Abstract: In this work, propagation of uncertainty during calibration
process of TRANUS, an integrated land use and transport model
(ILUTM), has been investigated. It has also been examined, through a
sensitivity analysis, which input parameters affect the variation of the
outputs the most. Moreover, a probabilistic verification methodology
of calibration process, which equates the observed and calculated
production, has been proposed. The model chosen as an application is
the model of the city of Grenoble, France. For sensitivity analysis and
uncertainty propagation, Monte Carlo method was employed, and a
statistical hypothesis test was used for verification. The parameters of
the induced demand function in TRANUS, were assumed as uncertain
in the present case. It was found that, if during calibration, TRANUS
converges, then with a high probability the calibration process is
verified. Moreover, a weak correlation was found between the inputs
and the outputs of the calibration process. The total effect of the
inputs on outputs was investigated, and the output variation was found
to be dictated by only a few input parameters.
Abstract: Metal matrix composites have been increasingly used
as materials for components in automotive and aerospace industries
because of their improved properties compared with non-reinforced
alloys. During machining the selection of appropriate machining
parameters to produce job for desired surface roughness is of great
concern considering the economy of manufacturing process. In this
study, a surface roughness prediction model using fuzzy logic is
developed for end milling of Al-SiCp metal matrix composite
component using carbide end mill cutter. The surface roughness is
modeled as a function of spindle speed (N), feed rate (f), depth of cut
(d) and the SiCp percentage (S). The predicted values surface
roughness is compared with experimental result. The model predicts
average percentage error as 4.56% and mean square error as 0.0729.
It is observed that surface roughness is most influenced by feed rate,
spindle speed and SiC percentage. Depth of cut has least influence.
Abstract: Machining is an important manufacturing process used to produce a wide variety of metallic parts. Among various machining processes, turning is one of the most important one which is employed to shape cylindrical parts. In turning, the quality of finished product is measured in terms of surface roughness. In turn, surface quality is determined by machining parameters and tool geometry specifications. The main objective of this study is to simultaneously model and optimize machining parameters and tool geometry in order to improve the surface roughness for AISI1045 steel. Several levels of machining parameters and tool geometry specifications are considered as input parameters. The surface roughness is selected as process output measure of performance. A Taguchi approach is employed to gather experimental data. Then, based on signal-to-noise (S/N) ratio, the best sets of cutting parameters and tool geometry specifications have been determined. Using these parameters values, the surface roughness of AISI1045 steel parts may be minimized. Experimental results are provided to illustrate the effectiveness of the proposed approach.
Abstract: Studying alternative raw materials for biodiesel production is of major importance. The use of mixtures with incorporation of wastes is an environmental friendly alternative and might reduce biodiesel production costs. The objective of the present work was: (i) to study biodiesel production using waste frying oil mixed with pork lard and (ii) to understand how mixture composition influences biodiesel quality. Biodiesel was produced by transesterification and quality was evaluated through determination of several parameters according to EN 14214. The weight fraction of lard in the mixture varied from 0 to 1 in 0.2 intervals. Biodiesel production yields varied from 81.7 to 88.0 (wt%), the lowest yields being the ones obtained using waste frying oil and lard alone as raw materials. The obtained products fulfilled most of the determined quality specifications according to European biodiesel quality standard EN 14214. Minimum purity (96.5 wt%) was closely obtained when waste frying oil was used alone and when 0.2% of lard was incorporated in the raw material (96.3 wt%); however, it ranged from 93.9 to 96.3 (wt%) being always close to the limit. From the evaluation of the influence of mixture composition in biodiesel quality, it was possible to establish a model to be used for predicting some parameters of biodiesel resulting from mixtures of waste frying oil with lard when different lard contents are used.
Abstract: The development of biomimetic micro-aerial-vehicles
(MAVs) with flapping wings is the future trend in military/domestic
field. The successful flight of MAVs is strongly related to the
understanding of unsteady aerodynamic performance of low Reynolds
number airfoils under dynamic flapping motion. This study explored
the effects of flapping frequency, stroke amplitude, and the inclined
angle of stroke plane on lift force and thrust force of a bio-inspiration
corrugated airfoil with 33 full factorial design of experiment and
ANOVA analysis. Unsteady vorticity flows over a corrugated thin
airfoil executing flapping motion are computed with time-dependent
two-dimensional laminar incompressible Reynolds-averaged
Navier-Stokes equations with the conformal hybrid mesh. The tested
freestream Reynolds number based on the chord length of airfoil as
characteristic length is fixed of 103. The dynamic mesh technique is
applied to model the flapping motion of a corrugated airfoil. Instant
vorticity contours over a complete flapping cycle clearly reveals the
flow mechanisms for lift force generation are dynamic stall, rotational
circulation, and wake capture. The thrust force is produced as the
leading edge vortex shedding from the trailing edge of airfoil to form a
reverse von Karman vortex. Results also indicated that the inclined
angle is the most significant factor on both the lift force and thrust
force. There are strong interactions between tested factors which mean
an optimization study on parameters should be conducted in further
runs.
Abstract: The aim of this paper is to experimentally discover the workability coefficient of the Inconel 718 material by using a slide turning machining. Two different types of cutting inserts, one made of carbide and the other one made of ceramic, are being used. The purpose is to compare measured results and recommend the appropriate materials and cutting parameters for a machining of the Inconel 718. Furthermore, the durability of inserts with the chosen wear criterion is being compared for different cutting speeds. Machinability of these materials is a crucial characteristic as it allows us to shorten the technological cycle time and increase the machining productivity. And this is of great importance from an economic point of view.
Abstract: In the oil and gas industry, energy prediction can help
the distributor and customer to forecast the outgoing and incoming
gas through the pipeline. It will also help to eliminate any
uncertainties in gas metering for billing purposes. The objective of
this paper is to develop Neural Network Model for energy
consumption and analyze the performance model. This paper
provides a comprehensive review on published research on the
energy consumption prediction which focuses on structures and the
parameters used in developing Neural Network models. This paper is
then focused on the parameter selection of the neural network
prediction model development for energy consumption and analysis
on the result. The most reliable model that gives the most accurate
result is proposed for the prediction. The result shows that the
proposed neural network energy prediction model is able to
demonstrate an adequate performance with least Root Mean Square
Error.
Abstract: Climate change has profound consequences for the agriculture of south-eastern Australia and its climate-induced water shortage in the Murray-Darling Basin. Post Keynesian Economics (PKE) macro-dynamics, along with Kaleckian investment and growth theory, are used to develop an ecological-economic system dynamics model of this complex nonlinear river basin system. The Murray- Darling Basin Simulation Model (MDB-SM) uses the principles of PKE to incorporate the fundamental uncertainty of economic behaviors of farmers regarding the investments they make and the climate change they face, particularly as regards water ecosystem services. MDB-SM provides a framework for macroeconomic policies, especially for long-term fiscal policy and for policy directed at the sustainability of agricultural water, as measured by socio-economic well-being considerations, which include sustainable consumption and investment in the river basin. The model can also reproduce other ecological and economic aspects and, for certain parameters and initial values, exhibit endogenous business cycles and ecological sustainability with realistic characteristics. Most importantly, MDBSM provides a platform for the analysis of alternative economic policy scenarios. These results reveal the importance of understanding water ecosystem adaptation under climate change by integrating a PKE macroeconomic analytical framework with the system dynamics modelling approach. Once parameterised and supplied with historical initial values, MDB-SM should prove to be a practical tool to provide alternative long-term policy simulations of agricultural water and socio-economic well-being.
Abstract: The concept of order reduction by least-squares moment matching and generalised least-squares methods has been extended about a general point ?a?, to obtain the reduced order models for linear, time-invariant dynamic systems. Some heuristic criteria have been employed for selecting the linear shift point ?a?, based upon the means (arithmetic, harmonic and geometric) of real parts of the poles of high order system. It is shown that the resultant model depends critically on the choice of linear shift point ?a?. The validity of the criteria is illustrated by solving a numerical example and the results are compared with the other existing techniques.
Abstract: This study aims to initiate sustainable water management for tourist accommodations in Amphawa, Samut Songkram Province, Thailand. Wastewater generated by tourist accommodation was conducted in 10 homestays and resorts in Amphawa during August – October, 2011. The prominent parameters which are of pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Fat Oil and Grease (FOG), Nitrate (No3-), and Phosphate (PO43-) were conducted monthly. The results revealed that some parameters were over national water quality standard (Class II). Especially, 90% of tourist accommodations have been recorded that FOG was over the standard of wastewater quality from accommodation (group I: total room of accommodation less than 200 rooms). Therefore, grease trap and natural treatment should be utilized in tourist accommodations in order to reduce the discharged of fat, oil, and grease from tourism activities. In addition, number of tourists also relate statistically with BOD and Nitrate at 0.05 level of significance.
Abstract: An evolutionary computing technique for solving initial value problems in Ordinary Differential Equations is proposed in this paper. Neural network is used as a universal approximator while the adaptive parameters of neural networks are optimized by genetic algorithm. The solution is achieved on the continuous grid of time instead of discrete as in other numerical techniques. The comparison is carried out with classical numerical techniques and the solution is found with a uniform accuracy of MSE ≈ 10-9 .
Abstract: Investigation of soil properties like Cation Exchange
Capacity (CEC) plays important roles in study of environmental
reaserches as the spatial and temporal variability of this property
have been led to development of indirect methods in estimation of
this soil characteristic. Pedotransfer functions (PTFs) provide an
alternative by estimating soil parameters from more readily available
soil data. 70 soil samples were collected from different horizons of
15 soil profiles located in the Ziaran region, Qazvin province, Iran.
Then, multivariate regression and neural network model (feedforward
back propagation network) were employed to develop a
pedotransfer function for predicting soil parameter using easily
measurable characteristics of clay and organic carbon. The
performance of the multivariate regression and neural network model
was evaluated using a test data set. In order to evaluate the models,
root mean square error (RMSE) was used. The value of RMSE and
R2 derived by ANN model for CEC were 0.47 and 0.94 respectively,
while these parameters for multivariate regression model were 0.65
and 0.88 respectively. Results showed that artificial neural network
with seven neurons in hidden layer had better performance in
predicting soil cation exchange capacity than multivariate regression.
Abstract: By utilizing the system of the recurrence equations, containing two parameters, the dynamics of two antagonistically interconnected populations is studied. The following areas of the system behavior are detected: the area of the stable solutions, the area of cyclic solutions occurrence, the area of the accidental change of trajectories of solutions, and the area of chaos and fractal phenomena. The new two-dimensional diagram of the dynamics of the solutions change (the fractal cabbage) has been obtained. In the cross-section of this diagram for one of the equations the well-known Feigenbaum tree of doubling has been noted.Keywordsbifurcation, chaos, dynamics of populations, fractals
Abstract: Fluid flow in cylinders of elliptic cross-section was investigated. Fluid used is Liquefied petroleum gas (LPG). LPG found in Nigeria contains majorly butane with percentages of propane. Commercial available code FLUENT which uses finite volume method was used to solve fluid flow governing equations. There has been little attention paid to fluid flow in cylindrical elliptic pipes. The present work aims to predict the LPG gas flow in cylindrical pipes of elliptic cross-section. Results of flow parameters of velocity and pressure distributions are presented. Results show that the pressure drop in elliptic pipes is higher than circular pipe of the same cross-sectional area. This is an important result as the pressure drop is related to the pump power needed to drive the flow. Results show that the velocity increases towards centre of the pipe as the flow moves downstream, and also increases towards the outlet of the pipe.
Abstract: This paper shows possibility of extraction Social,
Group and Individual Mind from Multiple Agents Rule Bases. Types
those Rule bases are selected as two fuzzy systems, namely
Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are
describing (modeling) agent behavior. Modifying of agent behavior
in the time varying environment will be provided by learning fuzzyneural
networks and optimization of their parameters with using
genetic algorithms in development system FUZNET. Finally,
extraction Social, Group and Individual Mind from Multiple Agents
Rule Bases are provided by Cognitive analysis and Matching
criterion.
Abstract: Bead-on-plate welds were carried out on AISI 316L
(N) austenitic stainless steel (ASS) using flux cored arc welding
(FCAW) process. The bead on plates weld was conducted as per L25
orthogonal array. In this paper, the weld bead geometry such as depth
of penetration (DOP), bead width (BW) and weld reinforcement (R)
of AISI 316L (N) ASS are investigated. Taguchi approach is used as
statistical design of experiment (DOE) technique for optimizing the
selected welding input parameters. Grey relational analysis and
desirability approach are applied to optimize the input parameters
considering multiple output variables simultaneously. Confirmation
experiment has also been conducted to validate the optimized
parameters.
Abstract: This research project aims to investigate difference in
relative rates concerning phosphoryl transfer relevant to biological
catalysis of DNA and RNA in the pH-independent reactions.
Activated Models of DNA and RNA for alkyl-aryl phosphate diesters
(with 4-nitrophenyl as a good leaving group) have successfully been
prepared to gather kinetic parameters. Eyring plots for the pH–
independent hydrolysis of 1 and 2 were established at different
temperatures in the range 100–160 °C. These measurements have
been used to provide a better estimate for the difference in relative
rates between the reactivity of DNA and RNA cleavage. Eyring plot
gave an extrapolated rate of kH2O = 1 × 10-10 s -1 for 1 (RNA model)
and 2 (DNA model) at 25°C. Comparing the reactivity of RNA
model and DNA model shows that the difference in relative rates in
the pH-independent reactions is surprisingly very similar at 25°. This
allows us to obtain chemical insights into how biological catalysts
such as enzymes may have evolved to perform their current
functions.
Abstract: In aircraft applications, according to the nature of
electrical equipment its location may be in unpressurized area or very
close to the engine; thus, the environmental conditions may change
from atmospheric pressure to less than 100 mbar, and the temperature
may be higher than the ambient one as in most real working
conditions of electrical equipment. Then, the classical Paschen curve
has to be replotted since these parameters may affect the discharge
ignition voltage. In this paper, we firstly investigate the domain of
validity of two corrective expressions on the Paschen-s law found in
the literature, in case of changing the air environment and known as
Peek and Dunbar corrections. Results show that these corrections are
no longer valid for combined variation of temperature and pressure.
After that, a new empirical expression for breakdown voltage is
proposed and is validated in the case of combined variations of
temperature and pressure.
Abstract: This paper presents performance analysis of the
Evolutionary Programming-Artificial Neural Network (EPANN)
based technique to optimize the architecture and training parameters
of a one-hidden layer feedforward ANN model for the prediction of
energy output from a grid connected photovoltaic system. The ANN
utilizes solar radiation and ambient temperature as its inputs while the
output is the total watt-hour energy produced from the grid-connected
PV system. EP is used to optimize the regression performance of the
ANN model by determining the optimum values for the number of
nodes in the hidden layer as well as the optimal momentum rate and
learning rate for the training. The EPANN model is tested using two
types of transfer function for the hidden layer, namely the tangent
sigmoid and logarithmic sigmoid. The best transfer function, neural
topology and learning parameters were selected based on the highest
regression performance obtained during the ANN training and testing
process. It is observed that the best transfer function configuration for
the prediction model is [logarithmic sigmoid, purely linear].
Abstract: This paper presents an on-going research work on the
implementation of feature-based machining via macro programming.
Repetitive machining features such as holes, slots, pockets etc can
readily be encapsulated in macros. Each macro consists of methods
on how to machine the shape as defined by the feature. The macro
programming technique comprises of a main program and
subprograms. The main program allows user to select several
subprograms that contain features and define their important
parameters. With macros, complex machining routines can be
implemented easily and no post processor is required. A case study
on machining of a part that comprised of planar face, hole and pocket
features using the macro programming technique was carried out. It
is envisaged that the macro programming technique can be extended
to other feature-based machining fields such as the newly developed
STEP-NC domain.