scholarly journals A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm

Author(s):  
Hamid Isakhani ◽  
Caihua Xiong ◽  
Shigang Yue ◽  
Wenbin Chen
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.


2012 ◽  
Vol 36 (10) ◽  
pp. 4898-4907 ◽  
Author(s):  
A.F.P. Ribeiro ◽  
A.M. Awruch ◽  
H.M. Gomes

2021 ◽  
Author(s):  
Mohammed Ahmed Al-Janabi ◽  
Omar F. Al-Fatlawi ◽  
Dhifaf J. Sadiq ◽  
Haider Abdulmuhsin Mahmood ◽  
Mustafa Alaulddin Al-Juboori

Abstract Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorithm to tackle the challenging task of optimally allocating the gas lift injection rate through numerical modeling and simulation studies to maximize the oil production of a Middle Eastern oil field with 20 production wells with limited amount of gas to be injected. The key objective of this study is to assess the performance of the wells of the field after applying gas lift as an artificial lift method and applying the genetic algorithm as an optimization algorithm while comparing the results of the network to the case of artificially lifted wells by utilizing ESP pumps to the network and to have a more accurate view on the practicability of applying the gas lift optimization technique. The comparison is based on different measures and sensitivity studies, reservoir pressure, and water cut sensitivity analysis are applied to allow the assessment of the performance of the wells in the network throughout the life of the field. To have a full and insight view an economic study and comparison was applied in this study to estimate the benefits of applying the gas lift method and the GA optimization technique while comparing the results to the case of the ESP pumps and the case of naturally flowing wells. The gas lift technique proved to have the ability to enhance the production of the oil field and the optimization process showed quite an enhancement in the task of maximizing the oil production rate while using the same amount of gas to be injected in the each well, the sensitivity analysis showed that the gas lift method is comparable to the other artificial lift method and it have an upper hand in handling the reservoir pressure reduction, and economically CAPEX of the gas lift were calculated to be able to assess the time to reach a profitable income by comparing the results of OPEX of gas lift the technique showed a profitable income higher than the cases of naturally flowing wells and the ESP pumps lifted wells. Additionally, the paper illustrated the genetic algorithm (GA) optimization model in a way that allowed it to be followed as a guide for the task of optimizing the gas injection rate for a network with a large number of wells and limited amount of gas to be injected.


2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>


2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Fayiz Abu Khadra ◽  
Jaber Abu Qudeiri ◽  
Mohammed Alkahtani

A control methodology based on a nonlinear control algorithm and optimization technique is presented in this paper. A controller called “the robust integral of the sign of the error” (in short, RISE) is applied to control chaotic systems. The optimum RISE controller parameters are obtained via genetic algorithm optimization techniques. RISE control methodology is implemented on two chaotic systems, namely, the Duffing-Holms and Van der Pol systems. Numerical simulations showed the good performance of the optimized RISE controller in tracking task and its ability to ensure robustness with respect to bounded external disturbances.


Author(s):  
Zaineb Nisar Jan

Abstract: In economic load dispatch problem scheduling of loads is done in order to achieve reliable power supply with reduced costs. With the increase in load demand with each passing year energy crisis is increasing hence an area of study where fuel costs can be reduced was proposed. This can be achieved using various methods among which the methods discussed in this paper are Lambda Iteration, Particle Swarm Operation and Genetic Algorithm. Based on numerical results the best optimization technique can be figured out among the discussed methods. Keywords: Lambda Iteration, PSO, GA, economic load dispatch, optimization solutions


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