The performance of a DEC system manufactured by RFX-PSS has been studied.
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The effect of using radiation shields on DEC systems has been investigated.
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Using the RFX-PSS-casing instead of metal-casing increases saturation efficiency.
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Inclined roof increases the saturation efficiency of DEC systems significantly.
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Glass wool-aluminum radiation shield decreases water consumption of DEC systems.
Abstract
The evaporation phenomenon is used as a passive cooling approach to absorb the sensible heat and convert it into the latent heat. The usage of evaporative cooling systems has boomed, recently, because of its low operational cost. The Direct Evaporative Cooling (DEC) systems can be used widely in warm dry environmental conditions, such as Middle East countries. Several factors can affect the DEC system such as water temperature, evaporative cooling pad thickness, air velocity, and solar radiation flux. In this study, the performance and water consumption of evaporative-based cooler manufactured by RFX Polycarbonate-Structured Sheet (PSS) are compared with the metal-casing one. The impacts of tilting the roof of the cooler and attaching a glass wool-aluminum foil on the cooler are evaluated. The assessments show about 20.6% reduction in the usage of water for the case of RFX Polycarbonate-Casing cooler compared to the metallic material. Results also revealed that tilting the roof saves 29% water compared with the metal-casing cooler while changing the inclined roof to the glass wool-aluminum foil radiation shield further saves the water by about 25%. The performance of the RFX-PSS cooler is enhanced by about 4.7% as compared to the metal-casing cooler. The improvement of 8.1% occurs by tilting the roof in RFX Polycarbonate-Casing cooler in comparison with the metallic DEC system and attaching the isolation foil of glass wool-aluminum instead of the inclined roof increases the saturation efficiency by 7%.
2.
Authors:
Mousa Rejvani,Ali Alipour, Seyed Masoud Vahedi*,Ali J. Chamkha, Somchai Wongwises
Tittle : Optimal characteristics and heat transfer efficiency of SiO2/water nanofluid for application of energy devices: A comprehensive study
In a comprehensive study, the thermal conductivity, dynamic viscosity, and the rheological behavior of a SiO2/water nanofluid are investigated experimentally at the temperatures, solid concentrations, and the shear rates of 25°C to 50°C, 0% to 1.5%, and 400 to 1400(s−1), respectively. The Response Surface Methodology (RSM) is utilized to obtain regression models for the thermal conductivity and the dynamic viscosity. Subsequently, the sensitivity of the aforementioned models to 10% changes in the temperature, and the nanofluid concentration is analyzed. Afterward, Nondominated Sorting Genetic Algorithm II (NSGA‐II) is utilized to find the maximum thermal conductivity and the minimum viscosity. The nondominated optimal points are presented through a fitted correlation on a Pareto front to make the results more practical. The measurements of the investigated nanofluid could be summarized as a paper of a handbook. The workability of the investigated nanofluid is also examined in both laminar and turbulent flow regimes through analysis of the heat transfer merit graphs. To this end, the ratio of the dynamic viscosity enhancement to the thermal conductivity enhancement and the Mouromtseff number are chosen as two criteria of the laminar and turbulent flow regimes, respectively. Finally, the results are compared with those for SiO2/glycerin and SiO2/ethylene glycol nanofluids to check the workability in different base fluids. From a thermal‐efficiency point of view, the SiO2/water nanofluid is not suggested for use in both laminar and turbulent pipe flows, except in temperatures higher than 30°C and volume concentrations lower than 1% for the case of laminar flow. This is because the favorable heat transfer enhancement of the nanofluid is more than the unfavorable increase of the pumping power. From the rheological point of view, though, a SiO2/water nanofluid would be a good choice in lubricating moving surfaces for both laminar and turbulent flow regimes. It is found that in higher nanofluid concentrations, the thermal conductivity of a SiO2/water nanofluid is highly influenced by temperature. Moreover, adding nanoparticles at temperatures of 35°C to 40°C would have the highest increasing effect on the thermal conductivity. It is also revealed that increasing the temperature does not significantly affect the viscosity when 1% SiO2 nanoparticles are suspended within the water.
3.
Authors:
Mousa Rejvani*,Seyfolah Seadodin, Mohammad Hemmat Esfe, Seyed Masoud Vahedi, Somchai Wongwises, Ali J. Chamkha.
Tittle : Experimental investigation of hybrid nano-lubricant for rheological and thermal engineering applications
Nowadays, various types of engine oils are widely used in lubricating and cooling internal combustion engines. In this study, the behavior of MWCNTs–SiO2 (30–70)/10W40 hybrid nanofluid as part of a new generation of engine oil is investigated experimentally. A mixture of SiO2, with 20–30 nm particle diameter, and MWCNT, with 3–5 nm inner and 5–15 nm outer nanoparticle diameter was dispersed into a base fluid of 10W40 engine oil. Then, the viscosity of the product was measured at nanofluid concentrations and temperatures, respectively, ranging from 0.05 to 1% and 5 to 55 °C, for different values of shear rate. Also, a sensitivity analysis to the solid volume fraction was performed at different temperatures. The results show that the behavior of the samples is well fitted with the pseudo-plastic Ostwald de Waele non-Newtonian model. The viscosity of the produced hybrid nano-lubricant is found to be 35% greater than that of pure engine oil. Because of the significant deviation between the measured viscosity and the values predicted by existing classical viscosity models, a new regression model is obtained. The R2 and adj. R2 for the model are computed as 0.988 and 0.977, respectively, signifying strong predictability with ± 3% margin of deviation.
4.
Authors:
Mousa Rejvani*, Mohammad Abdollahi Moghaddam, Pedram Alamdari
Tittle : Using statistical and optimization tools for determining optimal formulations and operating conditions for Al2O3/(EG + Water) nanofluids for cooling system
Obtaining optimum data for each nanofluid at various temperatures and concentrations.
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Proposing correlations based on thermal conductivity and viscosity for optimum data.
Abstract
In this research, Al2O3/Ethylene Glycol-water (40%–60%), Al2O3/EG-water (20%–80%) and Al2O3/EG-water (60%–40%) nanofluids have been optimized in various volume fractions and temperatures for heat transfer functions. The volume fractions between 0 and 1.5% and the temperatures between 20 °C and 60 °C were considered as designing variables, while the goal was reducing the dynamic viscosity and increasing thermal conductivity of nanofluids. Modeling the goal functions was conducted by presenting mathematical correlations for the viscosity and thermal conductivity of the mentioned nanofluids in terms of temperature and volume fraction. The modeling was performed by Non-dominated Sorting Genetic Algorithm (NSGA-II) so in order to obtain the optimal points of the designing, mathematical correlations were connected to optimization algorithm. By performing optimization, the optimal points were obtained for all the nanofluids for each volume fraction and temperature. These points showed the highest heat transfer coefficient and the lowest viscosity. In order to use the results of this study, some correlations were presented for the most optimal nanofluids. Through utilizing these correlations, a designer can determine the best temperature, volume fraction, and percentage of base fluid mixture according to his need and the significance of each of the goal functions.
5.
Authors:
Mohammad Abdollahi Moghaddam*, Mousa Rejvani, Pedram Alamdari.
Tittle : Determining optimal formulations and operating conditions for Al2O3/water nanofluid flowing through a micro channel heat sink for cooling system purposes using statistical and optimization tools
Obtaining optimum data for nanofluid at various operating conditions.
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Proposing correlations based on Nusselt number and friction factor for optimum data.
6.
Authors:
Mohammad Hemmat Esfe, Hossein Rostamian, MousaRejvani, Mohammad Reza Sarmasti Emami.
Tittle : Rheological behavior characteristics of ZrO2-MWCNT/10w40 hybrid nano-lubricant affected by temperature, concentration, and shear rate: An experimental study and a neural network simulating,
In this study, the ZrO2-MWCNT (70%–30%)/10w40 hybrid Nano-lubricant was experimentally evaluated in terms of rheological changes. ZrO2 (40 nm) and MWCNTs nanoparticles with an inner diameter of 3–5 nm were used to prepare the Nano-lubricant. Particles were weighted for solid volume fractions of 0.05%, 0.1%, 0.25%, 0.5%, 0.75%, and 1% and the nano-lubricant was prepared. Then, the viscosity of Nano-lubricants was measured at different shear rates between 5 and 55 °C. The results showed that the pure oil was non-Newtonian and the nano-lubricant was pseudoplastic. Calculation and checking the power law and consistency coefficients showed that the increase in temperature intensifies the non-Newtonian behavior. Based on the results, at temperature of 55 °C and a volume fraction of 1% condition, maximum of dynamic viscosity enhancement (DVE) was reported. Based on experimental data, a new correlation was proposed at different temperatures and the solid volume fraction, and the sensitivity of nano-lubricant was measured. An Artificial Neural Network (ANN) with two hidden layers and six neurons was designed to predict the viscosity. R2, MSE, and AARD values were obtained as much as 0.9905, 7.0631e-05, and 0.0051 ANN, respectively. Comparison of experimental data with new correlations and ANN showed that the performance of neural network was better in predicting the viscosity data.
7.
Authors:
Mohammad Hemmat Esfe, Saeed Esfandeh, Masoud Afrand, Mousa Rejvani, Seyed Hadi Rostamian
Tittle : Experimental evaluation, new correlation proposing and ANN modeling of thermal properties of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles for internal combustion engines applications
Thermal conductivity of EG based hybrid nanofluid containing ZnO-DWCNT nanoparticles was investigated experimentally at concentration of 0.045 to 1.9% and a temperature of 30–50 °C. ZnO particles (with an average diameter of 10–30 nm) and double wall carbon nanotubes (DWCNT) (internal diameter of 3–5 nm and 5–15 nm external diameter) were mix at a ratio of 90%: 10% and dispersed in ethylene glycol (EG) then its thermal conductivity was measured. The results showed that maximum relative thermal conductivity (TCR) at temperature of 50 °C and the concentration of 1.9%, equivalent to 24.9%. Economic evaluation and qualitative performance showed that nanofluids hybrid compared with ZnO and nanofluids containing MWCNT, in terms of increasing thermal conductivity (TCE) and economically, is quite effective. A new correlation to predict TCR in terms of concentration of nanoparticles and the temperature was proposed. This correlation has a coefficient of determination (R-squared) and the maximum error of 0.9826 and 2.9%, respectively. The greatest sensitivity was calculated at a maximum temperature and solid volume fraction. Based on the TCR data the artificial neural network (ANN) was developed. The best case ANN containing two hidden layer and 3 neurons in each layer was obtained. This ANN has an R-squared and MSE and was equal to 0.9966% AARD and 1.3127e-05 and 0.0489, respectively. The comparison between experimetnal data, correlation and ANN outputs shows the accuracy and capability of ANN in modeling the TCR data.
8.
Authors:
Mohammad Hemmat Esfe*, Ali Akbar Abbasian Arani, Rasool Shafiei Badi, Mousa Rejvani
Tittle : ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer
In this study, the thermal conductivity of SiO2–DWCNT/ethylene glycol hybrid nanofluid has been experimentally investigated on 0.03–1.71% solid volume fraction and temperatures from 30 to 50 °C. SiO2 and DWCNT’s nanoparticles dispersed in EG as base fluid, and its thermal conductivity was measured. The thermal conductivity was obtained 38% more than ethylene glycol thermal conductivity at some temperatures. A new correlation (R2 = 0.9925) was proposed to predict experimental thermal conductivity ratio as a function of volume concentration and temperature. Also an artificial neural network was designed for thermal conductivity ratio data predicting. The best artificial neural network topology has two hidden layers with five neurons in each layer. Comparing the experimental thermal conductivity ratio with artificial neural network outputs and the correlation shows the high capacity and accuracy of artificial neural network in thermal conductivity ratio data predicting.
9.
Authors:
Mohammad Hemmat Esfe*, Saeed Esfandeh, Mousa Rejvani.
Tittle : Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications.
In the present study, the thermal conductivity of SiO2-MWCNT/EG hybrid nanofluid has been investigated experimentally at solid volume fraction range from 0.025 to 0.86% and temperatures range from 30 to 50 °C. SiO2 particles and multi wall carbon nanotubes (MWCNTs) dispersed with the ratio of 70:30% by mass in ethylene glycol (EG) as the base fluid. The thermal conductivity ratio of mentioned hybrid nanofluid increased to 20.1% more than EG thermal conductivity at 50 °C and the solid volume fraction of 0.86%. Also in the present study, a new correlation was proposed to predict experimental TCR (thermal conductivity ratio) based on the solid volume fraction and the temperature. The R-squared for the proposed correlation is equal to 0.9864. The sensitivity of nanofluid’s thermal conductivity was increased with temperature and solid volume fraction increasing. Also, an ANN was designed for TCR data modeling and forecasting. The most optimal topology was an ANN contains two hidden layers and four neurons in each hidden layer. The R-squared, MSE, and AARD for proposed ANN are equal to 0.9989, 6.8344e−06, and 0.0105, respectively. The results indicated that the neural network is stronger than the correlation in the estimating and predicting experimental thermal conductivity ratio.
10.
Authors:
Mohammad Hemmat Esfe*, Hossein Rostamian, Mohammad Reza Sarlak, Mousa Rejvani, Ali Alirezaie
Tittle : Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: an experimental study and a neural network simulating
In this article, rheological behavior of TiO2-MWCNT (45–55%)/10w40 hybrid nano-oil was studied experimentally. The nano- oils were tested at temperature ranges of 5–55 °C and in shear rates up to 11,997 s−1. With respect to viscosity, shear stress and shear rate variations it was cleared that either of the base oil and nano-oil were non-Newtonian fluids. New equations which were based on thickness of the fluid were presented for different temperature values, R-squared values were between 0.9221 and 0.9998 (the precise of correlation changes depend on temperature). Also to predict the nano-oil behavior, neural network method was utilized. an artificial neural network (MLP type) were used to predict the viscosity in terms of temperature, solid volume fraction and shear stress. to compare the prediction precise of neural network and correlation the results of these two were compared with together. ANN showed more accurate results in comparison with correlation results. R2 and (MSE) were 0.9979 and 0.000016 respectively for the ANN.
Background: The artificial neural network has been employed to predict the thermal conductivity of the carbon nanotube–ethylene glycol (CNT-EG) nanofluid based on experimental data. The main aim of this study is to find the best training algorithm for modeling the thermal conductivity of nanofluids.
Methods: Different activating functions and two training algorithms have been tested to train the neurons. The architecture of this modeling is the same and consists of one hidden layer with two neurons. The input parameters of the network include 20 data of temperatures (15–55oC) and volume concentrations (2.2–7.8 vol.%), and the output of the network is the thermal conductivity coefficient.
Results: The results indicate that the trainbr algorithm with the Elliotsig activating function responses have a higher regression coefficient and a lower mean square error. The results show also that an artificial neural network can estimate the experimental results with high precision in a wide range of temperatures and concentrations of carbon nanotubes.
Conclusion: The comparative graph with experimental data and artificial neural network modeling results in terms of temperature for different volume fractions revealed that the neural network can estimate the experimental results with high precision at a wide range of temperatures and concentrations of CNTs. Also, the results indicated that the neural network was not a proper tool for outside of the available data and should be used in the same range in which it was trained.
12.
Authors:
Mohammad Hemmat Esfe*, Seyfolah Saedodin, Mousa Rejvani, Jalal Shahram.
Tittle : Experimental investigation, model development and sensitivity analysis of rheological behavior of ZnO/10W40 nano-lubricants for automotive applications
In the present study, rheological behavior of ZnO/10W40 nano-lubricant is investigated by an experimental approach. Firstly, ZnO nanoparticles of 10–30 nm were dispersed in 10W40 engine oil with solid volume fractions of 0.25–2%, then the viscosity of the composed nano-lubricant was measured in temperature ranges of 5–55 °C and in various shear rates. From analyzing the results, it was revealed that both of the base oil and nano-lubricants are non-Newtonian fluids which exhibit shear thinning behavior. Sensitivity of viscosity to the solid volume fraction enhancement was calculated by a new correlation which was proposed in terms of solid volume fraction and temperature. In order to attain an accurate model by which experimental data are predicted, an artificial neural network (ANN) with a hidden layer and 5 neurons was designed. This model was considerably accurate in predicting experimental data of dynamic viscosity as R-squared and average absolute relative deviation (AARD %) were respectively 0.9999 and 0.0502.
13.
Authors:
Mohammad Hemmat Esfe*, Mousa Rejvani, Rostam Karimpour, Ali Akbar Abbasian Arani.
Tittle : Estimation of thermal conductivity of Ethylene glycol-based nanofluid with hybrid suspensions of SWCNT-Al2O3 nanoparticles by correlation and ANN methods using experimental data.
In the present paper, the effects of temperature and volume fraction on thermal conductivity of SWCNT–Al2O3/EG hybrid nanofluid are investigated. Single-walled carbon nanotube with outer diameter of 1–2 nm and aluminum oxide nanoparticles with mean diameter of 20 nm with the ratio of 30 and 70%, respectively, were dispersed in the base fluid. The measurements were conducted on samples with volume fractions of 0.04, 0.08, 0.15, 0.3, 0.5, 0.8, 1.5 and 2.5. In order to investigate the effects of temperature on thermal conductivity of the nanofluid, this characteristic was measured in five different temperatures of 30, 35, 40, 45 and 50 °C. The results indicate that enhancement of nanoparticles’ thickness in low volume fractions and at any temperature causes a considerable increment in thermal conductivity of the nanofluid. In this study, the highest enhancement of thermal conductivity was 41.2% which was achieved at the temperature of 50 °C and volume fraction of 2.5%. Based on the experimental data, an experimental correlation and a neural network are presented and for thermal conductivity of the nanofluid in terms of volume fraction and temperature. Comparing outputs of the experimental correlation and the designed artificial neural network with experimental data, the maximum error values for the experimental correlation and the artificial neural network were, respectively, 2.6 and 1.94% which indicate the excellent accuracy of both methods in prediction of thermal conductivity.
14.
Authors:
Mohammad Hemmat Esfe*, Ali Alirezaie, Mousa Rejvani.
Tittle : An applicable study on the thermal conductivity of SWCNT-MgO hybrid nanofluid and price-performance analysis for energy management.
In this research, the viscosity of the aqueous nanofluid of TiO2 has been modeled by artificial neural networks using experimental data. Artificial neural networks are able to estimate the pattern of dynamic viscosity variation along with temperature and nanoparticles mass fraction with a high precision. A network with one hidden layer and 4 neurons has been used. The regression coefficient was obtained 0.9998 in this modeling, which shows very high precision of neural network with a very simple structure. In addition, a relationship in terms of mass fraction and temperature was presented in order to predict the viscosity of this nanofluid. This correlation can estimate the viscosity of TiO2–water nanofluid in a wide range of nanoparticles mass fraction with a maximum error of 0.5 %.
16.
Authors:
Mohammad Hemmat Esfe, Mousa Rejvani, Davood Toghraie*
Tittle : Modeling and estimation of thermal performance factor of MgO-water nanofluids flow by artificial neural network based on experimental data
Using a simple computational tool with a very high connection and the determining role of connections between neurons in identifying network function are the two similarities between natural and Artificial Neural Networks (ANNs). In this article, the very significant subjects of nanofluids efficiency and the thermal performance factor of these fluids operating as heat transfer have been investigated. To model the data in this article, ANNs are used. This modeling is presented for different ϕand modeling results have been compared with experimental data for MgO-water nanofluids flow. The data relating to the efficiency of these nanofluids use complicated patterns, so a type of ANNs has been used with the ability to distinguish the number of neurons required for modeling and following the data pattern. To evaluate the accuracy of the modeling using ANN, the experimental data have been compared with ANN results in different volume fraction of nanoparticles (The results show that ANN has a better agreement with experimental data in estimating the data with a higher Reynolds number.
1.
Authors:
Mousa Rejvani*,Seyfolah Seadodin,
Tittle : Experimental investigation of the viscosity and behavior of the hybrid nano-fluid as a coolant and heat engines lubricant in internal combustion engine
2.
Tittle : Identifying optimal characteristics of U-tube heat exchanger under flowing low concentration Fe3O4/water nanofluid for industrial applications (by experimental data)
3.
Tittle : Optimizing micro channel performance under flowing of Al2O3/water nanofluid for cooling applications through NSGA-II algorithm and ANOVA analysis
4.Tittle: Nanofluidics: Thermodynamic and Transport Properties-Efstathios Michaelides
Authors:
Seyfolah Saedodin, Mohammad Hemmat Esfe, Mousa Rejvani, Ali Alirezaie, Fereshteh Tahan
Type
Publisher
Language
ISBN
year
Translation
Semnan University
Persian
9786008424888
2019
1.Title: Production of Nanofluid Enriched with Silicon Oxide Nanoparticles and Multiwall Carbon Nanotubes as an Additive to Engine Oil by Two-Step Method
Inventors : Mousa Rejvani, Seyfolah Saedodin, Mohammad Hemmat Esfe, Semnan Science & Technology Park
Country
Int. Classfication
Declaration No.
Expire
year
Iran
C10M 00/169
139620140003006398
2037-August-21
2017
2.Title: 7.5 kJ Dynamic Testing Equipment for Energy Absorption by Drop Hammer “is of Scientific Approval”
Scientific Confirmation Grade :6.80 / 10.00 Among the top 75 Technologies of the Ministry of Science and Technology of Iran-7th IranLabExpo.
