New Functionality for Energy Parameter of Redlich-Kwong Equation of State for Density Calculation of Pure Carbon Dioxide and Ethane in Liquid, Vapor and Supercritical Phases

“…Table 4 shows the statistical results of the thermal conductivity of graphene/water nanofluid obtained using ANOVA. R 2 value closer to 1 indicates a good fit model based on good data [29]. The R square in this study was 0.984 that showed this model could account for over 0.984 of the variability in the response.…”

Section: Anova Analysissupporting

confidence: 49%

Assessment of the Influence of Graphene Nanoparticles on Thermal Conductivity of Graphene/Water Nanofluids Using Factorial Design of Experiments

Periasamy¹,

Ramesh²

2018

Period. Polytech. Chem. Eng.

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In this study, 23 factorial design of experiment was employed to evaluate the effect of parameters of hot fluid inlet temperature, graphene nanofluid concentration and hot fluid flow rate on thermal conductivity of graphene/water nanofluid. The levels of hot fluid inlet temperature are kept at 35°C and 85°C, nanofluid concentration is kept at 0.1 and 1.0 volume% (vol.%) and the hot fluid flow rate are kept at 2 lpm and 10 lpm. Experiments were conducted with 16 runs as per MINITAB design software using graphene/water nanofluids in the corrugated plate type heat exchanger. The nanofluid thermal conductivity was determined using the mixing rule for different nanofluid concentrations ranging from 0.1 to 1.0%. Normal, Pareto, Residual, Main and Interaction effects, Contour Plots were drawn. The Analysis of Variance (ANOVA) of test results depict that the hot fluid temperature and nanofluid concentration have significant effect on the thermal conductivity of graphene/water nanofluid (response variable).

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“…6, we conclude that the conducted modeling has been very successful and these networks can be used for modeling more extensive data and for different properties of nanofluids. The closer R 2 value to 1 shows the better prediction of experimental data by the proposed model [30]. As can be seen from Table 3, R-squared is 0.9918.…”

Section: Evaluating the Rule Premises Results Inmentioning

confidence: 53%

Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

Esfe

2017

Period. Polytech. Chem. Eng.

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In this article, thermal conductivity data of aqueous nanofluids of CuO have been modeled through one of the instruments of empirical data modeling. The input data of 5 different volume fractions of nanofluid obtained in four temperatures Keywords nanofluids, fuzzy networks, thermal conductivity, ANFIS IntroductionIncrease in energy cost in long time and growing need for energy have made scientists look for ways to conserve energy. One way for conserving energy in heat transfer field is to use operating fluids of heat transfer with better and more efficient transfer properties. Around 20 years ago, Choi [1] in his report proposed his solution for this problem by introducing suspensions called nanofluids. After scientists' familiarity with these fluids, a great deal of attention was drawn to them. When many researchers of heat transfer and mass field observed nanofluids potential in reducing energy consumption, they embarked on their researches on these new fluids and thousands of scientific articles in this field have been published so far.These articles have different subjects such as nanofluids thermal conductivity [2][3][4][5][6][7], viscosity [8][9][10][11], heat transfer coefficient [12][13][14][15][16][17][18] and the other subjects about nanofluids.In addition to experimental researches, a large number of analytical and numerical researches have been conducted on this field. Beyond this level, some researchers have begun studies on experimental data modeling. These researches are conducted with the purpose of nanofluids behavior modeling in thermophysical and hydrodynamic terms.A report of the studies conducted to model nanofluids behavior is provided in Table 1.In this article, thermal behavior of aqueous nanofluids containing CuO nanoparticles has been modeled by ANFIS network. The nanofluids were prepared through a two-step method and the thermal conductivity data were measured based on previous work [21,22] in five volume fractions and four temperatures and the modeling results were compared with experimental data. It should be noted that in this article Sugeno method has been used for modeling the data through ANFIS network. Based on the author's knowledge, there are no similar studies in literature on modeling the thermal conductivity by Adaptive Neuro-Fuzzy Inference System (ANFIS).

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New Functionality for Energy Parameter of Redlich-Kwong Equation of State for Density Calculation of Pure Carbon Dioxide and Ethane in Liquid, Vapor and Supercritical Phases

Abstract: Abstract

Cited by 12 publications

References 23 publications

The Investigation of Effects of Temperature and Nanoparticles Volume Fraction on the Viscosity of Copper Oxide-ethylene Glycol Nanofluids

The Investigation of Effects of Temperature and Nanoparticles Volume Fraction on the Viscosity of Copper Oxide-ethylene Glycol Nanofluids

Assessment of the Influence of Graphene Nanoparticles on Thermal Conductivity of Graphene/Water Nanofluids Using Factorial Design of Experiments

Thermal Conductivity Modeling of Aqueous CuO Nanofluids by Adaptive Neuro-Fuzzy Inference System (ANFIS) Using Experimental Data

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