Background and Objective: COVID-19 pandemic continues unabated due to the rapid spread of new mutant strains of the virus. Decentralized cluster containment is an efficient approach to manage the pandemic in the long term, without straining the healthcare system and economy. In this study, the objective is to forecast the peak and duration of COVID-19 spread in a cluster under different conditions, using a probabilistic cellular automata configuration designed to include the observed characteristics of the pandemic with appropriate neighbourhood schemes and transition rules. 

Methods: The cellular automata, initially configured to have only susceptible and exposed states, enlarges and evolves in discrete time steps to different infection states of the COVID-19 pandemic. The transition rules take into account the probability and proximity of contact between infected hosts and susceptible individuals. A transmittable and transition neighbourhoods are defined to identify the most probable individuals infected from a single host in a time step. 

Results: The model with novel neighbourhood schemes and transition rules reproduce the macroscopic behaviour of infection and recovery observed in pandemics. The temporal evolution of the pandemic trajectory is sensitive to lattice size, range, latent and recovery periods but has constraints in capturing the changes in the infectious period. A study of lockdown and migration scenarios shows strict social isolation is crucial in controlling the pandemic. The simulations also indicate that earlier vaccination with a higher capacity and rate is essential to mitigate the pandemic. A comparison of simulated and actual data shows a good match. 

Conclusions: The study concludes that social isolation during movement and interaction of people can limit the spread of new infections. Vaccinating a large proportion of the population reduces new cases in subsequent waves of the pandemic. The model and algorithm with real-world data as input can quickly forecast the trajectory of the pandemic, for effective response in cluster containment.

An experimental study on self-humidified air fed operation of polymer electrolyte membrane fuel cell using hydrogen as fuel shows that there is an optimum operating temperature at which the performance is high. A reduction in operating temperature decreased performance due to condensation of product water. An increase in operating temperature also resulted in performance loss due to evaporation of product water and ensuing dehydration of membrane. It is also found that, there is an optimum flow rate of air at which the performance is high. A lower air flow rate resulted in performance loss due to oxygen starvation whereas a higher air flow rate leads to performance loss due to excess water removal and dehydration of membrane. For hydrogen, there is no optimum flow rate but the minimum flow rate is found to give higher performance as it reduces the water removal through anode side as well as the fuel wastage.

The performance of a proton exchange membrane fuel cell strongly depends on the nature of reactant distribution and the effectiveness of liquid water removal. Three different configurations of a mixed flow distributor are studied to find out the effect of nonuniform under-rib convection on reactant and liquid water distribution in the cell. In a mixed flow distributor, the rate of under-rib convection is found to be different under each rib in the same flow sector which results in different rates of removal of liquid water. This helps to retain water to hydrate the membrane, whereas the excess is removed to avoid flooding. Under-rib convection aids to get better reactant distribution, reduces pressure drop, and provides better control over liquid water removal which is helpful in developing efficient water management strategies.

The possibility of achieving self-humidified operation of a polymer electrolyte membrane fuel cell is investigated experimentally. The flow distributor is designed such that the non-uniform under-rib convection aids to retain product water in the fuel cell enabling a dry feed operation. The fuel cell is operated in a pseudo co-flow and pseudo counter flow modes at a constant current density and the transient change in voltage and temperature are recorded. In the pseudo co-flow mode, the voltage drops at higher temperature and reactant stoichiometries which is attributed to membrane dehydration. In the pseudo counter-flow mode, voltage remains same at both low and high temperature operation and is found to be independent of reactant stoichiometry. A horizontal orientation of the flow field, in pseudo counter flow mode, is found to be ideal for self-humidified operation at low reactant stoichiometries and cell temperature.

Water management is one of the important factors which determine the performance of a Proton Exchange Membrane fuel cell using hydrogen as fuel. For developing efficient water management systems, it is important to know the potential locations of formation and the nature of distribution of liquid water in the fuel cell. A PEM fuel cell with three different types of flow distributors numerically simulated to find the water formation and distribution characteristics. It is found that the type of flow distributor used plays a major role in determining the distribution liquid water in the cell. A parallel flow distributor exhibits poor water removal capabilities whereas a serpentine flow distributor exhibits better water removal. A mixed flow distributor gives better water distribution characteristics compared to the parallel and serpentine distributors.