基于人工神经网络的油基混合纳米流体粘度的预测建模
Muhammad Furqan1, Fahim Raees1, Muhammad Khalid2
1Department of Mathematics, NED University of Engineering and Technology 75270 Karachi, Pakistan.
MethodsX
|December 29, 2025
概括
这项研究研究了铁纳米流体的粘度,发现它随着固体体积分和温度的提高而下降. 人工神经网络 (ANN) 模型比响应表面方法 (RSM) 提供更高的预测准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术 纳米技术
- 流体动力学 流体动力学
背景情况:
- 纳米流体在微电子和储能等行业中增强热性能.
- 使用纳米流体优化冷却和能量传输至关重要.
- 了解纳米流体粘度是它们应用的关键.
研究的目的:
- 为了研究铁纳米流体的粘度变化.
- 分析固体体积分和温度对粘度的影响.
- 开发纳米流体粘度的预测模型.
主要方法:
- 铁 (NiFe2O4) 是通过sol-gel自燃合成的纳米粒子.
- 使用X射线衍射,FTIR和SEM进行纳米粒子表征.
- 纳米流体的粘度测量 (0-1%度) 在40-80°C之间.
- 使用人工神经网络 (ANN) 和响应表面方法 (RSM) 的预测建模.
主要成果:
- 纳米流体粘度随着固体体积分 (φ) 和温度的增加而降低.
- 安恩获得了0.9999的高R2值和低的MSE (0.00001).
- RSM获得了0.9987的R2值和0.0002的MSE值,表明精度低于ANN.
结论:
- 该研究成功地将实验粘度数据与ANN和RSM预测相关联.
- 在预测油基混合纳米流体的粘度方面,ANN表现出卓越的准确性.
- 研究结果为优化纳米流体在传热和能源系统中的应用提供了宝贵的见解.
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