粘度IV-应用到124个工业重要流体的热量缩放
Viktor Martinek1, Ian Bell2, Roland Herzog1
1Interdisciplinary Center for Scientific Computing, Heidelberg University, 69120 Heidelberg, Germany.
Journal of chemical and engineering data
|February 19, 2025
概括
一个新的残留缩 (RES) 模型改善了纯流体的粘度预测. 这种增强模型的准确性与REFPROP相当,平均相对偏差明显较低.
科学领域:
- 热力学是一种热力学.
- 流体属性 流体属性
- 化学工程是化学工程的重要组成部分.
背景情况:
- 之前的残留缩 (RES) 模型将粘度与制冷剂的残留度联系起来,并扩展到124种纯流体.
- 在以前的RES模型中,手动优化导致平均绝对相对偏差 (AARD) 为3.36%,高于REFPROP 10.0.0.
- 在REFPROP 10.0中的现有模型实现了粘度预测的AARD值为2.74%.
研究的目的:
- 开发一个改进的剩余缩 (RES) 模型来预测流体粘度.
- 通过代调整全球和本地参数和数据选来优化RES模型.
- 减少 RES 模型的平均绝对相对偏差 (AARD),并将其性能与 REFPROP 10.0.0 进行比较.
主要方法:
- 对可再生能源模型的三期功率函数的代优化,适应全球和本地参数.
- 对实验粘度数据的选,以改进模型的准确性.
- 开发一个Python包,以实现优化的可再生能源模型的可访问性.
主要成果:
- 优化的RES模型实现了2.76%的AARD降低,与REFPROP 10.0的性能紧密相匹配.
- 新的RES模型显示,与REFPROP 10.0的0.7%相比,0.03%的平均相对偏差 (ARD) 显著较低.
- 改进后的模型采用了三期功率函数,简化了以前的四期方法.
结论:
- 增强的RES模型为广泛的纯流体提供了准确的粘度预测.
- 优化的模型提供了比REFPROP这样的既定软件具有竞争力的替代方案,并改进了偏差指标.
- 一个Python包的可用性促进了这种先进的粘度预测方法的实际应用.
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