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为JRT进行科学准备:大型射电望远镜的风压预测模型基于多传感器的真实数据
Rui Wu1, Zhong Cao1,2,3,4, Feng Wang2,3,4
1School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China.
Heliyon
|September 26, 2024
概括
这项研究为诸如京东120米射电望远镜 (JRT) 等大型射电望远镜提供了一种新的风压预测模型. 该模型使用自适应降噪和BiLSTM网络进行准确的实时风力负载预测.
科学领域:
- 无线电天文学工程 工程学
- 大气动力学和大气模型.
- 大规模仪器的结构工程.
背景情况:
- 大型射电望远镜,如即将推出的京东120米射电望远镜 (JRT),容易受到风力负荷的影响.
- 风引起的结构变形和指向错误可能会损害观测的准确性.
- 实时动态风力预测对于减轻这些影响至关重要.
研究的目的:
- 为大型射电望远镜开发和验证实时风压预测模型.
- 为了提高像JRT这样的望远镜的操作稳定性和指向精度.
- 为积极的风力负载减轻策略提供基础.
主要方法:
- 利用昆明40米射电望远镜 (KRT) 的传感器数据进行模型开发.
- 采用适应式噪声过和变化模式分解 (VMD) 来进行数据预处理.
- 实现了双向长期短期记忆 (BiLSTM) 神经网络,用于风压预测.
主要成果:
- 开发的模型在预测KRT反射器表面的风压方面取得了很高的准确性.
- 具有最低的平均绝对误差 (MAE),根平均平方误差 (RMSE) 和平均绝对百分比误差 (MAPE) 的表现卓越.
- 获得了高的确定系数 ( = 0.9392在45°的斜角),MAE,RMSE和MAPE值分别为1.2377,1.4923和1.82%.
结论:
- 建议的风压预测模型是有效的实时监测和减轻风载荷对射电望远镜的影响.
- 这项技术对于确保像JRT这样的大型射电望远镜的高精度运行至关重要.
- 该模型有助于调整控制参数,减少风载荷干扰并保持观测的准确性.
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