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Turgut, Oguz Emrah
Advanced Technology and Science (ATScience)
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Journal : International Journal of Intelligent Systems and Applications in Engineering

 1 
Crow Search based Multi-objective Optimization of Irreversible Air Refrigerators Turgut, Oguz Emrah
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018642064

Abstract

This study proposes the optimum performance of the irreversible air refrigerators through recently developed metaheuristic algorithm called Crow search algorithm by means of finite time thermodynamics. Finite time thermodynamics is based on choosing the optimum pathways for any kind of thermodynamic system in order to reach the maximum efficiency of the thermodynamic cycle. Handful of objectives for assessing the performance of the irreversible air refrigerators such as coefficient of performance (COP), exergetic efficiency (ηII)  , ecological coefficient of  performance (ECOP), thermoeconomic optimization (F), and thermoecologic optimization functions (ECF) have been  successfully applied on the system. Three optimization scenarios have been studied for the multi objective optimization of irreversible air refrigerators. First scenario evaluates the concurrent optimization of objectives including exergetic efficiency (ηII), coefficient of performance (COP), and ecological coefficient of performance (ECOP). In second scenario, coefficient of performance (COP), thermoeconomic parameter (F), and  thermoecological coefficient of  performance (ECOP) have been simultaneously maximized to retain optimum working point of the cycle. Third case studies the simultaneous optimization of the imposed objectives such as second law efficiency (ηII), coefficient of performance (COP), and thermoecological function (ECF).  Widely known decision-making theorems of LINMAP, TOPSIS, and Shannon’s entropy theorem have been applied on the Pareto curve constructed by the non-dominated solutions to decide the most favorable solution on the frontier. 
Multi objective design optimization of plate fin heat sinks using improved differential search algorithm Turgut, Oguz Emrah
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637924

Abstract

This study provides the multi-objective optimization of plate fin heat sinks equipped with flow – through and impingement-flow air-cooling system by using Improved Differential Search algorithm. Differential Search algorithm mimics the subsistence characteristics of the living beings through the migration process. Convergence speed of the algorithm is enhanced with the local search based perturbation schemes and this improvement yields favorable solution outputs according to the results obtained from the widely quoted optimization test problems. Improved algorithm is employed on multi-objective design optimization of plate fins heat sink considering the objective functions of entropy generation rate and total material cost. Total of seven decision variables such as oncoming stream velocity, number of fins on the plate, gap between consecutive fins, base thickness of the plate, width, length and height of the plate fin heat sink are selected to be optimized. Pareto frontiers are constructed for both flow-through and impingement flow air-cooling system design and best solutions are obtained   by means of widely reputed decision-making theories of LINMAP, TOPSIS, and Shannon’s entropy theory. Results retrieved from the case studies show that reliable outcomes could be achieved in terms of solution accuracy through   Improved Differential Search optimizer.   
Hybrid Artificial Cooperative Search – Crow Search Algorithm for Optimization of a Counter Flow Wet Cooling Tower Turgut, Mert Sinan; Turgut, Oguz Emrah
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 3 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017531425

Abstract

In this paper, an improved version of Artificial Cooperative Search (ACS) algorithm is applied on a counter flow wet-cooling tower design problem. The Merkel’s method is used to determine the characteristic dimensions of cooling tower, along with empirical correlations for the loss and overall mass transfer coefficients in the packing region of the tower.  Basic perturbation schemes of the Crow Search Algorithm, a recent developed metaheuristic algorithm inspired by the food searching behaviors intelligent crows, are incorporated into ACS to enhance the convergence speed and increase the solution diversity of the algorithm. In order to assess the solution performance of the proposed method, fourteen widely known optimization test function have been solved and corresponding convergence histories has been depicted.  .Then the improved ACS algorithm (IACS) is applied on six different examples of counter flow wet-cooling tower optimization problem. The results obtained by applying the proposed algorithm are compared with the results of some other algorithms in the literature. Optimization results show that IACS is an effective algorithm with rapid convergence performance for the optimization of counter flow wet-cooling towers. 
Global Best Algorithm Based Parameter Identification of Solar Cell Models Turgut, Oguz Emrah
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533892

Abstract

Effectivity of the solar energy systems is thoroughly dependent of successful modeling of the I-V characteristic curves. However, due to the lack of information about the precise model parameters those are profoundly involved in characterizing governing equations; an efficient design has not been accurately accomplished by researchers yet. This article proposes Global Best Algorithm (GBEST) in order to extract unknown parameters of solar cell models accurately. In order to test the performance of the proposed optimizer, nine different unconstrained optimization test functions are evaluated and their statistical results are compared with the recently developed metaheuristic algorithms. GBEST is applied on PV module, single and double diode models which are mathematically formulated as multi-dimensional and highly nonlinear in their nature.   Results reveal that GBEST is superior to the other methods in terms of solution accuracy and efficiency.
Co-Authors Turgut, Mert Sinan