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Öğe Analysis of bubble departure and lift-off boiling model using computational intelligence techniques and hybrid algorithms(Elsevier France-Editions Scientifiques Medicales Elsevier, 2024) Quadros, Jaimon Dennis; Mogul, Yakub Iqbal; Agbulut, Umit; Gurel, Ali Etem; Khan, Sher Afghan; Akhtar, Mohammad Nishat; Jilte, R. D.The bubble departure and lift-off boiling (BDL) model was studied using computational intelligence techniques and hybrid algorithms. Quite a few studies have predicted the relationship between wall heat fluxes and wall temperature in the form of flow boiling curves. The output wall temperature is a performance indicator that depends on many operating parameters. The current study, therefore, analyses the predictability of the wall temperature in terms of operating pressure, bulk flow velocity, and wall heat flux, based on the BDL model developed by Zenginer, which included two suppression factors namely, flow-induced and subcooling factors, respectively. The soft computing techniques used for prediction were - the artificial neural network (ANN), and the Fuzzy Mamdani model, and the hybrid algorithms were adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained particle swarm optimization (ANN-PSO). In addition, the ANN-PSO conducted a parametric analysis to evaluate the best model configuration by considering various factors. The comparison of all four techniques showed that the ANFIS model exhibited the prediction performance for wall temperature. Moreover, the results obtained from the ANFIS model have been compared with the different flow boiling curves from the literature and observed that the curve fitted well for higher bulk flow velocities with an MSE and R2 was found to be 0.85 % and 0.9933, respectively.Öğe Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation(Elsevier Sci Ltd, 2020) Gurel, Ali Etem; Agbulut, Umit; Bicen, YunusSolar radiation (SR) knowledge plays a vital role in the design, modelling, and operation of solar energy conversion systems and future energy investment policies of the governments. However, these data are not measured for all regions due to the non-availability of SR measurement equipment at the weather stations. Therefore, SR has to be accurately predicted using various prediction models. In this research, four models from different classes are being used to predict monthly average daily global SR data. The models used in this study are based on a machine-learning algorithm (feed-forward neural network), empirical models (3 Angstrom-type models), time series (Holt-Winters), and mathematical model (RSM). As the prediction locations, four provinces (Ankara, Karaman, Kilis, and Sirnak) in Turkey are selected. The dataset including pressure, relative humidity, wind speed, ambient temperature, and sunshine duration is supplied from the Turkish State Meteorological Service and it covers the years 2008-2018. In the study, monthly average daily global SR data for the year 2018 is being predicted, and the performance success of the models is discussed in terms of the following benchmarks R-2, MBE, RMSE, MAPE, and t-stat. In the results, R-2 value for all models is varying between 0.952 and 0.993 and MAPE and RMSE value for all models is smaller than 10% and 2 MJ/m(2)-day, respectively. Evaluation in terms of t-stat value, no models exceed the t-critic limit. Considering all the models together, ANN has presented the best results with an average R-2, MBE, RMSE, MAPE, and t-stat of 0.9911, 0.1323 MJ/m(2)-day, 0.78 MJ/m(2)-day, 4.9263%, and 0.582, respectively. Then Holt-Winters, RSM, and empirical models closely followed it, respectively. (C) 2020 Elsevier Ltd. All rights reserved.Öğe A detailed analysis of CPV/T solar air heater system with thermal energy storage: A novel winter season application(Elsevier, 2021) Ceylan, Ilhan; Gurel, Ali Etem; Ergun, Alper; Ali, Ismail Hamad Guma; Agbulut, Umit; Yildiz, GokhanThe interest in solar energy is increasing day by day because it is clean and limitless. Concentrated photovoltaic and thermal systems (CPV/T) are one of the systems that use in the winter and the summer, attract great attention among solar energy systems. The main purpose of this research is to discuss the capacity of a CPV/T to simultaneously convert solar energy into electrical energy and thermal energy, especially in winter seasons. While only thermal energy is obtained in many concentrated air collectors (CAC) used in the literature, in this study, energy is stored with the help of phase change material (PCM). Ethyl alcohol and water blend were utilized as a working fluid and paraffin wax was also utilized as a PCM. In this study, system performance was handled by applying energy, exergy and environmental economic analyzes. In the results, the average solar radiation was concentrated from 536 W/m(2) to 737 W/m(2). The average overall thermal efficiency and PV module efficiency of the CPV/T were calculated as 73% and 15%, respectively. In other words, the overall system efficiency of the CPV/T was obtained as 88%. The average exergy efficiency of the CPV/T was calculated as 10%. Concerning the environmental aspect, 1.11 kg of CO2 emission per hour into the atmosphere could be prevented by using such a system. In the conclusions, the present paper has reported that the integration of a PCM and air collector into a CPV/T system provided higher energy efficiency in the winter season.Öğe THE EFFECT OF MALFUNCTIONS IN AIR HANDLING UNITS ON ENERGY AND EXERGY EFFICIENCY(Begell House Inc, 2020) Ceylan, Ilhan; Yildiz, Gokhan; Gurel, Ali Etem; Ergun, Alper; Tosun, AbdulkerimIn this study, the effects of malfunctions and problems occurring in the system components of air handling units, which are the main elements of the air conditioning system, on the energy consumption were investigated. Investigations were carried out in 10 air handling units located in 5 different shopping centers of Turkey. The malfunctions and problems that may occur in operation of air handling units were determined and the problems causing the decrease in the efficiency prescribed by the design characteristics were determined. For this purpose, rod-type anemometer measuring the airflow in the air handling unit ducts, propeller-type anemometer, and thermal camera were used to measure air tightness and heat losses in the body structure. Also, the tension control of the belt of the fan motors, which is one of the main components of the energy consumption unit, and the pollution control of the air filter have also been carried out. The flow rate of water circulating in air handling units was determined, and losses were detected by energy and exergy analyses with thermodynamic parameters for summer and winter periods. As a result of the calculations, it was determined that the energy efficiency of the air handling units in the cooling period was 63.7% and the exergy efficiency was 59.6%. The energy loss is 471 kW and the exergy loss is 27 kW in the cooling period. The energy loss is 957 kW and the exergy loss is 127 kW in the heating period. The energy efficiency and the exergy efficiency during the heating period was calculated to be 75% and 41.7%, respectively.Öğe ENERGY, EXERGY, AND ENVIRONMENTAL (3E) ASSESSMENTS OF VARIOUS REFRIGERANTS IN THE REFRIGERATION SYSTEMS WITH INTERNAL HEAT EXCHANGER(Begell House Inc, 2020) Gurel, Ali Etem; Agbulut, Umit; Ergun, Alper; Yildiz, GokhanA comprehensive thermodynamic analysis of a refrigeration system with an internal heat exchanger was reported for four various refrigerants as an alternative to R134a. The preferred refrigerants in this paper have zero ozone-depleting potential and fairly low global warming potential value compared with reference R134a. These refrigerants are from both the HC group (R290 and R600a) and the HFO group (R1234yf and R1234ze(E)). Basically, the refrigeration system consists of a compressor, condenser, evaporator, expansion valve, and internal heat exchanger as well. Energy-exergy analyses and environmental impact assessments depending on the compressor energy consumptions are evaluated in the current study. The system performance was theoretically carried out at two different evaporation temperatures of 0 and -8 degrees C. Based on the obtained results from this study, the highest performance was achieved in R600a from HC group refrigerants and R1234ze(E) from HFO group refrigerants. As compared with R134a, in the COP value of R600a an increase of 3.2% at the evaporation temperature of 0 degrees C and 3.4% for the evaporation temperature of -8 degrees C was achieved. On the other hand, the COP value for R1234yf was decreased by 2% at the evaporation temperature of 0 degrees C and by 2.57% at the evaporation temperature of -8 degrees C. Considering the CO2 emissions, R600a was located at the first order in terms of the lowest CO2 emissions and R1234ze(E) follows R600a. In conclusion, R600a presented the highest performance compared with R134a in a refrigeration system with an internal heat exchanger.Öğe Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms(Pergamon-Elsevier Science Ltd, 2021) Karaagac, Mehmet Onur; Ergun, Alper; Agbulut, Umit; Gurel, Ali Etem; Ceylan, IlhanIn this paper, a concentrated photovoltaic-thermal solar dryer (CPV/TSD) using nano-enhanced PCM (Al2O3Paraffin wax) is experimentally studied. A comprehensive thermodynamic analysis of the system according to the first and second laws is discussed. Besides, the drying parameters (moisture content and moisture ratio) are predicted using the two machine learning algorithms (ANN and SVM) and compared the prediction success with four evaluation metrics (R2, rRMSE, MBE, and rMAE). The overall thermal energy efficiency and exergy efficiency of the CPV/TSD system are found to be 20% and 8%, respectively. Although solar radiation to the environment has decreased a lot, it has been found that the thermal energy transferred to the nano-enhanced PCM prevents the decrease in greenhouse temperature for the first 100 min. In the system, mushrooms are dried from the initial moisture content of 17.45 g water/g dry matter to the final moisture content of 0.0515 g water/g dry matter. Then the drying rate value for CPV/TSD system is calculated to be 0.436 g matter/g dry matter.min. On the other hand, even if both ANN and SVM algorithms have exhibited very satisfying results, ANN is coming to the fore in the prediction of the drying parameters considering all evaluation metrics together.Öğe Experimental and numerical analysis of the thermal performance of pebble solar thermal collector(Cell Press, 2024) Naik, N. Channa Keshava; Priya, R. Krishna; Agulut, Umit; Gurel, Ali Etem; Shaik, Saboor; Alzaed, Ali Nasser; Alwetaishi, MamdoohIn this work, pebbles of higher specific heat than the conventional absorber materials like aluminium or copper are proposed as a absorber in the solar flat plate collector. The proposed collector are integrated into the building design and constructed with masonry. Tests were conducted by varying the operating parameters which influence its performance, like the flow rate of the heat-absorbing medium, and the tilt of the collector using both coated and uncoated pebbles. The maximum temperature difference that could be measured for a conventional absorber was approximately 8 degrees C for a flow rate of 0.6 L/min. While for a coated and uncoated absorber, it was 7 degrees C and 5.5 degrees C respectively. This difference decreased with an increase in flow rates from 0.6 L/min to 1.2 L/min. For all the flow rates, it was observed that the average difference in efficiency between the coated and the conventional absorber collector is 5.82 %, while the difference between the coated and uncoated absorber collector is 15.68 %. Thus, it is very much evident that by replacing the conventional absorber with the proposed coated pebble absorber, the overall loss in efficiency is just 5.82 %, but the advantages are enormous. Along with the experimental study, numerical analysis was also carried out with CFD modeling. The numerical results agreed well with experimental results with the least error. Therefore, CFD simulation can be further used to optimize the design of the collector.Öğe Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles-diesel blends using different machine learning algorithms(Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Gurel, Ali Etem; Sandemir, SuatDeep learning (DL), Artificial Neural Network (ANN), Kernel Nearest Neighbor (k-NN), and Support Vector Machine (SVM) have been applied to numerous fields owing to their high-accuracy and ability to analyze the non-linear problems. In this study, these machine learning algorithms (MLAs) are used to predict emission and performance characteristics of a CI engine fuelled with various metal-oxide based nano particles (Al2O3, CuO, and TiO2) at a mass fractions of 200 ppm. Assessed parameters in the study are carbon dioxide (CO), nitrogen oxide (NOx), exhaust gas temperature (EGT), brake specific fuel consumption (BSFC), and brake thermal efficiency (BTE). To evaluate the success of algorithms, four metrics (R-2, RMSE, rRMSE, and MBE) are discussed in detail. Tests performed at varying engine speeds from 1500 rpm to 3400 rpm with the intervals of 100 rpm. The addition of nanoparticles simultaneously reduced CO and NOx emissions because they ensured more complete combustion thanks to their inherent oxygen, the higher surface to volume ratio, superior thermal conductivities and their catalytic activity role. Further, the nano-sized particles ensured an accelerated heat transfer from the combustion chamber. In comparison with that of neat diesel fuel, the reduction in NOx is found to be 3.28, 7.53, and 10.05%, and the reduction in CO is found to be 8.3, 11.6, and 15.5% for TiO2, Al2O3, and CuO test fuels, respectively. Moreover, the presence of nanoparticles in test fuels has improved engine performance. As compared with those of neat diesel fuel, the doping of nanoparticles drops the BSFC value by 5.54, 7.89, and 9.96% for TiO2, CuO, and Al2O3, respectively, and enhanced BTE value to be 6.15, 8.87, and 11.23% for TiO2, CuO, and Al2O3, respectively. On the other hand, it can be said that all algorithms presented very satisfying results in the prediction of CI engine responses. All R-2 has changed between 0.901 and 0.994, and DL has given the highest R-2 value for each engine response. In terms of rRMSE, all results (except for one result in k-NN) are categorized as excellent according to the classification in the literature. Considering all metrics together, DL is giving the best results in the prediction of engine responses for the dataset used in this paper. Then it is closely followed by ANN, SVM, and k-NN algorithms, respectively. In conclusion, this paper is proving that the nanoparticle addition for ICEs is significantly dropping the exhaust pollutants, and improving the engine performance, and further the results can be successfully predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved.Öğe The history of greenhouse gas emissions and relation with the nuclear energy policy for Turkey(Taylor & Francis Ltd, 2021) Agbulut, Umit; Ceylan, Ilhan; Gurel, Ali Etem; Ergun, AlperThe globalising world, rapidly developing technology and growing population have brought many problems and led to disrupting the world ecological balance. Today, existing energy sources reached such a level that cannot meet the current needs of the world. Due to the fact that fossil fuels will run out in near future, it has made mankind tending to seek alternative energy sources. The main issues addressed in this paper are the history of greenhouse gas emissions (GHGe) and relations with nuclear energy policy, particularly in Turkey. Currently, nuclear has much less GHGe and high energy-efficiency, and also meets 10.6% of the world primary electiricty energy demand. Therefore, countries seriously began to evaluate nuclear energy instead of fossil-fuels. In line with this, Turkey started to build third nuclear plants and aims to meet at least 15% of its primary electiricity energy demand. Hereby, Turkey is not only reducing dependence on fossil-fuels but also planning to reach the undertaken GHGe level as a country of signed the Kyoto protocol.Öğe Investigating the role of fuel injection pressure change on performance characteristics of a DI-CI engine fuelled with methyl ester(Elsevier Sci Ltd, 2020) Sandemir, Suat; Gurel, Ali Etem; Agbulut, Umit; Bakan, FarukThis paper intended to investigate the impact of corn oil methyl-ester and diesel blends on performance, cornbustion and emission characteristics at varying injection pressure (210 and 230 bar). The tests were performed at a constant engine speed of 2000 rpm, and at two different engine loads of 5 and 10 Nm. Corn oil methyl-ester was produced by transesterification method in the study and then blended at 10%, 20% and 50% by volume into neat diesel fuel. The results presented that corn oil methyl-ester could improve combustion process owing to its high oxygen content in comparison with that of B0 fuel. High-injection pressure reduced the droplet diameter and accelerated the combustion process. This case has generally caused to high cylinder pressures. With respect to emissions, it was observed that CO (down to 66.67% at 230 bar) and HC (down to 52.38% at 230 bar) were sharply reduced depending on the increment of the blending rates of biodiesel while NOx (up to 22.45% at 230 bar) increased significantly. Depending on the increasing rate of corn oil methyl-ester in the blends, more fuel mass was injected into the combustion chamber and specific fuel consumption in biodiesel content-fuels were, therefore, higher than that of low injection pressure. Additionally, thermal efficiency decreased with the increment of biodiesel content owing to the lower heating value of biodiesel.Öğe A New Hybrid System Design for Thermal Energy Storage(Springer, 2020) Ceylan, Ilhan; Ali, Ismail Hamad Guma; Ergun, Alper; Gurel, Ali Etem; Acar, Bahadir; Islam, NurselDue to some serious environmental problems like global warming and greenhouse effect, studies on solar energy systems are being conducted all over the world. The studies conducted in recent years are on hybrid designs in which solar energy systems can realize both electricity and heat production at the same time. In this way, both electrical energy and heat energy can be generated from the same system In this study, the design and analysis of a concentrated solar air collector with a heat storage unit were carried out.. In the solar air collector, heat energy was depot in paraffin wax, and the electrical energy which was stored in the battery using the PV (photovoltaic) modules in the system enabled the operation of the system fan. The experiments which aimed at determining system performance were carried out in winter when the ambient temperature was low. The experiments were performed with or without a heat storage unit, and a comparative analysis was made. It was found that the temperature of the air released from the collector ranged from 15 degrees C to 40 degrees C when the exterior temperature was -5 degrees C. The average efficiency of the concentrated system without the heat storage unit was calculated as 67%. The average efficiency of the concentrated system with the heat storage unit was calculated as 96%.Öğe Performance assessment of a novel design concentrated photovoltaic system coupled with self-cleaning and cooling processes(Wiley, 2020) Acar, Bahadir; Gurel, Ali Etem; Ergun, Alper; Ceylan, Ilhan; Agbulut, Umit; Can, AliThe generation of electrical energy with photovoltaic modules is a highly useful and environmentally friendly method. For this reason, studies to increase the electrical energy production from photovoltaic (PV) modules have gained great importance. Concentrated PV systems constitute a significant part of these studies. The major problems with the concentrated PV systems are the risks of lowering the efficiency of the cells (i.e., concentration process increases PV cell temperature) and the thermal damage that can occur with sudden temperature increases. In order to avoid these risks, various applications are used to cool concentrated PV modules. In this study, an active system, which was developed for cleaning and cooling PV modules, was tested. The aim of the present study was to ensure that the surfaces were clean and free of external, contaminating factors such as dust and dirt, and that the PV cells were cooled. During the experiments, two different systems were compared: the system with the cleaning-cooling processes and the one without these processes. Prior to starting experiments, a hydrophobic liquid onto the surfaces of the PV modules was applied to facilitate the cleaning process. The results of the experiments revealed that the temperature of the PV module was 50 degrees C in the cleaning-cooling process and 67 degrees C in the system without the cleaning-cooling process. On the other hand, it was observed that the proposed design increased the power output of PV module up to 40%.Öğe Performance Assessment of a Refrigeration System Charged with Different Refrigerants Using Infrared Image Processing Techniques(Springer Heidelberg, 2021) Katircioglu, Ferzan; Cingiz, Zafer; Cay, Yusuf; Gurel, Ali Etem; Kolip, AhmetThis study aims to investigate the performance of R417A, R422A, R422D and R438A refrigerants as alternatives to R22, in a commercial type refrigeration system operating with R22 refrigerant. To this end, first of all, the cooling capacity and coefficient of performance (COP) values were calculated for all refrigerants used in the experimental setup. Then, two methods were proposed, Pearson's Correlation Similarity Analysis (PCSA) and surface temperature-based COP (COPST), to evaluate the success of each alternative refrigerants, and R22 with infrared image analysis, separately. The COP values obtained for the refrigerants with the mathematical method are R22 4.07, R438A 3.88, R417A 3.63, R422D 3.37, and R422A 3.18, respectively. Both the COP values and the PCSA values (R438A 0.9425, R417A 0.9343, R422D 0.9167 and R422A 0.9080) show the proximity between the R22 refrigerant and other refrigerants. Similarly, the COPST method revealed the values of R22 6.8865, R438A 5.9539, R417A 5.3273, R422D 4.9898 and R422A 4.3057, and the fact that it has the same order with the other two methods demonstrates its operability in the performance test application with the developed infrared image processing. The compatibility of the order in the experimental results obtained from the PCSA and COPST methods and the COP calculation method and has proved that thanks to infrared imaging, the remote performance analysis of the refrigeration system can be successfully performed.Öğe Performance assessment of a V-trough photovoltaic system and prediction of power output with different machine learning algorithms(Elsevier Sci Ltd, 2020) Agbulut, Umit; Gurel, Ali Etem; Ergun, Alper; Ceylan, IlhanThis study carried out in two stages. In the first stage, four different-sized layers were designed and manufactured for a concentrated photovoltaic system. These layers were used to change the concentration ratio and area ratio of the system. Furthermore, a new power coefficient equation with this paper is proposed to the literature for the determination of the system performance. In the second stage of the study, the power outputs measured in the study were predicted with four machine-learning algorithms, namely support vector machine, artificial neural network, kernel and nearest-neighbor, and deep learning. To evaluate the success of these machine learning algorithms, coefficient of determination (R-2), root mean squared error (RMSE), mean bias error (MBE), t-statistics (t-stat) and mean absolute bias error (MABE) have been discussed in the paper. The experimental results demonstrated that the double-layer application for the concentrator has ensured better results and enhanced the power by 16%. The average concentration ratio for the double-layer was calculated to be 1.8. Based on these data, the optimum area ratio was determined to be 9 for this V-trough concentrator. Furthermore, the power coefficient was calculated to be 1.35 for optimum area ratio value. R-2 of all algorithms is bigger than 0.96. Support vector machine algorithm has generally presented better prediction results particularly with very satisfying R-2, RMSE, MBE, and MABE of 0.9921, 0.7082 W, 0.3357 W, and 0.6238 W, respectively. Then it is closely followed by kernel-nearest neighbor, artificial neural network, and deep learning algorithms, respectively. In conclusion, this paper is reporting that the proposed new power coefficient approach is giving more reliable results than efficiency data and the power output data of concentrated photovoltaic systems can be highly predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved.Öğe Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison(Pergamon-Elsevier Science Ltd, 2021) Agbulut, Umit; Gurel, Ali Etem; Bicen, YunusThe prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kirklareli, Tokat, Nevsehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018-31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R-2, RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R2, MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m(2), from 2.273 to 2.820 MJ/m(2), respectively. At all cases, k-NN exhibited the worst result in terms of R-2, RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively.Öğe A review of stability, thermophysical properties and impact of using nanofluids on the performance of refrigeration systems(Elsevier Sci Ltd, 2021) Yildiz, Gokhan; Agbulut, Umit; Gurel, Ali EtemThe popularity of the studies on improving the thermal properties of base fluids in thermal engineering applications is considerably increasing day by day. Recently, many researchers have proved that the use of nanoparticles along with the base fluids exhibits better thermal properties as well as better system performance. In line with this, it is noticed a respectful increase in the number of studies regarding nanoparticle use in refrigeration systems. Accordingly, the present paper aims to summarize the preparation of nanofluids, the variation of thermophysical properties, the stability of nanofluids, impacts on the system performances of nanofluid usage, limitations, and challenges of nanoparticle usage, particularly in the refrigeration systems. Previous studies revealed that the heat transfer mechanism of the lubricants and refrigerants is highly improved with nanoparticle addition. It is observed that the increase in thermal properties becomes more visible as nanoparticle fractions increase, but this case may worsen the viscosity of nanofluids. The enhanced thermal properties contribute to improving refrigeration system performance. Many papers emphasize that nanoparticle-doping triggers an increase in system performance by both reducing the compressor power input and increasing the cooling capacity of the refrigeration systems. However, some critical points such as stability, homogeneous distribution, agglomeration, and sedimentation considerably influence the sustainability of performance improvement. In conclusion, nanoparticle-doping for refrigeration systems can be accepted as a very promising way of improving the performance, nevertheless, some questions such as high cost, toxic effect, poor stabilization, erosion effect, high viscosity, clogging issues should be more addressed in the future. (c) 2021 Elsevier Ltd and IIR. All rights reserved.
