9,831 research outputs found
A new approach to upscaling fracture network models while preserving geostatistical and geomechanical characteristics
A new approach to upscaling two-dimensional fracture network models is proposed for preserving geostatistical and geomechanical characteristics of a smaller-scale “source” fracture pattern. First, the scaling properties of an outcrop system are examined in terms of spatial organization, lengths, connectivity, and normal/shear displacements using fractal geometry and power law relations. The fracture pattern is observed to be nonfractal with the fractal dimension D ≈ 2, while its length distribution tends to follow a power law with the exponent 2 < a < 3. To introduce a realistic distribution of fracture aperture and shear displacement, a geomechanical model using the combined finite-discrete element method captures the response of a fractured rock sample with a domain size L = 2 m under in situ stresses. Next, a novel scheme accommodating discrete-time random walks in recursive self-referencing lattices is developed to nucleate and propagate fractures together with their stress- and scale-dependent attributes into larger domains of up to 54 m × 54 m. The advantages of this approach include preserving the nonplanarity of natural cracks, capturing the existence of long fractures, retaining the realism of variable apertures, and respecting the stress dependency of displacement-length correlations. Hydraulic behavior of multiscale growth realizations is modeled by single-phase flow simulation, where distinct permeability scaling trends are observed for different geomechanical scenarios. A transition zone is identified where flow structure shifts from extremely channeled to distributed as the network scale increases. The results of this paper have implications for upscaling network characteristics for reservoir simulation
Co-community Structure in Time-varying Networks
In this report, we introduce the concept of co-community structure in
time-varying networks. We propose a novel optimization algorithm to rapidly
detect co-community structure in these networks. Both theoretical and numerical
results show that the proposed method not only can resolve detailed
co-communities, but also can effectively identify the dynamical phenomena in
these networks.Comment: 5 pages, 6 figure
Efficient one-step generation of large cluster states with solid-state circuits
Highly entangled states called cluster states are a universal resource for
measurement-based quantum computing (QC). Here we propose an efficient method
for producing large cluster states using superconducting quantum circuits. We
show that a large cluster state can be efficiently generated in just one step
by turning on the inter-qubit coupling for a short time. Because the
inter-qubit coupling is only switched on during the time interval for
generating the cluster state, our approach is also convenient for preparing the
initial state for each qubit and for implementing one-way QC via single-qubit
measurements. Moreover, the cluster state is robust against parameter
variations.Comment: 4 pages, 1 figur
Thermodynamical quantities of lattice full QCD from an efficient method
I extend to QCD an efficient method for lattice gauge theory with dynamical
fermions. Once the eigenvalues of the Dirac operator and the density of states
of pure gluonic configurations at a set of plaquette energies (proportional to
the gauge action) are computed, thermodynamical quantities deriving from the
partition function can be obtained for arbitrary flavor number, quark masses
and wide range of coupling constants, without additional computational cost.
Results for the chiral condensate and gauge action are presented on the
lattice at flavor number , 1, 2, 3, 4 and many quark masses and coupling
constants. New results in the chiral limit for the gauge action and its
correlation with the chiral condensate, which are useful for analyzing the QCD
chiral phase structure, are also provided.Comment: Latex, 11 figures, version accepted for publicatio
Renormalization of tensor-network states
We have discussed the tensor-network representation of classical statistical
or interacting quantum lattice models, and given a comprehensive introduction
to the numerical methods we recently proposed for studying the tensor-network
states/models in two dimensions. A second renormalization scheme is introduced
to take into account the environment contribution in the calculation of the
partition function of classical tensor network models or the expectation values
of quantum tensor network states. It improves significantly the accuracy of the
coarse grained tensor renormalization group method. In the study of the quantum
tensor-network states, we point out that the renormalization effect of the
environment can be efficiently and accurately described by the bond vector.
This, combined with the imaginary time evolution of the wavefunction, provides
an accurate projection method to determine the tensor-network wavfunction. It
reduces significantly the truncation error and enable a tensor-network state
with a large bond dimension, which is difficult to be accessed by other
methods, to be accurately determined.Comment: 18 pages 23 figures, minor changes, references adde
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