Effects of tube flattening on the fluid dynamic and heat transfer performance of nanofluids

In the present article, numerical simulation of Al₂O₃–water nanofluid flow in different flat tubes are performed to investigate the effects of tube flattening on the fluid dynamic and heat transfer performance of nanofluids. The numerical simulations of nanofluids are performed using two phase mixture model by FORTRAN programming language. The flow regime and the wall boundary conditions are assumed to be laminar and constant heat flux respectively. The simulated results are compared with previously published data and good agreement is observed. The effects of tube flattening on different parameters such as heat transfer coefficient, wall shear stress, nanoparticles distribution, temperature distribution, secondary flow and velocity profiles are presented and discussed. The results show that with increasing the flattening, the heat transfer coefficient and wall shear stress increase. The rate of increasing is soft for all flat tubes except for the tube with the most flattening which has a severe increasing in heat transfer and wall shear stress values.     

Evaluation of single-phase,discrete, mixture and combined model of discrete and mixture phases in predicting nanofluid heat transfer characteristics for laminar and turbulent flow regimes

It is essential to investigate the appropriate model for simulating nanofluid flow for different flow regimes because, at present, most previous studies do not agree with each other. It was, therefore, the purpose of this study to present a Computational Fluids Dynamics (CFD) investigation of heat transfer coefficients of internal forced convective flow of nanofluids in a circular tube subject to constant wall heat flux boundary conditions. A complete three-dimensional (3D) cylindrical geometry was used. Laminar and turbulent flow regimes were considered. Three two-phase models (mixture model, discrete phase model (DPM) and the combined model of discrete and mixture phases) and the single-phase homogeneous model (SPM) were considered with both constant and variable properties. For the turbulent flow regime, it was found that the DPM with variable properties closely predicted the local heat transfer coefficients with an average deviation of 9%, and the SPM deviated from the DPM model by 2%. It was also found that the mixture and the combined discrete and the mixture phase model gave unrealistic results. For laminar flow, the DPM model with variable properties predicted the heat transfer coefficients with an average deviation of 9%.     

Numerical study on heat transfer and entropy generation of developing laminar nanofluid flow in helical tube using two-phase mixture model

Nanofluids and helical tubes are among the best methods for heat transfer enhancement. In the present study, laminar, developing nanofluid flow in helical tube at constant wall temperature is investigated. The numerical simulation of Al₂O₃-water nanofluid with temperature dependent properties is performed using the two-phase mixture model by control volume method in order to study convective heat transfer and entropy generation. The numerical results is compared with three test cases including nanofluid forced convection in straight tube, velocity profile in curved tube and Nusselt number in helical tubes that good agreement for all cases is observed. Heat transfer coefficient in developing region inside a straight tube using mixture model shows a better prediction compared to the homogenous model. The effect of Reynolds number and nanoparticle volume fraction on flow and temperature fields, local and overall heat transfer coefficient, local entropy generation due to viscous dissipation and heat transfer, and the Bejan number is discussed in detail and compared with the base fluid. The results show that the nanofluid and the base fluid have almost the same axial velocity profile, but their temperature profile has significant difference in developing and fully developed region. Entropy generation ratio by nanofluid to the base fluid in each axial location along the coil length showed that the entropy generation is reduced by using nanofluid in at most length of the helical tube. Also, better heat transfer enhancement and entropy generation reduction can be achieved at low Reynolds number.     

RAMS+C informed decision-making with application to multi-objective optimization of technical specifications and maintenance using genetic algorithms

The role of technical specifications and maintenance (TSM) activities at nuclear power plants (NPP) aims to increase reliability, availability and maintainability (RAM) of Safety-Related Equipment, which, in turn, must yield to an improved level of plant safety. However, more resources (e.g. costs, task force, etc.) have to be assigned in above areas to achieve better scores in reliability, availability, maintainability and safety (RAMS). Current situation at NPP shows different programs implemented at the plant that aim to the improvement of particular TSM-related parameters where the decision-making process is based on the assessment of the impact of the change proposed on a subgroup of RAMS+C attributes.This paper briefly reviews the role of TSM and two main groups of improvement programs at NPP, which suggest the convenience of considering the approach proposed in this paper for the Integrated Multi-Criteria Decision-Making on changes to TSM-related parameters based on RAMS+C criteria as a whole, as it can be seem as a decision-making process more consistent with the role and synergic effects of TSM and the objectives and goals of current improvement programs at NPP. The case of application to the Emergency Diesel Generator system demonstrates the viability and significance of the proposed approach for the Multi-objective Optimization of TSM-related parameters using a Genetic Algorithm.     

Numerical analysis of conjugate natural and mixed convection heat transfer of nanofluids in a square cavity using the two-phase method

In the present study, the problem of conjugate natural and mixed convection of nanofluid in a square cavity containing several pairs of hot and cold cylinders is visualized using non-homogenous two-phase Buongiorno's model. Such configuration is considered as a model of heat exchangers in order to prevent the fluids contained in the pipelines from freezing or condensing. Water-based nanofluids with Cu, Al₂O₃, and TiO₂ nanoparticles at different diameters ($25\text{nm}?d_p?145\text{nm}$) are chosen for investigation. The governing equations together with the specified boundary conditions are solved numerically using the finite volume method based on the SIMPLE algorithm over a wide range of Rayleigh number (${10}^4?\mathit{Ra}?{10}^7$), Richardson number (${10}^{-2}?\mathit{Ri}?{10}^2$) and nanoparticle volume fractions ($0?\phi ?5\%$). Furthermore, the effects of three types of influential factors such as: orientation of conductive wall, thermal conductivity ratio ($0.2?K_r?25$) and conductive obstacles on the fluid flow and heat transfer rate are also investigated. It is found that the heat transfer rate is significantly enhanced by incrementing Rayleigh number and thermal conductivity ratio. It is also observed that at all Rayleigh numbers, the total Nusselt number rises and then reduces with increasing the nanoparticle volume fractions so that there is an optimal volume fraction of the nanoparticles where the heat transfer rate within the enclosure has a maximum value. Finally, the results reveal that by increasing the thermal conductivity of the nanoparticles and Rayleigh number, distribution of solid particles becomes uniform.