在有限空間且電子設備排列密度極高並以冷氣及風扇強迫對流裝置散熱的叢集電腦機房，如何排列強迫對流裝置以能有效的散熱，讓電子設備處於正常的運轉狀態，對機房的管理而言，是一個很重要的課題。 在侷促空間中，加上冷氣及風扇氣流的因素，機房的流場顯得非常紊亂，要以流體力學軟體來模擬流場狀態，以找到機房強迫對流裝置的合適佈局是非常困難的事，因此，本研究應用基因演算法，並以類神經網路學習模型建立適應函數，以找到機房強迫對流裝置佈局之最佳近似解。 To keep the electronic installation at normal operation, effective heat dissipation of the extremely crowded cluster computer room is needed. Usually, fans and air conditioning equipments are used to assist this heat dissipation work. In the cramped space of crowded cluster computer room, airflow is exceptionally disorderly. How to arrange the fans and the air conditioning equipments to make heat dissipation effective is an important problem. Using Computational Fluid Dynamics (CFD) software to simulate the airflow to find solutions for effective heat dissipation is extremely difficult. The purpose of this research is to apply neural networks model to establish a fitness function first, then, have this fitness function used by genetic algorithms in the finding of best approximate solutions for solving the problem of effective heat dissipation in cluster computer rooms.
