1,039 research outputs found
Elliptic supersonic jet morphology manipulation using sharp-tipped lobes
Elliptic nozzle geometry is attractive for mixing enhancement of supersonic
jets. However, jet dynamics, such as flapping, gives rise to high-intensity
tonal sound. We experimentally manipulate the supersonic elliptic jet
morphology by using two sharp-tipped lobes. The lobes are placed on either end
of the minor axis in an elliptic nozzle. The design Mach number and the aspect
ratio of the elliptic nozzle and the lobed nozzle are 2.0 and 1.65. The
supersonic jet is exhausted into ambient at almost perfectly expanded
conditions. Time-resolved schlieren imaging, longitudinal and cross-sectional
planar laser Mie-scattering imaging, planar Particle Image Velocimetry, and
near-field microphone measurements are performed to assess the fluidic behavior
of the two nozzles. Dynamic Mode and Proper Orthogonal Decomposition (DMD and
POD) analysis are carried out on the schlieren and the Mie-scattering images.
Mixing characteristics are extracted from the Mie-scattering images through the
image processing routines. The flapping elliptic jet consists of two dominant
DMD modes, while the lobed nozzle has only one dominant mode, and the flapping
is suppressed. Microphone measurements show the associated noise reduction. The
jet column bifurcates in the lobed nozzle enabling a larger surface contact
area with the ambient fluid and higher mixing rates in the near-field of the
nozzle exit. The jet width growth rate of the two-lobed nozzle is about twice
as that of the elliptic jet in the near-field, and there is a 40\% reduction in
the potential core length. Particle Image Velocimetry (PIV) contours
substantiate the results.Comment: 19 pages, 16 figures. Revised version submitted to Physics of Fluids
for peer review. URL of the Video files (Fig. 6 & Fig. 14) are given in the
text files (see in '/anc/*.txt'
Mimicking the QCD equation of state with a dual black hole
We present numerical and analytical studies of the equation of state of
translationally invariant black hole solutions to five-dimensional gravity
coupled to a single scalar. As an application, we construct a family of black
holes that closely mimics the equation of state of quantum chromodynamics at
zero chemical potential.Comment: 25 pages, 7 figure
User Pairing and Power Allocation for IRS-Assisted NOMA Systems with Imperfect Phase Compensation
In this letter, we analyze the performance of the intelligent reflecting
surface (IRS) assisted downlink non-orthogonal multiple access (NOMA) systems
in the presence of imperfect phase compensation. We derive an upper bound on
the imperfect phase compensation to achieve minimum required data rates for
each user. Using this bound, we propose an adaptive user pairing algorithm to
maximize the network throughput. We then derive bounds on the power allocation
factors and propose power allocation algorithms for the paired users to achieve
the maximum sum rate or ensure fairness. Through extensive simulations, we show
that the proposed algorithms significantly outperform the state-of-the-art
algorithms
A Legitimate Aspiration
Medical and Dental Council of India very recently made a provision for a single eligibility examination test named National Eligibility Entrance Test (NEET) for admission to medical and dental course. The Supreme Court of India has repealed the NEET dealing a jolt to uniform admission norms for medical, dental and postgraduate seats in medical and dental schools of the country. Keywords: Admission criteria, dental education, medical education
Supervised learning with quantum enhanced feature spaces
Machine learning and quantum computing are two technologies each with the
potential for altering how computation is performed to address previously
untenable problems. Kernel methods for machine learning are ubiquitous for
pattern recognition, with support vector machines (SVMs) being the most
well-known method for classification problems. However, there are limitations
to the successful solution to such problems when the feature space becomes
large, and the kernel functions become computationally expensive to estimate. A
core element to computational speed-ups afforded by quantum algorithms is the
exploitation of an exponentially large quantum state space through controllable
entanglement and interference. Here, we propose and experimentally implement
two novel methods on a superconducting processor. Both methods represent the
feature space of a classification problem by a quantum state, taking advantage
of the large dimensionality of quantum Hilbert space to obtain an enhanced
solution. One method, the quantum variational classifier builds on [1,2] and
operates through using a variational quantum circuit to classify a training set
in direct analogy to conventional SVMs. In the second, a quantum kernel
estimator, we estimate the kernel function and optimize the classifier
directly. The two methods present a new class of tools for exploring the
applications of noisy intermediate scale quantum computers [3] to machine
learning.Comment: Fixed typos, added figures and discussion about quantum error
mitigatio
Topology counts: force distributions in circular spring networks
Filamentous polymer networks govern the mechanical properties of many
biological materials. Force distributions within these networks are typically
highly inhomogeneous and, although the importance of force distributions for
structural properties is well recognized, they are far from being understood
quantitatively. Using a combination of probabilistic and graph-theoretical
techniques we derive force distributions in a model system consisting of
ensembles of random linear spring networks on a circle. We show that
characteristic quantities, such as mean and variance of the force supported by
individual springs, can be derived explicitly in terms of only two parameters:
(i) average connectivity and (ii) number of nodes. Our analysis shows that a
classical mean-field approach fails to capture these characteristic quantities
correctly. In contrast, we demonstrate that network topology is a crucial
determinant of force distributions in an elastic spring network.Comment: 5 pages, 4 figures. Missing labels in Fig. 4 added. Reference fixe
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