Abstract: In a travelling wave thermoacoustic device, the
regenerator sandwiched between a pair of (hot and cold) heat
exchangers constitutes the so-called thermoacoustic core, where the
thermoacoustic energy conversion from heat to acoustic power takes
place. The temperature gradient along the regenerator caused by the
two heat exchangers excites and maintains the acoustic wave in the
resonator. The devices are called travelling wave thermoacoustic
systems because the phase angle difference between the pressure and
velocity oscillation is close to zero in the regenerator. This paper
presents the construction and testing of a thermoacoustic engine
equipped with a ceramic regenerator, made from a ceramic material
that is usually used as catalyst substrate in vehicles- exhaust systems,
with fine square channels (900 cells per square inch). The testing
includes the onset temperature difference (minimum temperature
difference required to start the acoustic oscillation in an engine), the
acoustic power output, thermal efficiency and the temperature profile
along the regenerator.
Abstract: In large Internet backbones, Service Providers
typically have to explicitly manage the traffic flows in order to
optimize the use of network resources. This process is often referred
to as Traffic Engineering (TE). Common objectives of traffic
engineering include balance traffic distribution across the network
and avoiding congestion hot spots. Raj P H and SVK Raja designed
the Bayesian network approach to identify congestion hors pots in
MPLS. In this approach for every node in the network the
Conditional Probability Distribution (CPD) is specified. Based on
the CPD the congestion hot spots are identified. Then the traffic can
be distributed so that no link in the network is either over utilized or
under utilized. Although the Bayesian network approach has been
implemented in operational networks, it has a number of well known
scaling issues.
This paper proposes a new approach, which we call the Pragati
(means Progress) Node Popularity (PNP) approach to identify the
congestion hot spots with the network topology alone. In the new
Pragati Node Popularity approach, IP routing runs natively over the
physical topology rather than depending on the CPD of each node as
in Bayesian network. We first illustrate our approach with a simple
network, then present a formal analysis of the Pragati Node
Popularity approach. Our PNP approach shows that for any given
network of Bayesian approach, it exactly identifies the same result
with minimum efforts. We further extend the result to a more
generic one: for any network topology and even though the network
is loopy. A theoretical insight of our result is that the optimal routing
is always shortest path routing with respect to some considerations of
hot spots in the networks.
Abstract: Today, numerical simulation is a powerful tool to
solve various hydraulic engineering problems. The aim of this
research is numerical solutions of shallow water equations using
finite volume method for Simulations of dam break over wet and dry
bed. In order to solve Riemann problem, Roe-s approximate solver is
used. To evaluate numerical model, simulation was done in 1D and
2D states. In 1D state, two dam break test over dry bed (with and
without friction) were studied. The results showed that Structural
failure around the dam and damage to the downstream constructions
in bed without friction is more than friction bed. In 2D state, two
tests for wet and dry beds were done. Generally in wet bed case,
waves are propagated to canal sides but in dry bed it is not
significant. Therefore, damage to the storage facilities and
agricultural lands in wet bed case is more than in dry bed.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.