Abstract
Research findings on Machine Learning are discussed in a new report. According to news reporting from Jilin, People’s Republic of China, by NewsRx journalists, research stated, “This study proposes an innovative optimization framework to optimize channel structure and maximize power density by coupling multi-physics structure models, machine learning, and swarm intelligence algorithms. First, proton exchange membrane fuel cells (PEMFC) imitated water-drop block channels are employed for constructing multi-physics structure models.” Funders for this research include Jilin Provincial Science & Technology Department, National Natural Science Foundation of China (NSFC). The news correspondents obtained a quote from the research from Northeast Electric Power University, “A database of the PEMFC output performance under various structural parameters of imitated water-drop block is established. Then, a machine-learning-based surrogate model is constructed based on the adaptive boosting (AdaBoost) ensemble algorithm to predict the output performance under different channel parameters. Finally, the improved gray wolf optimizer (IGWO) fitness function is calculated using a surrogate model to establish an optimization framework for effectively optimizing the channel structure. Results show that the AdaBoost ensemble surrogate model predicts the PEMFC polarization curves with extremely high accuracy and efficiency within one second. The optimization framework is capable of swiftly predicting both the optimal channel structure and maximum power density in under two minutes. The predicted values are returned to the physical model for validation with an error of only 3.96%. Simultaneously, the optimal channel structure can effectively enhance the PEMFC performance.”
NETL
NSTL
Elsevier