计算机科学
稳健性(进化)
粒子(生态学)
算法
生物系统
粒径
人工智能
化学
地质学
海洋学
物理化学
生物化学
生物
基因
作者
Sibo Qu,Wei Zhang,Changfu You
标识
DOI:10.1016/j.powtec.2022.117939
摘要
Based on a reliable dataset of particle dynamics from DNS, a R–CNN model combining the functional structures of RNN and CNN was proposed to systematically learn both the temporal and spatial inhomogeneities of particulate flow and predict the particle dynamic. The particle pattern matrix sequence with length of 10 (ms) and β after a specific period of 1 ms were selected as the input and output. The matrix size was set as 25×25 according to the particle size of 0.5 mm and the proportion of particle size to the sampling domain of 1:25. Through validation and final testing, the R–CNN model maintained good predictive accuracy and robustness (validation: δ¯=0.13, R2 = 0.63; testing: δ¯=0.12, R2 = 0.64). The model was proven to be effective with an appropriate input sequence length of 3 (ms) and in an appropriate time span of 4 ms. The model performance is affected by the comprehensiveness of local particle pattern. The particle resolution corresponding to the best performance of the model was 0.8–1. Additionally, adding the feature (ur) that do not have a high correlation with the target is not necessarily effective in improving the model's performance. Overall, the feasibility of a physical–meaning–oriented R–CNN model with an optimizable combination of functional architecture and parameters was confirmed.
科研通智能强力驱动
Strongly Powered by AbleSci AI