ABSTRACT
The use of alternative energy sources, particularly solar energy, in buildings is rising and spreading around the globe. In this paper, a solar wall is analyzed using a numerical method. On the wall, a number of obstacles are placed in two shapes, rectangular (REC) and semicircular (SEC). The cavities are filled with organic phase-change materials. This study was performed in 7 h in the absence of solar radiation on the wall for different dimensions of obstacles in 5 different modes. Various temperatures have been investigated, including exhaust air temperature (TAR), Trombe wall temperature (TWL), and mean volume % of molten PCM in cavities. COMSOL software is used to carry out this numerical study. The results of this study showed that the use of SECs compared to RECs causes the TWL to be higher. In the most extreme case, at a 16 cm aspect ratio, the use of SECs gives a 2.1 °C increase in TWL relative to the REC one. The outlet TAR is also increased by the usage of SECs. The use of larger dimensions of the cavities has increased the TAR leaving the wall so that the TAR after 7 h of the absence of solar radiation, in the most significant case of SECs, was more than 295.5 K. The use of SECs also increases the PCM freezing time. In the largest case of cavities, using SECs increases the freezing time by 15 min compared to RECs.
ABSTRACT
On March 11, 2020, the World Health Organization has declared the outbreak of COVID-19 as Pandemic, which is the massive challenges faced globally. Previous studies have indicated that the meteorological parameters can play a vital role in transmissibility and Mortality. In the present work, the influence of Comorbidity and meteorological parameters are investigated for Mortality caused due to COVID. For this, the most affected city by COVID-19 is considered, i.e., Mumbai, India, as a case study. It was found that Comorbidity is the most influential parameter on the Mortality of COVID-19. The Spearman correlation coefficient for meteorological parameters lies between 0.386 and 0.553, whereas for Comorbidity was found as 0.964. A regression model is developed using particle swarm optimization to predict the mortality cases for Mumbai, India. Further, the developed model is validated for the COVID-19 cases of Delhi, India, to emphasize the utility of the developed model for other cities. The measured and predicted curve shows a good fit with a mean percentage error of 0.00957% and a coefficient of determination of 0.9828. Thus, particle swarm optimization techniques demonstrate very high potential for the prediction of Mortality caused due to COVID-19. It is insisted that by providing constant health monitoring and adequate care for the comorbidity patients, the Mortality can be suppressed drastically. The present work can serve as an input to the policymakers to overcome the COVID-19 pandemic in India as well as other parts of the world.
