Optimization Study of a Hybrid Solar Air Conditioner

2013 ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Global Optimum ◽  
Optimized Design ◽  
Air Conditioner ◽  
Air Conditioners ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Optimization Study ◽  
Systems Simulation

In order for the solar air conditioners (A/Cs) to become a real alternative to the conventional systems, their performance and total cost has to be optimized. In this study, an innovative hybrid solar A/C was simulated using the transient systems simulation (TRNSYS) program, which was coupled with MATLAB in order to carry out the optimization study. Two optimization problems were formulated with the following design variables: collector area, collector mass flow rate, storage tank volume, and number of batteries. The Genetic Algorithm (GA) was selected to find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, a Multi-Objective Genetic Algorithm (MOGA) was used to find the Pareto front within the design variables’ bounds while satisfying the constraints. The optimized design was also compared to a standard vapor compression cycle. The results show that coupling TRNSYS and MATLAB expands TRNSYS optimization capability in solving more complicated optimization problems.

Solar Hybrid Air Conditioner: Model Validation and Optimization

2016 ◽  
Vol 138 (3) ◽  
Author(s):  
Ali Al-Alili ◽  
Yunho Hwang ◽  
Reinhard Radermacher
Keyword(s):  
Genetic Algorithm ◽  
Energy Storage ◽  
Solar Collector ◽  
Optimization Problems ◽  
Electrical Energy ◽  
Global Optimum ◽  
Air Conditioner ◽  
Solar Thermal Energy ◽  
Design Variables ◽  
Optimization Study

Solar air conditioners (A/Cs) have attracted much attention in research, but their performance and cost have to be optimized in order to become a real alternative to conventional A/C systems. In this study, a hybrid solar A/C is simulated using the transient systems simulation program(trnsys), which is coupled with matlab in order to carry out the optimization study. The trnsys model is experimentally validated prior to the optimization study. Two optimization problems are formulated with the following design variables: solar collector area, solar collector mass flow rate, solar thermal energy storage volume, and solar electrical energy storage size. The genetic algorithm (GA) is selected to solve the single-objective optimization problem and find the global optimum design for the lowest electrical consumption. To optimize the two objective functions simultaneously, energy consumption and total cost (TC), a multi-objective genetic algorithm (MOGA) is used to find the Pareto curve within the design variables' bounds while satisfying the constraints. The overall cost of the optimized solar A/C design is also compared to a standard vapor compression cycle (VCC). The results show that coupling trnsys and matlab expands trnsys optimization capability in solving more complex optimization problems. The results also show that the optimized solar hybrid A/C is not very competitive when the electricity prices are low and no governmental support is provided.

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

Optimization for Crashworthiness of Urban Transit Trains Using Genetic Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1167-1172 ◽  
Author(s):  
Zhuo Jun Xie ◽  
Ping Xu ◽  
Yu Qi Luo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Urban Transit ◽  
Design Variables ◽  
Specific Goal ◽  
Design Function ◽  
Crash Model ◽  
Current Energy ◽  
Force Characteristics

As it is tough for the current energy absorb devices of urban vehicles to meet the crashworthiness requirements in the collision scenario of 25km/h, a methodology to improve the general crashworthiness is presented. A multi-criteria optimization, with the deformations and accelerations of all cars as the design functions and the force characteristics of end structures of cars as design variables, is defined and the Pareto Fonts are obtained. Then defining energy absorbed as design function, a single criteria optimization is made and the specific goal is achieved. No explicit relationship could be found between the design variables and the design functions, so a crash model of a train with velocity of 25km/h colliding to another train stopped is built and the genetic algorithm is chosen to solve the optimization problems. The results indicate that the crashworthiness performance of the trains is significantly improved and the crashworthiness requirements could be reached finally.

scholarly journals Using a Goal-Switching Selection Operator in Multi-Objective Genetic Algorithm Optimization Problems

2013 ◽  
Author(s):  
Daniel Shaefer ◽  
Scott Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Product Line ◽  
Algorithm Optimization ◽  
Nsga Ii ◽  
Solution Quality ◽  
Design Variables ◽  
Mp3 Player ◽  
Selection Operator

This paper demonstrates how solution quality for multiobjective optimization problems can be improved by altering the selection phase of a multiobjective genetic algorithm. Rather than the traditional roulette selection used in algorithms like NSGA-II, this paper adds a goal switching technique to the selection operator. Goal switching in this context represents the rotation of the selection operator among a problem’s various objective functions to increase search diversity. This rotation can be specified over a set period of generations, evaluations, CPU time, or other factors defined by the designer. This technique is tested using a set period of generations before switching occurs, with only one objective considered at a time. Two test cases are explored, the first as identified in the Congress on Evolutionary Computation (CEC) 2009 special session and the second a case study concerning the market-driven design of a MP3 player product line. These problems were chosen because the first test case’s Pareto frontier is continuous and concave while being relatively easy to find. The second Pareto frontier is more difficult to obtain and the problem’s design space is significantly more complex. Selection operators of roulette and roulette with goal switching were tested with 3 to 7 design variables for the CEC 09 problem, and 81 design variables for the MP3 player problem. Results show that goal switching improves the number of Pareto frontier points found and can also lead to improvements in hypervolume and/or mean time to convergence.

An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

2014 ◽  
Vol 962-965 ◽  
pp. 2903-2908
Author(s):  
Yun Lian Liu ◽  
Wen Li ◽  
Tie Bin Wu ◽  
Yun Cheng ◽  
Tao Yun Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Crossover Operator ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Great Ability ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

scholarly journals Large-Scale Truss Topology and Sizing Optimization by an Improved Genetic Algorithm with Multipoint Approximation

2021 ◽  
Vol 12 (1) ◽  
pp. 407
Author(s):  
Tianshan Dong ◽  
Shenyan Chen ◽  
Hai Huang ◽  
Chao Han ◽  
Ziqi Dai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Approximation Technique ◽  
Discrete Topology ◽  
Improved Genetic Algorithm ◽  
Approximation Accuracy ◽  
Design Variables ◽  
Metaheuristic Methods ◽  
Multipoint Approximation

Truss size and topology optimization problems have recently been solved mainly by many different metaheuristic methods, and these methods usually require a large number of structural analyses due to their mechanism of population evolution. A branched multipoint approximation technique has been introduced to decrease the number of structural analyses by establishing approximate functions instead of the structural analyses in Genetic Algorithm (GA) when GA addresses continuous size variables and discrete topology variables. For large-scale trusses with a large number of design variables, an enormous change in topology variables in the GA causes a loss of approximation accuracy and then makes optimization convergence difficult. In this paper, a technique named the label–clip–splice method is proposed to improve the above hybrid method in regard to the above problem. It reduces the current search domain of GA gradually by clipping and splicing the labeled variables from chromosomes and optimizes the mixed-variables model efficiently with an approximation technique for large-scale trusses. Structural analysis of the proposed method is extremely reduced compared with these single metaheuristic methods. Numerical examples are presented to verify the efficacy and advantages of the proposed technique.

scholarly journals A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization

2008 ◽  
pp. 103-126
Author(s):  
David W. Zingg ◽  
Marian Nemec ◽  
Thomas H. Pulliam
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Comparative Evaluation ◽  
Pareto Front ◽  
Optimization Problems ◽  
Aerodynamic Optimization ◽  
Design Variables ◽  
Gradient Based ◽  
Degree Of Convergence ◽  
Multipoint Optimization

A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples include singlepoint and multipoint optimization problems, as well as the computation of a Pareto front. The results demonstrate that both algorithms converge reliably to the same optimum. Depending on the nature of the problem, the number of design variables, and the degree of convergence, the genetic algorithm requires from 5 to 200 times as many function evaluations as the gradientbased algorithm.

Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Abhijit Deka ◽  
Dilip Datta
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Cross Sectional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

Sign in / Sign up

Export Citation Format

Share Document