The uneven particle distribution has posed a significant challenge to hydraulic fracturing operational efficiency. Previous investigations aimed to uncover the complex proppant behavior during injection. However, there still exist gaps in the fundamental proppant-proppant colliding interaction mechanisms and proppant deposition. The vigorous proppant-proppant and proppant-wall colliding behavior pose a challenge in understanding the overall slurry behavior due to the formation of turbulent eddies and vortices, especially during impulse injection in fractures. This study aims to shed light on proppant deposition behavior during impulse injection in various test conditions, namely inclusive and exclusive of gravitational effects. The injection was conducted using a microfracture model positioned in horizontal and vertical configurations to control the gravitational effect on deposition. The coupled application of high-speed imaging and particle image velocimetry (PIV) provided a space and time-resolved investigation of the flow patterns in the three-way proppant-fluid-fracture complex interaction problem. Additionally, the study utilized binary thresholding to quantify the coupled effect of injection solution viscosity and fracture angle on the deposition mechanisms, especially the uneven proppant distribution. The PIV investigation highlighted various unique vortex formation patterns at different fracture angles and solution viscosities. The horizontal (minimal gravitational effect) pure water (control-no viscosity alteration) experiments yielded a chaotic and unrepeatable deposition process with a spraying effect at the fracture junction. As the solution viscosity increased, injection stability was achieved by observing proppant clustering behavior indicative of potential fracture clogging behavior at the highest solution viscosity. No observable patterns of a specific proppant ratio deposition were made in the horizontal experiment. On the other hand, the experiments under gravitational effects indicated a minimal difference in deposition between pure water and the intermediate viscosity solution. As the solution viscosity was increased, particle agglomeration was observed, coupled with increased fracture deposition. Lastly, a few experiments highlighted higher deposition in downstream fractures when compared to upstream, which agrees with previously published numerical work.

A significant challenge to hydraulic fracturing is premature particle settling and uneven particle distribution in a formation during injection. Even though various research work were conducted on particle transport, gaps still exist in the fundamental proppant–proppant interaction mechanisms. This study utilizes an experimental approach to understand proppant interactions during gravitational settling in various test conditions. High-speed imaging coupled with particle image velocimetry (PIV) was implemented to provide a space and time-resolved investigation of multi-proppant interactions. The multi-perspective experimental study uncovered the coupled effect of viscosity and multi-particle mix ratio on slurry velocity. The PIV analysis highlights unique agglomeration and particle interactive patterns. The results indicate that the mix ratio has a significant effect on proppant interactive behavior and settling characteristics, especially as the solution viscosity increases. This conclusion was drawn from observing no signs of agglomeration in the low viscosity regime, although slight differences in proppant interactions were noted as the mix ratios were altered. On the other hand, the intermediate regime demonstrates formed agglomerates with unique patterns for different viscosity and mix ratios. The observed patterns were quantified using both velocity and proppant concentration analysis. Finally, the results indicate the existence of a reduced velocity condition at a given viscosity and particle mix ratio.

Nucleate boiling is perhaps one of the most efficient cooling methodologies due to its large heat flux with a relatively low superheat. Nucleate boiling often occurs on surfaces oriented at different angles; therefore, understanding the behavior of bubble growth on various surface orientations is of importance. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms on oriented surfaces, a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space- and time-resolved, local liquid temperature distributions surrounding a growing bubble on oriented surfaces that quantify the heat transfer from the superheated liquid layer during bubble growth. The dual tracer laser-induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions within a 0.3 ºC accuracy at a 30 μm resolution. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure, and the growing bubble works as a pump to remove heat from the surface with a temperature difference of up to 10 °C during its growth and departure. The experimental results are compared with data available in the literature to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid.

Boiling heat transfer associated with bubble growth is perhaps one of the most efficient cooling methodologies due to its large latent heat during phase change. Despite the significant advancements, numerous questions remain regarding the fundamentals of bubble growth mechanisms, which is a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space and time -resolved, local liquid temperature distributions surrounding a growing bubble to quantify the heat transfer in the superheated liquid layer during bubble growth. The dual tracer laser -induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions, that will render 3D temperature distributions by combining multiple 2D layers, within a 0.3 C accuracy at a 30 mu m resolution. Two fluorescent dyes, fluorescein and sulforhodamine B, were used to measure transient temperatures, by account of their temperaturesensitive emissions. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure. The growing bubble works as a pump to remove heat from the surface with a peak temperature difference of up to 10 C during its growth and departure. The experimental results were compared with previously reported studies to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid.

In this paper, a multilayer perceptron (MLP)-type artificial neural network model with a back-propagation training algorithm is utilized to model the bubble growth and bubble dynamics parameters in nucleate boiling with a non-uniform electric field. The influences of the electric field on different parameters that describe bubble’s behaviors including bubble waiting time, bubble departure frequency, bubble growth time, and bubble departure diameter are considered. This study models single bubble dynamic behaviors of R113 created on a heater in an inconsistent electric field by utilizing a MLP neural network optimized by four different swarm-based optimization algorithms, namely: Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC) algorithm, and Particle Swarm Optimization (PSO). For evaluating the model effectiveness, the MSE value (Mean-Square Error) of the artificial neural network model with various optimization algorithms is measured and compared. The results suggest that the optimal networks in the two-hidden layer and three-hidden layer models for the bubble departure diameter improve MSE by 33.85% and 35.27%, respectively, when compared with the best response in the one-hidden layer model. Additionally, for bubble growth time, the networks with two hidden layers and three hidden layers have the 44.51% and 45.85% reduction in error, when compared with the network with one hidden layer, respectively. For the departure frequency, the error reduction in the two-layer and three-layer networks is 46.85% and 62.32%, respectively. For bubble waiting time, the best networks in the two hidden-layer and three hidden-layer models improve MSE by 52.44% and 62.27% compared with the best 1HL model response, respectively. Also, the two algorithms of SSA and GWO are able to compete well (comparable MSE) with the PSO and ABC algorithms.

Boiling heat transfer associated with phase change is perhaps one of the most efficient cooling methodologies to manage extreme heat flux due to its large latent heat. Fin structures are used to further increase the magnitude of boiling heat transfer from the heated surface and have shown better performance than flat surface heat sinks. This work aims to experimentally investigate the heat transfer performance of two fin structures, namely regular and modified fins, in a pool boiling facility. The modified hollow fin structure is designed to enhance the regular fin’s heat transfer performance by adding an additional artificial nucleation site. Heat transfer rates and heat transfer coefficients of the two fin structures are estimated in atmospheric pressure conditions using deionized water and compared with the literature. The results show that the regular fin heat sink shows a better heat transfer rate than the plane surface, while the modified fin structure shows higher heat transfer performance than the regular fin. According to the boiling curves of two structures under various subcooling levels, the wall temperature of the modified fin is 2–4 [[EQUATION]] lower than that of the regular fin. This is attributed to the additional nucleation sites on the hollow fin, a better rewetting phenomenon, and therefore a favorable bubble growth and release mechanism. Also, a multilayer perceptron artificial neural network with a back-propagation training algorithm is applied for modeling the bubble departure diameter concerning wall superheat and subcooling level to predict the bubble behavior from the artificial nucleation site. The results show that the network can predict the desired parameter with a precision of R=0.92811.

This study investigates three-dimensional flow over dual particles at various separation distances and Re, mainly focusing on the particle's lift behavior in close proximity. Minimal changes in separation distance can result in various unique flow patterns and a non-linear augmentation in lift coefficient. Additionally, quantifying flow blockage between two particles suggests that repulsion behavior and blockage phenomenon are not mutually inclusive. Transient simulations suggest that periodic vortex shedding is not only a function of Re and separation distance, but they also suggest the existence of multiple combinations of separation distance and Re at which the shedding mechanism is triggered. Evident periodic vortex shedding was quantified at 0.25D and Re = 250, while miniature periodic instabilities at the same Re but for a larger separation distance (0.5D). An observable inverse relationship between lift and separation distance exists, while different patterns were quantified for the variation of lift at different Re.

Boiling heat transfer associated with phase change is perhaps one of the most efficient cooling methodologies to manage extreme heat flux due to its large latent heat. Fin structures are used to further increase the magnitude of boiling heat transfer from the heated surface and have shown better performance than flat surface heat sinks. This work aims to experimentally investigate the heat transfer performance of two fin structures, namely regular and modified fins, in a pool boiling facility. The modified hollow fin structure is designed to enhance the regular fin's heat transfer performance by adding an artificial nucleation site. Heat transfer rates and heat transfer coefficients of the two fin structures are estimated in atmospheric pressure conditions using deionized water and compared with the literature. The results show that the regular fin heat sink shows a better heat transfer rate than the plane surface, while the modified fin structure shows higher heat transfer performance than the regular fin. This is attributed to the additional nucleation sites on the hollow fin, a better rewetting phenomenon, and therefore a favorable bubble growth and release mechanism. Also, a multilayer perceptron artificial neural network with a back-propagation training algorithm is applied for modeling the bubble departure diameter concerning wall superheat and subcooling level to predict the bubble behavior from the artificial nucleation site.

Conservation of energy and reduction of carbon dioxide footprint are among the most crucial global challenges faced by humanity. Dozens of solutions have been so far introduced to address this issue. Nano‐phase change materials (nano‐PCMs) are new emerging technology that are vastly being studied which are believed to minimize energy waste. This study focuses on many aspects of nano‐PCMs with applications in the building industry. It was found that carbon‐based nanoparticles have the greatest thermal properties to be integrated into the PCMs. In this work, a summary of the previous review articles regarding PCM and nano‐PCM following the comparison of various types of nano‐particles in terms of their impact on thermal conductivity enhancement of PCMs is reported. Moreover, other influencial thermophyical properties of nanoparticles such as size, concentration, dispersion, shape, and inclination angle and their effect on thermal properties of PCMs are discussed.

In this paper, a multilayer perceptron (MLP) artificial neural network (ANN) with a back-propagation (BP) training algorithm is applied for modeling thermophysical properties and subcooled flow boiling performance of Al2O3/water nanofluid in a horizontal tube. The influence of nanofluid concentration, heat flux, and flow rate on different thermophysical parameters, including thermal conductivity, thermal conductivity enhancement, vis-cosity, viscosity enhancement, and heat transfer coefficient, are investigated. Specifically, flow boiling of Al2O3/ water nanofluid in a horizontal tube is modeled with the MLP neural network optimized by three novel swarm -based optimization algorithms: namely, Equilibrium Optimizer (EO), Marine Predators Algorithm (MPA), and Slime Mould Algorithm (SMA). To evaluate the effectiveness of different models, the MSE (Mean-Square Error) of the ANN model with varying optimization algorithms is calculated and compared. Additionally, the optimal network and regression values for each parameter are determined. The results show that the applied neural network and optimization algorithms could model the thermal conductivity, thermal conductivity enhancement, and viscosity better than the viscosity enhancement and heat transfer coefficient. The MSE of the best network for the thermal conductivity is 2.693 x 10-7, while the MSE of the best network for the viscosity enhancement is 0.0598. Also, the EO algorithm achieves the best optimization for the first three outputs, thermal conductivity, thermal conductivity enhancement, and viscosity. In comparison, the MPA algorithm extracts the optimal network for the other two outputs, viscosity enhancement, and heat transfer coefficient.

Microalgae have shown tremendous potentials as a feedstock for biofuel productions. Nevertheless, microalgae utilization is not economically sustainable mainly due to the high cost of harvesting. Among existing technologies, mechanical-based harvesting methods are considered the most effective means of microalgae recovery because these methods are highly reliable in separating suspended cultures from the growth medium, and they require no additives. To further advance these technologies in broader areas, a comprehensive review that covers the performance, limitations, and associated costs of these methods is needed. This review presents an inclusive assessment of all mechanical-based methods, particularly centrifugation and filtration, with a complete evaluation of their economic facets. The mechanical-based methods are used as a single-step harvesting process or as a sequence of operations for increased harvesting efficiency. This study reveals that centrifugation offers the utmost cell removal efficiency in a timely manner while having the highest capital and operational cost demands (2.18 $/kg produced oil). Conversely, the sedimentation process is comparatively inexpensive (0.3 $/kg produced oil) and is ideal as the initial step to reduce the load on the following steps, although it is a lengthy process. Flotation has to be utilized in conjunction with filtration to complete cell removal, and it may not be suitable for all agal cultures due to the necessity of chemical additives. The use of these additives essentially increases the maintenance requirements.

The elevated energy demand and high dependency on fossil fuels have directed researchers’ attention to promoting and advancing hydraulic fracturing (HF) operations for a sustainable energy future. Even though previous studies have demonstrated that the proppant suspension and positioning in slickwater play a vital role during the shut-in stage of the HF operations, minimal experimental work has been conducted on the fundamental proppant–proppant interaction mechanisms, especially a complete mapping of the interactions. This study utilizes high-speed imaging to provide a 2D space- and time-resolved investigation of two-particle (proppant models: 2 mm Ø, 2.6 g·cm−3) interactions during gravitational settling in different initial spatial configurations and rheological properties. The mapping facilitates the identification of various interaction regimes and newly observed particle trajectories. Pure water results at a settling particle Reynolds number (Rep) ~ 470 show an unstable particle–particle interaction regime characterized by randomness while altering pure water to a 25% (v/v) water–glycerin mixture (Rep ~ 200) transitions an unstable interaction to a stable prominent repulsion regime where particles’ final separation distance can extend up to four times the initial distance. This indicates the existence of Rep at which the stability of the interactions is achieved. The quantified trajectories indicate that when particles are within minimal proximity, a direct relation between repulsion and Rep exists with varying repulsion characteristics. This was determined by observing unique bottle-shaped trajectories in the prominent repulsion regimes and further highlighted by investigating the rate of lateral separation distance and velocity characteristics. Additionally, a threshold distance in which the particles do not interact (or negligibly interact) and settle independently seems to exist at the normalized 2D lateral separation distance.

In recent years, many studies have been conducted on hybrid solid oxide fuel cell and gas turbine which fed by renewable fuels to reach the high-efficiency power and the low pollution. In this paper, a thermodynamic study based on energy and exergy analyses of a hybrid solid oxide fuel cell and gas turbine hybrid system has been conducted in the presence of reforming with steam, in which the effects of using natural gas and other biofuels including Sewage biogas, Agricultural and Industrial waste biogas, Syngas, Biofuel and Gasified biomass, have been compared to this system. The purpose of this paper is to investigate the effect of each of the mentioned fuels on the performance of individual equipment and the entire system from the thermodynamic perspective. Initially, the modeling and energy balance of all components in the system were carried out in detail. Further, calculating the exergy destruction of all components of the system and the effects of applying different biofuels on the key parameters of the system, including total production power, operating pressure and temperature, compression ratio, the water-to-fuel ratio in the reforming, energy and exergy efficiency, and the amount of irreversibility of each component in the system have been analyzed. To compare more accurately between different fuels, an equal amount of each fuel is considered, and the effect of each fuel system on the hybrid system is analyzed by calculating the operating parameters of the system. By studying the results of modeling, it was determined that natural gas with 879.03 kW produced the highest production capacity and Gasified biomass with 45.44 kW has the least amount of power generation. By studying the thermodynamics of the system, if it is fed with Natural gas, the highest exergy destruction (687.45 kW) occurs and the lowest total exergy destruction rate is related to the system fed by gasified biomass (101.12 kW). Comparing the fuels, it was concluded that the electrical efficiency of the system fed with natural gas has the highest efficiency (72.71%) and Biofuel has the lowest total electrical efficiency (56.6%). Furthermore, by examining the exergy efficiency of the whole system, it was found that Natural gas with the highest efficiency of 61.29% and Gasified biomass with 46.06% the lowest exergy efficiency can be detected. Finally, the effect of the percentage combination of each fuel on the performance of all equipment from the perspective of energy and exergy has been studied. (C) 2020 Elsevier Ltd. All rights reserved.

The majority of existing water is saline water and it is crucial to find approaches and technologies to desalinate water in an efficient and reliable manner. Solar energy can be applied in desalination systems in order to provide required heat or generate needed electricity by using PV modules. Applying solar energy instead of fossil fuels leads to more environmentally benign technologies in desalinating saline water. Due to the severe worldwide water crisis, precise comprehension of desalination methods can pave the way toward potable water achievement at reasonable cost. In this paper, a comprehensive literature review is accomplished on various types of desalination systems and applications of solar energy in these technologies. Based on the reviewed studies, solar energy is a preferable source of energy for fresh water production with lower greenhouse gases emission and high operation reliability.

Exergy approach is essential for the design and possible operation of the absorption system with energy loss minimization. Operational limitations of absorption refrigeration system are due to Gibbs phase rules-base. Optimized operational decision rules are extracted from machine learning algorithm C4.5 which is suitable for different exergy evaluation methods and reference conditions. This study investigates proposed and investigated classification models for the same which are based on different thermodynamic features and operational design data. ANN model is used to predict the thermodynamic properties of the working fluids using improved thermodynamics properties of data patterns. Mathematical expressions are formulated from validated artificial neural network for predicting specific enthalpy and entropy of water–lithium bromide solution. Exergy design data are estimated on 356 thermodynamic design data pattern of absorption refrigeration system by MATLAB Simulators. Pearson’s correlation heatmap is used for extracting 14 thermodynamic features. 94.38% is the highest performance observed with 12 feature class six classification model.

The present study utilizes a microfluidic approach to investigate the effect of monoethanolamine (MEA) on increasing CO2 solubility in a light crude oil for low pressure immiscible enhanced oil recovery (EOR) operations. The CO2 microbubble area reduction over time was measured at varying MEA concentrations in oil to quantitatively estimate the effect of MEA on CO2 solubility, and as a result, its effect on gas-induced oil swelling and oil viscosity reduction. The maximum CO2 dissolution in 0.1% (v/v) MEA and 2% (v/v) MEA in oil was observed to be 77% and 86% dissolution, respectively. In addition, minor increases in CO2 dissolution rate were observed in 2% (v/v) MEA with oil when compared with 1% (v/v) MEA with oil and the control (pure oil). Furthermore, in order to test the feasibility of MEA employment in actual EOR operations, a series of immiscible CO2 flooding experiments were performed in a porous media to simulate an actual oil reservoir. In CO2 flooding experiments, the cumulative oil recovery efficiency was increased by 4% and 22% with 5% (v/v) MEA and 10% (v/v) MEA, respectively, due to the reduction of oil viscosity by CO2 dissolution. Unique phenomena such as trapping, displacement, re-filling, and drifting of oil blobs were observed during CO2 flooding. The MEA's strong effect on CO2 solubility, and as a result, its effect on various oil properties, indicates a potential for improving the recovery factor and reducing the cost of EOR operations.

In the current study, CuO nanoparticles were dispersed in a mixture of Ethylene Glycol-Water (60/40 wt. %) to prepare stable nanofluid in different concentrations (0.05 - 0.8 vol. %). The samples were used as the coolant fluid in a specific car radiator to evaluate the thermal performance of nanofluid and base fluid in the system. Five different and novel Machine-learning methods were applied over experimental data to predict the Nusselt number and output temperature of the coolant in the system. These methods are M5 tree regression, Linear and Cubic Multi-Variate Adaptive Regression Splines (MARS), Radial Basis Function (RBF), and Artificial Neural Network-Levenberg Marquardt Algorithm (ANN-LMA). Although all studied methods show acceptable accuracy in predicting experimental data, the ANN-LMA method in output temperature modeling and the MARS-Linear method in Nusselt number modeling has more precision.

In this investigation, neural networks were used to predict pressure drop of CuO-based nanofluid in a car radiator. For this purpose, the neural network with the multilayer perceptron structure was used to formulate a model for estimating the pressure drop In this way, different concentrations of copper oxide-based nanofluid were prepared. The base fluid was the mixture of ethylene glycol and pure water (60:40 wt%) which usually used as the cooling fluid in automotive industries. The prepared nanofluid samples were used in a car radiator and the pressure drop of nanofluid flows in the system at different Reynolds were measured. The main purpose of this study was developing the optimized neural networks for predicting the pressure drop of the system with sufficient precision. For the aim of designing the model’s structure, different neural networks were constructed and applied by changing the adjustable parameters (containing the transfer function, training rule, momentum’s amount, hidden layers’ number and the neurons’ number in hidden layer). In each case, the structure with the highest correlation coefficient was chosen as the final model. The selection of each parameters in the neural network model requires repeated tests and errors. So, genetic algorithm was used to optimize these parameters. Additionally, the pressure drop in the radiator of this method was investigated in neural network optimization. The outcomes indicated which a high accuracy in modeling and estimating the pressure drop of nanofluid flows in the studied system can be achieved by the neural network.

Abstract An attempt is made to improve the overall performances of channel heat exchangers. The techniques of baffles and nanofluids are combined to enhance the dynamic and thermal behaviors within the channel exchanger. Baffles under various attack angles are used as vortex generators. In addition, oil/multiwalled carbon nanotubes (MWCNT) is used as a working fluid. Both inclinations in the upstream and downstream directions were considered, referenced as Case A (UIB) and Case B (BIB), respectively. While the channel equipped with vertical baffles is referenced as Case C. The proposed models with combined techniques allowed a considerable enhancement in the overall efficiency. The comparison between the three cases revealed that the most significant value of thermal enhancement factor (TEF) of 5.634 was reached with vertical baffles (Case C) at the highest value of Reynolds number. When using inclined baffles, the 75° upstream attack angle (Case A) allowed the highest TEF of 4.814, compared with Case B.