首页|Reports from Xi'an Jiaotong University Describe Recent Advances in Machine Learning (Construction and Analysis of the Mesoscale Drag Force Model Based On Machine Learning Methods)
Reports from Xi'an Jiaotong University Describe Recent Advances in Machine Learning (Construction and Analysis of the Mesoscale Drag Force Model Based On Machine Learning Methods)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Machine Learning. According to news reporting originating in Xi'an, People's Republic of China, by NewsRx journalists, research stated, "The presence of mesoscale structures in gas-solid flows significantly complicates the constitutive relationship of the gassolid drag force in coarse-grid simulations. This study employs artificial neural networks to evaluate the performance of various filtered quantities in predicting the mesoscale drag force." Financial supporters for this research include Shaanxi Creative Talents Promotion Plan-Technological Innovation Team, National Natural Science Foundation of China (NSFC), Shaanxi Creative Talents Promotion Plan-Technological Innovation Team, Fundamental Research Funds for the Central Universities, HPC Platform, Xi'an Jiaotong University. The news reporters obtained a quote from the research from Xi'an Jiaotong University, "Our findings indicate that the drag model solely relying on local filtered quantities, such as solid volume fraction, slip velocity, or gas pressure gradient force, is unable to achieve the desired level of accuracy. The consideration of neighboring solid volume fractions significantly enhances the performance of the drag model, particularly in the dilute regions. In two-dimensional systems, the solid volume fractions at the eight grids closest to the considered grid are used. Additionally, the filtered solid volume fraction gradient and the filtered solid volume fraction at a second scale present a viable alternative to replace the eight neighboring solid volume fractions."
Xi'anPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningXi'an Jiaotong University
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