多模态飞机热管理系统双温协同架构设计与优化

doi:10.7527/S1000-6893.2026.33166

流动控制与热管理专刊

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多模态飞机热管理系统双温协同架构设计与优化

纪胜楠, 艾青(), 关鹏达, 刘梦, 帅永

哈尔滨工业大学能源科学与工程学院，哈尔滨 150001

收稿日期:2025-12-01 修回日期:2025-12-15 接受日期:2026-01-28 出版日期:2026-02-04 发布日期:2026-02-03
通讯作者: 艾青 E-mail:hitaiqing@hit.edu.cn
基金资助:
国家自然科学基金(52476066);国家自然科学基金(52406077)

Design and optimization of dual-temperature collaborative architecture for multi-mode aircraft thermal management system

Shengnan JI, Qing AI(), Pengda GUAN, Meng LIU, Yong SHUAI

School of Energy Science and Engineering，Harbin Institute of Technology，Harbin 150001，China

Received:2025-12-01 Revised:2025-12-15 Accepted:2026-01-28 Online:2026-02-04 Published:2026-02-03
Contact: Qing AI E-mail:hitaiqing@hit.edu.cn
Supported by:
National Natural Science Foundation of China(52476066)

摘要/Abstract

摘要：

现代飞机的热管理系统需要在多模式、多目标的任务环境下实现精确控制。提出一种自适应燃油热管理系统，旨在高度变化的热负荷下实现燃油与蒙皮双目标温度的协同调控。通过物理层热流路径重构与控制层自适应优化的协同设计，系统在隐身和非隐身模式转换过程中展现出卓越的动态性能。在物理层设计上，系统利用可重构三通阀网络构建了开环和闭环热传递路径，解决了燃油和蒙皮的热负荷竞争矛盾。在控制层优化方面，内嵌的自适应模糊PID策略通过实时调节压缩机转速、膨胀阀开度和液冷泵转速，实现了双目标温度的动态解耦控制。基于高保真动态模型的仿真结果表明，相较于传统PID控制，所提出的自适应热管理系统实现了燃油温度稳定时间缩短55 s，响应性能提高11.85%。蒙皮温度稳定时间减少83 s，响应速度提升23.38%。此外，自适应调控有效改善了执行设备的动态响应特性，降低了机械损耗和功率消耗。液冷泵转速峰值降低了400 r/min，压缩机转速峰值下降了700 r/min，膨胀阀开度过响应减少了15.10%。这种物理层解耦与控制层优化的协同设计，为现代飞机应对复杂多变的作战任务提供了高效可靠的热管理解决方案。

关键词: 飞机热管理系统, 动态模式切换, 双目标温度调节, 自适应模糊PID控制, 传热路径优化

Abstract:

The thermal management system of contemporary aircraft necessitates meticulous regulation in a multi-modal， multi-objective operational environment. This study introduces an adaptive fuel thermal management system designed to achieve synergistic control of dual target temperatures for fuel and airframe skin under highly dynamic thermal loads. Through the co-optimization of physical-layer thermal pathway reconfiguration and control-layer adaptive refinement， the system demonstrates superior transient performance during transitions between stealth and non-stealth operational modes. At the physical-layer level， the system employs a reconfigurable three-way valve architecture to establish both open-loop and closed-loop heat dissipation pathways， effectively mitigating thermal load contention between fuel and skin. At the control-layer level， an embedded adaptive fuzzy PID algorithm facilitates dynamic decoupling of dual-temperature regulation via real-time modulation of compressor rotational velocity， electronic expansion valve aperture， and liquid cooling pump operational speed. High-fidelity dynamic simulations reveal that， relative to conventional PID control， the proposed adaptive thermal management system attains a 55 s reduction in fuel temperature settling time， accompanied by an 11.85% enhancement in transient response. The skin temperature stabilization duration is curtailed by 83 s， with a 23.38% improvement in dynamic performance. Furthermore， adaptive control significantly refines the actuation dynamics of critical components， minimizing mechanical dissipation and energy expenditure. The liquid cooling pump’s peak rotational velocity diminishes by 400 r/min， the compressor’s maximum speed declines by 700 r/min， and the electronic expansion valve’s overshoot in aperture modulation is reduced by 15.1%. This synergistic integration of physical-layer decoupling and control-layer optimization furnishes a robust and efficient thermal management paradigm for next-generation aircraft， ensuring operational resilience in complex and evolving combat scenarios.

Key words: aircraft thermal management system, dynamic mode switching, dual target temperature regulation, adaptive fuzzy PID control, heat transfer path optimization

中图分类号:

纪胜楠, 艾青, 关鹏达, 刘梦, 帅永. 多模态飞机热管理系统双温协同架构设计与优化[J]. 航空学报, 2026, 47(13): 633166.

Shengnan JI, Qing AI, Pengda GUAN, Meng LIU, Yong SHUAI. Design and optimization of dual-temperature collaborative architecture for multi-mode aircraft thermal management system[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(13): 633166.

图/表 10

图 1

图 2

表1

自适应优化控制的输入和输出

控制主体	$E$	$E C$	输出参数
压缩机	$E a = T a - T a t$	$E C a = (T a - T a t) / d t$	$N c o m p = K P a E a + K I a ∫ 0 t E a d t + K D a d E a d t$
EEV	$E b = T b - T b t$	$E C b = (T b - T b t) / d t$	$A E E V = K P b E b + K I b ∫ 0 t E b d t + K D b d E b d t$
液冷泵	$E c = T c - T c t$	$E C c = (T c - T c t) / d t$	$N p u m p = K P c E c + K I c ∫ 0 t E c d t + K D c d E c d t$

表1

图 3

图 4

图 5

图 6

图 7

图 8

图 9

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

多模态飞机热管理系统双温协同架构设计与优化

Design and optimization of dual-temperature collaborative architecture for multi-mode aircraft thermal management system

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