可靠的智能模型用于估计通过不同尺寸的光滑通道在两相冷凝中的摩擦压力下降
M A Moradkhani1, S H Hosseini2, Mengjie Song3,4
1Department of Chemical Engineering, Ilam University, Ilam, 69315-516, Iran.
Scientific reports
|May 7, 2024
概括
这项研究开发了通用模型来预测在各种道的凝结过程中发生的摩擦压力下降 (FPD). 高斯过程回归 (GPR) 模型实现了高精度,超过了现有的方法.
科学领域:
- * 热力学和流体力学
- * 热传递和双相流程
背景情况:
- * 精确预测摩擦压力下降 (FPD) 对于双相流冷凝系统至关重要.
- * 现有的模型在不同的操作条件和通道大小 (从迷你/微型到宏观) 中具有适用性的局限性.
研究的目的:
- * 开发通用,智能模型来估计FPD在光滑的迷你/微型和常规通道的凝结过程中.
- * 克服目前文献模型的高偏差和有限的适用性.
主要方法:
- *从可靠的来源编制了一个广泛的数据集 (8037个样本,23种液体).
- * 斯皮尔曼的相关性分析,以确定关键的输入变量.
- *软计算范式的应用:基于洛克哈特-马丁尼利分离模型的多层感知器 (MLP),高斯过程回归 (GPR) 和辐射基函数 (RBF).
主要成果:
- *高斯过程回归 (GPR) 模型在测试阶段表现出卓越的性能,平均绝对相对误差 (AARE) 为4.10%,R平方值为99.23%.
- * 拟议的模型显著优于现有的文献相关性,在统计和视觉分析方面明显优越.
- *开发的预测工具准确地捕捉了冷凝FPD与操作参数的物理变化.
结论:
- *开发的软计算模型,特别是GPR,在广泛的道中在凝结过程中为FPD提供了准确和通用的预测.
- *这些新的工具在传统方法上提供了显著的进步,增强了系统设计和分析.
- * 灵敏度分析澄清了各种因素对冷凝FPD的影响.
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