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Twisted helical Tape's impact on heat transfer and friction in zinc oxide (ZnO) nanofluids for solar water heaters: Biomedical insight
Abstract
This study explores the influence of Zinc Oxide (ZnO) nanofluids on solar water heaters with Dimple Tubes and Helical Twisted Tape (DTHTT) surfaces. The helical twisted tape design enhances turbulence, improving nanofluid mixing and thermal exchange. Computational fluid dynamics (CFD) validates the efficiency of the parabolic trough solar water heater (PTSWH) with emphasis on solar light concentration and feed water flow velocity. Optimal conditions include a 0.3% ZnO nanofluid volume concentration, mass flow rates between 1.0 kg/min and 5.0 kg/min, and copper-type twisted helical tapes. Experimental results reveal Nusselt number enhancements of 15.1% and 20.96% at H/D = 10, and 16.72% and 32.12% at H/D = 3, for 0.3% ZnO nanofluid, at Reynolds numbers from 3000 to 8000. The twisted tape arrangement at H/D = 3 exhibits increased fluid mixing, leading to higher convective heat transfer. Friction factor enhancements at Reynolds numbers 3000 and 8000 are 0.26% and 0.38%, respectively, compared to the base fluid. At a 3.0 kg/min mass flow rate, thermal efficiency increases to 39.25%, a 13.25% gain over plain tapes. The model shows a ±3.24% deviation from the expected friction factor, with a total ±1.2% discrepancy between experimental and simulated findings, remaining within an acceptable range.
... A novel class of specific fluids with high thermal conductivity is suggested by the suspension of metallic particles at the nanoscale in industrial heat transfer fluids like ethylene glycol, engine oil, or water. The author created the word "nanofluids" (NFs) to describe this new class of designed heat transfer fluids that can be created using current nanophase technology and contain metallic particles with typical particle sizes of roughly 10 nanometres [6][7][8][9][10][11][12]. Koo and Kleinstreuer [13] presented a nanofluid model that provided more precise technique for forecasting the thermal conductivity of nanofluids, mostly in circumstances where traditional models failed to match experimental data. ...
... Differential system (Eqs. (8)(9)(10)) is tackled numerically and the computed results for velocity and temperature profiles are evaluated comprehensively to deliberate the complex rheological properties of the nano fluid parameters. The following variable as defined to peruse for computational procedure ...
This analysis examines the thermal and momentum physiognomies of copper oxide nanoparticle suspensions (nanofluids) past a permeable wall, including general magnetic effects. Understanding these dynamics is critical for enhancing heat transfer in numerous engineering applications, such as cooling and energy systems. The Koo-Kleinstreuer-Li (KKL) rheological model is used to develop a mathematical outline that incorporate for both static and Brownian thermal conductivities. Dual solutions for the velocity and temperature profiles are calculated under varying circumstances. An artificial neural network (ANN) method is applied to confirm the precision of the results, employing convergence plots of mean squared errors, histograms, and regression investigation. The outcomes disclose that the generalized magnetic effects along with permeability of surface meaningfully impact both the velocity and temperature distributions, with copper oxide nanoparticles enhancing thermal conductivity by up to 20 %. This effort extends existing models by integrating ANN-based optimization and providing more reliable predictions for nanofluid behavior in thermally variable environments. The results recommend that magnetic effects and thermal conductivity can be leveraged to enhance heat transfer processes in nanofluid-based systems.
... Bejan number and Eckert number were two factors that were noticed to possess larger impact on the heat transfer rates. The basic understanding and applications of nanofluids are discussed in [29][30][31]. Jakeer et al. [32] assessed the thermal features of Si-MgO-Ti particles subjected to applied magnetic field. The slandering sheet was the geometry in this problem. ...
Maximizing energy collection and optimizing the performance of solar photovoltaic cells (SPVCs) is essential for the day. In this context, using nanofluids of graphite-dopped titanium dioxide (G-TiO2) and aluminum oxide (G-Al2O3) in SPVC systems plays a crucial role in preventing PV panel overheating, thereby enhancing electrical efficiency and the panel's life span. For this purpose, the ultrasonication technique was used to dope graphite with TiO2 and Al2O3 and mixed with deionized water (DI-H2O) at concentrations of 0.5, 0.6, 0.7, and 0.8 % by vol. The results indicated that G-Al2O3 seems to give optimum results compared to G-TiO2. With G-Al2O3 the electrical and thermal efficiencies were increased to 16.2 % and 27.2 %, this is about 5.1 % and 6.3 % higher than the DI-H2O. With nanoparticle volume concentration of 0.7 %, G-Al2O3 causes a 3 % higher pressure drop, 3.6 % higher in friction factor, 8.6 % higher in Nu, and 9.5 % higher in temperature gradient (ΔT/Δx) in comparison to G-TiO2 at 14 h respectively. Hence it can be recommended that, with an increase in pump efficiency or by the use of a variable-speed pump, the extra resistance can be compensated for without excessive energy consumption.
The hybrid AI-based battery management system (HAI-BMS) is proposed to solve the complex problem of electric vehicle (EV) battery management. It combines conventional manipulation processes with system-gaining knowledge of neural networks and reinforcement learning algorithms. This simulation showcases the capability of AI-based BMS to transform electric-powered transportation by demonstrating substantial improvements to battery performance, lifespan, and average vehicle efficiency. By incorporating AI techniques into the BMSs of electric automobiles, the HAI-BMS is paving the manner for future transportation options that are sensible, bendy, and eco-friendly.
Meta-heuristic optimization algorithms are widely applied across various fields due to their intelligent behavior and fast convergence, but their use in optimizing engine behavior remains limited. This study addresses this gap by integrating the Design of Experiments-based Response Surface Methodology (RSM) with meta-heuristic optimization techniques to enhance engine performance and emissions characteristics using Tectona Grandi’s biodiesel with Elaeocarpus Ganitrus as an additive. Advanced Machine Learning (ML) models, including Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), and Random Trees (RT), were employed for predictive analysis, with ANN outperforming RSM in accuracy. The study identified the Teak biodiesel blend (TB20) with a 5 ml Elaeocarpus Ganitrus additive (TB20 + R5) as the optimal formulation, achieving the highest Brake Thermal Efficiency and reduced Brake-Specific Fuel Consumption. Desirability analysis further confirmed the blend’s superior performance and emissions characteristics, with a desirability rating of 0.9282. This work highlights the potential of hybrid optimization approaches for improving biodiesel performance and emissions without engine modifications, contributing to the advancement of sustainable energy practices in internal combustion engines.
This research examines the consequences of employing mixtures of diesel and sunflower biodiesel mixed with mesoporous silica nanoparticles (MSNs) made of Mobil Composition of Matter No. 41 (MCM-41) on the emission characteristics and performance of a single-cylinder diesel engine. The goal is to investigate if these nano-additives can lower hazardous gas emissions and increase engine efficiency, which is essential for developing cleaner and more sustainable engine technology. A single-cylinder DI diesel engine was used to study the effects of adding nanoparticles to sunflower biodiesel to reduce exhaust pollution levels and enhance engine efficiency. The study examines the effects of various doses of MCM-41 nanoparticles (50, 75, and 100 ppm) in combination with sunflower biodiesel fuel blend (B20) on CI engine characteristics at 3000 rpm and varying loads (500, 1000, 2000, 3000, and 4000 W). Nanoparticle additives are blended with fuel using an ultrasonication process to improve the mixture's stability. In comparison to B20 fuel operation, the brake thermal efficiency (BTE) increased by 12%, and the emissions such as hydrocarbon (HC) and carbon dioxide (CO2) deceased by 22.9% and 33.3%, respectively, with the addition of 100 ppm MCM-41 nanoparticles to B20 (B20MCM100). Nevertheless, all blends with the addition of nanoparticles exhibit a modest increase in nitrogen oxides. This study offers a novel strategy to enhancing the environmental and performance features of biodiesel, as it is the first to systematically investigate the effects of MCM-41 nanoparticles in biodiesel blends under varied engine loads. According to the research, biodiesel enriched with nanoparticles may be a key component in the development of cleaner and more effective fuel technologies.
div class="section abstract"> Nanofluids have emerged as effective alternatives to traditional coolants for enhancing thermal performance in automotive applications. This study conducts a comparative analysis of the viscosity and thermal conductivity of ZnO and Cu hybrid nanofluids. Nanofluids were prepared with ZnO and Cu nanoparticle concentrations of 0.1%, 0.3%, and 0.5% by volume and were characterized over temperatures ranging from 25°C to 100°C. The results demonstrate that ZnO and Cu hybrid nanofluids achieve an increase in thermal conductivity by up to 22% and 28%, respectively, compared to the base fluid. Concurrently, the viscosity of these nanofluids increases by up to 12% at the highest concentration and temperature. This study addresses a critical research gap by investigating the combined effects of ZnO and Cu nanoparticles in hybrid nanofluids, an area that has been underexplored. By providing new insights into optimizing both thermal conductivity and viscosity, this research contributes to the development of more efficient cooling systems for automotive applications.
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This study aims to characterize the twisting behavior of bi-directional functionally graded (FG) micro-tubes under torsional loads within the modified couple stress theory framework. The two material properties involved in the torsional static model of FG small-scale tubes, i.e., shear modulus and material length scale parameter, are assumed to possess smooth spatial variations in both radial and axial directions. Through the utilization of Hamilton’s principle, the governing equations and boundary conditions are derived, and then, the system of partial differential equations is numerically solved by using the differential quadrature method. A verification study is conducted by comparing limiting cases with the analytical results available in the literature to check the validity of the developed procedures. A detailed study is carried out on the influences of the phase distribution profile and geometric parameters upon twist angles and shear stresses developed in FG micro-tubes undergoing external distributed torques.
Pulsating heat pipe (PHP) is an implicit technique through a passive two-stage heat transfer system. This paper presents the experimentations on PHP contrived using copper with different inner tube diameters of 1, 1.5, 2, and 2.5 mm, respectively. The PHP is accused of acetone as a functional liquid with filling proportions varying from 50 to 90% of its volume with an increment of 10%. The effects of filling proportion and tube diameter on the thermal performance of PHP were investigated. The evaporator zone is electrically heated using a mica heater in the range of 20-80 W, and the condenser area is kept cool by the water circulation method. The results show that a 2 mm inner tube diameter performs best compared to other tube diameters, with a lower rate of thermal resistance of 0.49 K/W. Also, the performance of PHP is enhanced at a filling proportion of 60% for all the tube diameters. Further, CFD analysis was carried out for different filling ratios for a 2 mm diameter pipe at a constant heat input of 80 W, and it revealed that test outcomes were in line with CFD results. The deviation between the experimental and numerical studies was 10%. Considering the optimized parameters, i.e., tube diameter and filling ratio, the work was extended by adding SiO 2 nanoparticles to the base fluid with 1-5 % mass concentration. The results showed a lower thermal resistance value of 0.3 W/K and a higher heat transfer coefficient of 828.64 W/m 2°K was obtained at 2% mass concentration of SiO 2. Also, the propor-* Corresponding author. Case Studies in Thermal Engineering 55 (2024) 104065 2 E.R. Babu et al. tional rise in heat transfer coefficient at 60 W is 11.46, 17, 14, 4.15, and 1.94% for 1, 2, 3, 4, and 5% mass concentration of SiO 2 nanoparticles, respectively. Hence, the PHP operates better at a 2 mm diameter with a 60% filling ratio and 2% mass concentration of SiO 2 nanoparticles.
Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in the building, automotive, and packing industries. The novelty of the work is that the woven areca sheath fiber (ASF) with different fiber fraction epoxy composites has been fabricated and tested for its tribological responses on three-body abrasion wear testing machines along with its mechanical features. The impact of the fiber fraction on various features is examined. The study also revolves around the development and validation of a machine learning predictive model using the random forest (RF) algorithm, aimed at forecasting two critical performance parameters: the specific wear rate (SWR) and the coefficient of friction (COF). The void fraction is observed to vary between 0.261 and 3.8% as the fiber fraction is incremented. The hardness of the mat rises progressively from 40.23 to 84.26 HRB. A fair ascent in the tensile strength and its modulus is also observed. Even though a short descent in flexural strength and its modulus is seen for 0 to 12 wt % composite specimens, they incrementally raised to the finest values of 52.84 and 2860 MPa, respectively, pertinent to the 48 wt % fiber-loaded specimen. A progressive rise in the ILSS and impact strength is perceptible. The wear behavior of the specimens is reported. The worn surface morphology is studied to understand the interface of the ASF with the epoxy matrix. The RF model exhibited outstanding predictive prowess, as evidenced by high R-squared values coupled with low mean-square error and mean absolute error metrics. Rigorous statistical validation employing paired t tests confirmed the model’s suitability, revealing no significant disparities between predicted and actual values for both the SWR and COF.
Commercial simulation software like QFORM and DEFORM do not possess adequate material data for alloys produced through powder metallurgy. Therefore, in the present study, hot compression tests of Al-5.6%Zn–2% Mg aluminum alloy were conducted at temperature ranges (300 0C-500 ◦C) and strain rates (0.1 s 1-0.0001 s 1). The impact of compression temperature and strain rate on the flow curve behaviour and microstructure evolution-associated mechanisms were investigated through experimentally acquired flow curves and EBSD analysis. Four constitutive models were constructed; namely the Arrhenius-type, modified Johnson Cook (MJC), modified Zerilli-Armstrong (MZA), and an artificial neural network (ANN). The results demonstrated that among the models considered, the ANN and Arrhenius-type models exhibited the lowest AARE values of 0.486 % and 3.36 %, respectively. Conversely, the MZA and MJC models displayed higher AARE values of 8.84 % and 3.93 %, respectively. Notably, the Arrhenius-type model emerged as the most suitable prediction model due to its capability to handle nonlinear relationships between factors. However, in scenarios where material properties are unknown or experimental data is limited, the MJC model can serve as a simpler alternative. The MZA model was deemed unsuitable for accurately estimating flow stress in hot compression. Remarkably, the best-trained ANN model exhibited the highest predictive performance with an AARE of 0.486 % and an R-value of 0.99. This study offers fundamental insights to improve the accuracy of simulating hot compression procedures. EBSD analysis demonstrated that higher upsetting temperatures and lower strain rates promoted recrystallization, resulting in a more uniform microstructure.
Aluminium Matrix Composites (AMCs) have garnered significant attention due to their exceptional specific strength and stiffness compared to monolithic metals. Fly ash particulates are a commonly utilized and economically viable reinforcement for AMCs. This research discusses the influence of fly ash particulates on the microstructure assessment, physical characteristics, mechanical properties, and wear resistance of a stir-cast Al7075-fly ash composite. XRD analysis showed that the fly ash was uniformly distributed throughout the aluminium matrix without producing any intermetallic compound. UTS and Hardness of Al7075-fly ash composite increased with increasing % of fly ash reinforcement, going from 140 to 173 MPa and from 66 to 75 HV, respectively, and the error analysis is also presented. Results from studies of the microstructure of the aluminium alloy show that its fly ash component is evenly distributed throughout the material. The wear rate of a casted aluminum matrix composite was measured using the pin-on-disc tribotest with no lubrication. With a fixed sliding velocity of 300 rpm and a covering distance of 2000 mitres against an EN31 steel disc, wear tests were conducted at weights of 10, 20, and 30 N.
In this work, experiments were carried out in line with Design of Experiments (DOE) standards to assess the performance and emission features of 5% graphene nanoparticles added linseed bio- diesel. The engine was operated with the blends of B10, B20, and B30 with 5% graphene nano additives (designated as B10G5, B20G5, and B30G5). To find the parameter’s optimum values, the Desirability Function approach (DFA), Swarm Salp single objective, Multi Objective Bat al- gorithm (MOBA), Response surface methodology (RSM) and D-optimal design approach were employed. Advanced machine learning (ML) techniques were employed to anticipate these characteristics. It was found that B20G5 had a better brake thermal efficiency (BTE), when compared to the other samples (and around 11% higher than diesel fuel at full load). The emissions of Carbon monoxide (CO) and Hydrocarbon (HC) were lower for B20G5 blended fuel than for diesel (Around 23.52% lower than diesel). In comparison to Response surface method- ology (RSM), the overall coefficient of determination (R2) value using Artificial Neural Network (ANN) for was high. As a result, it was revealed that the ANN was typically better than the RSM in forecasting the various factors affecting the engine performance. The optimum outcomes were achieved by single objective (Salp Swarm algorithm) and multi-objective algorithms. According to multi-objective algorithm, a B20G5 nano additive biodiesel mix at its maximum Brake power (BP) produced the highest value of BTE with the lowest Nitrogen Oxides (NOx) emissions. The comparison shows that B20G5 can be used easily without making any modifications to engines.
A fuel cell, an energy conversion system, needs analysis for its performance at the design and off-design point conditions during its real-time operation. System performance evaluation with logical methodology is helpful in decision-making while considering efficiency and cross-correlated parameters in fuel cells. This work presents an overview and categorization of different fuel cells, leading to the developing of a method combining graph theory and matrix method for analyzing fuel cell system structure to make more informed decisions. The fuel cell system is divided into four interdependent sub-systems. The methodology developed in this work consists of a series of steps comprised of digraph representation, matrix representation, and permanent function representation. A mathematical model is evaluated quantitatively to produce a performance index numerical value. With the aid of case studies, the proposed methodology is explained, and the advantages of the proposed method are corroborated.
This study attempts to supply renewable energy efficiently and cost-effectively. Photovoltaic (PV) and hydro energy potential in Chupki, India, is examined using HOMER Pro v3.14. 161 homes can receive continuous energy from a hybrid renewable energy system (HRES). Optimized PV modules, hydro turbines, converters, and batteries make up the optimal HRES. The top four configurations were selected based on net present cost (NPC) and cost of energy production (COE). The optimal HRES configuration includes a 1510 kW solar array, 1059 kW hydro turbine, 3874 kWh lithium-ion batteries, and a 482 kW converter, achieving 100% renewable energy integration. The optimal HRES was evaluated for economic, technical and renewable energy factors. The findings and optimized HRES configuration can serve as a basis for similar initiatives in other rural regions, facilitating the transition towards renewable energy sources and enhancing energy access and reliability.
The thermal properties of paraffin wax were upgraded by utilizing rice husk-derived graphene nanoparticles. In terms of performance, the stability of NPEPCM is an essential aspect. The sta- bility of REPCM is checked using surfactants (CTAB, SDBS, SDS) and sonication time. An incre- ment in the thermal conductivity of paraffin wax was perceived. Maximum conductivity of 0.59 Wm−1K−1 was achieved at 304 K for 1.5 wt% of RGP’s. The maximum thermal conductivity of 0.69 Wm−1K−1 was achieved using pure Graphene at 304 K. Using RGPEPW at different con- centrations of RGP’s, the thermal efficiency was calculated for different flow rates of fluid and solar irradiance, and the consequence of the abridged temperature coefficient on the efficiency of FPSC observed. Maximum efficiencies of 67.5 % and 63.9 % were noted for 1 wt% of graphene nanoparticles and 1.5 wt% of RGPs at a 3 Lt/min flow rate, respectively. The improvement of 29.8 % and 22.8 % in the efficiency of FPSC is observed for 1 wt per cent of Graphene and 1.5 wt per cent of RGPs, respectively. Using RGPEPW with a 9.2 % increment in the outlet, the tem- perature was noticed in the afternoon at a 1.2 Lt/min flow rate. The bioengineering approach gives an understanding of engineering, biology, and environmental science involvement in the present study.