基于物理学的神经网络涉及过渡管道流动的不稳定摩擦
Yuyang Xu1, Ling Zhou1, Yanqing Lu1
1Hohai University, 210098, Nanjing, China.
Water research
|May 24, 2025
概括
这项研究引入了用于水分析的增强物理信息神经网络 (PINN),提高了预测准确性和数据效率. 这种新的方法整合了自适应函数和摩擦模型,用于强大的液压短暂预测.
科学领域:
- 流体动力学 流体动力学
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 水事件对管道系统构成重大风险.
- 传统的数值方法与实时数据和复杂的系统动态作斗争.
- 基于物理学的神经网络 (PINNs) 为液压瞬态分析提供了一个有希望的替代方案.
研究的目的:
- 开发一个强大的物理信息神经网络 (PINN) 以准确预测水事件期间的压力和流速.
- 通过解决非线性和稀疏数据挑战来提高PINN的性能.
- 用数值模拟和实验数据验证拟议的方法.
主要方法:
- 实施局部自适应激活函数 (LAAF),以提高PINN的稳定性,特别是高频数据.
- 整合一个不稳定的摩擦模型与自适应系数来考虑现实世界的不确定性,如管道摩擦和噪音.
- 除研究比较了四个具有不同组件的PINN方案,以评估优化影响.
主要成果:
- 通过LAAF增强的PINN在高频数据方面表现出更好的稳定性.
- 与布鲁诺纳模型的集成有效地减轻了本地最小值,并与参考解决方案达成了良好的协议.
- 实验验证表明,从有限的噪音传感器数据中成功地推断出液压信息 (相对误差<7.00e-2).
结论:
- 提议的优化PINN方法为复杂管道系统中的液压过渡分析提供了可行和准确的解决方案.
- 整合LAAF和先进的摩擦模型增强了PINN处理现实数据不确定性的能力.
- 训练点的战略位置,靠近液压过渡器,可以进一步改善PINN训练和物理洞察力提取.
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