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一种基于神经网络的方法,用于冷气推进系统中推力预测
Morteza Farhid1, Mohammad Reza Ghavidel Aghdam2, Moharram Shameli2
1Iranian Space Research Center, Tabriz, Iran. m.farhid@isrc.ac.ir.
Scientific reports
|July 22, 2025
概括
本研究引入了一种机器学习模型,用于预测冷气推进器性能. 开发的系统具有前神经网络和用户友好的界面,准确地估计推力,以改进推进系统设计.
科学领域:
- 航空航天工程 航空航天工程
- 计算科学 计算科学
背景情况:
- 准确的推力预测对于冷气推进器的设计和优化至关重要.
- 传统方法可能缺乏复杂操作参数所需的精度.
研究的目的:
- 开发和验证一种机器学习模型,用于冷气推进器中精确推力预测.
- 创建一个可访问的工具,用于实时推力估计和系统分析.
主要方法:
- 使用前神经网络 (FFNN),对关键操作参数进行训练.
- 集成的储存压力,质量流速,喷嘴长度,出口压力和推进剂质量密度.
- 开发了一个图形用户界面 (GUI),用于实际应用和实时预测.
主要成果:
- 该FFNN模型实现了高预测准确度 (0.98) 和F1得分 (0.981).
- 在各种测试案例中证明了稳定性和通用性.
- 图形用户界面促进了简化设计和性能优化.
结论:
- 机器学习为推进冷气推进器技术提供了一种强大的方法.
- 拟议的方法提高了推进系统开发的效率和创新.
- 这项工作为航空航天行业的工程师提供了一个实用的工具.
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