Frictional Pressure Drop and Cost Savings for Graphene Nanoplatelets Nanofluids in Turbulent Flow Environments

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.

Robust and universal predictive models for frictional pressure drop during two-phase flow in smooth helically coiled tube heat exchangers

There is a lack of well-verified models in the literature for the prediction of the frictional pressure drop (FPD) in the helically coiled tubes at different conditions/orientations. In this study, the robust and universal models for estimating two-phase FPD in smooth coiled tubes with different orientations were developed using several intelligent approaches. For this reason, a databank comprising 1267 experimental data samples was collected from 12 independent studies, which covers a broad range of fluids, tube diameters, coil diameters, coil axis inclinations, mass fluxes, saturation temperatures, and vapor qualities. The earlier models for straight and coiled tubes were examined using the collected database, which showed absolute average relative error (AARE) higher than 21%. The most relevant dimensionless groups were used as models’ inputs, and the neural network approach of multilayer perceptron and radial basis functions (RBF) were developed based on the homogenous equilibrium method. Although both intelligent models exhibited excellent accuracy, the RBF model predicted the best results with AARE 4.73% for the testing process. In addition, an explicit FPD model was developed by the genetic programming (GP), which showed the AARE of 14.97% for all data points. Capabilities of the proposed models under different conditions were described and, the sensitivity analyses were performed.

Experimental Investigation on Flow Boiling Heat Transfer and Pressure Drop of a Low-GWP Refrigerant R1234ze(E) in a Horizontal Minichannel

To protect the environment, a new low-GWP refrigerant R1234ze(E) was created to substitute R134a. However, its flow boiling performances have not received sufficient attention so far, which hinders its popularization to some extent. In view of this, an experimental investigation was carried out in a 1.88 mm horizontal circular minichannel. The saturation pressures were maintained at 0.6 and 0.7 MPa, accompanied by mass flux within 540–870 kg/m2 s and heat flux within 25–65 kW/m2. For nucleate boiling, a larger heat flux brings about a larger heat transfer coefficient (HTC), while for convective boiling, the mass flux and vapor quality appear to take the lead role. The threshold vapor quality of different heat transfer mechanisms is around 0.4. Additionally, larger saturation pressure results in large HTC. As for the frictional pressure drop (FPD), it is positively influenced by mass flux and vapor quality, while negatively affected by saturation pressure, and the influence of heat flux is negligible. Furthermore, with the measured data, several existing correlations are compared. The results indicate that the correlations of Saitoh et al. (2007) and Müller-Steinhagen and Heck (1986) perform best on flow boiling HTC and FPD with mean absolute deviations of 5.4% and 10.9%.

Soft-ANN based correlation for air-water two-phase flow pressure drop estimation in a vertical mini-channel

In this paper, an Artificial Neural Network soft matrix correlation to estimate the pressure drop of air-water two-phase flow is developed. The applicability of the model is extended by using dimensionless physical numbers as inputs (Air-Reynolds number, Water-Reynolds number, and the ratio of Air Inertial Forces to Water Inertial Forces), so the model can be implemented for vertical pipes with the proper combination of diameter-velocity-density-viscosity allowing estimations of dimensional numbers within the range of: Air-Reynolds numbers (430–6100), Water-Reynolds number (2400–7200), and Air-Water-Inertial forces ratio (1.6–1834), including the diameter range from 3 to 28 mm. Experimental measurements of frictional pressure drop of water-air mixtures are determined at different conditions. A search of the most suitable density, viscosity, and friction models was conducted and used in the model. The performance of the proposed ANN correlation is compared against published expressions showing good approximation to experimental data; results indicate that the most used correlations are within a mean relative error ( mre) of 23.9–30.7%, while the proposed ANN has a mre = 0.9%. Two additional features are discussed: i) the applicability and generality of the ANN using untrained data, ii) the applicability in laminar, transitional, and turbulent flow regimen. To take the approach beyond a robust performance mapping, the methodology to translate the ANN into a programmable equation is presented.

Analysis of Friction Factor of Two-Phase Flow in Helically Coiled Tubes

With compact structure and enhanced heat transfer capacity, helical-coiled once through steam generators (HTSGs) are widely used in the small modular reactors (SMRs). Nevertheless, the inside centrifugal forces make the flow more complicated, and increase the frictional pressure drop, which is closely related to the dual test of alternating thermal stress and flow instability. Therefore, the analysis of the friction factor in helically coiled tubes is significant to the efficient and safe operation of HTSGs. While the friction factor of single-phase flow in helically coiled tubes was fully studied and extensive correlations have been validated by a large amount of experimental data, the friction factor of two-phase flow still lacks feasible prediction due to its much more complexity. The existed correlations of two-phase flow in helically coiled tubes are mostly based on specified experimental parameters, so the applicable range is limited. Few scholars have tried to extend these correlations to broader applicability, but the trivial applicable range is unsuitable for program development or engineering design, which needs an accurate prediction of friction factor in a wider range. In this paper, existing frictional pressure drop correlations are investigated. The accuracy of single-phase frictional pressure drop correlations is verified through the comparison of calculation results. Since the known experimental data cannot cover a wide range of parameters, two assumptions are proposed, and the rationality is verified through the existing experimental data and calculation analysis. Based on the two assumptions and calculation, a set of calculation correlations for frictional pressure drop of two-phase flow in helically coiled tubes are proposed. The accuracy of this calculation model is validated by experimental data. The scope of application of this model is: D / d = 15–100, P = 0.12–6.3MPa, G = 200–1500kg / m2s, which is sufficient to support the design and operation of steam generators and the development of the simulation programs.