11,059 research outputs found
Critical slowing down and hyperuniformity on approach to jamming
Hyperuniformity characterizes a state of matter that is poised at a critical
point at which density or volume-fraction fluctuations are anomalously
suppressed at infinite wavelengths. Recently, much attention has been given to
the link between strict jamming and hyperuniformity in frictionless
hard-particle packings. Doing so requires one to study very large packings,
which can be difficult to jam properly. We modify the rigorous linear
programming method of Donev et al. [J. Comp. Phys. 197, 139 (2004)] in order to
test for jamming in putatively jammed packings of hard-disks in two dimensions.
We find that various standard packing protocols struggle to reliably create
packings that are jammed for even modest system sizes; importantly, these
packings appear to be jammed by conventional tests. We present evidence that
suggests that deviations from hyperuniformity in putative maximally random
jammed (MRJ) packings can in part be explained by a shortcoming in generating
exactly-jammed configurations due to a type of "critical slowing down" as the
necessary rearrangements become difficult to realize by numerical protocols.
Additionally, various protocols are able to produce packings exhibiting
hyperuniformity to different extents, but this is because certain protocols are
better able to approach exactly-jammed configurations. Nonetheless, while one
should not generally expect exact hyperuniformity for disordered packings with
rattlers, we find that when jamming is ensured, our packings are very nearly
hyperuniform, and deviations from hyperuniformity correlate with an inability
to ensure jamming, suggesting that strict jamming and hyperuniformity are
indeed linked. This raises the possibility that the ideal MRJ packings have no
rattlers. Our work provides the impetus for the development of packing
algorithms that produce large disordered strictly jammed packings that are
rattler-free.Comment: 15 pages, 11 figures. Accepted for publication in Phys. Rev.
How dense can one pack spheres of arbitrary size distribution?
We present the first systematic algorithm to estimate the maximum packing
density of spheres when the grain sizes are drawn from an arbitrary size
distribution. With an Apollonian filling rule, we implement our technique for
disks in 2d and spheres in 3d. As expected, the densest packing is achieved
with power-law size distributions. We also test the method on homogeneous and
on empirical real distributions, and we propose a scheme to obtain
experimentally accessible distributions of grain sizes with low porosity. Our
method should be helpful in the development of ultra-strong ceramics and high
performance concrete.Comment: 5 pages, 5 figure
Cell shape analysis of random tessellations based on Minkowski tensors
To which degree are shape indices of individual cells of a tessellation
characteristic for the stochastic process that generates them? Within the
context of stochastic geometry and the physics of disordered materials, this
corresponds to the question of relationships between different stochastic
models. In the context of image analysis of synthetic and biological materials,
this question is central to the problem of inferring information about
formation processes from spatial measurements of resulting random structures.
We address this question by a theory-based simulation study of shape indices
derived from Minkowski tensors for a variety of tessellation models. We focus
on the relationship between two indices: an isoperimetric ratio of the
empirical averages of cell volume and area and the cell elongation quantified
by eigenvalue ratios of interfacial Minkowski tensors. Simulation data for
these quantities, as well as for distributions thereof and for correlations of
cell shape and volume, are presented for Voronoi mosaics of the Poisson point
process, determinantal and permanental point processes, and Gibbs hard-core and
random sequential absorption processes as well as for Laguerre tessellations of
polydisperse spheres and STIT- and Poisson hyperplane tessellations. These data
are complemented by mechanically stable crystalline sphere and disordered
ellipsoid packings and area-minimising foam models. We find that shape indices
of individual cells are not sufficient to unambiguously identify the generating
process even amongst this limited set of processes. However, we identify
significant differences of the shape indices between many of these tessellation
models. Given a realization of a tessellation, these shape indices can narrow
the choice of possible generating processes, providing a powerful tool which
can be further strengthened by density-resolved volume-shape correlations.Comment: Chapter of the forthcoming book "Tensor Valuations and their
Applications in Stochastic Geometry and Imaging" in Lecture Notes in
Mathematics edited by Markus Kiderlen and Eva B. Vedel Jense
On Colorful Bin Packing Games
We consider colorful bin packing games in which selfish players control a set
of items which are to be packed into a minimum number of unit capacity bins.
Each item has one of colors and cannot be packed next to an item of
the same color. All bins have the same unitary cost which is shared among the
items it contains, so that players are interested in selecting a bin of minimum
shared cost. We adopt two standard cost sharing functions: the egalitarian cost
function which equally shares the cost of a bin among the items it contains,
and the proportional cost function which shares the cost of a bin among the
items it contains proportionally to their sizes. Although, under both cost
functions, colorful bin packing games do not converge in general to a (pure)
Nash equilibrium, we show that Nash equilibria are guaranteed to exist and we
design an algorithm for computing a Nash equilibrium whose running time is
polynomial under the egalitarian cost function and pseudo-polynomial for a
constant number of colors under the proportional one. We also provide a
complete characterization of the efficiency of Nash equilibria under both cost
functions for general games, by showing that the prices of anarchy and
stability are unbounded when while they are equal to 3 for black and
white games, where . We finally focus on games with uniform sizes (i.e.,
all items have the same size) for which the two cost functions coincide. We
show again a tight characterization of the efficiency of Nash equilibria and
design an algorithm which returns Nash equilibria with best achievable
performance
Heuristics with Performance Guarantees for the Minimum Number of Matches Problem in Heat Recovery Network Design
Heat exchanger network synthesis exploits excess heat by integrating process
hot and cold streams and improves energy efficiency by reducing utility usage.
Determining provably good solutions to the minimum number of matches is a
bottleneck of designing a heat recovery network using the sequential method.
This subproblem is an NP-hard mixed-integer linear program exhibiting
combinatorial explosion in the possible hot and cold stream configurations. We
explore this challenging optimization problem from a graph theoretic
perspective and correlate it with other special optimization problems such as
cost flow network and packing problems. In the case of a single temperature
interval, we develop a new optimization formulation without problematic big-M
parameters. We develop heuristic methods with performance guarantees using
three approaches: (i) relaxation rounding, (ii) water filling, and (iii) greedy
packing. Numerical results from a collection of 51 instances substantiate the
strength of the methods
Ant colony optimisation and local search for bin-packing and cutting stock problems
The Bin Packing Problem and the Cutting Stock Problem are two related classes of NP-hard combinatorial optimization problems. Exact solution methods can only be used for very small instances, so for real-world problems, we have to rely on heuristic methods. In recent years, researchers have started to apply evolutionary approaches to these problems, including Genetic Algorithms and Evolutionary Programming. In the work presented here, we used an ant colony optimization (ACO) approach to solve both Bin Packing and Cutting Stock Problems. We present a pure ACO approach, as well as an ACO approach augmented with a simple but very effective local search algorithm. It is shown that the pure ACO approach can compete with existing evolutionary methods, whereas the hybrid approach can outperform the best-known hybrid evolutionary solution methods for certain problem classes. The hybrid ACO approach is also shown to require different parameter values from the pure ACO approach and to give a more robust performance across different problems with a single set of parameter values. The local search algorithm is also run with random restarts and shown to perform significantly worse than when combined with ACO
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