25,670 research outputs found
Adjoint operator approach to shape design for internal incompressible flows
The problem of determining the profile of a channel or duct that provides the maximum static pressure rise is solved. Incompressible, laminar flow governed by the steady state Navier-Stokes equations is assumed. Recent advances in computational resources and algorithms have made it possible to solve the direct problem of determining such a flow through a body of known geometry. It is possible to obtain a set of adjoint equations, the solution to which permits the calculation of the direction and relative magnitude of change in the diffuser profile that leads to a higher pressure rise. The solution to the adjoint problem can be shown to represent an artificially constructed flow. This interpretation provides a means to construct numerical solutions to the adjoint equations that do not compromise the fully viscous nature of the problem. The algorithmic and computational aspects of solving the adjoint equations are addressed. The form of these set of equations is similar but not identical to the Navier-Stokes equations. In particular some issues related to boundary conditions and stability are discussed
No Open Cluster in the Ruprecht 93 Region
UBVI CCD photometry has been obtained for the Ruprecht 93 (Ru 93) region. We
were unable to confirm the existence of an intermediate-age open cluster in Ru
93 from the spatial distribution of blue stars. On the other hand, we found two
young star groups in the observed field: the nearer one (Ru 93 group) comprises
the field young stars in the Sgr-Car arm at d ~ 2.1 kpc, while the farther one
(WR 37 group) is the young stars around WR 37 at d ~ 4.8 kpc. We have derived
an abnormal extinction law (Rv = 3.5) in the Ruprecht 93 region.Comment: 6 pages, 6 figures, JKAS 2010, in press (Aug issue
Optimal and Suboptimal Detection of Gaussian Signals in Noise: Asymptotic Relative Efficiency
The performance of Bayesian detection of Gaussian signals using noisy
observations is investigated via the error exponent for the average error
probability. Under unknown signal correlation structure or limited processing
capability it is reasonable to use the simple quadratic detector that is
optimal in the case of an independent and identically distributed (i.i.d.)
signal. Using the large deviations principle, the performance of this detector
(which is suboptimal for non-i.i.d. signals) is compared with that of the
optimal detector for correlated signals via the asymptotic relative efficiency
defined as the ratio between sample sizes of two detectors required for the
same performance in the large-sample-size regime. The effects of SNR on the ARE
are investigated. It is shown that the asymptotic efficiency of the simple
quadratic detector relative to the optimal detector converges to one as the SNR
increases without bound for any bounded spectrum, and that the simple quadratic
detector performs as well as the optimal detector for a wide range of the
correlation values at high SNR.Comment: To appear in the Proceedings of the SPIE Conference on Advanced
Signal Processing Algorithms, Architectures and Implementations XV, San
Diego, CA, Jul. 1 - Aug. 4, 200
Neyman-Pearson Detection of Gauss-Markov Signals in Noise: Closed-Form Error Exponent and Properties
The performance of Neyman-Pearson detection of correlated stochastic signals
using noisy observations is investigated via the error exponent for the miss
probability with a fixed level. Using the state-space structure of the signal
and observation model, a closed-form expression for the error exponent is
derived, and the connection between the asymptotic behavior of the optimal
detector and that of the Kalman filter is established. The properties of the
error exponent are investigated for the scalar case. It is shown that the error
exponent has distinct characteristics with respect to correlation strength: for
signal-to-noise ratio (SNR) >1 the error exponent decreases monotonically as
the correlation becomes stronger, whereas for SNR <1 there is an optimal
correlation that maximizes the error exponent for a given SNR.Comment: To appear in the IEEE Transactions on Information Theor
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