scholarly journals Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mansur Mohammed Ali Gamel ◽  
Pin Jern Ker ◽  
Hui Jing Lee ◽  
Wan Emilin Suliza Wan Abdul Rashid ◽  
M. A. Hannan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Waste Heat ◽  
Antireflection Coating ◽  
Base Layer ◽  
Average Percentage ◽  
Cell Efficiency ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Dimensional Optimization

AbstractThe optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

scholarly journals Multi-Dimensional Optimization of In0.53Ga0.47As Thermophotovoltaic Cell using Real Coded Genetic Algorithm

2020 ◽  
Author(s):  
Mansur Gamel ◽  
Pin Jern Ker ◽  
Hui Jing Lee ◽  
Wan Emilin Suliza Wan Abdul Rashid ◽  
M.A. Hannan ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Output Power ◽  
Waste Heat ◽  
Antireflection Coating ◽  
Base Layer ◽  
Average Percentage ◽  
Cell Efficiency ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Dimensional Optimization

Abstract The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) under various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 K to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5% to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.

Genetic algorithm- and finite element-based design and analysis of nonprismatic piezolaminated beam for optimal vibration energy harvesting

2015 ◽  
Vol 230 (14) ◽  
pp. 2532-2548 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Harvesting ◽  
Output Power ◽  
Governing Equation ◽  
Optimization Technique ◽  
Vibration Energy ◽  
Fe Model ◽  
Vibration Energy Harvesting ◽  
Real Coded Genetic Algorithm

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.

Reliability-based design optimization of electromagnetic shielding structure using neural networks and real-coded genetic algorithm

2014 ◽  
Vol 228 (18) ◽  
pp. 3471-3481 ◽  
Author(s):  
Heeralal Gargama ◽  
Sanjay K Chaturvedi ◽  
Awalendra K Thakur
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Shielding ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Real Coded Genetic Algorithm

The conventional approaches for electromagnetic shielding structures’ design, lack the incorporation of uncertainty in the design variables/parameters. In this paper, a reliability-based design optimization approach for designing electromagnetic shielding structure is proposed. The uncertainties/variability in the design variables/parameters are dealt with using the probabilistic sufficiency factor, which is a factor of safety relative to a target probability of failure. Estimation of probabilistic sufficiency factor requires performance function evaluation at every design point, which is extremely computationally intensive. The computational burden is reduced greatly by evaluating design responses only at the selected design points from the whole design space and employing artificial neural networks to approximate probabilistic sufficiency factor as a function of design variables. Subsequently, the trained artificial neural networks are used for the probabilistic sufficiency factor evaluation in the reliability-based design optimization, where optimization part is processed with the real-coded genetic algorithm. The proposed reliability-based design optimization approach is applied to design a three-layered shielding structure for a shielding effectiveness requirement of ∼40 dB, used in many industrial/commercial applications, and for ∼80 dB used in the military applications.

Development of Multi-Stage and Hybrid Real-Coded Genetic Algorithms With Active Constraints

2002 ◽  
Author(s):  
Yasunari Mimura ◽  
Shinobu Yoshimura ◽  
Tomoyuki Hiroyasu ◽  
Mitsunori Miki
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Initial Point ◽  
Global Optimum ◽  
Feasible Region ◽  
Active Constraints ◽  
Multi Stage ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Optimum Solution

In this study, we propose multi-stage and hybrid real-coded genetic algorithm. In the proposed algorithm, there are two stages. In the first stage, Real-coded Genetic Algorithm with Active Constraints (RGAAC) is applied to find a solution that is close to the global optimum. In RGAAC, indviduals who are out of the feasible region are pulled back into feasible region. Therefore, the effective search can be carried out even in the constraints problems. In the second stage, Feasible Region Limiting Method (FRLM) is applied to obtain an optimum solution. FRLM uses the solution that is derived in the first stager as an initial point. In this study, RGAAC is applied to solve the truss structure problems. Through these problems, the effectiveness and the searching mechanism of RGAAC is discussed. The, the proposed algorithm is also applied to 2D problem. In this problem, there are about 1000 design variables. The proposed method can derive the reasonable solution. From these results, it is concluded that the proposed method is effective to solve optimzation problems of large scale structures.

Blending design of composite panels with lamination parameters

2016 ◽  
Vol 120 (1233) ◽  
pp. 1710-1725 ◽  
Author(s):  
P. Jin ◽  
X. Zhong ◽  
J. Yang ◽  
Z. Sun
Keyword(s):  
Genetic Algorithm ◽  
Least Squares ◽  
Numerical Results ◽  
Manufacturing Constraints ◽  
Stacking Sequence ◽  
Lamination Parameters ◽  
Least Squares Fitting ◽  
Design Variables ◽  
Real Coded Genetic Algorithm ◽  
Present Algorithm

ABSTRACTIn this paper, a new optimisation method incorporating lamination parameters and a guide-based blending model is proposed. Lamination parameters for a guide laminate and ply number of each panel are employed as design variables for optimisation with a parallel real-coded genetic algorithm incorporating structure behaviour and manufacturing constraints. During the optimisation process, with a form of least squares fitting adopted, another genetic algorithm is used to obtain the guide stacking sequence of the guide laminate from the guide lamination parameters, and then the laminate configurations of each panel are obtained from the guide stacking sequence and number of plies for each panel. The proposed framework is demonstrated via design of an 18-panel horseshoe configuration, where each panel is optimised individually with a buckling constraint. Numerical results indicate that the present algorithm is capable of obtaining fully blended designs.

A Novel Middleware-Based Model for Redundant Reader Elimination Using Genetic Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 881-885
Author(s):  
Shi Lei Lu ◽  
Shun Zheng Yu
Keyword(s):  
Genetic Algorithm ◽  
Performance Improvement ◽  
Radio Frequency Identification ◽  
Network Performance ◽  
Optimization Problem ◽  
Computing Time ◽  
Real Coded Genetic Algorithm ◽  
Frequency Identification ◽  
Simulation Results ◽  
Dimensional Optimization

Eliminating redundant readers is important for the performance improvement of the radio frequency identification (RFID) networks. This paper proposes an efficient approach for redundant reader elimination. A middleware-based model, which is formulated as a multi-dimensional optimization problem, is developed to evaluate the network performance. The real-coded genetic algorithm (RGA) is employed to search the optimal adjustable parameters. The simulation results reveal that the proposed approach outperforms other algorithms in terms of optimization precision and computing time.

Optimal vibration energy harvesting from non-prismatic axially functionally graded piezolaminated cantilever beam using genetic algorithm

2017 ◽  
Vol 28 (14) ◽  
pp. 1957-1976 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Energy Harvesting ◽  
Output Power ◽  
Cantilever Beam ◽  
Governing Equation ◽  
Axial Direction ◽  
Functionally Graded ◽  
Vibration Energy Harvesting ◽  
Functionally Graded Beam ◽  
Real Coded Genetic Algorithm

Optimal vibration–based energy harvesting from the axially functionally graded non-prismatic piezolaminated cantilever beam using finite element and genetic algorithm has been proposed in this article. The functionally graded material (i.e. non-homogeneous) in the axial direction is considered, where the cross section is varied (continuously decreasing from root to tip of such cantilever beam) using a proposed power law formula. The shape variations of piezolaminated cantilever (such as linear, parabolic and cubic) have been modelled using the Euler–Bernoulli beam theory. Hamilton’s principle is used in order to derive the governing equation of motion. The governing equation is solved by considering two-noded beam element with 2 degrees of freedom at each node. The responses (such as frequency, voltage and output power) are compared between uniform and the axially functionally graded beam with arbitrary power gradient index. The effects of taper (both in the width and height directions) on output power along with frequency and voltage are analysed for axially functionally graded beam. In order to maximise the output power within the allowable limits of voltage and stresses, a real-coded genetic algorithm–based constrained optimisation technique has been proposed.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

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