Transient temperature field prediction on anti-icing wing surface based on convolutional temporal networks

CHEN Zhiyong; YANG Xianfeng; CHEN Ningli; YI Xian; PENG Bo

doi:10.7638/kqdlxxb-2024.0099

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Transient temperature field prediction on anti-icing wing surface based on convolutional temporal networks

更新时间：2026-05-28

- Transient temperature field prediction on anti-icing wing surface based on convolutional temporal networks
- Vol. 43, Issue 12, Pages: 99-108(2025)
- 作者机构：
  
  1. 西南石油大学计算机与软件学院,成都,610500
  2. 中国空气动力研究与发展中心结冰与防除冰重点实验室,绵阳,621000
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0099
  CLC： V211.3;TP399
- Published：2025
- 稿件说明：
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陈志勇, 杨先凤, 陈宁立, 等. 基于卷积时序网络的防冰翼面瞬态温度场预测[J]. 空气动力学学报, 2025, 43(12): 99−108.

CHEN Zhiyong, YANG Xianfeng, CHEN Ningli, et al. Transient temperature field prediction on anti-icing wing surface based on convolutional temporal networks[J]. 2025, 43(12): 99-108.
陈志勇, 杨先凤, 陈宁立, 等. 基于卷积时序网络的防冰翼面瞬态温度场预测[J]. 空气动力学学报, 2025, 43(12): 99−108. DOI： 10.7638/kqdlxxb-2024.0099

CHEN Zhiyong, YANG Xianfeng, CHEN Ningli, et al. Transient temperature field prediction on anti-icing wing surface based on convolutional temporal networks[J]. 2025, 43(12): 99-108. DOI： 10.7638/kqdlxxb-2024.0099.

摘要

电加热防冰是防止飞机机翼结冰的关键技术之一，快速准确预测防冰翼面瞬态温度场对电加热系统的优化设计具有重要意义。为实现对防冰翼面瞬态温度场的快速预测，缩短电加热防冰系统优化设计周期，提出了一种结合本征正交分解（proper orthogonal decomposition

POD）和卷积时序网络的预测方法。该方法首先利用POD对温度数据做降维处理，随后以工作条件参数作为输入，使用降维后的模态时间系数作为输出，构建了基于一维卷积和时间卷积的卷积时序网络，并且引入多头注意力机制突出关键特征，最后使用样本准确率来评估模型对瞬态温度场预测的准确性。此外，为探究模型超参数对预测性能的影响，设置消融试验验证了提出的网络结构的有效性。结果表明，所提出的网络模型对防冰翼面瞬态温度场的预测具有较高的准确性，在测试集上的预测精度达94.4%。

Abstract

Electric heating for anti-icing is a crucial technique to prevent icing on aircraft wings. Accurate prediction of transient temperature fields on the anti-icing surface of a wing is essential for optimizing the design of electric heating systems. To achieve a quick prediction of these transient temperature fields and shorten the optimization cycle of electric anti-icing systems

we propose a predictive method that couples the proper orthogonal decomposition (POD) with the convolutional temporal networks. This method first utilizes POD to perform dimensionality reduction on the temperature data. Subsequently

with the

operation parameters as input and the reduced modal time coefficients as output

we construct a convolutional temporal network based on the 1D convolutional neural networks and the temporal convolutional networks

incorporating a multi-head attention mechanism to highlight key features. We introduce the sample accuracy (

) evaluation metric to evaluate the accuracy of transient temperature field predictions. Additionally

we explore the impact of model hyperparameters on the prediction performance and validate the effectiveness of the network structure through ablation experiments. Experimental results demonstrate that the proposed method achieves an

of 94.4% on the test set

indicating a high accuracy in predicting transient temperature fields on anti-icing wing surfaces.

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Keywords

references

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