Influencing factors of air thermal deicing temperature based on BP neural network and multi-factor weight analysis

XU Yang; CAI Anmin; WU Ziqiu; LIN Weirong; ZHANG Linwei; LI Shuiqing

doi:10.19666/j.rlfd.202208162

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Influencing factors of air thermal deicing temperature based on BP neural network and multi-factor weight analysis

Thermal Power Generation Vol. 51, Issue 12, Pages: 131-140(2022)

更新时间：2025-09-28

- Influencing factors of air thermal deicing temperature based on BP neural network and multi-factor weight analysis
- 作者机构：
  
  1. 中国华能集团清洁能源技术研究院有限公司
  2. 清华大学能源与动力工程系
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202208162
  CLC： TM614;TP183
- Published：2022
- 稿件说明：
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XU Yang, CAI Anmin, WU Ziqiu, et al. Influencing factors of air thermal deicing temperature based on BP neural network and multi-factor weight analysis[J]. Thermal Power Generation, 2022, 51(12): 131-140.
DOI：

XU Yang, CAI Anmin, WU Ziqiu, et al. Influencing factors of air thermal deicing temperature based on BP neural network and multi-factor weight analysis[J]. Thermal Power Generation, 2022, 51(12): 131-140. DOI： 10.19666/j.rlfd.202208162.

摘要

对典型叶片的气热除冰系统，建立全尺寸三维换热模型，对流固耦合传热过程进行模拟观察，同时，简化并构建一维换热模型，探究了环境温度等关键参数对除冰气流温度的影响；基于BP神经网络，建立环境条件与最低除冰气流温度之间的对应关系。结果显示：在叶片铺层结构与叶片流道布置的综合影响下，叶片前缘中部处气热除冰难度最大；利用BP神经网络可实现不同条件下的最低除冰气流温度的快速计算和预测；多因素权重分析显示，环境温度、PVC材料导热系数是影响最低除冰气流温度的两大关键参数；对于0.2r～r范围内的除冰，叶片长度也是影响最低除冰气流温度的关键参数，其影响权重达到14.3%。

Abstract

A full-scale three-dimensional heat transfer model is established for a typical blade air thermal deicing system

and the convection of solid coupling heat transfer process is simulated and observed. At the same time

the one-dimensional heat transfer model is simplified and constructed

and the influence of key parameters such as ambient temperature on the temperature of deicing air flow is explored. On the basis of BP neural network

the corresponding relationship between the environmental conditions and the minimum deicing air flow temperature is established. The results show that

under the comprehensive influence of the blade layer structure and the blade channel arrangement

the air thermal deicing at the middle of the blade leading edge is the most difficult. With the help of BP neural network

the rapid calculation and prediction of the minimum deicing air flow temperature under different conditions can be realized. The multi-factor weight analysis shows that

the environmental temperature and the thermal conductivity of PVC material are the two key parameters affecting the minimum deicing air flow temperature. For deicing in the range of 0.2r～r

the blade length is also a key parameter affecting the minimum deicing air flow temperature

with influence weight up to 14.3%.

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