In the present work, the semiconductor material is used to study the moisture diffusivity when a modified Moore–Gibson–Thompson (MGT) model is taken into account. The influence of moisture concentration is included in the governing equations throughout the photothermal transfer process. Based on the dissimilar relaxation durations of the coupled optoelectronic and thermoelastic waves, the MGT model is used to investigate the issue at hand. The method of the Laplace transform is used to obtain analytical solutions for the physical quantities, constitutive relationships, elastic waves, carrier density, heat equation conduction, and moisture diffusivity for the thermo-elastic medium. To extract the primary physical quantities in the space–time domain, the boundary conditions, temperature, plasma, displacement, and mechanical stress are inverted numerically using the Laplace transform. The effect of the new parameter like the reference moisture parameter with various values is discussed graphically on the primary physical quantities. The comparison between silicon and germanium is taken into account to achieve numerical computations.

The scarcity of drinkable water supplies is becoming a serious and troubling challenge to long-term development. This issue emphasises the urgency of implementing rapid water desalination options. However, desalination is a capital-intensive process that requires a significant amount of energy, and since it is now mostly powered by fossil fuels across the globe, it has the potential to leave a significant carbon footprint. In this light, transitioning to green energy sources for desalination is a necessity in today’s society. Humidification–dehumidification (HD) is one of the most effective desalination procedures to consider for distant places with modest fresh water demands for household and agricultural demands. This is mostly because it requires minimal operational and maintenance concerns. The current article examines the many kinds of HD desalination systems driven by solar energy sources and their key components. The current study established that the seawater HD system is viable for producing fresh water, especially for greenhouses and dispersed demand. HD is an effective technique for eluting fresh water from concentrated brines, boosting production, and minimising harmful environmental impact, benefitting ecosystems and human services. The packed bed humidifier is the most commonly used kind of humidifier in the HD water desalination system. Additionally, as compared to an indirect dehumidifier, the direct dehumidifier improves the performance of the HD desalination system. According to the findings of this research, hybrid energy systems have the greatest gain output ratio (GOR). Solar water heating significantly increases system productivity as compared to solar air heating. When combined with a heat pump, the HD delivers optimal system productivity and GOR at the lowest cost per litre. The conclusion is that, even though humidification–dehumidification is a promising method for decentralised small-scale fresh water production applications, it needs additional improvement to optimise system performance in terms of economy and gain output ratio.

Nanofluids have improved thermophysical properties compared to conventional fluids, which makes them promising successors in fluid technology. The use of nanofluids enables optimal thermal efficiency to be achieved by introducing a minimal concentration of nanoparticles that are stably suspended in conventional fluids. The use of nanofluids in technology and industry is steadily increasing due to their effective implementation. The improved thermophysical properties of nanofluids have a significant impact on their effectiveness in convection phenomena. The technology is not yet complete at this point; binary and ternary nanofluids are currently being used to improve the performance of conventional fluids. Therefore, this work aims to theoretically investigate the ternary nanofluid flow of a couple stress fluid in a vertical channel. A homogeneous suspension of alumina, cuprous oxide, and titania nanoparticles is formed by dispersing trihybridized nanoparticles in a base fluid (water). The effects of pressure gradient and viscous dissipation are also considered in the analysis. The classical ternary nanofluid model with couple stress was generalized using the fractal–fractional derivative (FFD) operator. The Crank–Nicolson technique helped to discretize the generalized model, which was then solved using computer tools. To investigate the properties of the fluid flow and the distribution of thermal energy in the fluid, numerical methods were used to calculate the solution, which was then plotted as a function of various physical factors. The graphical results show that at a volume fraction of 0.04 (corresponding to 4% of the base fluid), the heat transfer rate of the ternary nanofluid flow increases significantly compared to the binary and unary nanofluid flows.

Solar radiation is considered the main renewable energy source which reshapes the global sustainability plan for future development. Due to the lack of solar radiation measurements, this work investigates the performance of several temperature-based hybrid solar radiation models combining the parametric, statistical and satellite data approaches to estimate the global solar radiation on a horizontal surface. Over 35 years of meteorological data in the new location, Arar City, KSA (Latitude 30°96′ N and longitude 41°05′ E) are employed to establish and validate the models. These models are validated using two datasets with different averaging time spans to investigate the accuracy and reliability of different models as forecasting tools for the solar radiation. The mostly common statistical indicators are calculated to identify the most accurate model. The results show that Model (1) has the best performance among all models with high reliability as a solar radiation forecasting tool in this new location. This model is also validated against the widely-used datasets, namely NASA, On-Site measurements and PVGIS-SARAH data. The model shows excellent values for statistical indicators with high values of coefficient of determination, R² > 0.955, presenting the best performance regardless of the time span of the validation datasets.