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The growth of hybrid nanofluids can be connected to their enhanced thermal performance as pertains to the dynamics of automobile coolant among others. In addition to that, the thermal characteristics of water-based nanofluids carrying three different types of nanoparticles are incredible. Keeping in view this new idea, the current investigation explores ternary hybrid nanofluid flow over a stretching sheet. Joule heating and viscous dissipation are addressed in the heat equation. Three distinct kinds of nanoparticles, namely, magnesium oxide, copper, and MWCNTs, are suspended in water to form a ternary hybrid nanofluid with the combination MgO–Cu–MWCNTs–H 2 O. To stabilize the flow of the ternary hybrid nanofluid, transverse magnetic and electric fields have been considered in the fluid model. The production of entropy has been analyzed for the modeled problem. A comparative study for ternary, hybrid, and traditional nanofluids has also been carried out by sketching statistical charts. The equations that govern the problem are shifted to dimension-free format by employing transformable variables, and then they are solved by the homotopy analysis method (HAM). It has been revealed in this work that the flow of fluid opposes by magnetic parameter and supports by electric field the volumetric fraction of ternary hybrid nanofluid, while thermal profiles are gained by the growing values of these parameters. Boosting values of the electric field, magnetic parameters, and Eckert number support the Bejan number and oppose the production of entropy. Statistically, it has been established in this work that a ternary hybrid nanofluid has a higher thermal conductivity than hybrid or traditional nanofluids.

Hydromagnetic flow and heat transport have sustainable importance in conventional system design along with high-performance thermal equipment and geothermal energy structures. The current computational study investigates the energy transport and entropy production due to the pressure-driven flow of non-Newtonian fluid filled inside the wedge-shaped channel. The nonlinear radiation flux and uniform magnetic field are incorporated into the flow analysis. To be more precise, non-Newtonian fluid initiates from an inlet with the bound of the parabolic profile and leaves at outlet of a convergent/divergent channel. We assume that the channel flow is adiabatic and influenced by the wall friction. The leading flow equations are modeled via the Carreau fluid model using fundamental conservation laws. The thermodynamical aspect of the system is visualized using a two-phase model and analyses of the entropy equation due to fluid friction, ohmic heating, and diffusion of heat and mass fluxes. The modeled system of equations is normalized using a dimensionless variable mechanism. The system was elevated for the significant variation of controlling parameters. The outcomes obtained from the computational investigation are validated with the theoretical results that are available in the literature. An increasing semivertex angle and Reynolds number increase the converging channel flow. In the core flow zone, an increase in the divergent semiangle causes the flow to decelerate, while near and at the channel wall it causes a slight acceleration. Outcomes designate that the main contribution to the irreversibility is due to ohmic loss, frictional loss, and heat loss. The thermal performance and entropy production is dominant for a diverging flow. The outcomes of this research will assist in comprehending the process of entropy minimization in conjunction with the flow of nanomaterials in a nonuniform channel, which is essential in engineering processes such as the creation of micro machines, supersonic Jets, nozzles, and clean energy.

Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India. Between 1 January and 31 March 2022, the country had 136,604 fire points. They activated an alerting system that indicates the location of a forest fire detected using MODIS sensor data from NASA Aqua and Terra satellite images. However, the satellite passes the country only twice and sends the information to the state forest departments. The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial vehicle equipped with a GPS and a high-resolution camera can acquire quality images referencing the fire location. Further, deep learning frameworks can be applied to efficiently classify forest fires. In this paper, a cheaper UAV with extended MobileNet deep learning capability is proposed to classify forest fires (97.26%) and share the detection of forest fires and the GPS location with the state forest departments for timely action.

Atmospheric pressure plasma jets are gaining a lot of attention due to their widespread applications in the field of bio-decontamination, polymer modification, material processing, deposition of thin film, and nanoparticle fabrication. Herein, we are reporting the disinfection of Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli bacteria using plasma jet. In this regard, Ar–O2, Ar–N2, and Ar–O2–N2 mixture plasma is generated and characterized using optical and electrical characterization. Variation in plasma parameters like electron temperature, electron density, and reactive species production is monitored with discharge parameters such as applied voltage and feed gas concentration. Results show that the peak average power consumed in Ar–O2, Ar–N2, and Ar–O2–N2 mixture plasma is found to be 4.45, 2.93, and 4.35 W respectively, at 8 kV. Moreover, it is noted that by increasing applied voltage, the electron temperature, electron density, and reactive species production also increases. It is worth noting that electron temperature increases with increase in oxygen concentration in the mixture ( , while it decreases with increase in nitrogen concentration in the mixture (Ar–N2). Similarly, a decreasing trend in electron temperature is noted for Ar–O2–N2 mixture plasma. On the other hand, a decreasing trend in electron density is noted for all the mixtures. Reduction in viable colonies of Pseudomonas aeruginosa, Staphylococcus Aureus, and Escherichia coli were confirmed by the serial dilution method. The inactivation efficiency of pulsed DC plasma generated, in the Ar–N2 mixture at 8 kV and 6 KHz, was evaluated against P. aeruginosa, S. aureus and E. coli bacteria by measuring the number of surviving cells versus plasma treatment time. Results showed that after 240 s of plasma treatment, the number of survival colonies of the mentioned bacteria was reduced to less than 30 CFU/mL.