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人工神经网络计算混合纳米流体的热传输流在矩形几何体的计算
Saraj Khan1, Muhammad Imran Asjad1, F Maiz2
1Department of Mathematics, University of Management and Technology Lahore, 54770, Pakistan.
本研究使用机器学习方法研究混合纳米流体流中的热传递. 基于物理学的神经网络与原子轨道搜索 (PINNs-AOS) 模型准确地预测了各种参数下的流体动力学和温度概况.
科学领域:
- 流体动力学 流体动力学
- 热传递是一种热传递.
- 纳米材料是一种纳米材料.
背景情况:
- 混合纳米流体为传热应用提供了增强的热性能.
- 了解不稳定的流动动态对于优化工业流程至关重要.
- 磁场可以影响流体行为和传热特性.
研究的目的:
- 分析平行板之间石墨烯-Fe3O4/水混合纳米流体不稳定的挤压流.
- 为了研究磁场,吸入/注入和板拉伸对传热的影响.
- 开发和验证基于机器学习的框架 (PINNs-AOS) 来解决复杂的流体动力学方程.
主要方法:
- 使用非线性PDEs进行流体流动和热传递的数学建模.
- 通过相似性转换将PDE转化为ODE.
- 使用混合机器学习方法解决ODEs:具有原子轨道搜索 (PINNs-AOS) 的物理信息神经网络.
主要成果:
- 挤压参数 (Sq) 增加了速度,而吸入参数 (S) 减少了速度.
- 拉伸参数 (λ) 增加了靠近墙壁的速度,但进一步减少了它.
- 较高的吸入/注入和温度比 (δ) 增加温度;挤压参数显示轻微下降.
- 通过PINNs-AOS模型验证,显示了高准确度的最佳统计指标.
结论:
- 该PINNs-AOS框架准确地预测了混合纳米流体中的热传递.
- 这项研究提供了对优化混合纳米流体流向用于工业应用的见解.
- 开发的模型为复杂的流体动力学问题提供了可靠的计算工具.
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