Abstract: This study investigates and develops the structural optimization method. The effect of size constraints on practical solution of reinforced concrete (RC) building structure with shear wall is proposed. Cross-sections of beam and column, and thickness of shear wall are considered as design variables. The objective function to be minimized is total cost of the structure by using a simple and efficient automated MATLAB platform structural optimization methodology. With modification of mathematical formulations, the result is compared with optimal solution without size constraints. The most suitable combination of section sizes is selected as for the final design application based on linear static analysis. The findings of this study show that defining higher value of upper bound of sectional sizes significantly affects optimal solution, and defining of size constraints play a vital role in finding of global and practical solution during optimization procedures. The result and effectiveness of proposed method confirm the ability and efficiency of optimal solutions for 3D RC shear wall-frame structure.
Abstract: In this paper, it is aimed to improve autonomous flight
performance of a load-carrying (payload: 3 kg and total: 6kg)
unmanned aerial vehicle (UAV) through active wing and horizontal
tail active morphing and also integrated autopilot system parameters
(i.e. P, I, D gains) and UAV parameters (i.e. extension ratios of wing
and horizontal tail during flight) design. For this purpose, a loadcarrying
UAV (i.e. ZANKA-II) is manufactured in Erciyes
University, College of Aviation, Model Aircraft Laboratory is
benefited. Optimum values of UAV parameters and autopilot
parameters are obtained using a stochastic optimization method.
Using this approach autonomous flight performance of UAV is
substantially improved and also in some adverse weather conditions
an opportunity for safe flight is satisfied. Active morphing and
integrated design approach gives confidence, high performance and
easy-utility request of UAV users.
Abstract: This paper proposes an application of the differential
evolution (DE) algorithm for solving the economic dispatch problem
(ED). Furthermore, the regenerating population procedure added to
the conventional DE in order to improve escaping the local minimum
solution. To test performance of DE algorithm, three thermal
generating units with valve-point loading effects is used for testing.
Moreover, investigating the DE parameters is presented. The
simulation results show that the DE algorithm, which had been
adjusted parameters, is better convergent time than other optimization
methods.
Abstract: A new design of a planar passive T-micromixer with fin-shaped baffles in the mixing channel is presented. The mixing efficiency and the level of pressure loss in the channel have been investigated by numerical simulations in the range of Reynolds number (Re) 1 to 50. A Mixing index (Mi) has been defined to quantify the mixing efficiency, which results over 85% at both ends of the Re range, what demonstrates the micromixer can enhance mixing using the mechanisms of diffusion (lower Re) and convection (higher Re). Three geometric dimensions: radius of baffle, baffles pitch and height of the channel define the design parameters, and the mixing index and pressure loss are the performance parameters used to optimize the micromixer geometry with a multi-criteria optimization method. The Pareto front of designs with the optimum trade-offs, maximum mixing index with minimum pressure loss, is obtained. Experiments for qualitative and quantitative validation have been implemented.
Abstract: This paper presents a new feature based dense stereo
matching algorithm to obtain the dense disparity map via dynamic
programming. After extraction of some proper features, we use some
matching constraints such as epipolar line, disparity limit, ordering
and limit of directional derivative of disparity as well. Also, a coarseto-
fine multiresolution strategy is used to decrease the search space
and therefore increase the accuracy and processing speed. The
proposed method links the detected feature points into the chains and
compares some of the feature points from different chains, to
increase the matching speed. We also employ color stereo matching
to increase the accuracy of the algorithm. Then after feature
matching, we use the dynamic programming to obtain the dense
disparity map. It differs from the classical DP methods in the stereo
vision, since it employs sparse disparity map obtained from the
feature based matching stage. The DP is also performed further on a
scan line, between any matched two feature points on that scan line.
Thus our algorithm is truly an optimization method. Our algorithm
offers a good trade off in terms of accuracy and computational
efficiency. Regarding the results of our experiments, the proposed
algorithm increases the accuracy from 20 to 70%, and reduces the
running time of the algorithm almost 70%.
Abstract: This paper presents the use of Legendre pseudospectral
method for the optimization of finite-thrust orbital transfer for
spacecrafts. In order to get an accurate solution, the System-s
dynamics equations were normalized through a dimensionless method.
The Legendre pseudospectral method is based on interpolating
functions on Legendre-Gauss-Lobatto (LGL) quadrature nodes. This
is used to transform the optimal control problem into a constrained
parameter optimization problem. The developed novel optimization
algorithm can be used to solve similar optimization problems of
spacecraft finite-thrust orbital transfer. The results of a numerical
simulation verified the validity of the proposed optimization method.
The simulation results reveal that pseudospectral optimization method
is a promising method for real-time trajectory optimization and
provides good accuracy and fast convergence.
Abstract: Fluid flow and heat transfer of vertical full cone
embedded in porous media is studied in this paper. Nonlinear
differential equation arising from similarity solution of inverted cone
(subjected to wall temperature boundary conditions) embedded in
porous medium is solved using a hybrid neural network- particle
swarm optimization method.
To aim this purpose, a trial solution of the differential equation is
defined as sum of two parts. The first part satisfies the initial/
boundary conditions and does contain an adjustable parameter and
the second part which is constructed so as not to affect the
initial/boundary conditions and involves adjustable parameters (the
weights and biases) for a multi-layer perceptron neural network.
Particle swarm optimization (PSO) is applied to find adjustable
parameters of trial solution (in first and second part). The obtained
solution in comparison with the numerical ones represents a
remarkable accuracy.
Abstract: In recent years, environment regulation forcing
manufactures to consider recovery activity of end-of- life products
and/or return products for refurbishing, recycling,
remanufacturing/repair and disposal in supply chain management. In
this paper, a mathematical model is formulated for single product
production-inventory system considering remanufacturing/reuse of
return products and rate of return products follows a demand like
function, dependent on purchasing price and acceptance quality level.
It is useful in decision making to determine whether to go for
remanufacturing or disposal of returned products along with newly
produced products to satisfy a stationary demand. In addition, a
modified genetic algorithm approach is proposed, inspired by particle
swarm optimization method. Numerical analysis of the case study is
carried out to validate the model.