在各种工作模式下,地面控制站的自适应变频道散热控制
Dexin Wang1, Jiali Tao1, Jin Lei2
1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China.
Scientific reports
|February 15, 2025
概括
用于地面控制站 (GCS) 的新适应式冷却系统使用深度强化学习来显著提高冷却效率并降低能源消耗. 这种先进的系统优化了电子设备柜的热管理.
科学领域:
- 热管理系统 热管理系统
- 在工程领域的人工智能.
- 电子冷却技术 电子冷却技术
背景情况:
- 地上控制站 (GCS) 面临着由于在不同操作模式下来自多个模块的不同热量产生而面临的热量挑战.
- 现有的冷却解决方案可能没有足够的效率或适应性来处理动态热负荷,这可能会影响GCS性能和寿命.
研究的目的:
- 开发和评估GCS适应变频控制 (AVCC) 冷却方法,优化热管理.
- 通过智能热控制,提高GCS的冷却效率和运行耐久性.
- 为冷却具有多个热源的电子设备柜提供可扩展的解决方案.
主要方法:
- 设计了一个分布式冷却模块,用于可变通道冷却结构的多个风扇.
- 开发了一个多模块温度控制平台,模拟各种热量生成场景,用于算法训练.
- 实施了一个无模型控制策略,使用软行为者-关键 (SAC) 深度强化学习算法用于AVCC.
主要成果:
- 在最大负载下,AVCC方法成功地使多个GCS模块 (例如电压稳定器,电池,充电器,计算机) 在280秒内冷却到50°C.
- 与传统的基于规则的政策相比,冷却效率提高了40.4%.
- 与基于规则的方法相比,能源消耗减少了42.2%.
结论:
- 拟议的分布式冷却模块和AVCC方法有效地提高了GCS冷却性能,并减少了能源消耗.
- 这种智能冷却系统为电子设备的热管理提供了重大进步.
- 这项研究是设计和控制类似电子柜中的冷却系统的宝贵参考.
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