首页|Studies from Guangdong University of Technology Provide New Data on Machine Learning (Multi-objective Optimizations of Vaporliquid Adjustment Evaporator and Its Machine-learning Based Operational Control Strategy)
Studies from Guangdong University of Technology Provide New Data on Machine Learning (Multi-objective Optimizations of Vaporliquid Adjustment Evaporator and Its Machine-learning Based Operational Control Strategy)
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Current study results on Machine Learning have been published. According to news reporting originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “During flow boiling, there exists highly efficient heat transfer peaked at the high vapor quality. The vapor-liquid adjustment evaporator employs the liquid drainage and liquid refilling to redistribute the vapor quality and mass flux.” Funders for this research include Guangdong Basic and Applied Basic Research Foundation, University teachers’ Characteristic Innovation Research Project of Foshan, Supporting Project of Foshan City for Promoting the University Scientific and Technological Achievements to Service Industry in 2021. Our news editors obtained a quote from the research from the Guangdong University of Technology, “In this way, the efficient heat transfer could be repeated, leading to the improved heat transfer capacity and reduced pressure drop at the same time. However, the path arrangement and separation efficiencies have been not mutually coordinated to release the potential of the vapor-liquid adjustment evaporator at various conditions. In this study, a numerical model of this evaporator is developed and verified by experimental data. By implementing the multi-objective optimization algorithm, three optimal layouts, targeting to the lowest pressure drop, the highest heat transfer capacity and the compromised one, are obtained at the design conditions. Comparisons of their local characteristics reveals that the fifth path offers most benefits in terms of 50 % entire heat transfer capacity and up to 73 % reduced pressure drop. At various off-design conditions, the constant separation efficiencies in vapor-liquid adjustment evaporator could lead to the inferior performance to the conventional evaporator.”
GuangzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningGuangdong University of Technology
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