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A combined numerical simulation and optimization model for the cooling of IC chips under forced convection

机译:强制对流下IC芯片冷却的组合数值模拟和优化模型

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Three-dimensional steady state numerical simulations are carried out from seven protruding IC chips (Aluminium) of different sizes mounted at various positions on a high-power SMPS board (Bakelite) cooled under forced convection (q = 380 W/cm(2), V = 25 m/s). The objective is to predict the temperature distribution of these IC chips and to determine their optimal arrangement on the SMPS board; that will have the lowest maximum temperature excess among all the possible configurations. To achieve the same, a numerical analysis is carried out using the commercial software ANSYS-Icepak, and then the hybrid optimization strategy (Genetic Al-gorithm (GA) driven by Artificial Neural Network (ANN)) is employed to obtain the global optimal configuration of these IC chips. Preliminary analysis is performed using a dimensionless position parameter (lambda) which is found to be a strong function of the IC chip size, and their positioning on the substrate board. It is observed that the combined ANN-GA strategy is able to predict the optimal behavior of the IC chips more accurately. The larger size IC chips generating higher power should be placed at the substrate bottom for better cooling.
机译:三维稳态数值模拟从安装在强制对流(Q = 380W / cm(2)的高功率SMPS板(Bakelite)上的不同尺寸的不同尺寸的七个突出的IC芯片(铝)。(Q = 380W / cm(2), v = 25 m / s)。目的是预测这些IC芯片的温度分布,并确定其在SMPS板上的最佳布置;这将在所有可能的配置中具有最低的最高温度过剩。为了达到相同的是,使用商业软件ANSYS-ICEPAK进行数值分析,然后采用由人工神经网络(ANN)驱动的混合优化策略(遗传AL-GORITHIM(GA))来获得全局最佳配置这些IC芯片。使用无量纲位置参数(Lambda)进行初步分析,该初始位置参数(Lambda)被发现是IC芯片尺寸的强功能,以及它们在基板板上的定位。观察到,组合的Ann-GA战略能够更准确地预测IC芯片的最佳行为。产生更高功率的较大尺寸的IC芯片应放置在基板底部,以便更好地冷却。

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