PDF
Full
Outline
A deep-learning LSTM-based POD model （LSTM-POD） based on long short-term memory （LSTM） and proper orthogonal decomposition （POD） is developed for the turbulent wake of the square-back Ahmed automotive general model. A high time-resolution reconstruction is achieved by establishing the mapping relationship between the POD modal coefficients of the non-time-resolved planar velocity field and the time-resolved velocity signals at a number of discrete points， and the effect of different time-step configurations， i.e.， the single time step （LSTM-Sin） and multiple time steps （LSTM-Mul） on the reconstruction results is compared. The results show that the LSTM-POD model has strong learning and generalization ability in time series reconstruction， In addition， LSTM-Mul considers temporal continuity and correlation， the reconstructed mode coefficients （lower order） and velocity fields of which are more consistent with the POD reconstructed results compared with that of LSTM-Sin. The deep learning model proposed in this study can alleviate the problems of high resource consumption and low computational efficiency in obtaining high time resolution flow field data through experiments and high-precision numerical simulation.
PDF
320
123
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
