5,712 research outputs found
Performance-based optimization of structures: theory and applications
Performance-based Optimization of Structures introduces a method to bridge the gap between optimization theory and its practical applications to structural engineering. The performance-based optimization (PBO) method combines modern structural optimization theory with performance-based design concepts to produce a powerful technique for use in structural design. This book provides the latest PBO techniques for achieving optimal topologies and shapes of continuum structures with stress, displacement and mean compliance constraints.
The emphasis is strongly placed on practical applications of automated PBO techniques to the strut-and-tie modeling of structural concrete, which includes reinforced and prestressed concrete structures. Basic concepts underlying the development of strut-and-tie models, design optimization procedure, and detailing of structural concrete are described in detail. The design optimization of lateral load resisting systems for multi-story steel and steel-concrete composite buildings is also presented. Numerous practical design examples are given which illustrate the nature of the load transfer mechanisms of structures
Structural design optimization
Guest Editorial, Special Issue on Structural Design Optimization, Advances in Structural Engineering, An International Journal, 2007, Vol. 10, No.6
A channel Brownian pump powered by an unbiased external force
A Brownian pump of particles in an asymmetric finite tube is investigated in
the presence of an unbiased external force. The pumping system is bounded by
two particle reservoirs. It is found that the particles can be pumped through
the tube from a reservoir at low concentration to one at the same or higher
concentration. There exists an optimized value of temperature (or the amplitude
of the external force) at which the pumping capacity takes its maximum value.
The pumping capacity decreases with increasing the radius at the bottleneck of
the tube.Comment: 14 pages, 9 figure
Complex quantum network model of energy transfer in photosynthetic complexes
The quantum network model with real variables is usually used to describe the
excitation energy transfer (EET) in the Fenna-Matthews-Olson(FMO) complexes. In
this paper we add the quantum phase factors to the hopping terms and find that
the quantum phase factors play an important role in the EET. The quantum phase
factors allow us to consider the space structure of the pigments. It is found
that phase coherence within the complexes would allow quantum interference to
affect the dynamics of the EET. There exist some optimal phase regions where
the transfer efficiency takes its maxima, which indicates that when the
pigments are optimally spaced, the exciton can pass through the FMO with
perfect efficiency. Moreover, the optimal phase regions almost do not change
with the environments. In addition, we find that the phase factors are useful
in the EET just in the case of multiple-pathway. Therefore, we demonstrate
that, the quantum phases may bring the other two factors, the optimal space of
the pigments and multiple-pathway, together to contribute the EET in
photosynthetic complexes with perfect efficiency.Comment: 8 pages, 9 figure
Constrain intergalactic medium from the SZ effect map
In this paper, we try to detect the SZ effect in the 2MASS DWT clusters and
less bound objects in order to constrain the warm-hot intergalactic medium
distribution on large scales by cross-correlation analysis. The results of both
observed WMAP and mock SZ effect map indicate that the hot gas distributes from
inside as well as outside of the high density regions of galaxy clusters, which
is consistent with the results of both observation and hydro simulation.
Therefore, the DWT measurement of the cross-correlation would be a powerful
tool to probe the missing of baryons in the Universe.Comment: 9 pages,2 figures. Accepted for publication in Mod. Phys. Lett.
Reveal flocking of birds flying in fog by machine learning
We study the first-order flocking transition of birds flying in
low-visibility conditions by employing three different representative types of
neural network (NN) based machine learning architectures that are trained via
either an unsupervised learning approach called "learning by confusion" or a
widely used supervised learning approach. We find that after the training via
either the unsupervised learning approach or the supervised learning one, all
of these three different representative types of NNs, namely, the
fully-connected NN, the convolutional NN, and the residual NN, are able to
successfully identify the first-order flocking transition point of this
nonequilibrium many-body system. This indicates that NN based machine learning
can be employed as a promising generic tool to investigate rich physics in
scenarios associated to first-order phase transitions and nonequilibrium
many-body systems.Comment: 7 pages, 3 figure
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