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基于物理学的神经网络用于预测柴油发动机中的气体流动力学和未知参数
Kamaljyoti Nath1, Xuhui Meng2, Daniel J Smith3
1Division of Applied Mathematics, Brown University, Providence, RI, USA.
Scientific reports
|August 22, 2023
概括
这项研究引入了用于柴油发动机健康监测的物理信息神经网络 (PINN). 该方法准确地预测发动机动力学和识别参数,使变形几何轮增压器能够进行主动维护.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 具有变形几何学轮增压器和废气再循环的柴油发动机需要严格的健康监测.
- 准确的发动机动力学建模和参数识别对于预测维护需求至关重要.
- 由于引擎特定的经验公式,传统的平均值模型可能缺乏通用性.
研究的目的:
- 开发和评估用于柴油发动机健康监测的物理信息神经网络 (PINN).
- 用测量数据识别未知的参数并预测引擎动态.
- 将PINN与深度神经网络 (DNN) 结合起来,以实现更具适应性和全面的引擎状态表示.
主要方法:
- 将PINN模型应用于使用状态变量测量的柴油发动机.
- 整合DNNs以在平均值框架内建模引擎特定的实证公式.
- 使用实际的发动机运行条件来输入数据和模拟数据进行输出验证.
主要成果:
- 该PINN模型准确地预测未知的参数和引擎动态,使用干净和杂的数据.
- 损失函数中的自适应权重显著提高了合速度.
- 结合PINN和DNN的方法提供了灵活和适应性的发动机状态表示.
结论:
- PINNs提供了一种强大的基于物理的方法,用于在柴油发动机中预测现实世界的动态系统.
- 将PINN与DNN结合起来,可以提高发动机健康监测模型的适应性和特异性.
- 这种综合方法方便对柴油发动机进行准确的健康评估和积极的维护规划.
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