Two-phase mixture numerical and soft computing-based simulation of forced convection of biologically prepared water-silver nanofluid inside a double-pipe heat exchanger with converging sinusoidal wall: Hydrothermal performance and entropy generation analysis. (October 2022)

• Record Type: Journal Article
• Authors: Shahsavar, Amin; Alimohammadi, Saman; Askari, Ighball Baniasad; Shahmohammadi, Mohammad; Jamei, Mehdi; Pouyan, Neda
• Is Part Of: Engineering analysis with boundary elements. Volume 143(2022)
• Page Start: 43
• Page End: 60
• Publication Date: 2022-10
• Subjects: Converging wall -- Double-pipe heat exchanger -- Entropy analysis -- Nanofluid -- Two-phase mixture model, Soft computing; CDHX Converging double-pipe heat exchanger -- GP Gaussian process -- GPD Gaussian probability distribution -- GPR Gaussian process regression model -- MWCNT Multi-Walled carbon nanotube -- NF Nanofluid -- PDHX Parallel double-pipe heat exchanger -- PPD probability distribution -- R Correlation coefficient -- RAE Root absolute error -- RMSE root mean square error -- SCDHX Sinusoidal converging double-pipe heat exchanger; Boundary element methods -- Periodicals; Engineering mathematics -- Periodicals; Équations intégrales de frontière, Méthodes des -- Périodiques; Mathématiques de l'ingénieur -- Périodiques; Boundary element methods; Engineering mathematics; Periodicals; 620.00151
• DOI: 10.1016/j.enganabound.2022.06.008
• Languages: English
• ISSNs: 0955-7997

Abstract

Highlights: Performance of a heat exchanger with converging sinusoidal wall is examined. Two-phase mixture model is used to perform the simulations. Biologically prepared water-silver nanofluid is used as the coolant. The use of converging wavy wall entails a 407-439% increase in entropy generation. Using converging wavy wall improves overall performance up to the Reynolds 1000. Abstract: The cooling performance of biological water-silver nanofluid (NF) in three double-pipe heat exchangers with converging sinusoidal inner wall (SCDHX), converging inner wall (CDHX), and plain inner wall (PDHX) was examined numerically using the first and second laws of thermodynamics. The two-phase mixture model is employed to conduct the simulations. Based on the results, the sinusoidal wall increases the flow mixing, and thereby the convective heat transfer coefficient in SCDHX enhances by 50% and 18% over the PDHX type for Re s of 500 and 2000, respectively. Moreover, the highest NF frictional entropy generation rate ( S ˙ f, m, c ) was obtained for SCDHX; 67% and 80% higher than those for CDHX and PDHX, respectively. The efficiency criteria ratio (η) of SCDHX was obtained as roughly 1.1 for Re s of 500 and 1000, which is 27.27% higher than that for CDHX type. Also, the efficiency criteria ratio of SCDHX over the CDHX was in the range of 1.21-1.41. Besides, a robust soft computing, namely the Gaussian process regression (GPR) approach, was proposed to accurately estimate the total entropy of cold NF, the total entropy of hot NF, and the performance ratio based on the nanoparticle concentration and Reynolds number.