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.

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.

scholarly journals A hybrid air conditioning system using solar energy to save electrical energy with improving performance

2021 ◽  
Vol 288 ◽  
pp. 01066
Author(s):  
Ahmed Al–Okbi ◽  
Yuri Vankov ◽  
Hasanen Mohammad Hussain
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
Solar Collector ◽  
Air Conditioning ◽  
Electrical Energy ◽  
Coefficient Of Performance ◽  
Electricity Production ◽  
Air Conditioner ◽  
Solar Thermal Energy ◽  
Air Conditioning System

The process of operating an air conditioning system by hybrid energy that uses solar energy for purpose of saving electrical energy with improving the performance from modern and environmentally friendly systems. With high demand for air-conditioning systems in summer in hot regions, especially in Iraq due to high temperatures, the issue of using renewable energies becomes more attractive due to the continuous interruption of electrical energy. Air conditioners in Iraq consume more than half of the average electricity production. Therefore, saving energy leads to ensuring the reliability of electricity and reduces the consumption of fuel and gases that pollute the environment and negatively affect on the ozone layer. In the current research, the atmosphere of the city Baghdad was used to collect the solar thermal energy through a vacuum solar collector and combine it with a conventional air conditioner in order to reduce the electrical energy consumption on the compressor and increase the coefficient of performance. Several tests were conducted on the experimental device for comparing results with the conventional device and evaluating performance. The results showed that the performance with the vacuum solar collector became more efficient 8.97 instead of 4.27 than with the conventional system, and the energy consumption decreased by 52%.

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

scholarly journals Study of a solar air conditioning system with ejector

2020 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Ghodbane Mokhtar
Keyword(s):  
Solar Collector ◽  
Air Conditioning ◽  
Human Life ◽  
Electrical Energy ◽  
Well Being ◽  
Climatic Conditions ◽  
Power Grids ◽  
Air Conditioner ◽  
The Face ◽  
Thermal Generator

Air conditioning is one of the indispensable conditions of well-being in human life, so the face of this research to provide this basic necessity in remote areas and in desert places far from power grids. To achieve this goal, solar air conditioning has been adopted, where the compressor was replaced by an ejector, a parabolic trough solar collector and a small pump; this means that the solar air conditioner does not need a huge amount of electrical energy to operate. This paper is studding the thermodynamic cycles of this air conditioner as a function of changing the climatic conditions of Bouzaréah region in Algeria under several practical conditions of heat exchangers (Condenser, Evaporator and Generator). This study will allow the determination of the optical and thermal efficiency of the solar collector used as a solar thermal generator, refrigeration subsystem performance (COPEje) and system thermal ratio of the air conditioner, where the cooling load is estimated at 18 kW.

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.

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.

scholarly journals Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

2021 ◽  
Author(s):  
Nicolai Ree ◽  
Mads Koerstz ◽  
Kurt V. Mikkelsen ◽  
Jan H. Jensen
Keyword(s):  
Genetic Algorithm ◽  
Energy Storage ◽  
Storage Systems ◽  
Chemical Space ◽  
Tight Binding ◽  
Solar Spectrum ◽  
High Energy ◽  
Solar Thermal ◽  
Solar Thermal Energy ◽  
Energy Storage Systems

We present a computational methodology for the screening of a chemical space of 10²⁵ substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34,000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg.

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