Abstract: Using the quantum hydrodynamic (QHD) model the
nonlinear properties of ion-acoustic waves in are lativistically
degenerate quantum plasma is investigated by deriving a nonlinear
Spherical Kadomtsev–Petviashvili (SKP) equation using the
standard reductive perturbation method equation. It was found that
the electron degeneracy parameter significantly affects the linear
and nonlinear properties of ion-acoustic waves in quantum plasma.
Abstract: Electrohydraulic servo system have been used in industry in a wide number of applications. Its dynamics are highly nonlinear and also have large extent of model uncertainties and external disturbances. In this paper, a robust back-stepping control (RBSC) scheme is proposed to overcome the problem of disturbances and system uncertainties effectively and to improve the tracking performance of EHS systems. In order to implement the proposed control scheme, the system uncertainties in EHS systems are considered as total leakage coefficient and effective oil volume. In addition, in order to obtain the virtual controls for stabilizing system, the update rule for the system uncertainty term is induced by the Lyapunov control function (LCF). To verify the performance and robustness of the proposed control system, computer simulation of the proposed control system using Matlab/Simulink Software is executed. From the computer simulation, it was found that the RBSC system produces the desired tracking performance and has robustness to the disturbances and system uncertainties of EHS systems.
Abstract: The present study is concerned with the problem of determining the shape of the free surface flow in a hydraulic channel which has an uneven bottom. For the mathematical formulation of the problem, the fluid of the two-dimensional irrotational steady flow in water is assumed inviscid and incompressible. The solutions of the nonlinear problem are obtained by using the usual conformal mapping theory and Hilbert’s technique. An experimental study, for comparing the obtained results, has been conducted in a hydraulic channel (subcritical regime and supercritical regime).
Abstract: The paper develops a Non-Linear Model Predictive
Control (NMPC) of water quality in Drinking Water Distribution
Systems (DWDS) based on the advanced non-linear quality dynamics
model including disinfections by-products (DBPs). A special attention
is paid to the analysis of an impact of the flow trajectories prescribed
by an upper control level of the recently developed two-time scale
architecture of an integrated quality and quantity control in DWDS.
The new quality controller is to operate within this architecture in the
fast time scale as the lower level quality controller. The controller
performance is validated by a comprehensive simulation study based
on an example case study DWDS.
Abstract: The fatigue life of tubular joints commonly found in
offshore industry is not only dependent on the value of hot-spot stress
(HSS), but is also significantly influenced by the through-thethickness
stress distribution characterized by the degree of bending
(DoB). The determination of DoB values in a tubular joint is essential
for improving the accuracy of fatigue life estimation using the stresslife
(S–N) method and particularly for predicting the fatigue crack
growth based on the fracture mechanics (FM) approach. In the
present paper, data extracted from finite element (FE) analyses of
tubular KT-joints, verified against experimental data and parametric
equations, was used to investigate the effects of geometrical
parameters on DoB values at the crown 0°, saddle, and crown 180°
positions along the weld toe of central brace in tubular KT-joints
subjected to axial loading. Parametric study was followed by a set of
nonlinear regression analyses to derive DoB parametric formulas for
the fatigue analysis of KT-joints under axial loads. The tubular KTjoint
is a quite common joint type found in steel offshore structures.
However, despite the crucial role of the DoB in evaluating the fatigue
performance of tubular joints, this paper is the first attempt to study
and formulate the DoB values in KT-joints.
Abstract: Damage status of RC buildings is greatly influenced
by the characteristics of the imposed ground motion. Peak Ground
Acceleration and frequency contents are considered the main two
factors that affect ground motion characteristics; hence, affecting the
seismic response of RC structures and consequently their damage
state. A detailed investigation on the combined effects of these two
factors on damage assessment of RC buildings is carried out. Twenty
one earthquake records are analyzed and arranged into three groups,
according to their frequency contents. These records are used in an
investigation to define the expected damage state that would be
attained by RC buildings, if subjected to varying ground motion
characteristics. The damage assessment is conducted through
examining drift ratios and damage indices of the overall structure and
the significant structural components of RC building. Base and story
shear of RC building model, are also investigated, for cases when the
model is subjected to the chosen twenty one earthquake records.
Nonlinear dynamic analyses are performed on a 2-dimensional model
of a 12-story RC building.
Abstract: We present a refined multiscale Shannon entropy for
analyzing electroencephalogram (EEG), which reflects the underlying
dynamics of EEG over multiple scales. The rationale behind
this method is that neurological signals such as EEG possess
distinct dynamics over different spectral modes. To deal with the
nonlinear and nonstationary nature of EEG, the recently developed
empirical mode decomposition (EMD) is incorporated, allowing a
decomposition of EEG into its inherent spectral components, referred
to as intrinsic mode functions (IMFs). By calculating the Shannon
entropy of IMFs in a time-dependent manner and summing them over
adaptive multiple scales, it results in an adaptive subscale entropy
measure of EEG. Simulation and experimental results show that
the proposed entropy properly reveals the dynamical changes over
multiple scales.
Abstract: On the basis of the theory of nonlinear elasticity, the
effect of homogeneous stress on the propagation of Lamb waves in
an initially isotropic hyperelastic plate is analysed. The equations
governing the propagation of small amplitude waves in the prestressed
plate are derived using the theory of small deformations
superimposed on large deformations. By enforcing traction free
boundary conditions at the upper and lower surfaces of the plate,
acoustoelastic dispersion equations for Lamb wave propagation are
obtained, which are solved numerically. Results are given for an
aluminum plate subjected to a range of applied stresses.
Abstract: The purpose of the paper is to estimate the US small
wind turbines market potential and forecast the small wind turbines
sales in the US. The forecasting method is based on the application of
the Bass model and the generalized Bass model of innovations
diffusion under replacement purchases. In the work an exponential
distribution is used for modeling of replacement purchases. Only one
parameter of such distribution is determined by average lifetime of
small wind turbines. The identification of the model parameters is
based on nonlinear regression analysis on the basis of the annual
sales statistics which has been published by the American Wind
Energy Association (AWEA) since 2001 up to 2012. The estimation
of the US average market potential of small wind turbines (for
adoption purchases) without account of price changes is 57080
(confidence interval from 49294 to 64866 at P = 0.95) under average
lifetime of wind turbines 15 years, and 62402 (confidence interval
from 54154 to 70648 at P = 0.95) under average lifetime of wind
turbines 20 years. In the first case the explained variance is 90,7%,
while in the second - 91,8%. The effect of the wind turbines price
changes on their sales was estimated using generalized Bass model.
This required a price forecast. To do this, the polynomial regression
function, which is based on the Berkeley Lab statistics, was used. The
estimation of the US average market potential of small wind turbines
(for adoption purchases) in that case is 42542 (confidence interval
from 32863 to 52221 at P = 0.95) under average lifetime of wind
turbines 15 years, and 47426 (confidence interval from 36092 to
58760 at P = 0.95) under average lifetime of wind turbines 20 years.
In the first case the explained variance is 95,3%, while in the second
– 95,3%.
Abstract: This article presents two methods for the
compensation of harmonics generated by a nonlinear load. The first is
the classic method P-Q. The second is the controller by modern
method of artificial intelligence specifically fuzzy logic. Both
methods are applied to a shunt Active Power Filter (sAPF) based on a
three-phase voltage converter at five levels NPC topology. In
calculating the harmonic currents of reference, we use the algorithm
P-Q and pulse generation, we use the intersective PWM. For
flexibility and dynamics, we use fuzzy logic. The results give us clear
that the rate of Harmonic Distortion issued by fuzzy logic is better
than P-Q.
Abstract: Polymer Electrolyte Membrane Fuel Cell (PEMFC) is
such a time-vary nonlinear dynamic system. The traditional linear
modeling approach is hard to estimate structure correctly of PEMFC
system. From this reason, this paper presents a nonlinear modeling of
the PEMFC using Neural Network Auto-regressive model with
eXogenous inputs (NNARX) approach. The multilayer perception
(MLP) network is applied to evaluate the structure of the NNARX
model of PEMFC. The validity and accuracy of NNARX model are
tested by one step ahead relating output voltage to input current from
measured experimental of PEMFC. The results show that the obtained
nonlinear NNARX model can efficiently approximate the dynamic
mode of the PEMFC and model output and system measured output
consistently.
Abstract: The article presents two mathematical models of the
interaction between a rotating shaft and an incompressible fluid. The
mathematical model includes both the journal bearings and the
axially traversed hydrodynamic sealing gaps of hydraulic machines.
A method is shown for the identification of additional effects of the
fluid acting on the rotor of the machine, both for a linear and a nonlinear
model. The interaction is expressed by matrices of mass,
stiffness and damping.
Abstract: This paper is part of a study to develop robots for
farming. As such power requirement to operate equipment attach to
such robots become an important factor. Soil-tool interaction plays
major role in power consumption, thus predicting accurately the
forces which act on the blade during the farming is very important for
optimal designing of farm equipment. In this paper, a finite element
investigation for tillage tools and soil interaction is described by
using an inelastic constitutive material law for agriculture
application. A 3-dimensional (3D) nonlinear finite element analysis
(FEA) is developed to examine behavior of a blade with different
rake angles moving in a block of soil, and to estimate the blade force.
The soil model considered is an elastic-plastic with non-associated
Drucker-Prager material model. Special use of contact elements are
employed to consider connection between soil-blade and soil-soil
surfaces. The FEA results are compared with experimental ones,
which show good agreement in accurately predicting draft forces
developed on the blade when it moves through the soil. Also a very
good correlation was obtained between FEA results and analytical
results from classical soil mechanics theories for straight blades.
These comparisons verified the FEA model developed. For analyzing
complicated soil-tool interactions and for optimum design of blades,
this method will be useful.
Abstract: This work proposes a data-driven multiscale based
quantitative measures to reveal the underlying complexity of
electroencephalogram (EEG), applying to a rodent model of
hypoxic-ischemic brain injury and recovery. Motivated by that real
EEG recording is nonlinear and non-stationary over different
frequencies or scales, there is a need of more suitable approach over
the conventional single scale based tools for analyzing the EEG data.
Here, we present a new framework of complexity measures
considering changing dynamics over multiple oscillatory scales. The
proposed multiscale complexity is obtained by calculating entropies of
the probability distributions of the intrinsic mode functions extracted
by the empirical mode decomposition (EMD) of EEG. To quantify
EEG recording of a rat model of hypoxic-ischemic brain injury
following cardiac arrest, the multiscale version of Tsallis entropy is
examined. To validate the proposed complexity measure, actual EEG
recordings from rats (n=9) experiencing 7 min cardiac arrest followed
by resuscitation were analyzed. Experimental results demonstrate that
the use of the multiscale Tsallis entropy leads to better discrimination
of the injury levels and improved correlations with the neurological
deficit evaluation after 72 hours after cardiac arrest, thus suggesting an
effective metric as a prognostic tool.
Abstract: A cyclostationary Gaussian linearization method is
formulated for investigating the time average response of nonlinear
system under sinusoidal signal and white noise excitation. The
quantitative measure of cyclostationary mean, variance, spectrum of
mean amplitude, and mean power spectral density of noise are
analyzed. The qualitative response behavior of stochastic jump and
bifurcation are investigated. The validity of the present approach in
predicting the quantitative and qualitative statistical responses is
supported by utilizing Monte Carlo simulations. The present analysis
without imposing restrictive analytical conditions can be directly
derived by solving non-linear algebraic equations. The analytical
solution gives reliable quantitative and qualitative prediction of mean
and noise response for the Duffing system subjected to both sinusoidal
signal and white noise excitation.
Abstract: This paper presents a novel integrated hybrid
approach for fault diagnosis (FD) of nonlinear systems. Unlike most
FD techniques, the proposed solution simultaneously accomplishes
fault detection, isolation, and identification (FDII) within a unified
diagnostic module. At the core of this solution is a bank of adaptive
neural parameter estimators (NPE) associated with a set of singleparameter
fault models. The NPEs continuously estimate unknown
fault parameters (FP) that are indicators of faults in the system. Two
NPE structures including series-parallel and parallel are developed
with their exclusive set of desirable attributes. The parallel scheme is
extremely robust to measurement noise and possesses a simpler, yet
more solid, fault isolation logic. On the contrary, the series-parallel
scheme displays short FD delays and is robust to closed-loop system
transients due to changes in control commands. Finally, a fault
tolerant observer (FTO) is designed to extend the capability of the
NPEs to systems with partial-state measurement.
Abstract: This paper presents experimental investigation and
finite element analysis on buckling behavior of irregular section coldformed
steel columns under axially concentric loading. For the
experimental study, four different sections of columns were tested to
investigate effect of stiffening and width-to-thickness ratio on
buckling behavior. For each of the section, three lengths of 230, 950
and 1900 mm. were studied representing short, intermediate long and
long columns, respectively. Then, nonlinear finite element analyses
of the tested columns were performed. The comparisons in terms of
load-deformation response and buckling mode show good agreement
and hence the FEM models were validated. Parametric study of
stiffening element and thickness of 1.0, 1.15, 1.2, 1.5, 1.6 and 2.0
mm. was analyzed. The test results showed that stiffening effect pays
a large contribution to prevent distortional mode. The increase in wall
thickness enhanced buckling stress beyond the yielding strength in
short and intermediate columns, but not for the long columns.
Abstract: In this paper, we introduced a gradient-based inverse
solver to obtain the missing boundary conditions based on the
readings of internal thermocouples. The results show that the method
is very sensitive to measurement errors, and becomes unstable when
small time steps are used. The artificial neural networks are shown to
be capable of capturing the whole thermal history on the run-out
table, but are not very effective in restoring the detailed behavior of
the boundary conditions. Also, they behave poorly in nonlinear cases
and where the boundary condition profile is different.
GA and PSO are more effective in finding a detailed
representation of the time-varying boundary conditions, as well as in
nonlinear cases. However, their convergence takes longer. A
variation of the basic PSO, called CRPSO, showed the best
performance among the three versions. Also, PSO proved to be
effective in handling noisy data, especially when its performance
parameters were tuned. An increase in the self-confidence parameter
was also found to be effective, as it increased the global search
capabilities of the algorithm. RPSO was the most effective variation
in dealing with noise, closely followed by CRPSO. The latter
variation is recommended for inverse heat conduction problems, as it
combines the efficiency and effectiveness required by these
problems.
Abstract: In this paper, the problem of steady laminar boundary
layer flow and heat transfer over a permeable exponentially
stretching/shrinking sheet with generalized slip velocity is
considered. The similarity transformations are used to transform the
governing nonlinear partial differential equations to a system of
nonlinear ordinary differential equations. The transformed equations
are then solved numerically using the bvp4c function in MATLAB.
Dual solutions are found for a certain range of the suction and
stretching/shrinking parameters. The effects of the suction parameter,
stretching/shrinking parameter, velocity slip parameter, critical shear
rate and Prandtl number on the skin friction and heat transfer
coefficients as well as the velocity and temperature profiles are
presented and discussed.
Abstract: A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.