低温储罐的热性能估算：在液氢中的应用
Thermal performance estimation for cryogenic storage tanks: Application to liquid hydrogen
AI总结：
针对液氢低温储罐的漏热估算问题，推导了20 K液氢相比77 K液氮漏热量增加的理论界限（低于26%），并比较了热网络模型与有限元方法，证明前者在保持约1%误差的同时具备快速鲁棒的优势。
摘要：
低温液体存储解决方案的设计需要准确的方法来估算从材料层级到储罐层级的漏热量。对于隔热材料，通常使用环境条件和77 K的液氮作为边界温度来测量热性能。一个关键问题是，当在20 K下储存液氢LH$_2$时，漏热量会增加多少。我们推导了漏热量增加的理论界限，并表明其保持在26%以下。此外，我们证明漏热量对热边界温度的敏感度远高于冷边界温度。在储罐层级，我们比较了评估低温储罐稳态热性能的两种方法：热网络模型和使用有限元方法求解的热传导方程。后者对复杂几何形状具有高精度和适应性，而热网络模型则受益于简单性、速度和鲁棒性。我们将这两种方法应用于用于海运的自支撑LH$_2$储罐概念，并分析了对结构支撑厚度、热边界温度和隔热材料选择的敏感性。热网络模型估算漏热量的误差约为1%，估算冷点温度的误差小于1 K。
英文摘要：
The design of cryogenic liquid storage solutions requires accurate methods for estimating heat ingress, from the material level to the tank level. For insulation materials, thermal performance is usually measured using ambient conditions and liquid nitrogen at 77 K as boundary temperatures. A key question is how much heat ingress increases when storing liquid hydrogen LH$_2$ at 20 K. We derive theoretical bounds on the increased heat ingress, and show that it remains below 26%. Additionally, we demonstrate that heat ingress is much more sensitive to the warm boundary temperature than the cold boundary temperature. At the tank level, we compare two methods for assessing the steady-state thermal performance of cryogenic tanks: thermal network models and the heat equation solved with the finite element method. The latter offers high accuracy and adaptability for complex geometries, while thermal network models benefit from simplicity, speed and robustness. We apply both approaches to a self-supported LH$_2$ tank concept for maritime transport and analyze sensitivity to structural support thickness, warm boundary temperature, and choice of insulation material. The thermal network model can estimate heat ingress with $\sim$1% error and the cold-spot temperature with error less than 1 K.
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