首页|Findings from Shenzhen University Yields New Data on Machine Learning (Machine Learning-assisted High Precision Predictive Modelling of Convective Heat Transfer In Fluid Channels Fabricated By Laser Powder Bed Fusion)
Findings from Shenzhen University Yields New Data on Machine Learning (Machine Learning-assisted High Precision Predictive Modelling of Convective Heat Transfer In Fluid Channels Fabricated By Laser Powder Bed Fusion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in Machine Learning. According to news reporting from Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “Laser powder bed fusion (LPBF) has proven to be an effective tool in fabricating heat transfer devices with improved efficiency. However, the accurate prediction of convective heat transfer in LPBF-fabricated fluid channels remains a challenge.” Funders for this research include Shenzhen Science & Technology Project, NTUT-SZU Joint Research Program. The news correspondents obtained a quote from the research from Shenzhen University, “The classical Gnielinski model is regarded as the most accurate correlation for predicting forced convective heat transfer in traditional pipes. However, whether it is applicable to LPBFfabricated pipes is yet to be determined. To address this challenge, in this study, pipe samples with diameters of 3 mm, 4 mm, and 5 mm were designed and fabricated using LPBF along the building angles of 0 degrees, 45 degrees, and 90 degrees. The pressure loss and heat transfer characteristics of these samples were experimentally measured. Results showed that there was a maximum prediction error of 72.1 % between the classical Gnielinski model and experimental results.”
ShenzhenPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningShenzhen University
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