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Abstract This research explores the feasibility of using a nanocomposite from multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) for thermal engineering applications. The hybrid nanocomposites were suspended in water at various volumetric concentrations. Their heat transfer and pressure drop characteristics were analyzed using computational fluid dynamics and artificial neural network models. The study examined flow regimes with Reynolds numbers between 5000 and 17,000, inlet fluid temperatures ranging from 293.15 to 333.15 K, and concentrations from 0.01 to 0.2% by volume. The numerical results were validated against empirical correlations for heat transfer coefficient and pressure drop, showing an acceptable average error. The findings revealed that the heat transfer coefficient and pressure drop increased significantly with higher inlet temperatures and concentrations, achieving approximately 45.22% and 452.90%, respectively. These results suggested that MWCNTs-GNPs nanocomposites hold promise for enhancing the performance of thermal systems, offering a potential pathway for developing and optimizing advanced thermal engineering solutions.

1D and 2D carbon nanomaterials such as multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were investigated numerically. The thermophysical properties of water and nanofluids using MWCNTs in different outer diameters (ODs) and GNPs in different surface areas (SSA) were measured at an inlet temperature of 303.15 K and 0.1wt.%. The 3D geometry was solved under a fully developed turbulent flow of 6000 ≤ Re ≤ 16,000 using the model of k-ω SST via (ANSYS FLUENT 2022R2) software. Four numerical networks, Polyhedra, Polyhexacore, Hexacore, and Tetrahedral, were optimized. Moreover, seven parameters were discussed, namely wall surface temperature (Tw), heat transfer coefficient (htc), average Nusselt number (Nuavg), friction factor (f), pressure drop (ΔP), and total thermal performance index (PIth). Polyhexacore was the main grid over Polyhedra, Hexacore, and Tetrahedral with the average error (Dittus-Boelter: 2.754%, Gnielinski: 2.343%, Blasius: 1.441%, and Petukhov: 0.640%). Heat transfer increased by 18.38% with GNPs-300, 22.05% with GNPs-500, 23.25% with GNPs-750, 13.63% with CNT < 8 nm, and 11.42% with CNT 20–30 nm, relative to H2O at Re = 16,000. Pressure drop increased by about 42.01% with GNPs-300, 45.16% with GNPs-500, 44.84% with GNPs-750, 36.72% with CNT < 8 nm, and 34.39% with CNT 20-30 nm.

Coandă-effect-based collection stands out as the foremost technology in polymetallic-nodule mining due to the absence of direct contact between the collector and the ocean floor. Yet, this collection method disturbs the ocean floor, and minimizing such disturbance is crucial from an environmental viewpoint. To this end, a solid understanding of the interplay between the collector and the sediment bed is required. Therefore, we carried out a series of small-scale experiments, where a collector drives over a subaqueous clayey bed. These experiments provide the very first quantitative data on cohesive sediment erosion caused by a moving Coandă-effect-based collector, as well as on turbidity currents generated behind the collector head. This paper discusses the observations and findings derived from these experiments. Our findings reveal a logarithmic relationship between erosion depth and the flow impinging force applied on the clayey bed. An increased flow rate in the collection duct results in a slower turbidity current generated behind the collector head. This study enhances the ability to forecast sediment erosion caused by Coandă-effect-based collectors, offering the possibility to optimize the collector operational conditions and minimize the magnitude of the resulting sediment plumes.

The flat-plate solar collector (FPSC) three-dimensional (3D) model was used to numerically evaluate the energy and economic estimates. A laminar flow with 500 ≤ Re ≤ 1900, an inlet temperature of 293 K, and a solar flux of 1000 W/m2 were assumed the operating conditions. Two mono nanofluids, CuO-DW and Cu-DW, were tested with different shapes (Spherical, Cylindrical, Platelets, and Blades) and different volume fractions. Additionally, hybrid nanocomposites from CuO@Cu/DW with different shapes (Spherical, Cylindrical, Platelets and Blades), different mixing ratios (60% + 40%, 50% + 50% and 40% + 60%) and different volume fractions (1 volume%, 2 volume%, 3 volume% and 4 volume%) were compared with mono nanofluids. At 1 volume% and Re = 1900, CuO-Platelets demonstrated the highest pressure drop (33.312 Pa). CuO-Platelets achieved the higher thermal enhancement with (8.761%) at 1 vol.% and Re = 1900. CuO-Platelets reduced the size of the solar collector by 25.60%. Meanwhile, CuO@Cu-Spherical (40:60) needed a larger collector size with 16.69% at 4 vol.% and Re = 1900. CuO-Platelets with 967.61, CuO – Cylindrical with 976.76, Cu Platelets with 983.84, and Cu-Cylindrical with 992.92 presented the lowest total cost. Meanwhile, the total cost of CuO – Cu – Platelets with 60:40, 50:50, and 40:60 was 994.82, 996.18, and 997.70, respectively.

Flat plate solar collectors (FPSCs) are the most often used as solar collectors due to their easiness of installation and usage. The current research investigates the energy efficiency of FPSC using different mass concentration with varied base fluids containing Graphene nanofluids (T-Gr). Mass concentration of 0.1%-wt., 0.075%-wt., 0.050%-wt. and 0.025%-wt. were mixed with ethylene glycol (EG) and distilled water (DW) in different rations. The operating conditions were volumetric flowrate (1.5, 1 and 0.5) LPM 50 °C-input fluid temperature and 800 W/m2-global solar irradiation. Scanning electron microscope (SEM) and energy dispersive X-ray (EDX) were used to synthesize the thermally treated nanomaterial. The theoretical investigation indicated that using T-Gr nanosuspensions increased the FPSC efficiency in comparison with the host fluid for all examined mass concentrations and volumetric flowrates. In quantitative terms, the maximum thermal effectiveness improvement for the EG, (DW:70 + EG:30) and DW:EG (DW:50 + EG:50) and using flowrates of (1.5, 1 and 0.5) LPM were 12.54%, 12.46% and 12.48%. In addition, the research results pointed that the essential parameters (i.e., loss energy (FRUL)) and gain energy (FR(τα)) of the T-Gr nanofluids were increased significantly.

1D and 2D carbon nanomaterials such as multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were investigated numerically. The thermophysical properties of water and nanofluids using MWCNTs in different outer diameters (ODs) and GNPs in different surface areas (SSA) were measured at an inlet temperature of 303.15 K and 0.1wt.%. The 3D geometry was solved under a fully developed turbulent flow of 6000 ≤ Re ≤ 16,000 using the model of k-ω SST via (ANSYS FLUENT 2022R2) software. Four numerical networks, Polyhedra, Polyhexacore, Hexacore, and Tetrahedral, were optimized. Moreover, seven parameters were discussed, namely wall surface temperature (Tw), heat transfer coefficient (htc), average Nusselt number (Nuavg), friction factor (f), pressure drop (ΔP), and total thermal performance index (PIth). Polyhexacore was the main grid over Polyhedra, Hexacore, and Tetrahedral with the average error (Dittus-Boelter: 2.754%, Gnielinski: 2.343%, Blasius: 1.441%, and Petukhov: 0.640%). Heat transfer increased by 18.38% with GNPs-300, 22.05% with GNPs-500, 23.25% with GNPs-750, 13.63% with CNT < 8 nm, and 11.42% with CNT 20–30 nm, relative to H2O at Re = 16,000. Pressure drop increased by about 42.01% with GNPs-300, 45.16% with GNPs-500, 44.84% with GNPs-750, 36.72% with CNT < 8 nm, and 34.39% with CNT 20-30 nm. [ABSTRACT FROM AUTHOR]

The effectiveness of a flat-plate solar collector was studied by using SiO2, Al2O3, Graphene, and graphene nanoplatelets nanofluids with distilled water as the working fluids. The energy efficiency was theoretically compared using MATLAB programming. The prepared carbon and metallic oxides nanomaterials were structurally and morphologically characterized via field emission scanning electron microscope. The study was conducted under different operating conditions such as different volume fractions (0.25%, 0.5%, 0.75% and 1%), fluid mass flow rate (0.0085, 0.017, and 0.0255 kg/s), input temperatures (30, 40, and 50 °C), and solar irradiance (500, 750, and 1000 W/m2). Nanofluids showed better thermophysical properties compared to standard working fluids. With the addition of the nanofluids SiO2, Al2O3, Gr and GNPs to the FPSC the highest efficiency of 64.45%, 67.03%, 72.45%, and 76.56% respectively was reached. The results suggested that nanofluids made from carbon nanostructures and metallic oxides can be used in solar collectors to increase the parameters of heat absorbed/loss compared to water only usage.

This study aims to develop an adaptive mesh refinement (AMR) algorithm combined with Cut-Cell IBM using two-stage pressure–velocity corrections for thin-object FSI problems. To achieve the objective of this study, the AMR-immersed boundary method (AMR-IBM) algorithm discretizes and solves the equations of motion for the flow that involves rigid thin structures boundary layer at the interface between the structure and the fluid. The body forces are computed in proportion to the fraction of the solid volume in the IBM fluid cells to incorporate fluid and solid motions into the boundary. The corrections of the velocity and pressure is determined by using a novel simplified marker and cell scheme. The new developed AMR-IBM algorithm is validated using a benchmark data of fluid past a cylinder and the results show that there is good agreement under laminar flow. Simulations are conducted for three test cases with the purpose of demonstration the accuracy of the AMR-IBM algorithm. The validation confirms the robustness of the new algorithms in simulating flow characteristics in the boundary layers of thin structures. The algorithm is performed on a staggered grid to simulate the fluid flow around thin object and determine the computational cost.

For companies, notably in the realms of energy and power supply, the essential requirement for highly efficient thermal transport solutions has become a serious concern. Current research highlighted the use of metallic oxides and carbon-based nanofluids as heat transfer fluids. This work examined two carbon forms (PEG@GNPs & PEG@TGr) and two types of metallic oxides (Al2O3 & SiO2) in a square heated pipe in the mass fraction of 0.1 wt.%. Laboratory conditions were as follows: 6401 ≤ Re ≤ 11,907 and wall heat flux = 11,205 W/m2. The effective thermal–physical and heat transfer properties were assessed for fully developed turbulent fluid flow at 20–60 °C. The thermal and hydraulic performances of nanofluids were rated in terms of pumping power, performance index (PI), and performance evaluation criteria (PEC). The heat transfer coefficients of the nanofluids improved the most: PEG@GNPs = 44.4%, PEG@TGr = 41.2%, Al2O3 = 22.5%, and SiO2 = 24%. Meanwhile, the highest augmentation in the Nu of the nanofluids was as follows: PEG@GNPs = 35%, PEG@TGr = 30.1%, Al2O3 = 20.6%, and SiO2 = 21.9%. The pressure loss and friction factor increased the highest, by 20.8–23.7% and 3.57–3.85%, respectively. In the end, the general performance of nanofluids has shown that they would be a good alternative to the traditional working fluids in heat transfer requests.

Covalent-functionalized graphene nanoplatelets (CF-GNPs) inside a circular heated-pipe and the subsequent pressure decrease loss within a fully developed turbulent flow were discussed in this research. Four samples of nanofluids were prepared and investigated in the ranges of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%. Different tools such as field emission scanning electron microscopy (FE-SEM), ultraviolet-visible-spectrophotometer (UV-visible), energy-dispersive X-ray spectroscopy (EDX), zeta potential, and nanoparticle sizing were used for the data preparation. The thermophysical properties of the working fluids were experimentally determined using the testing conditions established via computational fluid dynamic (CFD) simulations that had been designed to solve governing equations involving distilled water (DW) and nanofluidic flows. The average error between the numerical solution and the Blasius formula was ~4.85%. Relative to the DW, the pressure dropped by 27.80% for 0.025 wt.%, 35.69% for 0.05 wt.%, 41.61% for 0.075 wt.%, and 47.04% for 0.1 wt.%. Meanwhile, the pumping power increased by 3.8% for 0.025 wt.%, 5.3% for 0.05 wt.%, 6.6% for 0.075%, and 7.8% for 0.1 wt.%. The research findings on the cost analysis demonstrated that the daily electric costs were USD 214, 350, 416, 482, and 558 for DW of 0.025 wt.%, 0.05 wt.%, 0.075 wt.%, and 0.1 wt.%, respectively.

Numerical studies were performed to estimate the heat transfer and hydrodynamic properties of a forced convection turbulent flow using three-dimensional horizontal concentric annuli. This paper applied the standard k–ε turbulence model for the flow range 1 × 104 ≤ Re ≥ 24 × 103. A wide range of parameters like different nanomaterials (Al2O3, CuO, SiO2 and ZnO), different particle nanoshapes (spherical, cylindrical, blades, platelets and bricks), different heat flux ratio (HFR) (0, 0.5, 1 and 2) and different aspect ratios (AR) (1.5, 2, 2.5 and 3) were examined. Also, the effect of inner cylinder rotation was discussed. An experiment was conducted out using a field-emission scanning electron microscope (FE-SEM) to characterize metallic oxides in spherical morphologies. Nano-platelet particles showed the best enhancements in heat transfer properties, followed by nano-cylinders, nano-bricks, nano-blades, and nano-spheres. The maximum heat transfer enhancement was found in SiO2, followed by ZnO, CuO, and Al2O3, in that order. Meanwhile, the effect of the HFR parameter was insignificant. At Re = 24,000, the inner wall rotation enhanced the heat transfer about 47.94%, 43.03%, 42.06% and 39.79% for SiO2, ZnO, CuO and Al2O3, respectively. Moreover, the AR of 2.5 presented the higher heat transfer improvement followed by 3, 2, and 1.5.

Increasing environmental and energy concerns constitutes an encouraging development of future automobiles and autonomous vehicles that utilize cleaner and renewable energy. Although primarily an aerodynamic device, car rear spoilers experience various vibrations and limited attention has been given to exploiting these vibrations for generating alternative energy. This paper aims to investigate the potential of using the flow-induced vibration of rear spoilers on automobiles for energy harvesting. To that end, a fluid-structure interaction analysis of an inverted NACA 2408 spoiler behind an Ahmed body was undertaken. An Arbitrary Lagrangian-Eulerian flow solver was coupled to a non-linear structural dynamic solver using a commercial software application. The vibrations of the rear spoiler placed at 0, 50 and 100 mm below the top face of the Ahmed body under Reynolds number Re = 2.7 × 106 were simulated. It was found that the vibration of the rear spoiler at the highest elevation showed the largest amplitudes and strain, but the vibration of the rear spoiler at the lowest elevation offers an extended period of vibration before reaching a steady state. With the rear spoiler positioned at the highest elevation, a vibration frequency of 70 Hz and a steady-state principal strain of 350  may be achieved. These vibration levels compared well with previous investigation to sufficiently charge storage capacitors for wireless transmitters. The rear spoiler vibration may offer potential means for energy harvesting, and warrants further experiments under actual driving conditions.

High-strength concrete (HSC) is highly applicable to the construction of heavy structures. However, shear strength (Ss) determination of HSC is a crucial concern for structure designers and decision makers. The current research proposes the novel models based on the combination of adaptive neuro-fuzzy inference system (ANFIS) with several meta-heuristic optimization algorithms, including ant colony optimizer (ACO), differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO), to predict the Ss of HSC slender beam. The proposed models were constructed using several input combinations incorporating several related dimensional parameters such as effective depth of beam (d), shear span (a), maximum size of aggregate (ag), compressive strength of concrete (fc), and percentage of tension reinforcement (ρ). To assess the impact of the non-homogeneity of the dataset on the prediction result accuracy, two possible modeling scenarios, (i) non-processed (initial) dataset (NP) and (ii) pre-processed dataset (PP), are inspected by several performance indices. The modeling results demonstrated that ANFIS-PSO hybrid model attained the best prediction accuracy over the other models and for the pre-processed input parameters. Several uncertainty analyses were examined (i.e., model, variables, and data), and results indicated predicting the HSC shear strength was more sensitive to the model structure uncertainty than the input parameters.