Proton exchange membrane fuel cell is essentially utilized to generate energy with zero emission. There are many drawbacks in PEMFC, such as the mal-distribution of reactants, water management between the catalyst layer and the GDL, and the mass transport issue of reactants. Flow field design parameters can overcome these problems to improve cell performance. Where the flow field is an essential element of the fuel cell, and it is designed to provide the required amount of both hydrogen and oxygen with the lowest possible pressure drop on the anode and cathode sides, respectively. In this paper, the cell performance with different flow field design parameters, such as conventional flow field configuration, nature-inspired flow field configuration, and geometric parameters, as well as their modifications, is reviewed in detail. It has been demonstrated through the current review paper that the flow field design parameters can significantly affect the overall behavior of PEMFC, and each design parameter has advantages and disadvantages that make the flow fields suitable for specific applications.

A propeller generates lift in the direction of revolution, similar to a revolving wing. Many previous propeller optimization techniques exist; nevertheless, they often find the optimal thrust coefficient at a constant power coefficient and vice versa. Using two types of algorithms, the genetic algorithm (GA), and the ant colony algorithm (ACO), and comparing with each other, this study will discover the optimal value of the thrust coefficient and the power coefficient combined to obtain the optimum value of the thrust and the lowest value of the power at the same time. A Simple Blade Element Theory Blade served as the foundation for all assumptions. This article examined over 80 various designs, brands, and types of propellers in a 2-blade configuration with diameters ranging from 2.5 to 19 inches and varying pitch values. The data for the baseline propeller was obtained from the UIUC Propeller Database. The inputs for the optimization are the propeller type, diameter, pitch angle, rotational speed, thrust coefficient, and power coefficient. The results show that by determining the factor of interest in the thrust coefficient (FITC), the algorithm can find the optimal propeller specifications. When the (FITC) is 100%, the algorithm will ignore the effect of the power coefficient and vice versa. In the instance (FITC) is 100 percent, the genetic algorithm performed much better than the ant colony algorithm (ACO). But the Ant colony algorithm is more accurate than the genetic algorithm.