For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

HVDC transformers are one of the main components for long distance transmission, for which many investigations have been done all over the world in order to increase their efficiency and reliability. Regarding their insulation, oil immersed pressboards are widely used, representing one of the most important elements playing a role in transformer behaviors. Under DC operation, space charges accumulating between the oil and pressboard have been reported as the influential cause possibly giving rise to unexpected service failures under the transient voltage from the grid. However, it might be possible to presume that the accumulation of space charge could be facilitated inside the multilayered pressboard causing insulation breakdown in a different way. In addition, other parameters could be potential issues to investigate: non homogeneous temperature distribution along the oil circulation path and the DC field intensity. The former could explain the conductivity variation causing the non-uniform DC field distribution, while the latter plays a role in the variation of conductivity. With these in mind, three parameters have mainly been put under experimental investigation. For this purpose, a DC voltage is applied to the oil immersed pressboard, and then the polarization and leakage current are measured and analyzed. The mobility of the space charge is calculated from the former as a function of the number of layers, while the conductivity is obtained from the latter.

In this study, hydrodynamic characteristics and operational feasibility of a floating liquefied natural gas (LNG) bunkering terminal moored side-by-side with an LNG carrier and two LNG bunkering shuttles were investigated, during the LNG transferring process. Experiments consisted of two configurations, namely the soft spring moorings individually and the side-by-side mooring system. Motion response amplitude operators (RAOs) and wave drift force RAOs in regular waves were measured from the former configuration while the latter configuration was used to obtain global motion responses, structural loads acting on ship-to-ship mooring lines and relative displacements between LNG loading arms and their manifolds. The gap flow effect was considered by conducting comparisons of the numerical and experimental results with various radiation body boundary damping values. The results provided insights into the structural reliability of side-by-side mooring lines and the applicability of a conventional rigid LNG loading arm.