Abstract: Large rotating systems, especially gear drives and gearboxes, occur as parts of many mechanical devices transmitting the torque with relatively small loss of power. With the increased demand for high speed machinery, mathematical modeling and
dynamic analysis of gear drives gained importance. Mathematical description of such mechanical systems is a complex task evolving for several decades. In gear drive dynamic models, which include flexible shafts, bearings and gearing and use the finite elements, nonlinear effects due to gear mesh and bearings are usually ignored, for such models have large number of degrees of freedom (DOF) and it is computationally expensive to analyze nonlinear systems with large number of DOF. Therefore, these models are not suitable for simulation of nonlinear behavior with amplitude jumps in frequency response. The contribution uses a methodology of nonlinear large rotating system modeling which is based on degrees of freedom (DOF) number reduction using modal synthesis method (MSM).
The MSM enables significant DOF number reduction while keeping
the nonlinear behavior of the system in a specific frequency range.
Further, the MSM with DOF number reduction is suitable for
including detail models of nonlinear couplings (mainly gear and
bearing couplings) into the complete gear drive models. Since each
subsystem is modeled separately using different FEM systems, it
is advantageous to parameterize models of subsystems and to use
the parameterization for optimization of chosen design parameters.
Final complex model of gear drive is assembled in MATLAB and
MATLAB tools are used for dynamical analysis of the nonlinear
system. The contribution is further focused on developing of a
methodology for investigation of behavior of the system by Nonlinear
Normal Modes with combination of the MSM using numerical
continuation method. The proposed methodology will be tested using
a two-stage gearbox including its housing.
Abstract: In recent years fuel cell vehicles are rapidly appearing
all over the globe. In less than 10 years, fuel cell vehicles have gone
from mere research novelties to operating prototypes and demonstration
models. At the same time, government and industry in development
countries have teamed up to invest billions of dollars in partnerships
intended to commercialize fuel cell vehicles within the early
years of the 21st century.
The purpose of this study is evaluation of model and performance
of fuel cell hybrid electric vehicle in different drive cycles. A fuel
cell system model developed in this work is a semi-experimental
model that allows users to use the theory and experimental relationships
in a fuel cell system. The model can be used as part of a complex
fuel cell vehicle model in advanced vehicle simulator (ADVISOR).
This work reveals that the fuel consumption and energy efficiency
vary in different drive cycles. Arising acceleration and speed in a
drive cycle leads to Fuel consumption increase. In addition, energy
losses in drive cycle relates to fuel cell system power request. Parasitic
power in different parts of fuel cell system will increase when
power request increases. Finally, most of energy losses in drive cycle
occur in fuel cell system because of producing a lot of energy by fuel
cell stack.
Abstract: This paper presents a simple three phase power flow
method for solution of three-phase unbalanced radial distribution
system (RDN) with voltage dependent loads. It solves a simple
algebraic recursive expression of voltage magnitude, and all the data
are stored in vector form. The algorithm uses basic principles of
circuit theory and can be easily understood. Mutual coupling between
the phases has been included in the mathematical model. The
proposed algorithm has been tested with several unbalanced radial
distribution networks and the results are presented in the article. 8-
bus and IEEE 13 bus unbalanced radial distribution system results
are in agreements with the literature and show that the proposed
model is valid and reliable.
Abstract: Reliable water level forecasts are particularly
important for warning against dangerous flood and inundation. The
current study aims at investigating the suitability of the adaptive
network based fuzzy inference system for continuous water level
modeling. A hybrid learning algorithm, which combines the least
square method and the back propagation algorithm, is used to
identify the parameters of the network. For this study, water levels
data are available for a hydrological year of 2002 with a sampling
interval of 1-hour. The number of antecedent water level that should
be included in the input variables is determined by two statistical
methods, i.e. autocorrelation function and partial autocorrelation
function between the variables. Forecasting was done for 1-hour until
12-hour ahead in order to compare the models generalization at
higher horizons. The results demonstrate that the adaptive networkbased
fuzzy inference system model can be applied successfully and
provide high accuracy and reliability for river water level estimation.
In general, the adaptive network-based fuzzy inference system
provides accurate and reliable water level prediction for 1-hour ahead
where the MAPE=1.15% and correlation=0.98 was achieved. Up to
12-hour ahead prediction, the model still shows relatively good
performance where the error of prediction resulted was less than
9.65%. The information gathered from the preliminary results
provide a useful guidance or reference for flood early warning
system design in which the magnitude and the timing of a potential
extreme flood are indicated.
Abstract: In general, reports are a form of representing data in
such way that user gets the information he needs. They can be built in
various ways, from the simplest (“select from") to the most complex
ones (results derived from different sources/tables with complex
formulas applied). Furthermore, rules of calculations could be written
as a program hard code or built in the database to be used by dynamic
code. This paper will introduce two types of reports, defined in the
DB structure. The main goal is to manage calculations in optimal
way, keeping maintenance of reports as simple and smooth as
possible.
Abstract: In this paper a comprehensive model of a fossil fueled
power plant (FFPP) is developed in order to evaluate the
performance of a newly designed turbine follower controller.
Considering the drawbacks of previous works, an overall model is
developed to minimize the error between each subsystem model
output and the experimental data obtained at the actual power plant.
The developed model is organized in two main subsystems namely;
Boiler and Turbine. Considering each FFPP subsystem
characteristics, different modeling approaches are developed. For
economizer, evaporator, superheater and reheater, first order models
are determined based on principles of mass and energy conservation.
Simulations verify the accuracy of the developed models. Due to the
nonlinear characteristics of attemperator, a new model, based on a
genetic-fuzzy systems utilizing Pittsburgh approach is developed
showing a promising performance vis-à-vis those derived with other
methods like ANFIS. The optimization constraints are handled
utilizing penalty functions. The effect of increasing the number of
rules and membership functions on the performance of the proposed
model is also studied and evaluated. The turbine model is developed
based on the equation of adiabatic expansion. Parameters of all
evaluated models are tuned by means of evolutionary algorithms.
Based on the developed model a fuzzy PI controller is developed. It
is then successfully implemented in the turbine follower control
strategy of the plant. In this control strategy instead of keeping
control parameters constant, they are adjusted on-line with regard to
the error and the error rate. It is shown that the response of the
system improves significantly. It is also shown that fuel consumption
decreases considerably.
Abstract: The automatic transmission (AT) is one of the most
important components of many automobile transmission systems. The
shift quality has a significant influence on the ride comfort of the
vehicle. During the AT shift process, the joint elements such as the
clutch and bands engage or disengage, linking sets of gears to create a
fixed gear ratio. Since these ratios differ between gears in a fixed gear
ratio transmission, the motion of the vehicle could change suddenly
during the shift process if the joint elements are engaged or disengaged
inappropriately, additionally impacting the entire transmission system
and increasing the temperature of connect elements.The objective was
to establish a system model for an AT powertrain using
Matlab/Simulink. This paper further analyses the effect of varying
hydraulic pressure and the associated impact on shift quality during
both engagment and disengagement of the joint elements, proving that
shift quality improvements could be achieved with appropriate
hydraulic pressure control.
Abstract: The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.
Abstract: This paper describes the development, modeling, and
testing of skyhook and MiniMax control strategies of semi-active
suspension. The control performances are investigated using
Matlab/Simulink [1], with a two-degree-of-freedom quarter car semiactive
suspension system model. The comparison and evaluation of
control result are made using software-in-the-loop simulation (SILS)
method. This paper also outlines the development of a hardware-inthe-
loop simulation (HILS) system. The simulation results show that
skyhook strategy can significantly reduce the resonant peak of body
and provide improvement in vehicle ride comfort. Otherwise,
MiniMax strategy can be employed to effectively improve drive
safety of vehicle by influencing wheel load. The two strategies can
be switched to control semi-active suspension system to fulfill
different requirement of vehicle in different stages.
Abstract: Modeling of the distributed systems allows us to
represent the whole its functionality. The working system instance
rarely fulfils the whole functionality represented by model; usually
some parts of this functionality should be accessible periodically.
The reporting system based on the Data Warehouse concept seams to
be an intuitive example of the system that some of its functionality is
required only from time to time. Analyzing an enterprise risk
associated with the periodical change of the system functionality, we
should consider not only the inaccessibility of the components
(object) but also their functions (methods), and the impact of such a
situation on the system functionality from the business point of view.
In the paper we suggest that the risk attributes should be estimated
from risk attributes specified at the requirements level (Use Case in
the UML model) on the base of the information about the structure of
the model (presented at other levels of the UML model). We argue
that it is desirable to consider the influence of periodical changes in
requirements on the enterprise risk estimation. Finally, the
proposition of such a solution basing on the UML system model is
presented.
Abstract: This paper demonstrates a model of an e-Learning
system based on nowadays learning theory and distant education
practice. The relationships in the model are designed to be simple
and functional and do not necessarily represent any particular e-
Learning environments. It is meant to be a generic e-Learning
system model with implications for any distant education course
instructional design. It allows online instructors to move away from
the discrepancy between the courses and body of knowledge. The
interrelationships of four primary sectors that are at the e-Learning
system are presented in this paper. This integrated model includes
[1] pedagogy, [2] technology, [3] teaching, and [4] learning. There
are interactions within each of these sectors depicted by system loop
map.
Abstract: This paper shows how we can integrate
communication modeling into the design modeling at early stages of
the design flow. We consider effect of incorporating noise such as
impulsive noise on system stability. We show that with change of the
system model and investigate the system performance under the
different communication effects. We modeled a unmanned aerial
vehicle (UAV) as a demonstration using SystemC methodology.
Moreover the system is modeled by joining the capabilities of UML
and SystemC to operate at system level.
Abstract: This research was conducted in the Lower Ping River
Basin downstream of the Bhumibol Dam and the Lower Wang River
Basin in Tak Province, Thailand. Most of the tributary streams of the
Ping can be considered as ungauged catchments. There are 10-
pumping station installation at both river banks of the Ping in Tak
Province. Recently, most of them could not fully operate due to the
water amount in the river below the level that would be pumping,
even though included water from the natural river and released flow
from the Bhumibol Dam. The aim of this research was to increase the
performance of those pumping stations using weir projects in the
Ping. Therefore, the river analysis system model (HEC-RAS) was
applied to study the hydraulic behavior of water surface profiles in
the Ping River with both cases of existing conditions and proposed
weirs during the violent flood in 2011 and severe drought in 2013.
Moreover, the hydrologic modeling system (HMS) was applied to
simulate lateral streamflow hydrograph from ungauged catchments of
the Ping. The results of HEC-RAS model calibration with existing
conditions in 2011 showed best trial roughness coefficient for the
main channel of 0.026. The simulated water surface levels fitted to
observation data with R2 of 0.8175. The model was applied to 3
proposed cascade weirs with 2.35 m in height and found surcharge
water level only 0.27 m higher than the existing condition in 2011.
Moreover, those weirs could maintain river water levels and increase
of those pumping performances during less river flow in 2013.
Abstract: Airbag deployment has been known to be responsible
for huge death, incidental injuries and broken bones due to low crash
severity and wrong deployment decisions. Therefore, the authorities
and industries have been looking for more innovative and intelligent
products to be realized for future enhancements in the vehicle safety
systems (VSSs). Although the VSSs technologies have advanced
considerably, they still face challenges such as how to avoid
unnecessary and untimely airbag deployments that can be hazardous
and fatal. Currently, most of the existing airbag systems deploy
without regard to occupant size and position. As such, this paper will
focus on the occupant and crash sensing performances due to frontal
collisions for the new breed of so called smart airbag systems. It
intends to provide a thorough discussion relating to the occupancy
detection, occupant size classification, occupant off-position
detection to determine safe distance zone for airbag deployment,
crash-severity analysis and airbag decision algorithms via a computer
modeling. The proposed system model consists of three main
modules namely, occupant sensing, crash severity analysis and
decision fusion. The occupant sensing system module utilizes the
weight sensor to determine occupancy, classify the occupant size,
and determine occupant off-position condition to compute safe
distance for airbag deployment. The crash severity analysis module is
used to generate relevant information pertinent to airbag deployment
decision. Outputs from these two modules are fused to the decision
module for correct and efficient airbag deployment action. Computer
modeling work is carried out using Simulink, Stateflow,
SimMechanics and Virtual Reality toolboxes.
Abstract: Ferroresonance is an electrical phenomenon in
nonlinear character, which frequently occurs in power system due to
transmission line faults and single or more-phase switching on the
lines as well as usage of the saturable transformers. In this study, the
ferroresonance phenomena are investigated under the modeling of the
West Anatolian Electric Power Network of 380 kV in Turkey. The
ferroresonance event is observed as a result of removing the loads at
the end of the lines. In this sense, two different cases are considered.
At first, the switching is applied at 2nd second and the ferroresonance
affects are observed between 2nd and 4th seconds in the voltage
variations of the phase-R. Hence the ferroresonance and nonferroresonance
parts of the overall data are compared with each
others using the Fourier transform techniques to show the
ferroresonance affects.
Abstract: The paper presents new results of a recent industry
supported research and development study in which an efficient
framework for evaluating practical and meaningful power system
reliability and quality indices was applied. The system-wide
integrated performance indices are capable of addressing and
revealing areas of deficiencies and bottlenecks as well as
redundancies in the composite generation-transmission-demand
structure of large-scale power grids. The technique utilizes a linear
programming formulation, which simulates practical operating
actions and offers a general and comprehensive framework to assess
the harmony and compatibility of generation, transmission and
demand in a power system. Practical applications to a reduced
system model as well as a portion of the Saudi power grid are also
presented in the paper for demonstration purposes.
Abstract: In automotive systems almost all steps concerning the
calibration of several control systems, e.g., low idle governor or
boost pressure governor, are made with the vehicle because the timeto-
production and cost requirements on the projects do not allow for
the vehicle analysis necessary to build reliable models. Here is
presented a procedure using parametric and NN (neural network)
models that enables the generation of vehicle system models based
on normal ECU engine control unit) vehicle measurements. These
models are locally valid and permit pre and follow-up calibrations so
that, only the final calibrations have to be done with the vehicle.
Abstract: Dynamic of phytoplankton blooms in the Baltic Sea
has been analyzed applying the numerical ecosystem model 3D
CEMBS. The model consists of the hydrodynamic model (POP,
version 2.1) and the ice model (CICE, version 4.0), which are
imposed by the atmospheric data model (DATM7). The 3D
model has an ecosystem module, activated in 2012 in the operational
mode. The ecosystem model consists of 11 main variables: biomass
of small-size phytoplankton and large-size phytoplankton
and cyanobacteria, zooplankton biomass, dissolved and molecular
detritus, dissolved oxygen concentration, as well as concentrations of
nutrients, including: nitrates, ammonia, phosphates and silicates. The
3D-CEMBS model is an effective tool for solving problems related to
phytoplankton blooms dynamic in the Baltic Sea
Abstract: The System Identification problem looks for a
suitably parameterized model, representing a given process. The
parameters of the model are adjusted to optimize a performance
function based on error between the given process output and
identified process output. The linear system identification field is
well established with many classical approaches whereas most of
those methods cannot be applied for nonlinear systems. The problem
becomes tougher if the system is completely unknown with only the
output time series is available. It has been reported that the
capability of Artificial Neural Network to approximate all linear and
nonlinear input-output maps makes it predominantly suitable for the
identification of nonlinear systems, where only the output time series
is available. [1][2][4][5]. The work reported here is an attempt to
implement few of the well known algorithms in the context of
modeling of nonlinear systems, and to make a performance
comparison to establish the relative merits and demerits.
Abstract: This paper is concerned with the application of the vision control algorithm for robot's point placement task in discontinuous trajectory caused by obstacle. The presented vision control algorithm consists of four models, which are the robot kinematic model, vision system model, parameters estimation model, and robot joint angle estimation model.When the robot moves toward a target along discontinuous trajectory, several types of obstacles appear in two obstacle regions. Then, this study is to investigate how these changes will affect the presented vision control algorithm.Thus, the practicality of the vision control algorithm is demonstrated experimentally by performing the robot's point placement task in discontinuous trajectory by obstacle.