混合管束交叉流热流性能的数值研究与预测

Selma Akcay, Selim Buyrukoglu
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摘要

在这项研究中,对交错配置的混合管上空气横流的流体动力学和热行为进行了数值研究。在 ANSYS Fluent 程序的帮助下进行了 CFD(计算流体动力学)研究。在研究中,创建了 12 个不同的管束模型,其中既有圆形管束,也有翼形管束。翼形管以不同的攻击角(θ:0° 和 180°)放置在管束中。使用的雷诺数范围为 4000 ≤ Re ≤ 12000。为了观察混合管和雷诺数对热场和流场的影响,采集了速度、压力和温度等值线。研究结果表明,所有管束模型的努塞尔特数(Nu)和性能标准(PC)均随 Re 值的增加而增加,摩擦因数(f)则随 Re 值的增加而降低。混合管束的摩擦因数高于翼形管束,但大大低于圆形管束。在混合模型中,模型 6 的传热效果最好,模型 11 的摩擦因数最低,模型 4 的 PC 效果最好。此外,还创建了三种不同的叠加集合模型来预测 Nu、f 和 PC 值,用于 CFD 分析。这些模型分别是 XGBoost 集合模型、DNN 集合模型以及 DNN、XGBoost 和 RF 集合模型。研究结果表明,在 CFD 分析中,XGBoosts 集合比其他集合更有利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Numerical study and prediction of thermohydraulic performance in crossflow over hybrid tube bundles

Numerical study and prediction of thermohydraulic performance in crossflow over hybrid tube bundles

In this study, the hydrodynamics and thermal behavior in the crossflow of air passing over hybrid tubes in the staggered configuration were numerically investigated. CFD (Computational Fluid Dynamics) study were conducted with the help of the ANSYS Fluent program. In the study, 12 different tube bundle models were created with a combination of circular and wing-shaped tubes. The wing-shaped tubes were placed in the tube bundle at different attack angles (θ: 0° and 180°). Reynolds numbers in the range of 4000 ≤ Re ≤ 12,000 were used. To observe the effects of hybrid tubes and Reynolds numbers on thermal and flow fields, velocity, pressure, and temperature contours were acquired. According to the findings, Nusselt number (Nu) and performance criteria (PC) increased, and friction factor (f) decreased with increasing Re for all tube bundle models. The friction factor of hybrid tube bundles was higher than wing-shaped tube bundles but considerably lower than circular tube bundles. Among the hybrid models, the best heat transfer was obtained in Model 6, the lowest friction factor in Model 11, and the best PC in Model 4. Also, three different stacked ensemble models were created to predict Nu, f, and PC values for CFD analysis. These models are the ensemble of XGBoosts, the ensemble of DNNs, and the ensemble of DNN, XGBoost, and RF. This study revealed that ensemble of XGBoosts is more beneficial than the other in the CFD analysis.

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