通过综合生物仿真遗传算法方法优化用于热能存储应用的TES的设计
Nadiya Mehraj1, Carles Mateu1, Gabriel Zsembinszki1
1GREiA Research Group, Universitat de Lleida, Pere de Cabrera s/n, 25001 Lleida, Spain.
Biomimetics (Basel, Switzerland)
|April 25, 2025
概括
这项研究使用人工智能和仿生技术来设计热能储,将传热面积增加29%,同时确保高效储能的可制造性.
科学领域:
- 储能 储能 储能 储能 储能 储能
- 生物仿真工程 生物仿真工程
- 计算设计的计算设计.
背景情况:
- 现有的热能储能 (TES) 设计往往缺乏最佳的热效率.
- 之前对TES设计的仿生方法依赖于预定义的几何配置.
研究的目的:
- 开发一种新的计算框架,用于生成优化的TES水箱几何形状.
- 整合人工智能 (AI) 与仿生原理,用于系统的设计探索.
- 在可制造性限制范围内增强传热表面积.
主要方法:
- 利用了整合遗传算法 (GA) 与仿生原理的计算框架.
- 探索了13个几何参数,灵感来自于自然血管网络.
- 应用经过实验验证的尺寸限制 (150毫米直径,155毫米高度).
主要成果:
- 产生了新的TES水箱几何形状,传热表面积增加了29%.
- 保证的设计符合制造能力的限制.
- 在经过验证的维度限制内开发出由人工智能驱动的生物灵感结构.
结论:
- 建立了TES水箱架构的系统和可扩展的方法.
- 展示了人工智能驱动的仿生生物技术在高性能,可制造的TES解决方案中的潜力.
- 为更高效和可持续的储能应用铺平了道路.
相关概念视频
01:27Thermal expansion and Thermal stress: Problem Solving
San Francisco's Golden Gate Bridge is exposed to temperatures ranging from -15 °C to 40 °C. At its coldest, the main span of the bridge is 1275 m long. Assuming that the bridge is made entirely of steel, what is the change in its length between these temperatures?
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55 °C.
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in temperature (ΔT) is 55 °C.
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