Abolfazl Fattahi’s research while affiliated with University of Kashan and other places

The growing demand for precise and efficient thermal management in microfluidic heat exchange systems has led to increasing interest in nano-encapsulated phase change materials (NEPCMs) for enhanced heat transfer and thermal energy storage. This study performs a comprehensive numerical simulation and machine learning-based prediction of the thermo-hydrodynamic behavior of NEPCM slurries in microchannels with secondary flow passages. The research aims to quantify the influence of microchannel geometry, flow conditions (Reynolds number: 100–200), and NEPCM concentration (0–10%) on heat transfer and pressure drop characteristics. Energy and entropy analyses are conducted by applying the first and second laws of thermodynamics to assess system efficiency. Furthermore, an artificial neural network (ANN) model is trained to predict the Nusselt number and performance evaluation criterion (PEC) based on input parameters with high accuracy. The simulation results indicate that incorporating NEPCMs enhances heat transfer performance, increasing the average Nusselt number by up to 50% compared to a simple microchannel. However, this improvement comes at the cost of higher pressure drop, with the friction factor showing a variation of up to 100% across different configurations. Entropy generation analysis reveals that thermal entropy generation dominates at lower Reynolds numbers, whereas frictional entropy generation becomes significant at higher Reynolds numbers. The ANN model achieves an R2 value of 0.98, with a prediction error of less than 1.5%, demonstrating its effectiveness. These findings provide quantitative insights for optimizing microchannel-based thermal management systems, balancing heat transfer enhancement, pressure drop, and entropy generation for improved performance in microfluidic applications.

The emergence of artificial intelligence (AI) and, more particularly, machine learning (ML), has had a significant impact on engineering and the fundamental sciences, resulting in advances in various fields. The use of ML has significantly enhanced data processing and analysis, eliciting the development of new and improved technologies. Specifically, ML is projected to play an increasingly significant role in helping researchers better understand and predict the behavior of porous media. Furthermore, ML models will be able to make use of sizable datasets, such as subsurface data and experiments, to produce accurate predictions and simulations of porous media systems. This capability could help optimize the design of porous materials for specific applications and improve the effectiveness of industrial processes. To this end, this review paper attempts to provide an overview of the present status quo in this context, i.e., the interface of ML and porous media in six different applications, namely, heat exchanger and storage, energy storage and combustion, electrochemical devices, hydrocarbon reservoirs, carbon capture and sequestration, and groundwater, stressing the advances made in the application of ML to porous media and offering insights into the challenges and opportunities for future research. Each section also entails a supplementary database of the literature as a spreadsheet, which includes the details of ML models, datasets, key findings, etc., and mentions relevant available online datasets that can be used to train ML models. Future research trends include employing hybrid models by combining ML models with physics-based models of porous media to improve predictions concerning accuracy and interpretability.

Cooling of the electrical vehicles’ battery is of crucial importance, as it affects the performance of the electrical power system, discharging duration, and subsequently their market acceptance. Present numerical work aims at analysing the improvement of the thermal management system by topological changes, which can be easily and affordably performed. The influence of AgO-water nanofluid with a volume fraction of 3% is also evaluated. A pack involving 10 cylindrical batteries with a constant heat flux is studied, which is merged by the nanofluid flow. Effects of changing the location of the inlet/outlet ports and inserting one or two plates to guide the fluid flow are assessed. The analysis was performed for a range of Reynolds number (based on the inlet pack diameter) from 1000 to 2000. It is shown that the topological modifications can improve the Nusselt number by more than 25%, while the Reynolds number rising from 1000 to 2000 makes a maximum increase of 30%. However, it follows a maximum 50% increment in the pressure drop. The proposed geometries indicate a more uniform temperature distribution compared to the simple cooling system without guiding plates by about 50%.

An increase in the thermal performance of heat exchangers leaves a dramatic influence on the energy consumption of industries, addressing why such studies are of interest. The current numerical work, therefore, aims to increase heat transfer in a shell-and-tube heat exchanger by innovative, novel topological changes, using Cassini cross-sectional tubes and proposed segmental curved baffles. Cassini oval and triple Cassini cross sections in horizontal, vertical, and oblique tube arrangements are applied, not investigated yet. Further, the heat transfer is augmented by adding carbon nanotubes to the pure water. The inlet Reynolds number is chosen between 10,000 and 30,000 and the nanotube volume fraction falls in the range of 0 and 2%. The friction factor, Nusselt number, performance evaluation criteria as well as the second law of thermodynamics analysis, including thermal and frictional entropy generation, are monitored. The Witte–Shamsunder efficiency is also detected to consider both the first and second low. Using the water as the working fluid and irrespective of the baffle geometry, the case with the triple Cassini cross-section tube has the highest value of the Nusselt number up to 100, while the circular tube case sets in the lowest rank, with the value near 50. Additionally, the circular tubes show the worst PEC value, while triple Cassini tubes pretend as the most valuable case, with values of 40–50% higher than those of circular tubes, highlighted more at the lower Reynolds numbers. The cases with curved baffles make the PEC increment in all cases up to 15% compared to those with simple baffles. The entropy generation reduces by using the curved baffles up to 20%.

Energy and mass storage in various single-phase fluid flows is of particular interest, as the world currently faces energy challenges. Double-diffusive natural convection in an n-shaped storage tank is numerically studied which can be a general guideline to maintain a storage tank with higher exergy. Lattice-Boltzmann's approach in an in-house computational code is used to simulate the problem. To display the results, it is considered that the Rayleigh number lies between 10³ and 10⁵, and the Lewis number in the range of 0.1 and 10. The average Nusselt and Sherwood number, as well as entropy generation, showing the energy loss, are illustrated. It is observed that the average Nusselt and Sherwood number rises with increasing Rayleigh number and buoyancy ratio. Further, the average Sherwood number boosts by increasing the Lewis number. The most promising parameter in increasing the heat and mass transfer are found to be Rayleigh and Lewis number, respectively, with a maximum 300 percent improvement. The flow friction can be regarded as the main source of entropy generation, with a share of 90 percent. The Rayleigh number increment from 10³ to 10⁵ leads to the rise in the total entropy generation by approximately fivefold.