped seismic response using a hybrid model
SiSeRHMap is a computerized methodology capable of drawing up prediction maps of
seismic response. It was realized on the basis of a hybrid model which combines different
approaches and models in a new and non-conventional way. These approaches
5 and models are organized in a code-architecture composed of five interdependent
modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered
computational structure based on the concept of lithodynamic units and zones,
aims at reproducing a parameterized layered subsoil model. A metamodeling process
confers a hybrid nature to the methodology. In this process, the one-dimensional linear
10 equivalent analysis produces acceleration response spectra of shear wave velocitythickness
profiles, defined as trainers, which are randomly selected in each zone. Subsequently,
a numerical adaptive simulation model (Spectra) is optimized on the above
trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm
(EA) and the Levenberg–Marquardt Algorithm (LMA) as the final optimizer. In the fi15
nal step, the GCM Maps Executor module produces a serial map-set of a stratigraphic
seismic response at different periods, grid-solving the calibrated Spectra model. In addition,
the spectra topographic amplification is also computed by means of a numerical
prediction model. This latter is built to match the results of the numerical simulations
related to isolate reliefs using GIS topographic attributes. In this way, different sets
20 of seismic response maps are developed, on which, also maps of seismic design response
spectra are defined by means of an enveloping technique
Steric engineering of metal-halide perovskites with tunable optical band gaps
Owing to their high energy-conversion efficiency and inexpensive fabrication
routes, solar cells based on metal-organic halide perovskites have rapidly
gained prominence as a disruptive technology. An attractive feature of
perovskite absorbers is the possibility of tailoring their properties by
changing the elemental composition through the chemical precursors. In this
context, rational in silico design represents a powerful tool for mapping the
vast materials landscape and accelerating discovery. Here we show that the
optical band gap of metal-halide perovskites, a key design parameter for solar
cells, strongly correlates with a simple structural feature, the largest
metal-halide-metal bond angle. Using this descriptor we suggest continuous
tunability of the optical gap from the mid-infrared to the visible. Precise
band gap engineering is achieved by controlling the bond angles through the
steric size of the molecular cation. Based on these design principles we
predict novel low-gap perovskites for optimum photovoltaic efficiency, and we
demonstrate the concept of band gap modulation by synthesising and
characterising novel mixed-cation perovskites.Comment: This manuscript was submitted for publication on March 6th, 2014.
Many of the results presented in this manuscript were presented at the
International Conference on Solution processed Semiconductor Solar Cells,
held in Oxford, UK, on 10-12 September 2014. The manuscript is 37 pages long
and contains 8 figure
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