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预测一个对流波中的热分布,使用一种新的训练物理信息的神经网络方法
K Chandan1, Rania Saadeh2, Ahmad Qazza3
1Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, India.
Scientific reports
|March 26, 2024
概括
基于物理学的神经网络 (PINNs) 分析了带有内部热量生成的波状中的热传递. 较高的导热率改善了热分布,而增加的对流导效应则降低了温度概况.
科学领域:
- 热传递工程 热传递工程
- 计算流体动力学的流体动力学.
- 在工程领域的人工智能.
背景情况:
- 翅膀是热交换机的关键组件,因为它们的成本效益,轻量化和紧的设计.
- 了解结构中的热传递,特别是内部热生成和对流效应,对于优化热性能至关重要.
研究的目的:
- 在带有内部热量生成的对流条件下研究波的热分布.
- 探索物理信息神经网络 (PINNs) 的应用,以分析结构中复杂的热传递现象.
- 检查不同超参数对非线性传热方程PINN模型准确性的影响.
主要方法:
- 开发了一种波状的一维稳定状态热传递模型.
- 管理的非线性普通微分方程 (ODE) 被缩小到一个无维的形式.
- 物理信息神经网络 (PINNs) 作为机器学习策略来解决ODE.
- 在数值验证中使用了Runge-Kutta Fehlberg的第四-第五顺序 (RKF-45) 方法.
- 一个PINN模型使用基于平均平方误差的损失函数进行训练,绕过传统的数据驱动方法.
主要成果:
- 这项研究表明,热导率的增加增强了波纹内整体热分布.
- 发现对流电导电变量的增加会降低温度概况.
- 在不依赖传统数据驱动方法的情况下,PINNs有效地预测了传热特性.
结论:
- PINNs提供了一种强大而新的方法来解决工程应用中复杂的传热问题,例如波.
- 这些发现为优化设计提供了宝贵的见解,通过了解导热率和对流效应之间的相互作用.
- 这项研究强调了机器学习策略在推进热系统分析方面的潜力.
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