通过机器学习,通过超音速流量从鼻释放的短暂冷却液喷流的预测
Ali Basem1, M Yasiri2, Pooya Ghodratallah3,4
1Faculty of Engineering, Warith Al-Anbiyaa University, Karbala, 56001, Iraq.
Scientific reports
|January 28, 2025
概括
这项研究结合了长期短期记忆 (LSTM) 和正确直角分解 (POD) 以准确预测冷却剂注射流量. 混合模型显示可接受的性能,特别是80%的训练数据,尽管复杂的 vortex 区域的错误较高.
科学领域:
- 流体动力学 流体动力学
- 计算建模计算建模
- 航空航天工程是航空航天工程.
背景情况:
- 准确预测短暂冷却液注入对于航空航天应用至关重要.
- 对于复杂的流体现象,全订单模型 (FOM) 可能在计算上昂贵.
- 替代模型为流量变量估计提供了一个计算效率高的替代方案.
研究的目的:
- 开发和评估一个用于短暂冷却液注入的预测代用模型.
- 结合长短期记忆 (LSTM) 和正确直角分解 (POD) 进行流量变量估计.
- 分析代理模型和全订单模型 (FOM) 之间的差异.
主要方法:
- 使用混合方法,结合正确直角分解 (POD) 和长短期记忆 (LSTM) 网络.
- 采用POD来识别主导的流体结构并减少维度.
- 在短暂冷却液注入数据上训练并测试POD+LSTM模型,评估不同数据分割的性能.
主要成果:
- 通过POD+LSTM混合技术,在短暂冷却液注入过程中成功估计了速度,压力和质量分数.
- 当80%的数据用于培训时,模型性能被认为是可以接受的.
- 对冷却液质量分数和轴向速度观察到更高的预测错误,这是由于循环区中复杂的旋动态.
结论:
- POD+LSTM混合模型为预测短暂冷却液注入流量变量提供了一种高效且可接受的方法.
- 了解代用模型和全订单模型之间的差异来源是模型改进的关键.
- 可能需要进一步的研究来解决复杂的流量区域 (如循环回收区) 中较高的误差.
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