基于分布参数模型的降膜式蒸发器性能智能仿真研究

乔波涛; 杜燊; 李明佳; 宋霞

doi:10.7652/xjtuxb202409005

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基于分布参数模型的降膜式蒸发器性能智能仿真研究

—————————————————————— | 更新时间：2025-07-28

- 基于分布参数模型的降膜式蒸发器性能智能仿真研究
- Intelligent Simulation of Performance of Falling Film Evaporator Based on Distributed Parameter Model
- 西安交通大学学报 2024年58卷第9期页码：38-47+82
- 作者机构：
  
  1. 西安交通大学能源与动力工程学院,西安,710049
  2. 北京理工大学机械与车辆学院,北京,100081
  3. 广东美的暖通设备有限公司,广东,佛山,528311
- 作者简介：
  
  : 2024-03-09。作者简介: 乔波涛(1999—),男,硕士生
  杜燊(通信作者),男,助理教授。基金项目: 国家自然科学基金资助项目(52306272,52076161)。
- 基金信息：
  
  : 2024-03-09。作者简介: 乔波涛(1999—),男,硕士生;杜燊(通信作者),男,助理教授。基金项目: 国家自然科学基金资助项目(52306272,52076161)。
- DOI：10.7652/xjtuxb202409005
  中图分类号： TK124
- 纸质出版：2024
- 稿件说明：
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乔波涛, 杜燊, 李明佳, 等. 基于分布参数模型的降膜式蒸发器性能智能仿真研究[J]. 西安交通大学学报, 2024,58(9):38-47+82.

QIAO Botao, DU Shen, LI Mingjia, et al. Intelligent Simulation of Performance of Falling Film Evaporator Based on Distributed Parameter Model[J]. 2024, 58(9): 38-47+82.
乔波涛, 杜燊, 李明佳, 等. 基于分布参数模型的降膜式蒸发器性能智能仿真研究[J]. 西安交通大学学报, 2024,58(9):38-47+82. DOI： 10.7652/xjtuxb202409005.

QIAO Botao, DU Shen, LI Mingjia, et al. Intelligent Simulation of Performance of Falling Film Evaporator Based on Distributed Parameter Model[J]. 2024, 58(9): 38-47+82. DOI： 10.7652/xjtuxb202409005.

摘要

为对大型水平管降膜式蒸发器进行高精度的设计计算和优化分析

建立了降膜式蒸发器的智能换热仿真模型

并分析研究了蒸发器的性能。首先

采用分布参数法建立了单出口水平管降膜式蒸发器的换热仿真模型; 其次

通过贝叶斯优化算法利用蒸发器实验数据对模型进行参数辨识

使得换热仿真模型的预测精度和适应性有所提高; 最后

探究了传热系数和热流密度沿管长方向的变化情况

以及满液区换热面积所占比例对蒸发器换热量的影响规律。仿真结果表明:结合分布参数法和贝叶斯优化算法的智能换热仿真模型对大型水平管降膜式蒸发器19个工况换热量的预测误差不超过±6%; 在较大的换热量范围内

单出口水平管降膜式蒸发器的最优池沸腾换热面积比例维持不变; 从管内对流换热和二次布液两个角度优化降膜式蒸发器能有效提升其换热性能。

Abstract

To carry out high-precision design calculations and optimization analyses for a large horizontal-tube falling film evaporator

an intelligent simulation model for heat exchange of the falling film evaporator is established

and performance analysis research is conducted on the evaporator. Firstly

a simulation model for heat exchange of a single-outlet horizontal-tube falling film evaporator is constructed based on a distributed parameter model. Secondly

the Bayesian optimization algorithm and experimental data from the evaporator are used to identify the parameters of the model. This approach improves the prediction accuracy and adaptability of the simulation model for heat exchange. Finally

the changes in heat transfer coefficient and heat flux along the axis of the tube are explored

and the influence of the proportion of heat transfer area in the flooded zone on the heat transfer of the evaporator is examined. The research findings show that the intelligent simulation model

integrating the distributed parameter method and Bayesian optimization algorithm

achieves a ±6% prediction error for heat transfer in the evaporator across 19 operating conditions. Within a wide range of heat exchange

the optimal proportion of heat transfer area in the flooded zone remains unchanged. Optimization from the perspectives of convective heat transfer inside the tube and secondary liquid distribution will effectively improve the performance of heat transfer of the falling film evaporator.

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Keywords

references

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