Nanofluid, the fluid suspensions of a metallic nanoparticle, became a coolant fluid that is used when a promising enhancement in heat transfer is required. In the current study, the characteristics of fluid flow and heat transfer are numerically investigated using different nanofluids (Al₂O₃–H₂O, TiO₂–H₂O, and SiO₂–H₂O) and different micro-channel heat sink (MCHS) configurations (rectangular, triangular, trapezoidal, and circular). In this numerical investigation, the effect of Re number ranged from 890 to 1500, and the effect of nanoparticle concentration ranged from 1% to 7% at constant heat flux q = 10⁶ W/m², and constant fluid inlet temperature of 288 K, were studied. The average heat transfer coefficient, h, and pressure drop, $\Delta p$, are used to quantify the fluid flow and heat transfer characteristics in each MCHS configuration and for each nanoparticle concentration. It is revealed that a better heat transfer coefficient is obtained for Al₂O–H₂O compared with other types of nanoparticles and pure water, such as 8.58% heat transfer coefficient improvement obtained at Re = 1500 and $\mathsf{\phi }=7\%$ more than that of pure water. It is also inferred that the maximum heat transfer coefficient is obtained by the triangular MCHS; however, it has the highest pressure drop because of the lowest hydraulic diameter.

Nanofluids have gained attention for their potential to solve overheating problems in various industries. They are a mixture of a base fluid and nanoparticles dispersed on the nanoscale. The nanoparticles can be metallic, ceramic, or carbon based, depending on the desired properties. While nanofluids offer advantages, challenges such as nanoparticle agglomeration, stability, and cost effectiveness remain. Nonetheless, ongoing research aims to fully harness the potential of nanofluids in addressing overheating issues and improving thermal management in different applications. The current study is concerned with the fluid flow and heat transfer characteristics of different nanofluids using different types of nanoparticles such as Al₂O₃, SiO₂, and ZnO mixed with different base fluids. Pure water and ethylene glycol–water (EG–H₂O) mixtures at different EG–H₂O ratios (ψ = 0%, 10%, 30%, 40%) are used as the base fluid. Furthermore, a rectangular microchannel heat sink is used. Mesh independent study and validation are performed to investigate the current model, and a good agreement is achieved. The numerical analysis evaluates the influence on the heat transfer coefficient and flow characteristics of nanofluids for Reynolds numbers 500 to 1200 at a 288 K inlet flow temperature. The results show that ZnO nanofluid and 40% EG–H₂O increase the heat transfer coefficient by 63% compared to ZnO–H₂O nanofluid obtained at Re = 1200 and φ = 5%. Conversely, the pressure drop by ZnO is nearly double that obtained by Al₂O₃ and SiO_2.

In this study, the effect of inlet air cooling on greenhouse gas (GHG) emissions and engine performance for a land-based gas turbine engine was investigated under varying ambient temperatures (15–55 °C). The study aimed to reduce GHG emissions while improving output power and fuel efficiency during hot weather operating conditions. For illustrative purposes, a representative gas turbine engine model, approximating the 250 MW class General Electric (GE) engine, was analyzed in a simple cycle. A refrigeration process was integrated with a turboshaft gas turbine engine to chill the incoming air, and the power required for cooling was extracted from the gas turbine’s output power. This mechanical chiller was assumed to provide a 15 °C inlet air temperature. Without inlet air cooling, at 55 °C ambient temperature, the engine’s power output was calculated to decrease by 15.06%, while power-specific fuel consumption and GHG emissions increased by 6.09% and 5.84%, respectively. However, activating the refrigeration or cooling system in the inlet made it possible to mitigate most of the adverse effects of hot weather on the engine’s performance and GHG emissions. Therefore, with inlet air cooling, the power output loss reduces to 3.28%, indicating an 11.78% recovery compared to the 15.06% loss without cooling. Similarly, the rise in power-specific fuel consumption caused by high ambient temperature decreases from 6.09% to 3.43%, reflecting a 2.66% improvement. An important finding of the study is that with inlet air cooling, the increase in GHG emissions reduces from 5.84% to 3.41%, signifying a 2.43% improvement on a hot day with a temperature of 55 °C.

The increasing integration of renewable energy sources, particularly solar and wind, has created a growing need for efficient and reliable energy storage technologies (ESTs) because of their intermittent nature. This review critically examines established and emerging ESTs, including batteries, supercapacitors, and mechanical, thermal, hydrogen, and superconducting magnetic energy storage systems, with emphasis on their technical performance, durability, response time, economic feasibility, and environmental considerations. The reviewed technologies exhibit substantial differences in their energy and power characteristics. Lithium-ion batteries, for example, provide an energy density of approximately 100–265 Wh/kg, with reported lifetimes of 2000–10,000 cycles, whereas supercapacitors offer only 5–10 Wh/kg but can withstand more than 100,000 cycles and deliver energy within seconds. Flywheel systems can achieve power densities of 1000–10,000 W/kg and operate for up to 100,000 cycles, while pumped hydro storage has a comparatively low energy density of approximately 0.5–1.5 Wh/kg but can provide service over periods exceeding 50 years. Thermal storage technologies also show considerable potential, with reported energy densities ranging from approximately 0.25 Wh/kg for sensible heat storage to 120–383 Wh/kg for thermochemical storage, depending on the materials and system configuration. Overall, the findings demonstrate that no single storage technology is optimal for all applications. Technology selection should, therefore, consider the required energy and power capacity, response time, lifetime, cost, and environmental impact, particularly when integrating variable renewable energy into modern energy systems.