70,519 research outputs found
Response characteristics of MHOST (MARC Hot-Section Technology) for 3-D inelastic analysis of hot-section components
The advantages of a newly developed code are demonstrated by comparisons of the analysis with existing theoretical data as well as with other available finite element programs. The new program shows promise to significantly reduce the computer time. It also permits accurate and efficient structural analysis of engine hot section components
Relativistic Equation of State for Core-Collapse Supernova Simulations
We construct the equation of state (EOS) of dense matter covering a wide
range of temperature, proton fraction, and density for the use of core-collapse
supernova simulations. The study is based on the relativistic mean-field (RMF)
theory, which can provide an excellent description of nuclear matter and finite
nuclei. The Thomas--Fermi approximation in combination with assumed nucleon
distribution functions and a free energy minimization is adopted to describe
the non-uniform matter, which is composed of a lattice of heavy nuclei. We
treat the uniform matter and non-uniform matter consistently using the same RMF
theory. We present two sets of EOS tables, namely EOS2 and EOS3. EOS2 is an
update of our earlier work published in 1998 (EOS1), where only the nucleon
degree of freedom is taken into account. EOS3 includes additional contributions
from hyperons. The effect of hyperons on the EOS is
negligible in the low-temperature and low-density region, whereas it tends to
soften the EOS at high density. In comparison with EOS1, EOS2 and EOS3 have an
improved design of ranges and grids, which covers the temperature range
-- MeV with the logarithmic grid spacing (92 points including T=0), the proton fraction
range --0.65 with the linear grid spacing (66
points), and the density range --
with the logarithmic grid spacing (110 points).Comment: 43 pages, 10 figure
Relativistic Equation of State of Nuclear Matter for Supernova Explosion
We construct the equation of state (EOS) of nuclear matter at finite
temperature and density with various proton fractions within the relativistic
mean field (RMF) theory for the use in the supernova simulations. The
Thomas-Fermi approximation is adopted to describe the non-uniform matter where
we consider nucleus, alpha-particle, proton and neutron in equilibrium. We
treat the uniform matter and non-uniform matter consistently using the RMF
theory. We tabulate the outcome as the pressure, free energy, entropy etc, with
enough mesh points in wide ranges of the temperature, proton fraction, and
baryon mass density.Comment: 22 pages, LaTeX, 9 ps-figures, Submitted to Prog.Theor.Phy
Sensor Selection Based on Generalized Information Gain for Target Tracking in Large Sensor Networks
In this paper, sensor selection problems for target tracking in large sensor
networks with linear equality or inequality constraints are considered. First,
we derive an equivalent Kalman filter for sensor selection, i.e., generalized
information filter. Then, under a regularity condition, we prove that the
multistage look-ahead policy that minimizes either the final or the average
estimation error covariances of next multiple time steps is equivalent to a
myopic sensor selection policy that maximizes the trace of the generalized
information gain at each time step. Moreover, when the measurement noises are
uncorrelated between sensors, the optimal solution can be obtained analytically
for sensor selection when constraints are temporally separable. When
constraints are temporally inseparable, sensor selections can be obtained by
approximately solving a linear programming problem so that the sensor selection
problem for a large sensor network can be dealt with quickly. Although there is
no guarantee that the gap between the performance of the chosen subset and the
performance bound is always small, numerical examples suggest that the
algorithm is near-optimal in many cases. Finally, when the measurement noises
are correlated between sensors, the sensor selection problem with temporally
inseparable constraints can be relaxed to a Boolean quadratic programming
problem which can be efficiently solved by a Gaussian randomization procedure
along with solving a semi-definite programming problem. Numerical examples show
that the proposed method is much better than the method that ignores dependence
of noises.Comment: 38 pages, 14 figures, submitted to Journa
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