129 research outputs found
A Weyl-Dirac Cosmological Model with DM and DE
In the Weyl-Dirac (W-D) framework a spatially closed cosmological model is
considered. It is assumed that the space-time of the universe has a chaotic
Weylian microstructure but is described on a large scale by Riemannian
geometry. Locally fields of the Weyl connection vector act as creators of
massive bosons having spin 1. It is suggested that these bosons, called
weylons, provide most of the dark matter in the universe. At the beginning the
universe is a spherically symmetric geometric entity without matter. Primary
matter is created by Dirac's gauge function very close to the beginning. In the
early epoch, when the temperature of the universe achieves its maximum,
chaotically oriented Weyl vector fields being localized in micro-cells create
weylons. In the dust dominated period Dirac's gauge function is giving rise to
dark energy, the latter causing the cosmic acceleration at present. This
oscillatory universe has an initial radius identical to the Plank length =
1.616 exp (-33) cm, at present the cosmic scale factor is 3.21 exp (28) cm,
while its maximum value is 8.54 exp (28) cm. All forms of matter are created by
geometrically based functions of the W-D theory.Comment: 25 pages. Submitted to GR
Shock Compression in Granular Media Using Discrete Finite Element Method
Los Alamos National Laboratory Research Contract LANL 6911J00149
Neural Network-Based Material Modeling
A neural network - based material modeling methodology for engineering materials
is developed in this study. With this approach, the complex stress - strain behavior
of an engineering material can be captured within the weight structure of a multilayer
feedforward neural network trained directly on the stress- strain data obtained from
experiments. The feasibility of this approach is verified through constructing neural
network-based constitutive models of plain concrete in biaxial stress states and in
uniaxial cyclic compression. A composite material model simulating the stress-strain
behavior of reinforced concrete as a generic composite material in a biaxial stress state
is built with experimental data from Vecchio and Collins' tests on reinforced concrete
panels in both pure shear and combined shear with normal stresses.
An adaptive neural network simulator is developed by implementing a dynamic
node creation scheme and a higher order learning algorithm. Representation schemes,
network architectures. training and testing methods, stress- and strain -based approaches
for material modeling are investigated. An elastic unloading mechanism is
studied with a concrete material model in biaxial compression. Main issues concerning
the implementation of neural network material models in finite element solution
procedures arc discussed. The results on the stress-strain relations of a material
predicted by a neural network-based model are compared with experimental data. All
neural network material models developed in this study match well with experimental
results and the network testing results are reasonable. The developed approach shows
promise in the constitutive modeling of composite materials
Modeling of Hysteretic Behavior of Beam-Column Connections Based on Self-Learning Simulation
Current AISC-LRFD code requires that the moment-rotation characteristics of
connections be known. Moreover, it requires that these characteristics be incorporated in
the analysis and member design under factored loads (AISC, 2001). Conventional
modeling approaches to improve the prediction of cyclic behavior starts with a choice of a phenomenological model followed by calibration of the model parameters. However, not only is the improvement limited due to inherent limitations of this approach, but also test results indicate a large variability in load-carrying capacity under earthquake loading.
In this research, a new neural network (NN) based cyclic material model is applied to
inelastic hysteretic behavior of connections. In the proposed model, two energy-based internal variables are introduced to expedite the learning of hysteretic behavior of materials or structural components. The model has significant advantages over conventional models in that it can handle complex behavior due to local buckling and
tearing of connecting elements. Moreover, its numerical implementation is more efficient than the conventional models since it does not need an interaction equation and a plastic potential. A new approach based on a self-learning simulation algorithm is used to characterize the hysteretic behavior of the connections from structural tests. The proposed approach is verified by applying it to both synthetic and experimental examples. For its practical application in semi-rigid connections, design variables are included as inputs to the model through a physical principle based module. The extended model also gives reasonable predictions under earthquake loads even when it is presented with new geometrical properties and loading scenario as well.published or submitted for publicatio
Hybrid Mathematical-Informational Modeling of Beam-to-Column Connections
The analysis of steel and composite frames has traditionally been carried out by
idealizing beam-to-column connections as either rigid or pinned. Although some
advanced analysis methods have been proposed to account for semi-rigid connections, the
performance of these methods strongly depends on the proper modeling of connection
behavior. The primary challenge of modeling beam-to-column connections is their
inelastic response and continuously varying stiffness, strength, and ductility. In this report,
two distinct approaches???mathematical models and informational models???are proposed
to account for the complex hysteretic behavior of beam-to-column connections. The
performance of the two approaches is examined and is then followed by a discussion of
their merits and deficiencies. To capitalize on the merits of both mathematical and
informational representations, a new approach, a hybrid modeling framework, is
developed and demonstrated through modeling beam-to-column connections.
Component-based modeling is a compromise spanning two extremes in the field
of mathematical modeling: simplified global models and finite element models. In the
component-based modeling of angle connections, the five critical components of
excessive deformation are identified. Constitutive relationships of angles, column panel
zones, and contact between angles and column flanges, are derived by using only
material and geometric properties and theoretical mechanics considerations. Those of slip
and bolt hole ovalization are simplified by empirically-suggested mathematical
representation and expert opinions. A mathematical model is then assembled as a macroelement
by combining rigid bars and springs that represent the constitutive relationship of
components. Lastly, the moment-rotation curves of the mathematical models are
compared with those of experimental tests. In the case of a top-and-seat angle connection
with double web angles, a pinched hysteretic response is predicted quite well by complete
mechanical models, which take advantage of only material and geometric properties. On
the other hand, to exhibit the highly pinched behavior of a top-and-seat angle connection
without web angles, a mathematical model requires components of slip and bolt hole
ovalization, which are more amenable to informational modeling.
An alternative method is informational modeling, which constitutes a fundamental
shift from mathematical equations to data that contain the required information about
underlying mechanics. The information is extracted from observed data and stored in
neural networks. Two different training data sets, analytically-generated and
experimental data, are tested to examine the performance of informational models. Both
informational models show acceptable agreement with the moment-rotation curves of the
experiments. Adding a degradation parameter improves the informational models when
modeling highly pinched hysteretic behavior. However, informational models cannot
represent the contribution of individual components and therefore do not provide an
insight into the underlying mechanics of components.
In this study, a new hybrid modeling framework is proposed. In the hybrid
framework, a conventional mathematical model is complemented by the informational
methods. The basic premise of the proposed hybrid methodology is that not all features of
system response are amenable to mathematical modeling, hence considering
informational alternatives. This may be because (i) the underlying theory is not available
or not sufficiently developed, or (ii) the existing theory is too complex and therefore not
suitable for modeling within building frame analysis. The role of informational methods
is to model aspects that the mathematical model leaves out. Autoprogressive algorithm
and self-learning simulation extract the missing aspects from a system response. In a
hybrid framework, experimental data is an integral part of modeling, rather than being
used strictly for validation processes. The potential of the hybrid methodology is
illustrated through modeling complex hysteretic behavior of beam-to-column connections.
Mechanics-based components of deformation such as angles, flange-plates, and column
panel zone, are idealized to a mathematical model by using a complete mechanical
approach. Although the mathematical model represents envelope curves in terms of initial
stiffness and yielding strength, it is not capable of capturing the pinching effects.
Pinching is caused mainly by separation between angles and column flanges as well as
slip between angles/flange-plates and beam flanges. These components of deformation
are suitable for informational modeling. Finally, the moment-rotation curves of the
hybrid models are validated with those of the experimental tests. The comparison shows
that the hybrid models are capable of representing the highly pinched hysteretic behavior
of beam-to-column connections. In addition, the developed hybrid model is successfully
used to predict the behavior of a newly-designed connection.unpublishednot peer reviewe
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