高雷诺数流动条件下针翅散热器的可变晶格密度优化
Variable-Lattice-Density Optimization of Pin-Fin Heat Sinks under High-Reynolds-Number Flow Conditions
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中文总结 AI 辅助
研究针对高雷诺数流动下针翅散热器设计，将可变晶格密度优化扩展至此场景，通过双网格框架及相关分析确定参数并优化，能捕捉不同条件下流动趋势，可用于实际设计探索以确定候选构型。
中文摘要 AI 辅助
本研究将可变晶格密度优化扩展到高雷诺数流动，用于周期性排列的针翅散热器设计。通过对圆柱形针翅阵列的单胞雷诺平均纳维-斯托克斯分析确定有效渗透率和阻力系数，并纳入基于达西-福希海默定律的简化模型进行宏观设计探索。引入双网格框架以实现高雷诺数条件下的稳定优化。在简化模型中，基础条件下温度偏差的\(L_2\)范数从4.38K降至1.00K，重建设计的几何解析分析证实从5.98K降至1.17K。更高入口速度和修改出口位置的额外计算表明，该方法能捕捉不同工况和几何条件下的主要流动再分布趋势。结果表明该方法可用于高雷诺数条件下针翅散热器候选构型的实际设计探索。
英文摘要
This study extends variable-lattice-density optimization to high-Reynolds-number flows for the design of periodically arranged pin-fin heat sinks. Effective permeability and drag coefficient are identified from unit-cell Reynolds-averaged Navier--Stokes analyses of cylindrical pin-fin arrays and incorporated into a reduced model based on the Darcy--Forchheimer law for macroscopic design exploration. To enable stable optimization under high-Reynolds-number conditions, a dual-mesh framework is introduced, in which the flow field and sensitivities are evaluated on a fine mesh, whereas the design variables are updated on a coarse mesh corresponding to the unit-cell arrangement. For the base condition, the $L_2$ norm of the temperature deviation from the area-averaged temperature is decreased from 4.38 K to 1.00 K in the reduced model, and geometry-resolved analysis of the reconstructed design confirms a reduction from 5.98 K to 1.17 K. Additional calculations with higher inlet velocities and modified outlet locations show that the proposed method captures the dominant flow-redistribution trends under different operating and geometric conditions, although the thermal objective became less accurate when local solid-temperature variations became pronounced. These results indicate that the proposed approach is useful as practical design-exploration for identifying candidate pin-fin heat sink configurations under high-Reynolds-number conditions.
