首页|Research Conducted at Huazhong University of Science and Technology Has Updated Our Knowledge about Machine Learning (Arrangement Optimization of Spherical Dimp les Inside Tubes Based On Machine Learning for Realizing the Optimal Flow Pattern)
Research Conducted at Huazhong University of Science and Technology Has Updated Our Knowledge about Machine Learning (Arrangement Optimization of Spherical Dimp les Inside Tubes Based On Machine Learning for Realizing the Optimal Flow Pattern)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on Machine Learning is now available.According to news reporting from Wuhan,People's Republic of China,by NewsRx journalists,research stated,"Optimizing the arrangement of dimples i n a dimpled tube can alter the flow field structure and improve its heat transfe r performance.However,direct optimization through numerical simulation techniq ues is challenging due to the high nonlinearity and time-consuming nature of the heat transfer process." Funders for this research include National Natural Science Foundation of China ( NSFC),National Key Research and Development Program of China,Core Technology R esearch Project of Shunde District,Foshan City,China.The news correspondents obtained a quote from the research from the Huazhong Uni versity of Science and Technology,"This study proposes a novel optimization met hod based on a surrogate model combined with machine learning technology.By con sidering the relative positions of the dimples as design variables and the compr ehensive performance of the dimpled tube as the optimization objective,the opti mal arrangement of dimples is achieved,and the flow field structure under the o ptimal layout configuration is determined.The results indicate that the optimal design involves five dimples forming a V-shaped dimpled strip,which deflects t he flow and disrupts the boundary layer development.The optimal flow pattern wi th multi-longitudinal vortexes is formed which promotes the mixing between the c old and hot fluids and intensifies heat transfer with a moderate pressure loss.The performance of the optimal structure is evaluated within a Reynolds number r ange of 600 to 1200.As a result,the heat transfer capacity is increased by 5.3 76-8.819 times compared to plain tubes,with an increase in the flow resistance factor by 3.102-5.264.The comprehensive performance achieves a range of 3.683-5 .069.Furthermore,the research conducted particle image velocimetry experiments to observe the flow field structure inside the optimized tube,thus validating the effectiveness of the optimization."
WuhanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningHuazhong University of Scienc e and Technology
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