基于最佳预后元模型的颗粒污垢特性全局敏感性分析

doi:10.16085/j.issn.1000-6613.2023-1156

摘要/Abstract

摘要：

通过敏感性分析可识别出重要变量和无关变量，有效筛选数据降低模型复杂程度，并辅助优化设计。以内置转子传热管内的颗粒污垢为研究对象，采用欧拉-欧拉模型和颗粒污垢模型进行数值实验获取训练数据空间，基于最佳预后元模型开展全局敏感性分析，定量比较了颗粒直径、颗粒浓度、入口流速和入口温度的影响程度。对颗粒污垢四种沉积速率分析表明，入口温度对扩散沉积和热泳沉积的影响最显著，总影响分别为65.4%、58.6%；颗粒直径对湍流泳沉积和重力沉积的影响最明显，总影响分别为53.9%、75.0%。在此基础上，进一步分析了沉积率和污垢热阻的总影响、主影响及交互影响。结果表明：颗粒直径对沉积率和污垢热阻的影响均最大，总影响分别为52.7%、60.2%，各输入变量对沉积率和污垢热阻的交互影响的和分别为59.7%、42.5%，且随着四个输入变量的增加，沉积率和污垢热阻均增大。颗粒直径是影响内置转子传热管颗粒污垢问题的首要因素，在换热器设计中应予以重点考虑，其次是流体温度与流速。

关键词: 结垢, 两相流, 数值模拟, 全局敏感性, 元模型

Abstract:

Sensitivity analysis can identify important and irrelevant variables, effectively filter the data to reduce the complexity of the model and assistant optimization design. In this paper, the particulate fouling in the heat transfer tube with an inserted rotor was taken as the research object, and the Eulerian-Eulerian model and the particulate fouling model were used to conduct numerical experiments to obtain the training data space. The global sensitivity analysis was carried out based on the metamodel of optimal prognosis, and the influence degree of particle diameter, particle concentration, inlet velocity, and inlet temperature was quantitatively compared. The analysis of four deposition rates showed that the inlet temperature had the most significant influence on diffusion deposition and thermophoretic deposition, and the total effects were 65.4% and 58.6%, respectively. The influence of particle diameter on turbulent flow deposition and gravity deposition was the most obvious, and the total effects were 53.9% and 75.0%, respectively. On this basis, the total, main, and interaction effects of deposition rate and fouling resistance were further analyzed. The results showed that particle diameter had the greatest influence on deposition rate and fouling resistance, and the total effects were 52.7% and 60.2%, respectively. The sum of interaction effects of each input variable on the deposition rate and the fouling thermal resistances were 59.7% and 42.5%, respectively. With the increase in the four input variables, both deposition rate and fouling resistance increased. In order to solve the fouling problem of a heat transfer tube with an inserted rotor, it was necessary to first consider reducing the particle diameter while controlling the fluid temperature and flow rate.

Key words: fouling, two-phase flow, numerical simulation, global sensitivity, metamodel

中图分类号:

TK172

谢广烁, 张斯亮, 何松, 肖娟, 王斯民. 基于最佳预后元模型的颗粒污垢特性全局敏感性分析[J]. 化工进展, 2024, 43(1): 328-337.

XIE Guangshuo, ZHANG Siliang, HE Song, XIAO Juan, WANG Simin. Global sensitivity analysis for particulate fouling performance based on metamodel of optimal prognosis[J]. Chemical Industry and Engineering Progress, 2024, 43(1): 328-337.

图/表 13

图1 几何模型(单位：mm)

表1 不同温度下水的黏度[29]

温度/K	水的动力黏度/Pa·s
293	0.0010040
303	0.0008015
313	0.0006533
323	0.0005494
333	0.0004699
343	0.0004061

表2 物性参数

物性	密度/kg·m^-3	比热容/J·kg^-1·K^-1	热导率/W·m^-1·K^-1
水	988	4182	0.6
氧化镁	1740	873	25.0
铝	2719	871	202.4
铁	8030	502.48	16.3

图2 基于MOP的全局敏感性分析

图3 网格独立性测试

图4 内置转子传热管Nu数计算验证

图5 污垢热阻模拟值与实验数据对比曲线

图6 四个沉积速率的总影响CoP矩阵

图7 沉积率和污垢热阻的总影响CoP矩阵

图8 md的主影响和交互影响柱状图

图9 Rf的主影响和交互影响柱状图

图10 预测混合项对md的响应

图11 预测混合项对Rf的响应

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