Dynamic Performance Comparison of R134a and R1234yf Refrigerants for a Vapor Compression Refrigeration Cycle

M achines like air conditioners and refrigerators, which cause significant energy consumption in countries around the world, are widely used in industry and residences. Analyzing and studying the behavior of these machines with computer simulations can optimize performance of them. In this study, thermodynamic modelling and dynamic simulation of a vapor compression refrigeration cycle is handled. R134a and R1234yf are used as the primary fluid and water is used as the secondary fluid in the refrigeration cycle. R1234yf is a refrigerant, which has low Global Warming Potential GWP and Ozone Depletion Potential ODP and is recently has been begun to use as a substitute of R134a. In this study, dynamic behaviors of these two refrigerants are examined in a vapor compression refrigerant cycle with fixed operating conditions. Finite Difference Method is utilized for the modelling of the evaporator and condenser and Gungrr-Winterton and Travis et al. correlations are used for the modelling of the evaporation and condensation proccesses respectively. Orifice equation is utilized for the modelling of the expansion valve and modelling of the compressor is carried out by first dynamically simuating the heat transfer between the gas and surroundings until the gas reaches to compression chamber and after that the polytropic compression process in the chamber. For the realization of the dynamical simulation, refrigerant fluid mass flow rate is applied to the system as step input. Response of the system to the input is observed with transient p-h and coefficient of performance COP diagrams. The results showed that COP is started off with the values of 2.079 for R134a and 1.711 R1234yf, reached the maximum points of 2.577 for R134a and 2.02 for R1234yf, then slowly declined with fluctuations. In the p-h diagram, due to temperature rise of inner walls of the evaporator and condenser, condenser outlet and compressor inlet enthalpy values started off with 395,945 kJ/kg and 231,714 kJ/kg for R134a, 361,557 kJ/kg and 230,750 kJ/kg for R1234yf, then approached to the saturation curve with time and reached the values of 393,957 kJ/kg and 233,808 kJ/kg for R134a, 359,547 kJ/kg and 231,917 kJ/kg for R1234 y

Eagle strategy based on modified barnacles mating optimization and differential evolution algorithms for solving transient heat conduction problems

Solving time-dependent heat conduction problems through a conventional solution procedure of iterative root-finding method may sometimes cause difficulties in obtaining accurate temperature distribution across the heat transfer medium. Analytical root-finding methods require good initial estimates for finding exact solutions, however locating these promising regions is some kind of a black-box process. One possible answer to this problem is to convert the root-finding equation into an optimization problem, which eliminates the exhaustive process of determining the correct initial guess. This study proposes an Eagle Strategy optimization framework based on modified mutation equations of Barnacles Mating Optimizer and Differential Evolution algorithm for solving one-dimensional transient heat conduction problems. A test suite of forty optimization benchmark problems have been solved by the proposed algorithm and the respective solution outcomes have been compared with those found by the reputed literature optimizers. Finally, a case study associated with a transient heat conduction problem have been solved. Results show that Eagle strategy can provide efficient and feasible results for various types of solution domains. © 2021, Ismail Saritas. All rights reserved

An oppositional Salp Swarm: Jaya algorithm for thermal design optimization of an Organic Rankine Cycle

This study proposes a hybrid metaheuristic algorithm to tackle both single and multi objective optimization problems that are subjected to hard constraints.Twenty-four single objective optimization benchmark problems comprising unimodal and multi modal test functions have been solved by the proposed hybrid algorithm (OPSSAJ) and numerical results have been compared with those acquired by some of the new emerged metaheuristic optimizers. The proposed OPSSAJ shows a significant accuracy and robustness in most of the cases and proves its efficiency in solving high dimensional problems. As a real-world case study, seventeen operational design parameters of an organic rankine cycle (ORC) operating with a binary mixture of R227EA and R600 refrigerants are optimized by the proposed hybrid OPSSAJ to obtain the optimum values of contradicting dual objectives of second law efficiency and Specific Investment Cost. A Pa reto curve composed of non-dominated solutions is constructed through the weighted sum method and the final solution is chosen by the reputed TOPSIS decision-maker. The pareto curve and best-compromising result obtained by utilizing the OPPSAJ are compared with that of acquired by using nondominated sorting genetic algorithm II (NSGA-II) and multiple objective particle swarm optimization (MOPSO) algorithms. The multi-objective ORC design obtained with the OPSSAJ yields a significant improvement in thermal efficiency and cost values compared to designs found by the NSGA-II and MOPSO algorithms. Furthermore, a sensitivity analysis is performed to observe the influences of the selected design variables on problem objectives

Multi-objective optimisation of a novel organic Rankine-Goswami cycle operating with different refrigerants in the organic Rankine cycle

This paper tackles the multi-objective design optimisation and comparative investigation of a cascade organic Rankine-Goswami cycle (ORC-GW) operating with various refrigerants, namely R600, R236ea, and R245fa, in the organic Rankine cycle (ORC). The solution of the multi-objective optimisation problem is achieved by applying the African vultures optimisation algorithm (AVA). The results show that the ORC-GW operating ammonia-water/R600 working fluid pair has increased total cost and second law efficiency compared to its competitors, respectively 24.575 M euro and 0.440

Optimal control of a refrigeration cycle that uses smart and artificial intelligence systems

In this thesis, the methods that improve the energy efficiency of the refrigeration cycles have been analyzed. One of these methods is dynamic simulation analysis of a vapor compression refrigeration cycle, and the other is optimal control of a vapor compression refrigeration cycle with using the artificial neural networks. Utilizing the artificial neural networks emphasizes the application of the artificial intelligence and machine learning algorithms, which are growingly popular nowadays, to the efficiency of the energy systems. In the dynamic simulation analysis, dynamic modeling of a vapor compression refrigeration cycle has been accomplished, thereafter, two refrigerants are employed in the cycle seperately, R134a and R1234yf, and their cooling performances are compared with each other. As a result, it has been found out that the cooling performance of the cycle is greater when R134a is employed as the main refrigerant. In the optimal control with using the artificial neural networks study, dynamic modeling of a vapor compression refrigeration cycle has been accomplished, afterwards, an artificial neural network has been trained with the simulation data and the optimal control of the system has been done by utilizing the artificial neural network as the main model of the system. Performances of the four different controllers have been compared with each other and the second law controllers has came up with the best overall second law efficiency performance, on the other hand, the entropy generation controller has found the most desirable energy consumption value through the simulation time.Bu tezde, soğutma çevrimlerinin enerji verimliliğini arttırıcı yöntemler incelenmiştir. İncelenen bu yöntemlerden birisi buhar sıkıştırmalı soğutma çevriminin dinamik simülasyon analizi, diğeri ise yapay sinir ağları kullanarak bir buhar sıkıştırmalı soğutma çevriminin optimal kontrolüdür. Yapay sinir ağlarının kullanılması günümüzde gittikçe popülerleşen yapay zeka ve makine öğrenmesi algoritmalarının enerji sistemlerinin verimliliğine uygulanmasını vurgulamaktadır. Dinamik simülasyon analizinde, bir buhar sıkıştırmalı soğutma çevrimi dinamik olarak modellenmiş, soğutucu akışkanlar olarak R134a ve R1234yf kullanılarak bu iki akışkanın soğutma performansları karşılaştırılmıştır. Sonuç olarak, R134a temel soğutucu akışkan olarak kullanıldığında çevrimin soğutma performansının daha üstün olduğu görülmüştür. Yapay sinir ağları destekli optimal kontrol çalışmasında ise, bir buhar sıkıştırmalı soğutma çevriminin dinamik modellenmesi gerçekleştirilmiş, ardından simülasyon verileriyle bir yapay sinir ağı eğitilmiş ve bu yapay sinir ağı sistem modeli olarak kullanılıp sistemin optimal kontrolü gerçekleştirilmiştir. Dört farklı kontrolcünün performansları birbirleriyle karşılaştırılmış ve ikinci yasa kontrolcüsü en iyi ortalama ikinci yasa verimi performansını sergilerken, diğer taraftan, entropi üretimi kontrolcüsü simulasyon süresi boyunca en uygun enerji tüketimi değerini bulmuştur

Global best-guided oppositional algorithm for solving multidimensional optimization problems

Turgut, Oguz Emrah/0000-0003-3556-8889WOS: 000520227700004This paper presents an alternative optimization algorithm to the literature optimizers by introducing global best-guided oppositional-based learning method. the procedure at hand uses the active and recent manipulation schemes of oppositional learning procedure by applying some modifications to them. the first part of the algorithm deals with searching the optimum solution around the current best solution by means of the ensemble learning-based strategy through which unfeasible and semi-optimum solutions have been straightforwardly eliminated. the second part of the algorithm benefits the useful merits of the quasi-oppositional learning strategy to not only improve the solution diversity but also enhance the convergence speed of the whole algorithm. A set of 22 optimization benchmark functions have been solved and corresponding results have been compared with the outcomes of the well-known literature optimization algorithms. Then, a bunch of parameter estimation problem consisting of hard-to-solve real world applications has been analyzed by the proposed method. Following that, eight widely applied constrained benchmark problems along with well-designed 12 constrained test cases proposed in CEC 2006 session have been solved and evaluated in terms of statistical analysis. Finally, a heat exchanger design problem taken from literature study has been solved through the proposed algorithm and respective solutions have been benchmarked against the prevalent optimization algorithms. Comparison results show that optimization procedure dealt with in this study is capable of achieving the utmost performance in solving multidimensional optimization algorithms

Comparative investigation and multi objective design optimization of R744/R717, R744/R134a and R744/R1234yf cascade rerfigeration systems

WOS: 000457651400017This study aims to make a comparative investigation on performance analysis of cascade refrigeration systems using R744/R717, R744/R134a, and R744/R1234yf refrigerant pairs. Artificial Cooperative Search methaheuristic algorithm is put into practice to obtain the optimal values of eight design parameters including Condenser and evaporator temperature, R744 condensing temperature, temperature difference in the cascade condenser, and amount of subcooling and superheating at the bottom and the top of the cascade cycle. Second law efficiency and total annual cost of the cascade refrigeration system are chosen as design objectives to be optimized individually and concurrently in order to obtain the optimal operating conditions of the system. Single optimization results show that R744/R1234yf system has the lowest operating cost while having the highest second law efficiency compared to other cycle configurations. A set of non-dominated solutions obtained through multi objective Artificial Cooperative Search algorithm is represented in the form of Pareto front and the best result is chosen from the well-reputed decision makers of TOPSIS and LINMAP for each cycle configuration. Multi objective optimization results reveal that design variables of the refrigeration system can create a trade off between problem objectives. A sensitivity analysis is performed to investigate the influences of varying values of design variables upon problem objectives while the system is operated under optimal conditions

Ensemble Shuffled Population Algorithm for multi-objective thermal design optimization of a plate frame heat exchanger operated with Al2O3/water nanofluid

WOS: 000438775200014This study proposes a brand new optimization algorithm entitled Ensemble Shuffled Population Algorithm for solving multidimensional optimization problems. The proposed algorithm adopts the perturbation equations of the Crow Search and Differential Search algorithms with useful modifications on them and aims to maintain a reasonable balance between the intensification and diversification phases of the algorithm. A batch of 22 benchmark problems consisting of unimodal and multimodal unconstrained optimization test functions are applied using this algorithm to assess its performance on multi dimensional problems. Statistical results obtained from the proposed Ensemble Shuffled Population Algorithm are compared to those found by eleven well known metaheuristic optimizers. The comparison results show that the Ensemble Shuffled Population Algorithm outperforms the compared optimizers with regards to solution accuracy and convergence speed. After that, the proposed algorithm is applied on a multi objective optimization of a plate frame heat exchanger operated with Al2O3 nanofluid. The optimization results show that utilizing nanoparticles instead of base fluid not only increases the overall heat transfer coefficient rates but also entails a huge decline in total cost values. A Pareto frontier is constructed for these two conflicting objectives to select the final optimum solution from the set of non-dominated solutions by virtue of three famous decision making methods of LINMAP, TOPSIS, and Shannon's entropy theory. Then, sensitivity analysis is performed to observe the variational effects of the design variables on the optimization objectives. (C) 2018 Elsevier B.V. All rights reserved