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Experimental and numerical study of nanofluid in heat exchanger fitted by modified twisted tape: exergy analysis and ANN prediction model

机译:改进的扭曲带安装在换热器中的纳米流体的实验和数值研究:火用分析和ANN预测模型

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摘要

Present study provides an experimental investigation of the exergetic efficiency due to the flow and heat transfer of nanofluids in different geometries and flow regimes of the double pipe heat exchangers. The experiments with different Geometrical Progression Ratio (GPR) of twists as the new modified twisted tapes and different nanofluid concentration were performed under similar operation condition. Pitch length of the proposed twisted tapes and consequently the twist ratios changed along the twists with respect to the Geometrical Progression Ratio (GPR) whether reducer (RGPR < 1) or increaser (IGPR > 1). Regarding the experimental data, utilization of RGPR twists together with nanofluids tends to increase exergetic efficiency. Since the Prediction of exergetic efficiency from experimental process is complex and time consuming, artificial neural networks for identification of the relationship, which may exist between the thermal and flow parameters and exergetic efficiency, have been utilized. The network input consists of five parameters (Re, Pr, φ,Tr, GPR) that crucially dominate the heat transfer process. The results proved that the introduced ANN model is reliable and capable in proposing a proper development plan for a heat exchanger and/or to determine the optimal plan of operation for heat transfer process.
机译:本研究提供了对由于在双管式换热器的不同几何形状和流动方式下纳米流体的流动和传热而产生的能效的实验研究。在相似的操作条件下进行了不同的扭曲几何进展比(GPR)作为新的改性扭曲带和不同纳米流体浓度的实验。无论是减速机(RGPR <1)还是增量机(IGPR> 1),建议的绞合带的节距长度以及相应的绞合率都相对于几何级数比(GPR)沿绞合方向变化。关于实验数据,RGPR捻度与纳米流体一起使用往往会增加能量利用效率。由于从实验过程预测能效效率是复杂且费时的,因此已经利用人工神经网络来识别热和流量参数与能效效率之间可能存在的关系。网络输入由五个参数(Re,Pr,φ,Tr,GPR)组成,这些参数至关重要地控制着传热过程。结果证明,所引入的人工神经网络模型是可靠的,能够为热交换器提出适当的开发计划和/或确定传热过程的最佳运行计划。

著录项

  • 来源
    《Heat and mass transfer》 |2017年第4期|1413-1423|共11页
  • 作者单位

    Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran;

    Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Iran;

    Department of Chemistry, Saveh Branch, Islamic Azad University, Saveh, Iran;

    Department of Chemistry, Saveh Branch, Islamic Azad University, Saveh, Iran;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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