机器学习智能化基于Soret和Dufour效应下的聚合/分离通道中的水磁热传输:一种混合进化数值算法
Muhammad Naeem Aslam1,2, Nadeem Shaukat1,2, Arshad Riaz3
1Department of Physics and Applied Mathematics (DPAM), Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, 45650, Pakistan.
Scientific reports
|December 11, 2023
概括
这项研究使用人工神经网络 (ANN) 和人工蜂群 (ABC) 优化来分析形通道中的水磁流和热传输. 混合方法准确地预测了的产生和能量传输,这对于热系统设计至关重要.
科学领域:
- 流体动力学 流体动力学
- 热传递是一种热传递.
- 非牛顿流体力学 不牛顿流体力学
背景情况:
- 水磁流和热传输在热系统中至关重要.
- 了解非均通道中的产量对于系统效率至关重要.
- 索雷特和杜福效应显著影响能源运输动态.
研究的目的:
- 在索雷特和杜福效应下的形通道中分析水磁流和热传输.
- 研究非牛顿流体流动中的能量传输和产生.
- 使用人工神经网络开发和验证准确的预测模型.
主要方法:
- 利用人工神经网络 (ANN) 来建模复杂的流动动力学.
- 采用混合优化方法:人工蜂群 (ABC) 与神经网络算法 (NNA) 集成.
- 通过与分析和数值方法进行比较来验证结果,包括多个独立运行和错误分析.
主要成果:
- 达到1.32 × 10-8的最低健身功能的值.
- 在混合模型和HAM.之间证明了低绝对误差 (3.55 × 10−7到1.90 × 10−8)
- 混合ANN-ABC方法在预测流量特征方面被证明是强大而可靠的.
结论:
- 混合ANN-ABC模型准确地捕捉了水磁流和热传输动态.
- 这种方法提供了一种可靠的方法,用于在非均通道流中管理.
- 这些发现对设计传统系统和高性能热设备有重大影响.
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