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Molecular identification, isolation and functional characterization of a glutathione S-transferase gene CsGST in saffron (Crocus sativus L.)
Molecular Microheterogeneity: Structure Formation in Amine-Water Mixtures
Perceived stress and its association with social support and social factors among lower-middle income mothers of young children in Tunisia
The unfortunate embossing of Subsector XZ-74
Dynamic Nanocrystal-Ligand Boundaries: Reversible Photoinduced Ligand Detachment from Quantum Dots in Solution
Testing the causal relationship between interpersonal closeness and inter-brain synchrony
The relationship between secondary school exam performance and lifestyle behaviors at the onset of university education
Click-to-Release Reactions for Tertiary Amines and Pyridines
Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models
Li <sub> 1+ <i>x</i> </sub> TaO <sub> <i>x</i> </sub> F <sub> 6– <i>x</i> </sub> Oxyfluoride Solid Electrolytes with Amorphization-Driven Enhancement of Ion Conduction Channels for 5 V All-Solid-State Batteries
The dynamics of bicycles for disaster recovery and relief
Cancer Metastasis Blockage by Exosome-Neutrophil Nanocoupler
Solid oxide fuel cell simulation model tuning using operating conditions dependent optimization techniques
Abstract This paper discusses the mathematical model and simulation of Solid Oxide Fuel Cell (SOFC), where the conventional and reversible SOFC are studied. Their performance is studied at the steady state to get the V-I polarization curves, as well as in case of changing the electric current drawn from the cell to get the change in voltage over time. Unlike most existing studies, the focus is on the time needed by the cell to reach voltage stability after a change, in preparation for studying the cell integration into an electrical network, and all of these studies are carried out when changing the cell operating conditions such as temperatures and the flow rate of reactive gases. Then optimization techniques are used, such as Walrus Optimization Algorithm (WaOA), Secretary Bird Optimization Algorithm (SBOA), Chaos Game Optimization (CGO) and Teaching–Learning Based Optimization (TLBO) to increase model accuracy and make its results closer to the published laboratory results. The results demonstrate that WaOA achieves the lowest modeling error among the investigated optimization techniques and provides the most accurate model calibration. The proposed WaOA-based tuning reduces the modeling error by more than five orders of magnitude compared to curve-fitting approaches reported in the literature, and is further employed to optimize reactant flow rates, resulting in an output power increase of approximately 6% compared to nominal operating conditions.