基于神经网络的稳定状态温度系统的重建,其材料组成不明
Silvester Sabathiel1, Hèlios Sanchis-Alepuz2, Andrew S Wilson2
1Silicon Austria Labs GmbH, 8010, Graz, Austria. silvester.sabathiel@silicon-austria.com.
本研究介绍了一种神经网络方法,可以从有限的传感器数据中重建完整的温度场. 这种方法可以准确地预测热系统中的温度,即使测量很少.
科学领域:
- 热力工程是热力工程中的一个.
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 有效的热管理对于设备的性能和寿命至关重要.
- 由于不完整的观测数据,难以准确地确定温度分布.
- 现有的方法可能需要材料属性信息,限制了适用性.
研究的目的:
- 开发一种基于神经网络的方法,从部分观测中重建完整的温度场.
- 为了克服热系统中不完整的温度数据的局限性.
- 在不需要空间材料属性数据的情况下验证模型的有效性.
主要方法:
- 采用了基于物理的全卷积自动编码器.
- 集成了一个先前的空间传播器网络模块.
- 该模型在模拟和现实世界的热系统中使用虚拟传感验证.
主要成果:
- 神经网络方法成功地从稀疏的数据中重建了完整的温度场,包括具有挑战性的网边采样 (3.12%的采样比率).
- 在温度场重建中达到1.1%的相对平均误差.
- 在预测最大系统温度方面超过了Kriging方法,并且在有限的测量中表现出更高的稳定性.
结论:
- 拟议的神经网络方法为从不完整的数据中进行温度场重建提供了有效的解决方案.
- 这种方法可以提高热管理,因为它可以在没有材料属性信息的情况下准确地预测温度.
- 该方法在各种热系统中的虚拟传感应用中显示出重大前景.
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