Abstract
Investigators publish new report on Ma chine Learning. According to news reporting originating from Tianjin, People's R epublic of China, by NewsRx correspondents, research stated, "The flow field dis tribution of compressor blades is critical to the performance of aero-engine. To efficiently obtain the aerodynamic loads on the blades, this study employs mach ine learning models to predict the aerodynamic characteristics of compressor bla de surfaces." Funders for this research include National Natural Science Foundation of China ( NSFC), National Natural Science Foundation of China (NSFC), Natural Science Foun dation of Tianjin. Our news editors obtained a quote from the research from the School of Aeronauti cs and Astronautics, "The predictive performances of these models are evaluated by applying random forest, multi-layer perceptron (MLP), one-dimensional convolu tional neural network, and long short-term memory network based on simulation da ta of computational fluid dynamics (CFD). The results indicate that the MLP mode l performs exceptionally well among all test metrics, with its predictions close ly matching the CFD simulation results. Further analysis using SHapley Additive exPlanations methods is performed to interpret the MLP model and reveal the impo rtance of various input features."
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