查詢結果分析
來源資料
頁籤選單縮合
題名 | 應用類神經網路於銀奈米流體熱管之性能預測=Performance Forecasting of Silver Nano-Fluid Heat Pipe Using Artificial Neural Network |
---|---|
作者 | 陳育堂; | 書刊名 | 德霖學報 |
卷期 | 27 2014.01[民103.01] |
頁次 | 頁39-47 |
分類號 | 440.137 |
關鍵詞 | 銀奈奈米流體; 溝槽式熱管; 倒傳遞類神經網路; Silver nano-fluid; Grooved heat pipe; Artificial neural network; |
語文 | 中文(Chinese) |
中文摘要 | 本研究以銀奈米流體為溝槽式熱管之工作流體,實驗使用10nm、35nm 兩種不同粒徑之銀奈米粒子, 並配成1ppm、5ppm、10ppm、50ppm、100ppm 等五種不同濃度之奈米流體,於不同加熱功率:30W、40W、 50W、60W,完成39 組實驗並分析熱阻值。另以MATLAB 內建Neural Network Toolbox 套裝程式進行倒 傳遞類神經網路學習,以加熱功率區分為:全部功率(30W、40W、50W 以及60W)、低功率(30W、40W)、 高功率(50W、60W)以及各單一功率,來進行交叉比對,藉此來預測具有非線性關係之熱管熱阻值。結果 顯示,在使用本實驗預設函數以及隱藏層公式時,皆可準確有效預測溝槽式熱管之熱阻值,且隨機抽取之驗證準確率達100%。 |
英文摘要 | In this study, silver nano-fluid was utilized to be the working fluid on grooved heat pipes. The silver nano-particles in two different particle sizes (10nm and 35nm) were used in this experiment. Also, combined with nano-fluid in five different kinds of concentration:1pm, 5ppm, 10ppm, 50ppm, and 100ppm, the 39 sets of thermal resistance values in this experiment were completed under different heating power:30W, 40W, 50W, and 60W. Besides, the suite program “Neural Network Toolbox” embedded in METLAB was utilized to perform the Back-Propagation Artificial Neural Network Learning. The heating power were divided to the whole power (30W, 40W, 50W, and 60W), low power (30W, and 40W), high power (50W, and 60W), and respective single power, for performing cross reference, so as to predict the thermal resistance values of the heat pipes having non-linear relations. The findings indicated: When the preset functions and hidden layer formulas in this experiment were utilized, the thermal resistance values of the grooved heat pipes could be predicted accurately and effectively. Also, the verification precision rate of random sampling could reach 100%. |
本系統之摘要資訊系依該期刊論文摘要之資訊為主。