Nonlinear function estimation fuzzy system (NFEFS) as a novel statistical approach to estimate nanofluids’ thermal conductivity according to empirical data

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>This paper aims to propose a new nonlinear function estimation fuzzy system as a novel statistical approach to estimate nanofluids’ thermal conductivity.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>A fuzzy system having a product inference engine, a singleton fuzzifier, a center average defuzzifier and Gaussian membership functions is proposed for this purpose.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>Results indicate that the proposed fuzzy system can predict the thermal conductivity of Al<jats:sub>2</jats:sub>O<jats:sub>3</jats:sub>/paraffin nanofluid with appropriate precision and generalization and it also outperforms the classic interpolation methods.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>A new nonlinear function estimation fuzzy system was introduced as a novel statistical approach to estimate nanofluids’ thermal conductivity for the first time.</jats:p> </jats:sec>

Hydrodynamic and thermal flow in nanochannel to study effects of roughness by estimation the atoms positions via MD method

“…In this step, the external magnetic force (F=qvB sin(wt)) inserted on fluid atoms in non-ideal nanochannel and equilibrated atomic system for 1 ns. After this time energy calculation of all atoms show that, this time is sufficient for equilibration of system (Qi et al , 2019; Zhao et al , 2019a; Mei et al , 2019; Wang et al , 2019; Safaei et al , 2019; Vo et al , 2019; Zhai et al , 2019; Zhao et al , 2019b; Sarafraz et al , 2019c; Tlili et al , 2018a; Aman et al , 2018; Tlili et al , 2018b; Khan and Tlili, 2018; Sarafraz et al , 2019a, 2019b; Tlili et al , 2019; Shadloo, 2019; Hopp-Hirschler et al , 2019; Sadeghi et al , 2018; Hopp-Hirschler et al , 2018; Shadloo and Hadjadj, 2017; Sharma et al , 2019; Méndez et al , 2018; Piquet et al , 2019; Nguyen et al , 2019; Shenoy et al , 2019; Karimipour et al , 2014; Karimipour et al , 2015; Goodarzi et al , 2018; Ranjbarzadeh et al , 2017a, 2017b; Karimipour et al , 2018a; Afrand et al , 2016; Alrashed et al , 2018; Esfe et al , 2014; Karimipour et al , 2018b; Arabpour et al , 2018; Safaei et al , 2018; Karimipour et al , 2016; Jalali and Karimipour, 2019; Goodarzi et al , 2019; Tian et al , 2019; Gholamalizadeh et al , 2019; Mozaffari et al , 2019; Salimpour et al , 2019; Nazarafkan et al , 2019; Rajabzadeh et al , 2019; D’Orazio and Karimipour, 2019; Sulgani and Karimipour, 2019; Nafchi et al , 2019; Ershadi and Karimipour, 2018; Nojoomizadeh et al , 2018). Figures 3 and 4 show potential energy of system.…”

Turbulent flows in a spiral double-pipe heat exchanger

“…By analyzing the results obtained from simulations, the most suitable model for problem-solving is selected. In Figures 3 and 4, the results of the simulation and distribution of temperature contour for the three models of standard k-ε, logic k-ε and k-ε RNG are shown for the first time (Shadloo, 2019; Hopp-Hirschler et al , 2019; Sadeghi et al , 2018; Hopp-Hirschler et al , 2018; Toghyani et al , 2019; Nasiri et al , 2018; Piquet et al , 2019; Shenoy et al , 2019; Nguyen et al , 2019; Lebon et al , 2018; Sharma et al , 2019; Mendez-Gonzlez et al , 2018; Shadloo and Hadjadj, 2017; Rashidi et al , 2017; Karimipour et al , 2019; Bahrami et al , 2019; Shadloo et al , 2016; Jalali and Karimipour, 2019; Arabpour et al , 2018a; Arabpour et al , 2018b; Safaei et al , 2018; Pordanjani et al , 2019; Jiang et al , 2019; Safaei et al , 2019; Bagherzadeh et al , 2019; Salimpour et al , 2019; Mozaffari et al , 2019; Gholamalizadeh et al , 2019; Tian et al , 2019; Goodarzi et al , 2019; Cheloii et al , 2019; Aghakhani et al , 2018; Dehkordi and Abdollahi., 2018; Abdollahi et al , 2018; Dehghani et al , 2019; Sedeh et al , 2019; Norouzipour et al , 2019; Bakhshi et al , 2019; Gheynani et al , 2019). In all simulations, the same conditions are used (the element size of the 2 μm , which was selected as the most appropriate element size in the previous section, and the water-Al 2 O 3 nanofluid with 1 per cent of nanoparticle volume fraction and Reynolds number of 1,700).…”

Thermal Conductivity and Dynamic Viscosity of Highly Mineralized Water

“…high mineralized waters with purities of 99.9% and 99.5%, respectively, were used in the current study. An important challenge with nanofluids is that nanoparticles tend to accumulate due to molecular interactions such as van der Waals forces [16][17][18][19][20][21]. Therefore, as the percentage of filler increases, the accumulation of particles also increases.…”