21,706 research outputs found
Robust Algorithm to Generate a Diverse Class of Dense Disordered and Ordered Sphere Packings via Linear Programming
We have formulated the problem of generating periodic dense paritcle packings
as an optimization problem called the Adaptive Shrinking Cell (ASC) formulation
[S. Torquato and Y. Jiao, Phys. Rev. E {\bf 80}, 041104 (2009)]. Because the
objective function and impenetrability constraints can be exactly linearized
for sphere packings with a size distribution in -dimensional Euclidean space
, it is most suitable and natural to solve the corresponding ASC
optimization problem using sequential linear programming (SLP) techniques. We
implement an SLP solution to produce robustly a wide spectrum of jammed sphere
packings in for and with a diversity of disorder
and densities up to the maximally densities. This deterministic algorithm can
produce a broad range of inherent structures besides the usual disordered ones
with very small computational cost by tuning the radius of the {\it influence
sphere}. In three dimensions, we show that it can produce with high probability
a variety of strictly jammed packings with a packing density anywhere in the
wide range . We also apply the algorithm to generate various
disordered packings as well as the maximally dense packings for
and 6. Compared to the LS procedure, our SLP protocol is able to ensure that
the final packings are truly jammed, produces disordered jammed packings with
anomalously low densities, and is appreciably more robust and computationally
faster at generating maximally dense packings, especially as the space
dimension increases.Comment: 34 pages, 6 figure
Sliced rotated sphere packing designs
Space-filling designs are popular choices for computer experiments. A sliced
design is a design that can be partitioned into several subdesigns. We propose
a new type of sliced space-filling design called sliced rotated sphere packing
designs. Their full designs and subdesigns are rotated sphere packing designs.
They are constructed by rescaling, rotating, translating and extracting the
points from a sliced lattice. We provide two fast algorithms to generate such
designs. Furthermore, we propose a strategy to use sliced rotated sphere
packing designs adaptively. Under this strategy, initial runs are uniformly
distributed in the design space, follow-up runs are added by incorporating
information gained from initial runs, and the combined design is space-filling
for any local region. Examples are given to illustrate its potential
application
An Efficient Linear Programming Algorithm to Generate the Densest Lattice Sphere Packings
Finding the densest sphere packing in -dimensional Euclidean space
is an outstanding fundamental problem with relevance in many
fields, including the ground states of molecular systems, colloidal crystal
structures, coding theory, discrete geometry, number theory, and biological
systems. Numerically generating the densest sphere packings becomes very
challenging in high dimensions due to an exponentially increasing number of
possible sphere contacts and sphere configurations, even for the restricted
problem of finding the densest lattice sphere packings. In this paper, we apply
the Torquato-Jiao packing algorithm, which is a method based on solving a
sequence of linear programs, to robustly reproduce the densest known lattice
sphere packings for dimensions 2 through 19. We show that the TJ algorithm is
appreciably more efficient at solving these problems than previously published
methods. Indeed, in some dimensions, the former procedure can be as much as
three orders of magnitude faster at finding the optimal solutions than earlier
ones. We also study the suboptimal local density-maxima solutions (inherent
structures or "extreme" lattices) to gain insight about the nature of the
topography of the "density" landscape.Comment: 23 pages, 3 figure
Solving Irregular Strip Packing Problems With Free Rotations Using Separation Lines
Solving nesting problems or irregular strip packing problems is to position
polygons in a fixed width and unlimited length strip, obeying polygon integrity
containment constraints and non-overlapping constraints, in order to minimize
the used length of the strip. To ensure non-overlapping, we used separation
lines. A straight line is a separation line if given two polygons, all vertices
of one of the polygons are on one side of the line or on the line, and all
vertices of the other polygon are on the other side of the line or on the line.
Since we are considering free rotations of the polygons and separation lines,
the mathematical model of the studied problem is nonlinear. Therefore, we use
the nonlinear programming solver IPOPT (an algorithm of interior points type),
which is part of COIN-OR. Computational tests were run using established
benchmark instances and the results were compared with the ones obtained with
other methodologies in the literature that use free rotation
Optimal Packings of Superballs
Dense hard-particle packings are intimately related to the structure of
low-temperature phases of matter and are useful models of heterogeneous
materials and granular media. Most studies of the densest packings in three
dimensions have considered spherical shapes, and it is only more recently that
nonspherical shapes (e.g., ellipsoids) have been investigated. Superballs
(whose shapes are defined by |x1|^2p + |x2|^2p + |x3|^2p <= 1) provide a
versatile family of convex particles (p >= 0.5) with both cubic- and
octahedral-like shapes as well as concave particles (0 < p < 0.5) with
octahedral-like shapes. In this paper, we provide analytical constructions for
the densest known superball packings for all convex and concave cases. The
candidate maximally dense packings are certain families of Bravais lattice
packings. The maximal packing density as a function of p is nonanalytic at the
sphere-point (p = 1) and increases dramatically as p moves away from unity. The
packing characteristics determined by the broken rotational symmetry of
superballs are similar to but richer than their two-dimensional "superdisk"
counterparts, and are distinctly different from that of ellipsoid packings. Our
candidate optimal superball packings provide a starting point to quantify the
equilibrium phase behavior of superball systems, which should deepen our
understanding of the statistical thermodynamics of nonspherical-particle
systems.Comment: 28 pages, 16 figure
2D multi-objective placement algorithm for free-form components
This article presents a generic method to solve 2D multi-objective placement
problem for free-form components. The proposed method is a relaxed placement
technique combined with an hybrid algorithm based on a genetic algorithm and a
separation algorithm. The genetic algorithm is used as a global optimizer and
is in charge of efficiently exploring the search space. The separation
algorithm is used to legalize solutions proposed by the global optimizer, so
that placement constraints are satisfied. A test case illustrates the
application of the proposed method. Extensions for solving the 3D problem are
given at the end of the article.Comment: ASME 2009 International Design Engineering Technical Conferences &
Computers and Information in Engineering Conference, San Diego : United
States (2009
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