Abstract: The air transport impact on environment is more than
ever a limitative obstacle to the aeronautical industry continuous
growth. Over the last decades, considerable effort has been carried
out in order to obtain quieter aircraft solutions, whether by changing
the original design or investigating more silent maneuvers. The
noise propagated by rotating surfaces is one of the most important
sources of annoyance, being present in most aerial vehicles. Bearing
this is mind, CEIIA developed a new computational chain for
noise prediction with in-house software tools to obtain solutions in
relatively short time without using excessive computer resources. This
work is based on the new acoustic tool, which aims to predict the
rotor noise generated during steady and maneuvering flight, making
use of the flexibility of the C language and the advantages of GPU
programming in terms of velocity. The acoustic tool is based in the
Formulation 1A of Farassat, capable of predicting two important
types of noise: the loading and thickness noise. The present work
describes the most important features of the acoustic tool, presenting
its most relevant results and framework analyses for helicopters and
UAV quadrotors.
Abstract: Estimation of model parameters is necessary to predict
the behavior of a system. Model parameters are estimated using
optimization criteria. Most algorithms use historical data to estimate
model parameters. The known target values (actual) and the output
produced by the model are compared. The differences between the
two form the basis to estimate the parameters. In order to compare
different models developed using the same data different criteria are
used. The data obtained for short scale projects are used here. We
consider software effort estimation problem using radial basis
function network. The accuracy comparison is made using various
existing criteria for one and two predictors. Then, we propose a new
criterion based on linear least squares for evaluation and compared
the results of one and two predictors. We have considered another
data set and evaluated prediction accuracy using the new criterion.
The new criterion is easy to comprehend compared to single statistic.
Although software effort estimation is considered, this method is
applicable for any modeling and prediction.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The aim of this paper is to perform experimental
modal analysis (EMA) of reinforced concrete (RC) square slabs.
EMA is the process of determining the modal parameters (Natural
Frequencies, damping factors, modal vectors) of a structure from a
set of frequency response functions FRFs (curve fitting). Although,
experimental modal analysis (or modal testing) has grown steadily in
popularity since the advent of the digital FFT spectrum analyzer in
the early 1970’s, studying all types of members and materials using
such method have not yet been well documented. Therefore, in this
work, experimental tests were conducted on RC square slab
specimens of dimensions 600mm x 600mmx 40mm. Experimental
analysis was based on freely supported boundary condition.
Moreover, impact testing as a fast and economical means of finding
the modes of vibration of a structure was used during the
experiments. In addition, Pico Scope 6 device and MATLAB
software were used to acquire data, analyze and plot Frequency
Response Function (FRF). The experimental natural frequencies
which were extracted from measurements exhibit good agreement
with analytical predictions. It is showed that EMA method can be
usefully employed to investigate the dynamic behavior of RC slabs.
Abstract: Urban areas have been expanded throughout the
globe. Monitoring and modelling urban growth have become a
necessity for a sustainable urban planning and decision making.
Urban prediction models are important tools for analyzing the causes
and consequences of urban land use dynamics. The objective of this
research paper is to analyze and model the urban change, which has
been occurred from 1990 to 2000 using CORINE land cover maps.
The model was developed using drivers of urban changes (such as
road distance, slope, etc.) under an Artificial Neural Network
modelling approach. Validation was achieved using a prediction map
for 2006 which was compared with a real map of Urban Atlas of
2006. The accuracy produced a Kappa index of agreement of 0,639
and a value of Cramer's V of 0,648. These encouraging results
indicate the importance of the developed urban growth prediction
model which using a set of available common biophysical drivers
could serve as a management tool for the assessment of urban
change.
Abstract: The use OF adhesive anchors for wooden constructions is an efficient technology to connect and design timber members in new timber structures and to rehabilitate the damaged structural members of historical buildings. Due to the lack of standard regulation in this specific area of structural design, designers’ choices are still supported by test analysis that enables knowledge, and the prediction, of the structural behaviour of glued in rod joints. The paper outlines an experimental research activity aimed at identifying the tensile resistance capacity of several new adhesive joint prototypes made of epoxy resin, steel bar and timber, Oak and Douglas Fir species. The development of new adhesive connectors has been carried out by using epoxy to glue stainless steel bars into pre-drilled holes, characterised by smooth and rough internal surfaces, in timber samples. The realization of a threaded contact surface using a specific drill bit has led to an improved bond between wood and epoxy. The applied changes have also reduced the cost of the joints’ production. The paper presents the results of this parametric analysis and a Finite Element analysis that enables identification and study of the internal stress distribution in the proposed adhesive anchors.
Abstract: The 6th version of Universal modeling method for
centrifugal compressor stage calculation is described. Identification
of the new mathematical model was made. As a result of
identification the uniform set of empirical coefficients is received.
The efficiency definition error is 0,86 % at a design point. The
efficiency definition error at five flow rate points (except a point of
the maximum flow rate) is 1,22 %. Several variants of the stage with
3D impellers designed by 6th version program and quasi threedimensional
calculation programs were compared by their gas
dynamic performances CFD (NUMECA FINE TURBO).
Performance comparison demonstrated general principles of design
validity and leads to some design recommendations.
Abstract: Vegetation affects the mean and turbulent flow
structure. It may increase flood risks and sediment transport.
Therefore, it is important to develop analytical approaches for the bed
shear stress on vegetated bed, to predict resistance caused by
vegetation. In the recent years, experimental and numerical models
have both been developed to model the effects of submerged
vegetation on open-channel flow. In this paper, different analytic
models are compared and tested using the criteria of deviation, to
explore their capacity for predicting the mean velocity and select the
suitable one that will be applied in real case of rivers. The
comparison between the measured data in vegetated flume and
simulated mean velocities indicated, a good performance, in the case
of rigid vegetation, whereas, Huthoff model shows the best
agreement with a high coefficient of determination (R2=80%) and the
smallest error in the prediction of the average velocities.
Abstract: The California Bearing Ratio (CBR) has been
acknowledged as an important parameter to characterize the bearing
capacity of earth structures, such as earth dams, road embankments,
airport runways, bridge abutments and pavements. Technically, the
CBR test can be carried out in the laboratory or in the field. The CBR
test is time-consuming and is infrequently performed due to the
equipment needed and the fact that the field moisture content keeps
changing over time. Over the years, many correlations have been
developed for the prediction of CBR by various researchers,
including the dynamic cone penetrometer, undrained shear strength
and Clegg impact hammer. This paper reports and discusses some of
the results from a study on the prediction of CBR. In the current
study, the CBR test was performed in the laboratory on some finegrained
subgrade soils collected from various locations in Victoria.
Based on the test results, a satisfactory empirical correlation was
found between the CBR and the physical properties of the
experimental soils.
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: Universal modeling method well proven for industrial
compressors was applied for design of the high flow rate supersonic
stage. Results were checked by ANSYS CFX and NUMECA Fine
Turbo calculations. The impeller appeared to be very effective at
transonic flow velocities. Stator elements efficiency is acceptable at
design Mach numbers too. Their loss coefficient versus inlet flow
angle performances correlates well with Universal modeling
prediction. The impeller demonstrates ability of satisfactory operation
at design flow rate. Supersonic flow behavior in the impeller inducer
at the shroud blade to blade surface Φ des deserves additional study.
Abstract: The number of Ground Motion Prediction Equations
(GMPEs) used for predicting peak ground acceleration (PGA) and
the number of earthquake recordings that have been used for fitting
these equations has increased in the past decades. The current PF-L
database contains 3550 recordings. Since the GMPEs frequently
model the peak ground acceleration the goal of the present study was
to refit a selection of 44 of the existing equation models for PGA in
light of the latest data. The algorithm Levenberg-Marquardt was used
for fitting the coefficients of the equations and the results are
evaluated both quantitatively by presenting the root mean squared
error (RMSE) and qualitatively by drawing graphs of the five best
fitted equations. The RMSE was found to be as low as 0.08 for the
best equation models. The newly estimated coefficients vary from the
values published in the original works.
Abstract: Fracture in hot precision forging of engine valves was
investigated in this paper. The entire valve forging procedure was
described and the possible cause of the fracture was proposed. Finite
Element simulation was conducted for the forging process, with
commercial Finite Element code DEFORMTM. The effects of
material properties, the effect of strain rate and temperature were
considered in the FE simulation. Two fracture criteria were discussed
and compared, based on the accuracy and reliability of the FE
simulation results. The selected criterion predicted the fracture
location and shows the trend of damage increasing with good
accuracy, which matches the experimental observation. Additional
modification of the punch shapes was proposed to further reduce the
tendency of fracture in forging. Finite Element comparison shows a
great potential of such application in the mass production.
Abstract: In this paper, Bayesian online inference in models of
data series are constructed by change-points algorithm, which
separated the observed time series into independent series and study
the change and variation of the regime of the data with related
statistical characteristics. variation of statistical characteristics of time
series data often represent separated phenomena in the some
dynamical system, like a change in state of brain dynamical reflected
in EEG signal data measurement or a change in important regime of
data in many dynamical system. In this paper, prediction algorithm
for studying change point location in some time series data is
simulated. It is verified that pattern of proposed distribution of data
has important factor on simpler and smother fluctuation of hazard
rate parameter and also for better identification of change point
locations. Finally, the conditions of how the time series distribution
effect on factors in this approach are explained and validated with
different time series databases for some dynamical system.
Abstract: In this study a ternary system containing sodium
chloride as solute, water as primary solvent and ethanol as the
antisolvent was considered to investigate the application of artificial
neural network (ANN) in prediction of sodium solubility in the
mixture of water as the solvent and ethanol as the antisolvent. The
system was previously studied using by Extended UNIQUAC model
by the authors of this study. The comparison between the results of
the two models shows an excellent agreement between them
(R2=0.99), and also approves the capability of ANN to predict the
thermodynamic behavior of ternary electrolyte systems which are
difficult to model.
Abstract: The purpose of this paper is to examine gas transport
behavior of mixed matrix membranes (MMMs) combined with
porous particles. Main existing models are categorized in two main
groups; two-phase (ideal contact) and three-phase (non-ideal contact).
A new coefficient, J, was obtained to express equations for estimating
effect of the particle porosity in two-phase and three-phase models.
Modified models evaluates with existing models and experimental
data using Matlab software. Comparison of gas permeability of
proposed modified models with existing models in different MMMs
shows a better prediction of gas permeability in MMMs.
Abstract: This paper presents the scaling laws that provide the
criteria of geometry and dynamic similitude between the full-size
rotor-shaft system and its scale model, and can be used to predict the
torsional vibration characteristics of the full-size rotor-shaft system by
manipulating the corresponding data of its scale model. The scaling
factors, which play fundamental roles in predicting the geometry and
dynamic relationships between the full-size rotor-shaft system and its
scale model, for torsional free vibration problems between scale and
full-size rotor-shaft systems are firstly obtained from the equation of
motion of torsional free vibration. Then, the scaling factor of external
force (i.e., torque) required for the torsional forced vibration problems
is determined based on the Newton’s second law. Numerical results
show that the torsional free and forced vibration characteristics of a
full-size rotor-shaft system can be accurately predicted from those of
its scale models by using the foregoing scaling factors. For this reason,
it is believed that the presented approach will be significant for
investigating the relevant phenomenon in the scale model tests.
Abstract: Examining existing experimental results for shallow
rigid foundations subjected to vertical centric load (N), accompanied
or not with a bending moment (M), two main non-linear mechanisms
governing the cyclic response of the soil-foundation system can be
distinguished: foundation uplift and soil yielding. A soil-foundation
failure limit, is defined as a domain of resistance in the two
dimensional (2D) load space (N, M) inside of which lie all the
admissible combinations of loads; these latter correspond to a pure
elastic, non-linear elastic or plastic behavior of the soil-foundation
system, while the points lying on the failure limit correspond to a
combination of loads leading to a failure of the soil-foundation
system. In this study, the proposed resistance domain is constructed
analytically based on mechanics. Original elastic limit, uplift
initiation limit and iso-uplift limits are constructed inside this
domain. These limits give a prediction of the mechanisms activated
for each combination of loads applied to the foundation. A
comparison of the proposed failure limit with experimental tests
existing in the literature shows interesting results. Also, the
developed uplift initiation limit and iso-uplift curves are confronted
with others already proposed in the literature and widely used due to
the absence of other alternatives, and remarkable differences are
noted, showing evident errors in the past proposals and relevant
accuracy for those given in the present work.
Abstract: The thermal conductivity of a fluid can be
significantly enhanced by dispersing nano-sized particles in it, and
the resultant fluid is termed as "nanofluid". A theoretical model for
estimating the thermal conductivity of a nanofluid has been proposed
here. It is based on the mechanism that evenly dispersed
nanoparticles within a nanofluid undergo Brownian motion in course
of which the nanoparticles repeatedly collide with the heat source.
During each collision a rapid heat transfer occurs owing to the solidsolid
contact. Molecular dynamics (MD) simulation of the collision
of nanoparticles with the heat source has shown that there is a pulselike
pick up of heat by the nanoparticles within 20-100 ps, the extent
of which depends not only on thermal conductivity of the
nanoparticles, but also on the elastic and other physical properties of
the nanoparticle. After the collision the nanoparticles undergo
Brownian motion in the base fluid and release the excess heat to the
surrounding base fluid within 2-10 ms. The Brownian motion and
associated temperature variation of the nanoparticles have been
modeled by stochastic analysis. Repeated occurrence of these events
by the suspended nanoparticles significantly contributes to the
characteristic thermal conductivity of the nanofluids, which has been
estimated by the present model for a ethylene glycol based nanofluid
containing Cu-nanoparticles of size ranging from 8 to 20 nm, with
Gaussian size distribution. The prediction of the present model has
shown a reasonable agreement with the experimental data available
in literature.
Abstract: The recommended limit for cadmium concentration in
potable water is less than 0.005 mg/L. A continuous biosorption
process using indigenous red seaweed, Gracilaria corticata, was
performed to remove cadmium from the potable water. The process
was conducted under fixed conditions and the breakthrough curves
were achieved for three consecutive sorption-desorption cycles. A
modeling based on Artificial Neural Network (ANN) was employed
to fit the experimental breakthrough data. In addition, a simplified
semi empirical model, Thomas, was employed for this purpose. It
was found that ANN well described the experimental data (R2>0.99)
while the Thomas prediction were a bit less successful with R2>0.97.
The adjusted design parameters using the nonlinear form of Thomas
model was in a good agreement with the experimentally obtained
ones. The results approve the capability of ANN to predict the
cadmium concentration in potable water.