The Influence of Forced Convective Heat Transfer on Hybrid Nanofluid Flow in a Heat Exchanger with Elliptical Corrugated Tubes: Numerical Analyses and Optimization

The capabilities of nanofluids in boosting the heat transfer features of thermal, electrical and power electronic devices have widely been explored. The increasing need of different industries for heat exchangers with high efficiency and small dimensions has been considered by various researchers and is one of the focus topics of the present study. In the present study, forced convective heat transfer of an ethylene glycol/magnesium oxide-multiwalled carbon nanotube (EG/MgO-MWCNT) hybrid nanofluid (HNF) as single-phase flow in a heat exchanger (HE) with elliptical corrugated tubes is investigated. Three-dimensional multiphase governing equations are solved numerically using the control volume approach and a validated numerical model in good agreement with the literature. The range of Reynolds numbers (Re) 50 Re 1000 corresponds to laminar flow. Optimization is carried out by evaluation of various parameters to reach an optimal case with the maximum Nusselt number (Nu) and minimum pressure drop. The use of hybrid nanofluid results in a greater output temperature, a higher Nusselt number, and a bigger pressure drop, according to the findings. A similar pattern is obtained by increasing the volume fraction of nanoparticles. The results indicate that the power of the pump is increased when EG/MgO-MWCNT HNFs are employed. Furthermore, the thermal entropy generation reduces, and the frictional entropy generation increases with the volume fraction of nanoparticles and Re number. The results show that frictional and thermal entropy generations intersect by increasing the Re number, indicating that frictional entropy generation can overcome other effective parameters. This study concludes that the EG/MgO-MWCNT HNF with a volume fraction (VF) of 0.4% is proposed as the best-case scenario among all those considered

Viscosity of nanofluids based on an artificial intelligence model

By using an FCM-based Adaptive neuro-fuzzy inference system (FCM-ANFIS) and a set of experimental data, models were developed to predict the effective viscosity of nanofluids. The effective viscosity was selected as the target parameter, and the volume concentration, temperature and size of the nanoparticles were considered as the input (design) parameters. To model the viscosity, experimental data from literature were divided into two sets: a train and a test data set. The model was instructed by the train set and the results were compared with the experimental data set. The predicted viscosities were compared with experimental data for four nanofluids, which were Al2O3, CuO, TiO2 and SiO2, and with water as base fluid. The viscosities were also compared with several of themost cited correlations in literature. The results, which were obtained by the proposed FCM-ANFIS model, in general compared very well with the experimental measurement.NRF, Stellenbosch University/University of Pretoria Solar Hub, CSIR, EEDSM Hub and NAC.http://www.elsevier.com/locate/ichmthb201

Application of the FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network approaches to modelling the thermal conductivity of alumina-water nanofluids

By using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial neural network as well as experimental data, two models were established in order to predict the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In these models, the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. The empirical data were divided into train and test sections for developing the models. Therefore, they were instructed by 80% of the experimental data and the remaining data (20%) were considered for benchmarking. The results, which were obtained by the proposed FCM-based Neuro-Fuzzy Inference System (FCMANFIS) and Genetic Algorithm-Polynomial Neural Network (GA-PNN) models, were provided and discussed in detail.http://www.elsevier.com/locate/ichmtai201

Modelling and multi-objective optimisation of the convective heat transfer characteristics and pressure drop of low concentration TiO2-water nanofluids in the turbulent flow regime

In the research for this paper, a GA–PNN hybrid system was used for modelling the convective heat transfer characteristics and pressure drop of TiO2–water a nanofluid in a fully developed turbulent flow based on an experimentally obtained train and test data set. Models were developed for the Nusselt number and the pressure drop of the nanofluid as a function of Reynolds and Prandtl numbers, nanofluid volume concentration and average nanoparticle diameter. The results of the proposed models were compared with experimental data and with existing correlations. The validity of the proposed models was benchmarked by using statistical criteria and NSGA-II was used for multi-objective optimisation for the convective heat transfer. In the optimisation procedure model, the Nusselt number and pressure drop were considered as the objective functions. However, when the set of decision variables was selected based on the Pareto set, it ensures the best possible combination of objectives. The Pareto front of multi-objective optimisation of the Nusselt number and pressure drop proposed models were also shown and discussed. It was found that application of the multi-objective optimisation method for the turbulent convective heat transfer characteristics and pressure drop of TiO2–water nanofluid could lead to finding the best design points based on the importance of the objective function in the design procedure.The NRF, Stellenbosch University/University of Pretoria Solar Hub, CSIR, EEDSM Hub, RDP and NAC.http://www.elsevier.com/locate/ijhmthb2013ai201

Impact of fusion temperature on hydrothermal features of flow within an annulus loaded with nanoencapsulated phase change materials (NEPCMs) during natural convection process

Please read abstract in the article.http://www.hindawi.com/journals/mpe/am2022Mechanical and Aeronautical Engineerin

Effect of inclined magnetic field on the entropy generation in an annulus filled with NEPCM suspension

The encapsulation technique of phase change materials in the nanodimension is an innovative approach to improve the heat transfer capability and solve the issues of corrosion during the melting process. (is new type of nanoparticle is suspended in base fluids call NEPCMs, nanoencapsulated phase change materials. (e goal of this work is to analyze the impacts of pertinent parameters on the free convection and entropy generation in an elliptical-shaped enclosure filled with NEPCMs by considering the effect of an inclined magnetic field. To reach the goal, the governing equations (energy, momentum, and mass conservation) are solved numerically by CVFEM. Currently, to overcome the low heat transfer problem of phase change material, the NEPCM suspension is used for industrial applications. Validation of results shows that they are acceptable.(eresults reveal that the values of Nuave descend with ascending Ha while Ngen has a maximum at Ha 16. Also, the value of NT,MF increases with ascending Ha. (e values of Nuave and Ngen depend on nondimensional fusion temperature where good performance is seen in the range of 0.35 < θf < 0.6. Also, Nuave increases 19.9% and ECOP increases 28.8% whereasNgen descends 6.9% when ϕ ascends from 0 to 0.06 at θf 0.5. Nuave decreases 4.95% while Ngen increases by 8.65% when Ste increases from 0.2 to 0.7 at θf 0.35.http://www.hindawi.com/journals/mpeam2022Mechanical and Aeronautical Engineerin

Experimental investigation on viscosity of nanofluids prepared from banana fibre - nanoparticles

In this research for the first time, banana-fibre nanoparticles produced for nanofluid application and the viscosity of resultant nanofluids were measured.A Transmission Electron Microscope (TEM) and Scanning Electron Microscope (SEM) were used to analyzethe sizes of the particles produced(200nm). This paper presents new findings on the synthesis of natural fibre to obtain nanoparticles and subsequently produced nanofluids. Nanofluids are prepared by dispersing Banana fibre- nanoparticles in deionized water.An ultrasonic sonicator was used to ensure proper mixtures of different volume fractions (0.3%, 0.6 %, 0.9 % 1.2 % and 1.5%) of Banana fibre nanoparticles into base fluid (DI water). A Vibro Viscometer machine (SV-10) is used to measure the viscosity of the prepared nanofluids more conveniently. For minimum and maximum volume fractions of Banana fibre-nanoparticles (0.3% and 1.5%) in deionized water,the viscosity was found to be 1.08 mPa.s and 1.23mPa.s, which increases slightly with an increase of particle volume fraction and decreases as the temperature increases.The experimental results show a maximum of 22% increasing of viscosity for 1.5% volume fraction of nanofluids as compared with the deionized water (base fluid). From the experimental study on prepared nanofluids conducted, results show that all the values of viscosities at different volume fractions of the prepared nanofluids were found to be substantially higher than the values of the base fluids (deionized water). The experiments were conducted at varying temperature range (20oC through 60oC).Papers presented to the 12th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Costa de Sol, Spain on 11-13 July 2016

A review of thermal conductivity models for nanofluids

Nanofluids, as new heat transfer fluids, are at the center of attention of researchers, while their measured thermal conductivities are more than for conventional heat transfer fluids. Unfortunately, conventional theoretical and empirical models cannot explain the enhancement of the thermal conductivity of nanofluids. Therefore, it is important to understand the fundamental mechanisms as well as the important parameters that influence the heat transfer in nanofluids. Nanofluids’ thermal conductivity enhancement consists of four major mechanisms: Brownian motion of the nanoparticle, nanolayer, clustering, and the nature of heat transport in the nanoparticles. Important factors that affect the thermal conductivity modeling of nanofluids are particle volume fraction, temperature, particles size, pH, and the size and property of nanolayer. In this paper, each mechanism is explained and proposed models are critically reviewed. It is concluded that there is a lack of a reliable hybrid model that includes all mechanisms and influenced parameters for thermal conductivity of nanofluids. Furthermore, more work needs to be conducted on the nature of heat transfer in nanofluids. A reliable database and experimental data are also needed on the properties of nanoparticles.http://www.tandfonline.com/loi/uhte202016-09-30hb201

Influence of injection pressure on the dual-fuel mode in CI engines fueled with blends of ethanol and tamanu biodiesel

DATA AVAILABILITY : The data used to support the findings of this study are included within the article.The acceleration of global warming is primarily attributable to nonrenewable energy sources such as conventional fossil fuels. The primary source of energy for the automobile sector is petroleum products. Petroleum fuel is depleting daily, and its use produces a significant amount of greenhouse emissions. Biofuels would be a viable alternative to petroleum fuels, but a redesign of the engine would be required for complete substitution. The use of CNG in SI engines is not new, but it has not yet been implemented in CI engines. This is due to the fuel having a greater octane rating. The sole use of CNG in a CI engine results in knocking and excessive vibration. This study utilizes CNG under dual-fuel conditions when delivered through the intake manifold. In a dual-fuel mode, compressed natural gas (CNG) is utilized as the secondary fuel and a blend of 90% tamanu methyl ester and 10% ethanol (TMEE10) is used as the primary fuel. The injection pressure (IP) of the primary fuel changes between 200 and 240 bar, while the CNG induction rate is kept constant at 0.17 kg/h. The main combustion process is governed by the injection pressure of the pilot fuel. It could be affecting factors such as the vaporization characteristics of the fuel, the homogeneity of the mixture, and the ignition delay. Originally, tamanu methyl ester (TME) and diesel were used as base fuels in the investigation. As a result of its inherent oxygen content, TME emits more NOx than diesel. The addition of 10% ethanol to TME (TMEE10) marginally reduces NOx emissions in a CI mode because of its high latent heat of vaporization characteristics. Under peak load conditions, NOx emissions of TMEE10 are 6.2% lower than those of neat TME in the CI mode. Furthermore, the experiment was conducted using TMEE10 as the primary fuel and CNG as the secondary fuel. In the dual-fuel mode, the TMEE10 blend showed higher combustion, resulting in an increase in performance and a significant decrease in emission characteristics. As a result of the CNG’s high-energy value and rapid burning rate, the brake thermal efficiency (BTE) of TMEE10 improves to 29.09% compared to 27.09% for neat TME. In the dual-fuel mode of TMEE10 with 20.2% CNG energy sharing, the greatest reduction in fuel consumption was 2.9%. TMEE10 with CNG induction emits 7.8%, 12.5%, and 15.5% less HC, CO, and smoke, respectively, than TME operation.http://www.hindawi.com/journals/ijce/am2023Mechanical and Aeronautical Engineerin

Development of Optimized Maintenance Program for a Steam Boiler System Using Reliability-Centered Maintenance Approach

Reliability centered maintenance (RCM) is a new strategic framework for evaluating system maintenance requirements in its operating conditions. Some industries employ predictive maintenance strategies in addition to preventive maintenance (PM) strategies, which increase production costs. As the breakdown maintenance (BDM) technique is used, the maintenance cost increases. The RCM approach is a mixture of these maintenance strategies that can be used to optimize the maintenance costs and to ensure the availability of the system. The RCM method was applied to the steam boiler system used in the textile industries for the research work reported in this paper. The RCM methodology stated in the literature cannot be implemented, as it is in Indian textile industries due to the lack of knowledge of RCM principles, a labor-oriented nature, the use of partially computerized information systems, an inadequate maintenance database, and information about maintenance costs and production loss. To resolve these issues, a modified RCM approach involving a large number of experts is developed. To apply this RCM methodology, critical components are identified through reliability and failure mode effect and criticality analysis (FMECA). Finally, scheduled maintenance strategies and their intervals are recommended to ensure that the system continues to operate properly. According to this study, implementing the RCM technique effectively will increase boiler system reliability and availability by 28.15 percent and 0.16 percent, respectively. Additionally, up to 20.32 percent of the maintenance cost can be saved annually by applying these scheduled maintenance programs. © 2022 by the authors