Abstract: In this paper, a direct power control (DPC)
strategies have been investigated in order to control a high
power AC/DC converter with time variable load. This converter
is composed of a three level three phase neutral point clamped
(NPC) converter as rectifier and an H-bridge four quadrant
current control converter. In the high power application,
controller not only must adjust the desire outputs but also
decrease the level of distortions which are injected to the network
from the converter. Regarding to this reason and nonlinearity
of the power electronic converter, the conventional controllers
cannot achieve appropriate responses. In this research, the
precise mathematical analysis has been employed to design the
appropriate controller in order to control the time variable
load. A DPC controller has been proposed and simulated using
Matlab/ Simulink. In order to verify the simulation result, a real
time simulator- OPAL-RT- has been employed. In this paper,
the dynamic response and stability of the high power NPC
with variable load has been investigated and compared with
conventional types using a real time simulator. The results proved
that the DPC controller is more stable and has more precise
outputs in comparison with conventional controller.
Abstract: The formulated problem of optimization of the
technological process of water treatment for thermal power plants is
considered in this article. The problem is of multiparametric nature.
To optimize the process, namely, reduce the amount of waste water, a
new technology was developed to reuse such water. A mathematical
model of the technology of wastewater reuse was developed.
Optimization parameters were determined. The model consists of a
material balance equation, an equation describing the kinetics of ion
exchange for the non-equilibrium case and an equation for the ion
exchange isotherm. The material balance equation includes a
nonlinear term that depends on the kinetics of ion exchange. A direct
problem of calculating the impurity concentration at the outlet of the
water treatment plant was numerically solved. The direct problem
was approximated by an implicit point-to-point computation
difference scheme. The inverse problem was formulated as relates to
determination of the parameters of the mathematical model of the
water treatment plant operating in non-equilibrium conditions. The
formulated inverse problem was solved. Following the results of
calculation the time of start of the filter regeneration process was
determined, as well as the period of regeneration process and the
amount of regeneration and wash water. Multi-parameter
optimization of water treatment process for thermal power plants
allowed decreasing the amount of wastewater by 15%.
Abstract: In this paper we present the efficient parallel
implementation of elastoplastic problems based on the TFETI (Total
Finite Element Tearing and Interconnecting) domain decomposition
method. This approach allow us to use parallel solution and compute
this nonlinear problem on the supercomputers and decrease the
solution time and compute problems with millions of DOFs. In
our approach we consider an associated elastoplastic model with
the von Mises plastic criterion and the combination of linear
isotropic-kinematic hardening law. This model is discretized by
the implicit Euler method in time and by the finite element
method in space. We consider the system of nonlinear equations
with a strongly semismooth and strongly monotone operator. The
semismooth Newton method is applied to solve this nonlinear
system. Corresponding linearized problems arising in the Newton
iterations are solved in parallel by the above mentioned TFETI. The
implementation of this problem is realized in our in-house MatSol
packages developed in MatLab.
Abstract: In this paper we consider the equation of motion for
the F (R, T) gravity on their property of conformal invariance. It
is shown that in the general case, such a theory is not conformal
invariant. Studied special cases for the functions v and u in which
can appear properties of the theory. Also we consider cosmological
aspects F (R, T) theory of gravity, having considered particular case
F (R, T) = μR+νT^2. Showed that in this case there is a nonlinear
dependence of the parameter equation of state from time to time,
which affects its evolution.
Abstract: Using the pseudopotential technique the Sagdeev
potential equation has been derived in a plasma consisting of twotemperature
nonisothermal electrons, negatively charged dust grains
and warm positive ions. The study shows that the presence of
nonisothermal two-temperature electrons and charged dust grains
have significant effects on the excitation and structure of the ionacoustic
double layers in the model plasma under consideration. Only
compressive type double layer is obtained in the present plasma
model. The double layer solution has also been obtained by including
higher order nonlinearity and nonisothermality, which is shown to
modify the amplitude and deform the shape of the double layer.
Abstract: New physical insights into the nonlinear Lorenz
equations related to flow resistance is discussed in this work. The
chaotic dynamics related to Lorenz equations has been studied in
many papers, which is due to the sensitivity of Lorenz equations to
initial conditions and parameter uncertainties. However, the physical
implication arising from Lorenz equations about convectional motion
attracts little attention in the relevant literature. Therefore, as a first
step to understand the related fluid mechanics of convectional motion,
this paper derives the Lorenz equations again with different forced
conditions in the model. Simulation work of the modified Lorenz
equations without the viscosity or buoyancy force is discussed. The
time-domain simulation results may imply that the states of the
Lorenz equations are related to certain flow speed and flow resistance.
The flow speed of the underlying fluid system increases as the flow
resistance reduces. This observation would be helpful to analyze the
coupling effects of different fluid parameters in a convectional model
in future work.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: In this paper, the problem of fault detection and
isolation in the attitude control subsystem of spacecraft formation
flying is considered. In order to design the fault detection method, an
extended Kalman filter is utilized which is a nonlinear stochastic state
estimation method. Three fault detection architectures, namely,
centralized, decentralized, and semi-decentralized are designed based
on the extended Kalman filters. Moreover, the residual generation
and threshold selection techniques are proposed for these
architectures.
Abstract: This study estimates the seismic demands of tall
buildings with central symmetric setbacks by using nonlinear time
history analysis. Three setback structures, all 60-story high with
setback in three levels, are used for evaluation. The effects of
irregularities occurred by setback are evaluated by determination of
global-drift, story-displacement and story drift. Story-displacement is
modified by roof displacement and first story displacement and story
drift is modified by global drift. All results are calculated at the
center of mass and in x and y direction. Also the absolute values of
these quantities are determined. The results show that increasing of
vertical irregularities increases the global drift of the structure and
enlarges the deformations in the height of the structure. It is also
observed that the effects of geometry irregularity in the seismic
deformations of setback structures are higher than those of mass
irregularity.
Abstract: Unmanned aircraft systems (UAS) are playing
increasingly prominent roles in defense programs and defense
strategies around the world. Technology advancements have
enabled the development of it to do many excellent jobs as
reconnaissance, surveillance, battle fighters, and communications
relays. Simulating a small unmanned aerial vehicle (SUAV)
dynamics and analyzing its behavior at the preflight stage is too
important and more efficient. The first step in the UAV design is
the mathematical modeling of the nonlinear equations of motion. .
In this paper, a survey with a standard method to obtain the full
non-linear equations of motion is utilized, and then the
linearization of the equations according to a steady state flight
condition (trimming) is derived. This modeling technique is
applied to an Ultrastick-25e fixed wing UAV to obtain the valued
linear longitudinal and lateral models. At the end the model is
checked by matching between the behavior of the states of the nonlinear
UAV and the resulted linear model with doublet at the
control surfaces.
Abstract: The seismic responses of steel buildings with semirigid
post-tensioned connections (PC) are estimated and compared
with those of steel buildings with typical rigid (welded) connections
(RC). The comparison is made in terms of global and local response
parameters. The results indicate that the seismic responses in terms of
interstory shears, roof displacements, axial load and bending
moments are smaller for the buildings with PC connection. The
difference is larger for global than for local parameters, which in turn
varies from one column location to another. The reason for this
improved behavior is that the buildings with PC dissipate more
hysteretic energy than those with RC. In addition, unlike the case of
buildings with WC, for the PC structures the hysteretic energy is
mostly dissipated at the connections, which implies that structural
damage in beams and columns is not significant. According to these
results, steel buildings with PC are a viable option in high seismicity
areas because of their smaller response and self-centering connection
capacity as well as the fact that brittle failure is avoided.
Abstract: In order to study the aerodynamic performance of a
semi-flexible membrane wing, Fluid-Structure Interaction simulations
have been performed. The fluid problem has been modeled using
two different approaches which are the vortex panel method and the
numerical solution of the Navier-Stokes equations. Nonlinear analysis
of the structural problem is performed using the Finite Element
Method. Comparison between the two fluid solvers has been made.
Aerodynamic performance of the wing is discussed regarding its
lift and drag coefficients and they are compared with those of the
equivalent rigid wing.
Abstract: This study examines analytically the effect of tsunami loads on reinforced concrete (RC) frame buildings. The impact of tsunami wave loads and waterborne objects are analyzed using a typical substandard full-scale two-story RC frame building tested as part of the EU-funded Ecoleader project. The building was subjected to shake table tests in bare condition, and subsequently strengthened using Carbon Fiber Reinforced Polymers (CFRP) composites and retested. Numerical models of the building in both bare and CFRP-strengthened conditions are calibrated in DRAIN-3DX software to match the test results. To investigate the response of wave loads and impact forces, the numerical models are subjected to nonlinear dynamic analyses using force time-history input records. The analytical results are compared in terms of displacements at the floors and at the “impact point” of a boat. The results show that the roof displacement of the CFRP-strengthened building reduced by 63% when compared to the bare building. The results also indicate that strengthening only the mid-height of the impact column using CFRP is more effective at reducing damage when compared to strengthening other parts of the column. Alternative solutions to mitigate damage due to tsunami loads are suggested.
Abstract: Neurons in the nervous system communicate with
each other by producing electrical signals called spikes. To
investigate the physiological function of nervous system it is essential
to study the activity of neurons by detecting and sorting spikes in the
recorded signal. In this paper a method is proposed for considering
the spike sorting problem which is based on the nonlinear modeling
of spikes using exponential autoregressive model. The genetic
algorithm is utilized for model parameter estimation. In this regard
some selected model coefficients are used as features for sorting
purposes. For optimal selection of model coefficients, self-organizing
feature map is used. The results show that modeling of spikes with
nonlinear autoregressive model outperforms its linear counterpart.
Also the extracted features based on the coefficients of exponential
autoregressive model are better than wavelet based extracted features
and get more compact and well-separated clusters. In the case of
spikes different in small-scale structures where principal component
analysis fails to get separated clouds in the feature space, the
proposed method can obtain well-separated cluster which removes
the necessity of applying complex classifiers.
Abstract: Geometric and mechanical properties all influence the
resistance of RC structures and may, in certain combination of
property values, increase the risk of a brittle failure of the whole
system.
This paper presents a statistical and probabilistic investigation on
the resistance of RC beams designed according to Eurocodes 2 and 8,
and subjected to multiple failure modes, under both the natural
variation of material properties and the uncertainty associated with
cross-section and transverse reinforcement geometry. A full
probabilistic model based on JCSS Probabilistic Model Code is
derived. Different beams are studied through material nonlinear
analysis via Monte Carlo simulations. The resistance model is
consistent with Eurocode 2. Both a multivariate statistical evaluation
and the data clustering analysis of outcomes are then performed.
Results show that the ultimate load behaviour of RC beams
subjected to flexural and shear failure modes seems to be mainly
influenced by the combination of the mechanical properties of both
longitudinal reinforcement and stirrups, and the tensile strength of
concrete, of which the latter appears to affect the overall response of
the system in a nonlinear way. The model uncertainty of the
resistance model used in the analysis plays undoubtedly an important
role in interpreting results.
Abstract: In this paper, a nonlinear Finite Element Analysis
(FEA) was carried out using ANSYS software to build a model able
of predicting the behavior of Reinforced Concrete (RC) beams with
unbonded reinforcement. The FEA model was compared to existing
experimental data by other researchers. The existing experimental
data consisted of 16 beams that varied from structurally sound beams
to beams with unbonded reinforcement with different unbonded
lengths and reinforcement ratios. The model was able to predict the
ultimate flexural strength, load-deflection curve, and crack pattern of
concrete beams with unbonded reinforcement. It was concluded that
when the when the unbonded length is less than 45% of the span,
there will be no decrease in the ultimate flexural strength due to the
loss of bond between the steel reinforcement and the surrounding
concrete regardless of the reinforcement ratio. Moreover, when the
reinforcement ratio is relatively low, there will be no decrease in
ultimate flexural strength regardless of the length of unbond.
Abstract: High Peak to Average Power Ratio (PAPR) of the
transmitted signal is a serious problem in multicarrier systems (MC),
such as Orthogonal Frequency Division Multiplexing (OFDM), or in
Multi-Carrier Code Division Multiple Access (MC-CDMA) systems,
due to large number of subcarriers. This effect is possible reduce with
some PAPR reduction techniques. Spreading sequences at the
presence of Saleh and Rapp models of high power amplifier (HPA)
have big influence on the behavior of system. In this paper we
investigate the bit-error-rate (BER) performance of MC-CDMA
systems. Basically we can see from simulations that the MC-CDMA
system with Iterative algorithm can be providing significantly better
results than the MC-CDMA system. The results of our analyses are
verified via simulation.
Abstract: Recent research in neural networks science and
neuroscience for modeling complex time series data and statistical
learning has focused mostly on learning from high input space and
signals. Local linear models are a strong choice for modeling local
nonlinearity in data series. Locally weighted projection regression is
a flexible and powerful algorithm for nonlinear approximation in
high dimensional signal spaces. In this paper, different learning
scenario of one and two dimensional data series with different
distributions are investigated for simulation and further noise is
inputted to data distribution for making different disordered
distribution in time series data and for evaluation of algorithm in
locality prediction of nonlinearity. Then, the performance of this
algorithm is simulated and also when the distribution of data is high
or when the number of data is less the sensitivity of this approach to
data distribution and influence of important parameter of local
validity in this algorithm with different data distribution is explained.
Abstract: Building loss estimation methodologies which have
been advanced considerably in recent decades are usually used to
estimate socio and economic impacts resulting from seismic structural
damage. In accordance with these methods, this paper presents the
evaluation of an annual loss probability of a reinforced concrete
moment resisting frame designed according to Korean Building Code.
The annual loss probability is defined by (1) a fragility curve obtained
from a capacity spectrum method which is similar to a method adopted
from HAZUS, and (2) a seismic hazard curve derived from annual
frequencies of exceedance per peak ground acceleration. Seismic
fragilities are computed to calculate the annual loss probability of a
certain structure using functions depending on structural capacity,
seismic demand, structural response and the probability of exceeding
damage state thresholds. This study carried out a nonlinear static
analysis to obtain the capacity of a RC moment resisting frame
selected as a prototype building. The analysis results show that the
probability of being extensive structural damage in the prototype
building is expected to 0.01% in a year.
Abstract: In this paper a real-time obstacle avoidance approach
for both autonomous and non-autonomous dynamical systems (DS) is
presented. In this approach the original dynamics of the controller
which allow us to determine safety margin can be modulated.
Different common types of DS increase the robot’s reactiveness in
the face of uncertainty in the localization of the obstacle especially
when robot moves very fast in changeable complex environments.
The method is validated by simulation and influence of different
autonomous and non-autonomous DS such as important
characteristics of limit cycles and unstable DS. Furthermore, the
position of different obstacles in complex environment is explained.
Finally, the verification of avoidance trajectories is described through
different parameters such as safety factor.