基于神经网络与遗传算法的热气防冰结构优化

楚紫滢; 耿技; 杨倩; 易贤

doi:10.7638/kqdlxxb-2024.0211

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基于神经网络与遗传算法的热气防冰结构优化

研究论文 | 更新时间：2026-05-28

- 基于神经网络与遗传算法的热气防冰结构优化
- Optimization of hot-air anti-icing structures based on neural networks and genetic algorithms
- 空气动力学学报 2026年44卷第4期页码：18-28
- 作者机构：
  
  1. 电子科技大学信息与软件工程学院,成都,611731
  2. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
  3. 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：
  
  楚紫滢（2000—），女，陕西西安人，研究方向：神经网络在飞行器结冰与防除冰领域的应用. E-mail：chuzy@std.uestc.edu.cn
  杨倩*，工程师，研究方向：空气动力学. E-mail：qianyang@sjtu.edu.cn
- 基金信息：
  
  国家重大科技专项（2019-III-0010-0054）;国家自然科学基金青年科学基金（12402276，12302308）
- DOI：10.7638/kqdlxxb-2024.0211
  中图分类号： V221+.92
- 纸质出版：2026
- 稿件说明：
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楚紫滢, 耿技, 杨倩, 等. 基于神经网络与遗传算法的热气防冰结构优化[J]. 空气动力学学报, 2026, 44(4): 18−28.

Chu Ziying, Geng Ji, Yang Qian, et al. Optimization of hot-air anti-icing structures based on neural networks and genetic algorithms[J]. 2026, 44(4): 18-28.
楚紫滢, 耿技, 杨倩, 等. 基于神经网络与遗传算法的热气防冰结构优化[J]. 空气动力学学报, 2026, 44(4): 18−28. DOI： 10.7638/kqdlxxb-2024.0211

Chu Ziying, Geng Ji, Yang Qian, et al. Optimization of hot-air anti-icing structures based on neural networks and genetic algorithms[J]. 2026, 44(4): 18-28. DOI： 10.7638/kqdlxxb-2024.0211.

摘要

热气防冰是飞行器结冰防护的重要技术手段，但传统设计方法多通过数值模拟预测防冰表面温度来实现其结构参数优化，存在设计周期长、设计效率低等问题。本文提出了一种基于混合卷积神经网络（convolutional neural network

CNN）和门控循环单元（gate recurrent unit

GRU）的飞行器防冰表面温度分布预测模型，建立了基于该预测模型和遗传算法的多目标优化设计方法，并将其应用于涡扇发动机进气道热气防冰系统。结果表明：所建立的神经网络模型能够快速且准确地预测防冰表面的温度分布，平均绝对误差小于1.5 K，预测准确度高于95%；基于快速非支配排序遗传算法获得了进气道热气防冰系统一系列满足约束条件的防冰结构优化设计方案。与传统设计方法相比，该优化方法所得结构设计在显著降低引气量的同时提高了防冰区域平均温度，有效提升了热气防冰系统设计效率。本文研究为热气防冰系统的快速高效优化设计提供了一种具有工程应用潜力的数据驱动创新方法。

Abstract

Hot-air anti-icing systems are critical for ensuring the safety and reliability of aircraft operations in icing conditions. Traditional design methods for these systems depend heavily on numerical simulations to predict the surface temperature distribution and optimize the structural parameters

which are inefficient. This study proposed an innovative optimization method that integrated neural networks with genetic algorithms to address these challenges. A multi-objective optimization method based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to minimize the required bleed air quantity and maximize the average temperature within the anti-icing region. The method efficiently identifies a Pareto-optimal solution set

offering diverse design options that meet the constraints. Experimental results show that the optimized designs significantly reduce the required air bleed while enhancing the anti-icing performance. A data-driven prediction model

named Multi-CNNs with GRU (MCG)

for the surface temperature distribution

was developed using optimized Latin Hypercube Sampling (OLHS) and high-dimensional numerical simulation data. The proposed neural network model demonstrates high accuracy

achieving a mean absolute error of less than 1.5 K and a mean prediction accuracy exceeding 96%. The optimization framework used the MCG model as an individual evaluation algorithm to achieve fast prediction of high-dimensional temperature distributions with a computation time of about 5.5 ms per sample

which is thousands of times more efficient compared to traditional numerical simulations. Comparative analysis with a reduced-order model

POD-AlexNet

highlights the superior accuracy and stability of the MCG model

particularly in predicting complex three-dimensional surface temperature distributions. Additionally

the optimization results demonstrate that the proposed method provides a reliable trade-off between improving anti-icing performance and minimizing energy consumption

thereby enhancing the practical applicability. This study provides an efficient tool for optimizing hot-air anti-icing structures

enabling rapid and accurate decision-making in engineering design. The proposed framework holds significant potential for advancing the development of anti-icing systems in aviation and related fields. This study presents a novel data-driven approach with potential for engineering application

facilitating rapid and efficient optimization design of hot-air-anti-icing systems.

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references

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