Search Results (2,007)

Nanofluids are an innovative working medium in solar hot water installations (DHWs), thanks to their increased thermal conductivity and heat transfer coefficient. The aim of this work was to assess the effect of Al₂O₃ nanofluids in a water–ethylene glycol base (40:60%) and with the addition of Tween 80 surfactant (0.2 wt%) on thermal efficiency (ε) and exergy (η_ex) in a plate heat exchanger at DHW flows of 3 and 12 L/min. The numerical NTU–ε model was used with dynamic updating of thermophysical properties of nanofluids and the solution of the ODE system using the ode45 method, and the validation was carried out against the literature data. The results showed that the nanofluids achieved ε ≈ 0.85 (vs. ε ≈ 0.87 for the base fluid) and η_ex ≈ 0.72 (vs. η_ex ≈ 0.74), with higher entropy generation. The addition of Tween 80 reduced the viscosity by about 10–15%, resulting in a slight increase of Re and h-factor; however, the impact on ε and η_ex was marginal. The environmental analysis with an annual demand of Q = 3000 kWh/year and an emission factor of 0.2 kg CO₂/kWh showed that for ε < 0.87 the nanofluids increased the emissions by ≈16 kg CO₂/year, while at ε ≈ 0.92, a reduction of ≈5% was possible. This paper highlights the need to optimize nanofluid viscosity and exchanger geometry to maximize energy and environmental benefits. Nowadays, due to the growing problems of global warming, the analysis of energy efficiency and carbon footprint related to the functioning of a building seems to be crucial. Full article

Background: Unambiguous clinical interpretation of PCR results for urinary tract infections (UTIs) remains a challenge. Here we compare and correlate multiplex qPCR results (quantification cycle values) with traditional microbial culture results (colony forming units) for clinical samples. Methods: Serial dilutions [10⁸ to 10⁰ colony forming units (CFU)/mL] were performed on five Gram-negative and two Gram-positive UTI-causing bacterial pathogens. For each dilution, quantitative cultures on solid media to confirm CFU/mL values and a real-time PCR UTI panel employing a nanofluidic Open Array^TM platform producing quantification cycle (Cq) values were performed. Cq values were correlated with CFU/mL values, generating a semi-quantitative interpretive scale for clinical samples. The clinical utility of the scale was then assessed using PCR and culture data from 168 clinical urine samples. Results: For Gram-negative bacteria, Cq values of <23, 23 to 28, and >28 corresponded with ≥10⁵ CFU/mL, <10⁵ CFU/mL and negative cultures, respectively. For Gram-positive bacteria, Cq values of <26, 26 to 30, and >30 corresponded with ≥10⁵ CFU/mL, <10⁵ CFU/mL and negative cultures, respectively. Among 168 urine specimens (including 138 Gram-negative and 30 Gram-positive bacteria), there was 83.3% agreement (n = 140/168) and 16.6% non-agreement (n = 28/168) between culture CFU/mL and qPCR Cq. Gram-negative bacteria had higher agreement (87.6%, 121/138) than Gram-positive bacteria (63.3%, 19/30). Conclusions: This study demonstrates that qPCR Cq results can be directly correlated with traditional urine quantitative culture results and reliably identify the clinically relevant cutoff of 10⁵ CFU/mL for detected uropathogens. Full article

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 10⁷–10¹⁰ pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties. Full article

Microchannel heat sinks (MCHSs) have emerged as an alternative for dissipating high heat rates. However, manufacturing MCHSs can be expensive, so exploring low-cost additive manufacturing using 3D printing is warranted. Before fabrication, the entropy minimization method helps to optimize MCHSs, enhancing their cooling capacity while maintaining their power consumption. We employed this method through computational simulation of laminar water flow in rectangular microchannels ($\mathsf{\mu }$C) and minichannels (mC), considering two heat fluxes (10 and 50 kW/m²). The results showed that the frictional entropy is only appreciable in the smallest and largest channels. These computational results enabled the fabrication of the optimal $\mathsf{\mu }$C and mC, whose experimental implementation validated the computational findings. Moreover, we computationally studied the effect of using rGO-Ag water-based nanofluids as a coolant. In general, a reduction in total entropy generation was observed at a heat flux of 50 kW/m². Although at lower heat flux (10 kW/m²), mC was the best option. Channels with lower heights were more effective at higher heat fluxes (≥50 kW/m²). Our findings offer a cost-effective strategy for fabricating high-performance cooling systems while also highlighting the interplay among heat flux, entropy generation, and nanofluid-enhanced cooling. Full article

Based on admirable porous media performance and the popularity of additive manufacturing technology, gradient porous media are progressively being applied in increasing fields. In this study, convection heat transfer within a square vented cavity, partially occupied by two copper metal foam layers of $10$ and $20$ PPI saturated with nanofluid, was assessed numerically. The left wall was heated uniformly and non-uniformly by applying multi-frequency spatial heating following a sinusoidal function. Governing equations, including continuity, the Darcy–Brinkmann–Forchheimer model, and local thermal non-equilibrium energy equations, were adopted and solved by employing the finite volume method. The influences of relevant parameters, including nanoparticle concentrations $\left(0\%\le \phi \le 10\%\right)$, Reynolds number ($1\le Re\le 100$), inlet and outlet port aspect ratios $\left(0.1\le D/H\le 0.4\right)$, three outlet vent opening locations ($\left(S_o=0\right)$ left, ($S_o=\left(H/2-D/2\right)$) middle, and ($S_o=\left(H-D\right)$) right), sinusoidal offset temperature (${\theta }_o=0.5$, $1$), frequency ($f=1, 3, 5$), and amplitude ($A=0-1$), were examined. The results demonstrate that flow and heat transfer fields are impacted mainly by these parameters. Streamlines are more intensified at the upper-left corner when the outlet opening vent is shifted towards the right-corner upper wall. Fluid- and solid-phase Nusselt number increases $Re$, $D/H$, ${\theta }_o$, $A$, and $f$ are raised, specifically when $A\ge 0.3$. The Nusselt number remains constant when the frequency is raised from $3$ to $5$, definitely when $D/H\ge 0.25$. In uniform and non-uniform heating cases, the Nusselt number of both phases remains constant as the outlet port is shifted right for $Re\le 10$ and slightly for higher $Re$ as the outlet vent location is translated from left to right. Full article

Water, acetone, and TiO₂/nano-silver water (NSW) nanofluids were investigated as working fluids in loop thermosyphon battery thermal management systems (LTBMS) under simulated electric vehicle (EV) conditions to evaluate scalability and robustness across inclinations (0° to 60°) and ambient temperatures (−10 °C to 20 °C). Experimental conditions were established with 60 °C as the reference temperature, corresponding to the onset of battery thermal runaway, to ensure relevance to critical thermal management scenarios. Results indicate that LTBMS A maintained battery cell temperatures at 50.4 °C with water and 31.6 °C with acetone under a 50 W heat load. In contrast, LTBMS B achieved cell temperatures of 41.8 °C with water and 42.8 °C with 0.01 vol% TiO₂ nanofluid, however, performance deteriorated at higher nanofluid concentrations due to increased viscosity and related thermophysical constraints. In heating mode, LTBMS A elevated cell temperatures by 16 °C at an ambient temperature of −10 °C using acetone, while LTBMS B attained 52–55 °C at a 100 W heat load with nanofluids. The lightweight LTBMS design demonstrated superior thermal performance compared to conventional air-cooling systems and performance comparable to liquid-cooling systems. Pure water proved to be the most effective working fluid, while nanofluids require further optimization to enhance their practical applicability in EV thermal management. Full article

This study presents an experimental evaluation of the thermal and exergetic performance of an air-to-air heat pipe heat exchanger using a cobalt oxide (Co₃O₄)-based non-Newtonian nanofluid, with the additional incorporation of carbon black (CB). Nanofluids were synthesized via a two-step method and tested under turbulent flow conditions across varying Reynolds numbers. The results demonstrated that increasing the Co₃O₄ nanoparticle concentration and adding CB substantially improved both the thermal and exergetic performance compared to deionized water. Specifically, maximum thermal efficiency improvements of 62.7% and 75.4% were recorded for nanofluids containing 1% and 2% Co₃O₄, respectively. The addition of CB further enhanced the thermal efficiency, achieving a maximum improvement of 79.2%. Furthermore, the maximum reduction in thermal resistance reached 61.4% with CB incorporation, while the 2% Co₃O₄ nanofluid achieved a maximum decrease of 50.2%. The use of nanofluids led to a significant reduction in exergy loss, with exergy-saving efficiencies reaching up to 33.6%. These findings highlight the considerable potential of Co₃O₄- and CB-based hybrid nanofluids in advancing waste heat recovery technologies and enhancing the thermodynamic performance of air-to-air heat pipe heat exchanger systems. Full article

This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. The Mountain Gazelle Optimizer is a recently developed metaheuristic algorithm that simulates the foraging behaviors of Mountain Gazelles. However, it suffers from premature convergence due to an imbalance between its exploration and exploitation mechanisms. A two-step improvement procedure is implemented to enhance the overall search efficiency of the original algorithm. The first step concerns substituting uniformly random numbers with chaotic numbers to refine the solution quality to better standards. The second step is to develop a novel manipulation equation that integrates different variants of quasi-dynamic oppositional learning search schemes, guided by a novel intelligently devised adaptive switch mechanism. The efficiency of the proposed algorithm is evaluated using the challenging benchmark functions from various CEC competitions. Finally, the thermo-economic design of a shell-and-tube heat exchanger operated with different nanoparticles is solved by the proposed improved metaheuristic algorithm to obtain the optimal design configuration. The predictive results indicate that using water + SiO₂ instead of ordinary water as the refrigerant on the tube side of the heat exchanger reduces the total cost by 16.3%, offering the most cost-effective design among the configurations compared. These findings align with the demonstration of how biologically inspired metaheuristic algorithms can be successfully applied to engineering design. Full article

We use Co doping to alter the magnetic relaxation dynamics in superparamagnetic nanofluids made of 18 nm average diameter Fe₃O₄ nanoparticles immersed in Isopar M. Ac-susceptibility data recorded at different frequencies and temperatures, χ″vs. T|_f, reveals a major (~100 K) increase in the superspin blocking temperature of the Co_0.2Fe_2.8O₄-based fluid (CFO) compared to its Fe₃O₄ counterpart (FO). We ascribe this behavior to the strengthening of the interparticle magnetic dipole interactions upon Co doping, as demonstrated by the relative χ″-peak temperature variation per frequency decade $\mathsf{\Phi }={\displaystyle \frac{∆\mathrm{T}}{\mathrm{T}·∆\mathrm{l}\mathrm{o}\mathrm{g}\left(\mathrm{f}\right)}}$, which decreases from Φ~0.15 in FO to Φ~0.025 in CFO. In addition, χ″vs. T|_f datasets from the CFO fluid reveal two magnetic events at temperatures T_p1 = 240 K and T_p2 = 275 K, both above the fluid’s freezing point (T_F = 197 K). We demonstrate that the physical rotation of the nanoparticles within the fluid, the Brown mechanism, is entirely responsible for the collective superspin relaxation observed at T_p1, whereas the Néel mechanism, the superspin flip across an energy barrier within the particle, is dominant at T_p2. We confirm this finding through fits of models that describe the temperature dependence of the relaxation time via the two mechanisms: ${\mathsf{\tau }}_{\mathrm{B}}(\mathrm{T})={\displaystyle \frac{3{\mathsf{\eta }}_0{\mathrm{V}}_{\mathrm{H}}}{{\mathrm{k}}_{\mathrm{B}}\mathrm{T}}}\exp \left[{\displaystyle \frac{{\mathrm{E}}^{\prime }}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{{\mathrm{T}}_0}^{\prime }\right)}}\right]$ and ${\mathsf{\tau }}_{\mathrm{N}}\left(\mathrm{T}\right)={\mathsf{\tau }}_0\exp \left[{\displaystyle \frac{{\mathrm{E}}_{\mathrm{B}}}{{\mathrm{k}}_{\mathrm{B}}\left(\mathrm{T}-{\mathrm{T}}_0\right)}}\right]$. The best fits yield ${\mathsf{\gamma }}_0={\displaystyle \frac{3{\mathsf{\eta }}_0{\mathrm{V}}_{\mathrm{H}}}{{\mathrm{k}}_{\mathrm{B}}}}$= 1.5 × 10⁻⁸ s·K, E′/k_B = 7 03 K, and T₀′ = 201 K for the Brown relaxation, and E_B/k_B = 2818 K and T₀ = 143 K for the Néel relaxation. Full article

A minichannel heat sink combining flow boiling heat transfer with nanofluid is an ideal solution for the long-term cooling of high-power equipment. In the present paper, three mass fractions for 0.01 wt%, 0.05 wt%, and 0.1 wt% graphene/R141b and Al₂O₃/R141b nanofluids are prepared by ultrasonic vibration. The flow boiling heat transfer performance for graphene/R141b and Al₂O₃/R141b nanofluids was contrastively investigated in a 3D printing 10-minichannel heat sink with a single channel dimension of 198 mm × 1.5 mm × 1.5 mm. The results indicate that the heat transfer performance of graphene/R141b and Al₂O₃/R141b nanofluids are enhanced after adding nanoparticles in pure R141b, and the maximum average heat transfer coefficients of graphene/R141b and Al₂O₃/R141b nanofluids, respectively, increase by 35.4% and 31.7% compared with that of pure R141b. The heat transfer performance of graphene/R141b and Al₂O₃/R141b nanofluids increases nonlinearly with the increase in mass concentration; the heat transfer coefficient reaches its maximum at the mass concentration of 0.02 wt%, and then, it decreases slightly, which is mainly caused by nanoparticle deposition, leading to silted channel surface cavities during the flow boiling experiment. Moreover, it has been discovered that the heat transfer coefficient of graphene/R141b is larger than that of Al₂O₃/R141b under the same conditions. The average heat transfer coefficient of graphene/R141b increased by 19.7% compared with that of Al₂O₃/R141b. The main reason for this is that graphene nanosheets have a larger contact area with the liquid working medium compared with nanoparticle Al₂O₃, and the graphene/R141b thermal conductivity is also significantly higher than that of Al₂O₃/R141b nanofluids. The research results can provide a basis for the practical application of nanofluids in heat sinks. Full article

This study explores the impact of using water-Al₂O₃ nanofluids, at different nanoparticle concentrations, in solar thermal collectors for solar cooling applications. Improving the seasonal energy performance of solar cooling systems is a current research priority, and this work investigates whether nanofluids can significantly enhance system efficiency compared to traditional heat transfer fluids. A transient simulation was carried out using a dynamic model developed in TRNSYS (TRANsient SYstem Simulation), evaluating the system performance throughout the cooling season. The results show that in July, under low volumetric flow conditions and with nanoparticle concentrations of 0.6% and 0.3%, the solar fraction reaches a maximum value of 1. Using a nanofluid at 0.6% concentration leads to significantly higher fractional energy savings compared to pure water. Despite increased pumping energy, the overall energy savings—which include the contribution from an auxiliary boiler—exceed 80% when nanofluids are used. This study goes beyond previous work by providing a dynamic, system-level simulation of nanofluid-enhanced solar cooling performance under realistic operating conditions. The findings demonstrate the practical potential of nanofluids as a valid and more energy-efficient alternative in solar thermal applications. Full article

Nanoparticle additives are used to increase the cooling efficiency of cutting fluids in machining. In this study, changing dynamic viscosity values depending on the addition of nanoparticles to cutting oils was investigated. Mono nanofluids were prepared by adding hBN (hexagonal boron nitride), ZnO, MWCNT (multi-walled carbon nanotube), TiO₂, and Al₂O₃ as nanoparticles, hybrid nanofluids were prepared by using two types of nanoparticles (ZnO + MWCNT, hBN + MWCNT etc.), and ternary nanofluids were prepared by using three types of nanoparticles. GPR (Gaussian process regression) was used to estimate unmeasured dynamic viscosity values using the dynamic viscosity values measured for different temperatures. Dynamic viscosity results are a precise determination (R² = 1). An augmented dataset was obtained by adding the dynamic viscosity values estimated with high accuracy. A fitness function based on dynamic viscosity and nanoparticle unit costs was proposed for the cost analysis. With the help of the proposed fitness function, it was observed that the best performing nanoparticles were the ZnO and ZnO hybrid mixtures according to different dynamic viscosity and cost effects. The study showed that the most suitable nanofluid selection focused on performance and cost could be made without performing experiments under various operating conditions by increasing the limited experimental measurements with strong GPR estimates and using the proposed fitness function. Full article