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.

Gas turbines play a crucial role in power generation and aviation, where effective maintenance strategies are essential to ensure reliability. Traditional condition monitoring methods often rely on scheduled inspections, leading to potential downtime and increased maintenance costs. This study presents an AI-driven approach for thermal condition monitoring and the predictive maintenance of gas turbines using machine learning. An Extreme Gradient Boosting (XGBoost)-based classification model was developed to distinguish between healthy and faulty operating conditions based on thermal load data. The dataset, collected over six months from strategically placed thermocouples in the exhaust gas section, was processed to extract key statistical features such as mean temperature, standard deviation, and skewness. The proposed XGBoost model achieved a classification accuracy (CA) of 97.2%, with an F1-score of 96.8%, precision of 97.5%, and recall of 96.1%, demonstrating its effectiveness in detecting anomalies. The results indicate that the integration of machine learning in gas turbine monitoring significantly enhances fault detection capabilities, enabling proactive maintenance strategies and reducing the risk of critical failures. This study provides valuable insights for data-driven maintenance strategies, optimizing operational efficiency and extending the lifespan of gas turbine components. Future work will focus on real-time deployment and further validation with extended datasets.