精确预测的动态粘度的polyalpha-olefin化纳米流体使用机器学习
Yazeed AbuShanab1, Wahib A Al-Ammari1, Samer Gowid1
1Department of Mechanical & Industrial Engineering, College of Engineering, Qatar University, Qatar.
Heliyon
|June 9, 2023
概括
机器学习准确地预测了Polyalpha-Olefin-hexagonal化 (PAO-hBN) 纳米流体的粘度. 人工神经网络 (ANN) 比其他模型表现出更高的性能和效率,为工业应用实现了高精度.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
背景情况:
- 精确预测纳米流体动态粘度对于优化它们在各种工业应用中的性能至关重要.
- 传统模型经常难以捕捉影响纳米流体粘度的复杂关系,导致预测不准确.
研究的目的:
- 评估和比较支持向量回归 (SVR),人工神经网络 (ANN) 和适应性神经模糊推理系统 (ANFIS) 对于聚亚尔法-奥莱芬-六角性化 (PAO-hBN) 纳米流体粘度的预测准确性.
- 确定最有效的机器学习模型,以高精度预测PAO-hBN纳米流体动态粘度.
主要方法:
- 用540个PAO-hBN纳米流体的实验数据点训练和验证了三种机器学习模型 (SVR,ANN,ANFIS).
- 使用平均平方误差 (MSE) 和确定系数 (R2) 评估模型性能.
- 优化了ANN模型,排除了剪速参数,以提高预测准确度.
主要成果:
- 这三种模型都准确地预测了PAO-hBN纳米流体粘度,ANFIS和ANN的表现优于SVR.
- 经过优化后的ANN模型获得了0.99994的R2,这表明了极高的准确性.
- 除了剪速参数,ANN准确度在广泛的温度范围内 (-19.7°C-70°C) 提高到<1.89%的绝对相对误差,明显优于传统模型 (11%).
结论:
- 机器学习模型,特别是ANN,对于准确预测PAO-hBN纳米流体的动态粘度非常有效.
- 优化的ANN模型比传统方法有了显著的改进,为预测热力学特性提供了可靠的工具.
- 这些发现对纳米流体在各种工业领域的高效设计和应用有重大影响.
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