Abstract: The objective of this article is to improve the passive vibration damping of solar array (SA) used in space structures, by the effective application of numerical optimization. A case study of a SA is used for demonstration. A finite element (FE) model was created and verified by experimental testing. Optimization was then conducted by implementing the FE model with the genetic algorithm, to find the optimal placement of aluminum circular patches, to suppress the first two bending mode shapes. The results were verified using experimental testing. Finally, a parametric study was conducted using the FE model where patch locations, material type, and shape were varied one at a time, and the results were compared with the optimal ones. The results clearly show that through the proper application of FE modeling and numerical optimization, passive vibration damping of space structures has been successfully achieved.
Abstract: This study presents a hybrid metaheuristic algorithm
to obtain optimum designs for steel space buildings. The optimum
design problem of three-dimensional steel frames is mathematically
formulated according to provisions of LRFD-AISC (Load and
Resistance factor design of American Institute of Steel Construction).
Design constraints such as the strength requirements of structural
members, the displacement limitations, the inter-story drift and the
other structural constraints are derived from LRFD-AISC
specification. In this study, a hybrid algorithm by using teachinglearning
based optimization (TLBO) and harmony search (HS)
algorithms is employed to solve the stated optimum design problem.
These algorithms are two of the recent additions to metaheuristic
techniques of numerical optimization and have been an efficient tool
for solving discrete programming problems. Using these two
algorithms in collaboration creates a more powerful tool and
mitigates each other’s weaknesses. To demonstrate the powerful
performance of presented hybrid algorithm, the optimum design of a
large scale steel building is presented and the results are compared to
the previously obtained results available in the literature.
Abstract: The composite flour blend consisting of corn, pearl
millet, black gram and wheat bran in the ratio of 80:5:10:5 was taken
to prepare the extruded product and their effect on physical properties
of extrudate was studied. The extrusion process was conducted in
laboratory by using twin screw extruder. The physical characteristics
evaluated include lateral expansion, bulk density, water absorption
index, water solubility index, and rehydration ratio and moisture
retention. The Central Composite Rotatable Design (CCRD) was
used to decide the level of processing variables i.e. feed moisture
content (%), screw speed (rpm), and barrel temperature (oC) for the
experiment. The data obtained after extrusion process were analyzed
by using response surface methodology. A second order polynomial
model for the dependent variables was established to fit the
experimental data. The numerical optimization studies resulted in
127°C of barrel temperature, 246 rpm of screw speed, and 14.5% of
feed moisture as optimum variables to produce acceptable extruded
product. The responses predicted by the software for the optimum
process condition resulted in lateral expansion 126%, bulk density
0.28 g/cm3, water absorption index 4.10 g/g, water solubility index
39.90%, rehydration ratio 544% and moisture retention 11.90% with
75% desirability.
Abstract: This study presents the numerical simulation of three-dimensional incompressible steady and laminar fluid flow and conjugate heat transfer of a trapezoidal microchannel heat sink using water as a cooling fluid in a silicon substrate. Navier-Stokes equations with conjugate energy equation are discretized by finite-volume method. We perform numerical computations for a range of 50 ≦ Re ≦ 600, 0.05W ≦ P ≦ 0.8W, 20W/cm2 ≦q"≦ 40W/cm2. The present study demonstrates the numerical optimization of a trapezoidal microchannel heat sink design using the response surface methodology (RSM) and the genetic algorithm method (GA). The results show that the average Nusselt number increases with an increase in the Reynolds number or pumping power, and the thermal resistance decreases as the pumping power increases. The thermal resistance of a trapezoidal microchannel is minimized for a constant heat flux and constant pumping power.
Abstract: Nowadays, with the increasing of the wafer's size and
the decreasing of critical size of integrated circuit manufacturing in
modern high-tech, microelectronics industry needs a maximum
attention to challenge the contamination control. The move to 300
[mm] is accompanied by the use of Front Opening Unified Pods for
wafer and his storage. In these pods an airborne cross contamination
may occur between wafers and the pods. A predictive approach using
modeling and computational methods is very powerful method to
understand and qualify the AMCs cross contamination processes.
This work investigates the required numerical tools which are
employed in order to study the AMCs cross-contamination transfer
phenomena between wafers and FOUPs. Numerical optimization and
finite element formulation in transient analysis were established.
Analytical solution of one dimensional problem was developed and
the calibration process of physical constants was performed. The least
square distance between the model (analytical 1D solution) and the
experimental data are minimized. The behavior of the AMCs
intransient analysis was determined. The model framework preserves
the classical forms of the diffusion and convection-diffusion
equations and yields to consistent form of the Fick's law. The
adsorption process and the surface roughness effect were also
traduced as a boundary condition using the switch condition Dirichlet
to Neumann and the interface condition. The methodology is applied,
first using the optimization methods with analytical solution to define
physical constants, and second using finite element method including
adsorption kinetic and the switch of Dirichlet to Neumann condition.
Abstract: The motion planning technique described in this paper has been developed to eliminate or reduce the residual vibrations of belt-driven rotary platforms, while maintaining unchanged the motion time and the total angular displacement of the platform. The proposed approach is based on a suitable choice of the motion command given to the servomotor that drives the mechanical device; this command is defined by some numerical coefficients which determine the shape of the displacement, velocity and acceleration profiles. Using a numerical optimization technique, these coefficients can be changed without altering the continuity conditions imposed on the displacement and its time derivatives at the initial and final time instants. The proposed technique can be easily and quickly implemented on an actual device, since it requires only a simple modification of the motion command profile mapped in the memory of the electronic motion controller.
Abstract: This paper is focused on issues of process modeling
and two model based control strategies of a fed-batch sugar
crystallization process applying the concept of artificial neural
networks (ANNs). The control objective is to force the operation into
following optimal supersaturation trajectory. It is achieved by
manipulating the feed flow rate of sugar liquor/syrup, considered as
the control input. The control task is rather challenging due to the
strong nonlinearity of the process dynamics and variations in the
crystallization kinetics. Two control alternatives are considered –
model predictive control (MPC) and feedback linearizing control
(FLC). Adequate ANN process models are first built as part of the
controller structures. MPC algorithm outperforms the FLC approach
with respect to satisfactory reference tracking and smooth control
action. However, the MPC is computationally much more involved
since it requires an online numerical optimization, while for the FLC
an analytical control solution was determined.
Abstract: Among many different methods that are used for
optimizing different engineering problems mathematical (numerical)
optimization techniques are very important because they can easily
be used and are consistent with most of engineering problems. Many
studies and researches are done on stability analysis of three
dimensional (3D) slopes and the relating probable slip surfaces and
determination of factors of safety, but in most of them force
equilibrium equations, as in simplified 2D methods, are considered
only in two directions. In other words for decreasing mathematical
calculations and also for simplifying purposes the force equilibrium
equation in 3rd direction is omitted. This point is considered in just a
few numbers of previous studies and most of them have only given a
factor of safety and they haven-t made enough effort to find the most
probable slip surface. In this study shapes of the slip surfaces are
modeled, and safety factors are calculated considering the force
equilibrium equations in all three directions, and also the moment
equilibrium equation is satisfied in the slip direction, and using
nonlinear programming techniques the shape of the most probable
slip surface is determined. The model which is used in this study is a
3D model that is composed of three upper surfaces which can cover
all defined and probable slip surfaces. In this research the meshing
process is done in a way that all elements are prismatic with
quadrilateral cross sections, and the safety factor is defined on this
quadrilateral surface in the base of the element which is a part of the
whole slip surface. The method that is used in this study to find the
most probable slip surface is the non-linear programming method in
which the objective function that must get optimized is the factor of
safety that is a function of the soil properties and the coordinates of
the nodes on the probable slip surface. The main reason for using
non-linear programming method in this research is its quick
convergence to the desired responses. The final results show a good
compatibility with the previously used classical and 2D methods and
also show a reasonable convergence speed.
Abstract: The purpose of this paper is applied Taguchi method on the optimization for PEMFC performance, and a representative Computational Fluid Dynamics (CFD) model is selectively performed for statistical analysis. The studied factors in this paper are pressure of fuel cell, operating temperature, the relative humidity of anode and cathode, porosity of gas diffusion electrode (GDE) and conductivity of GDE. The optimal combination for maximum power density is gained by using a three-level statistical method. The results confirmed that the robustness of the optimum design parameters influencing the performance of fuel cell are founded by pressure of fuel cell, 3atm; operating temperature, 353K; the relative humidity of anode, 50%; conductivity of GDE, 1000 S/m, but the relative humidity of cathode and porosity of GDE are pooled as error due to a small sum of squares. The present simulation results give designers the ideas ratify the effectiveness of the proposed robust design methodology for the performance of fuel cell.
Abstract: A novel method of individual level adaptive mutation rate control called the rank-scaled mutation rate for genetic algorithms is introduced. The rank-scaled mutation rate controlled genetic algorithm varies the mutation parameters based on the rank of each individual within the population. Thereby the distribution of the fitness of the papulation is taken into consideration in forming the new mutation rates. The best fit mutate at the lowest rate and the least fit mutate at the highest rate. The complexity of the algorithm is of the order of an individual adaptation scheme and is lower than that of a self-adaptation scheme. The proposed algorithm is tested on two common problems, namely, numerical optimization of a function and the traveling salesman problem. The results show that the proposed algorithm outperforms both the fixed and deterministic mutation rate schemes. It is best suited for problems with several local optimum solutions without a high demand for excessive mutation rates.
Abstract: This paper reports the study results on neural network
training algorithm of numerical optimization techniques multiface
detection in static images. The training algorithms involved are scale
gradient conjugate backpropagation, conjugate gradient
backpropagation with Polak-Riebre updates, conjugate gradient
backpropagation with Fletcher-Reeves updates, one secant
backpropagation and resilent backpropagation. The final result of
each training algorithms for multiface detection application will also
be discussed and compared.
Abstract: The incorporation of computational fluid dynamics in the design of modern hydraulic turbines appears to be necessary in order to improve their efficiency and cost-effectiveness beyond the traditional design practices. A numerical optimization methodology is developed and applied in the present work to a Turgo water turbine. The fluid is simulated by a Lagrangian mesh-free approach that can provide detailed information on the energy transfer and enhance the understanding of the complex, unsteady flow field, at very small computing cost. The runner blades are initially shaped according to hydrodynamics theory, and parameterized using Bezier polynomials and interpolation techniques. The use of a limited number of free design variables allows for various modifications of the standard blade shape, while stochastic optimization using evolutionary algorithms is implemented to find the best blade that maximizes the attainable hydraulic efficiency of the runner. The obtained optimal runner design achieves considerably higher efficiency than the standard one, and its numerically predicted performance is comparable to a real Turgo turbine, verifying the reliability and the prospects of the new methodology.
Abstract: In this paper usefulness of quasi-Newton iteration
procedure in parameters estimation of the conditional variance
equation within BHHH algorithm is presented. Analytical solution of
maximization of the likelihood function using first and second
derivatives is too complex when the variance is time-varying. The
advantage of BHHH algorithm in comparison to the other
optimization algorithms is that requires no third derivatives with
assured convergence. To simplify optimization procedure BHHH
algorithm uses the approximation of the matrix of second derivatives
according to information identity. However, parameters estimation in
a/symmetric GARCH(1,1) model assuming normal distribution of
returns is not that simple, i.e. it is difficult to solve it analytically.
Maximum of the likelihood function can be founded by iteration
procedure until no further increase can be found. Because the
solutions of the numerical optimization are very sensitive to the
initial values, GARCH(1,1) model starting parameters are defined.
The number of iterations can be reduced using starting values close
to the global maximum. Optimization procedure will be illustrated in
framework of modeling volatility on daily basis of the most liquid
stocks on Croatian capital market: Podravka stocks (food industry),
Petrokemija stocks (fertilizer industry) and Ericsson Nikola Tesla
stocks (information-s-communications industry).
Abstract: This study presents the numerical simulation of
optimum pin-fin heat sink with air impinging cooling by using
Taguchi method. 9 L ( 4 3 ) orthogonal array is selected as a plan for
the four design-parameters with three levels. The governing
equations are discretized by using the
control-volume-based-finite-difference method with a power-law
scheme on the non-uniform staggered grid. We solved the coupling of
the velocity and the pressure terms of momentum equations using
SIMPLEC algorithm. We employ the k −ε two-equations
turbulence model to describe the turbulent behavior. The parameters
studied include fin height H (35mm-45mm), inter-fin spacing a , b ,
and c (2 mm-6.4 mm), and Reynolds number ( Re = 10000- 25000).
The objective of this study is to examine the effects of the fin
spacings and fin height on the thermal resistance and to find the
optimum group by using the Taguchi method. We found that the fin
spacings from the center to the edge of the heat sink gradually
extended, and the longer the fin’s height the better the results. The
optimum group is 3 1 2 3 H a b c . In addition, the effects of parameters are
ranked by importance as a , H , c , and b .