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研究生:徐溢隆
論文名稱:利用類神經網路分析具不同粒徑下堆疊球流道之溫度分佈
論文名稱(外文):Prediction of a Packed Spheres Channel Temperature Distribution with Different Sphere Diameter Using Artificial Neural Network
指導教授:王紀瑞
學位類別:碩士
校院名稱:建國科技大學
系所名稱:自動化工程系暨機電光系統研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
畢業學年度:97
語文別:中文
論文頁數:91
中文關鍵詞:類神經網路堆疊球流道強制對流
外文關鍵詞:Artificial Neural Networkstack copper beadsforced convection
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本研究係以類神經網路探討在不同空氣流量之強制對流條件下堆疊球流道之溫度分佈,以尋求最佳化溫度分佈分析之類神經網路。研究中使用類神經網路估算不同流量之堆疊球流道之溫度分佈情形,並可推估在流道中不同位置之溫度,此對溫度分佈之分析相當重要。為驗證此網路學習資料之正確性,除流道溫度預測以外,量測溫度之感測器是否正常運作也是非常重要之關鍵。實驗中記錄器以熱電偶(Thermocouple)連接測試段,為避免因訊號線故障造成部份資料截取錯誤,形成類神經網路分析、訓練之雜訊。網路本身可進行對無法正常運作之感測器進行修正,以增強網路之可用性,使網路除溫度分佈預測外,並具備補正偏差之能力。實驗設計模型可模擬在不同流量之溫度分佈情況,分析結果顯示本研究所建構之類神經網路,能有效預測在不同流量等參數情況之流道溫度分佈,研究結果經驗證後與實驗測量值之平均誤差在1.1%以內,足證類神經網路能在不同之操作環境下,提供熱傳分析所需之溫度數據,有效預測流道之溫度分佈,並可檢測量測數據的正確性。
關鍵字:類神經網路、堆疊球流道、強制對流
The Artificial Neural Network is adopted in this research to perform (an analysis) on the temperature distribution of stack copper beads flow channel under the forced convection conditions of different air flow volumes; it attempts to do an analysis calculation of the optimum temperature distribution. The Artificial Neural Network is being applied in this research to calculate the temperature distribution of stack copper beads flow channel of different air flow volumes and it is able to estimate the temperatures of different locations in the flow channel; this is relative crucial to the analysis of temperature distribution. In order to verify the accuracy of this network learning data, in addition to the prediction of flow channel temperatures, that whether the sensors used to measure the temperatures are functioning normally is also a problem of comparative importance. Recording devices used in the experiment are connected through its testing segments by thermo couple; this is to avoid the partial data access errors caused by malfunction of signal wiring, the error may become the noise of Artificial Neural Network analysis and training. The network itself could perform correcting action on the sensors that could not function normally to enhance the network availability; the network is also provided with the capability of deviation compensation in addition to performing the prediction of temperature distribution. The empirical design model could simulate the temperature distribution conditions of different flow volumes; the analysis results manifests that the Artificial Neural Network constructed in this research could effectively predict, for cases of different flow volumes parameters, the flow channel temperature distribution conditions. The research results have been verified that its average errors comparing to the experimental measurement value is within 1.1%. This certainly proves that Artificial Neural Network could effectively predict the temperature distribution of flow channel under different operating environments and provide the temperature data required by thermal transmission analysis, and, it could be used to verify the accuracy of the measurement data.
Keywords: Artificial Neural Network, stack copper beads,
forced convection .
目 錄 頁次
中文摘要 ..................................I
英文摘要 .................................II
謝誌 .................................... IV
目錄 ......................................V
表目錄 ..................................VII
圖目錄 .................................VIII
符號說明 ..................................X
第一章 緒論 ...............................1
1-1 研究背景與目的 ........................1
1-2 文獻回顧 ..............................3
1-3 研究流程 ..............................6
1-4 研究架構 ..............................7
第二章 實驗方法 ............................8
2-1 實驗設備 ..............................8
2-2 實驗步驟 .............................12
2-3 誤差分析 .............................17
第三章 類神經網路 ..........................21
3-1 類神經網路概述 ........................21
3-2 多層類神經網路與倒傳遞演算法簡介 ........36
3-3 倒傳遞網路的改善方式 ...................43
第四章 類神經網路設計與討論 .................45
4-1 資料正規化 ............................45
4-2 網路訓練 ..............................47
4-3 網路參數對學習品質之影響 ................50
4-4 結果與討論 ............................57
第五章 結論與未來展望 ......................61
5-1 結論 .................................61
5-2 未來展望 ............................. 62
參考文獻 ...................................63
附錄一 .....................................66
附錄二 .....................................71
[1] J. C. Y. Koh and R. Colony, Analysis of cooling effectiveness for porous material in a coolant passage, ASME Journal of Heat Transfer, pp. 1026~1033. (1974)
[2] C. C. Wu, Chao, C. H., and G. J. Hwang, 1995, Investigation of non-darcian forecd convection in an asymmetrically heated sintered porous channel, Journal of Heat Transfer, Vol. 117, pp. 752~732.
[3] S. Tada, Echigo, R., and Yoshida, H., “A New Concept of Porous Thermoelectric Module Using a Reciprocating Flow for Cooling / Heating System, ” Intemational Conference on Thermoelectrics, IEEE, Vol. 15, pp. 264~268. (1996)
[4] J. J. Hwang, G. J., Hwang, R. H. Yeh, and C. H. Chao, 2002, “Measurement of Interstitial Convective Heat Transfer and Frictional Drag for Flow Across Metal Foams, “ Transactions of the ASME, Vol. 124, 120~129.
[5] W. McCulloch and W. Pitts, A logic calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, Vol. 5, 115-133, 1943.
[6] M.Minsky and S. Papert, Perceptrons, Cambridge, MA:MIT Press, 1969.
[7] D. E. Rumelhart, G. E. Hinton and R.J Williams, Learning representations by back-propagation errors, Nature, vol. 323, 533-536,1986.
[8] S. A. Kalongirou, Application of artificial neural-networks for energy systems, 67 (2000) 17-35.
[9] Arturo Pacheco-Vega, Mihir Sen, K. T. Yang, Rodney L. McClain, Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data, International Journal of Heat and Mass Transfer 44 (2001) 763-700.
[10] Y. Islamoglu, A. Kurt and C. Parmaksizoglu, Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach, Energy Conversion and Management 46 (2005) 223-232.
[11] T. H. Lee, J. Y. Yun, J. S. Lee, J. J. Park and K. S. Lee, Determination of airside heat transfer coefficient on wire-on-tube type heat exchanger, International Journal of Heat and Mass Transfer 44 (2001) 1767-1776.
[12] H. M. Ertunc, M. Hosoz, Artificial neural network analysis of a refrigeration system with an evaporative condenser, Applied Thermal Engineering 26 (2006) 627-635.
[13] K. S. Yigit, H. M. Ertunc, Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks, International Communications in Heat and Mass Transfer 33 (2006) 898-907.
[14] 吳明潮、曾憲中、王紀瑞、林金標、馬偉平、吳俊岳,” 具可變流向堆疊旁通流道之流阻與熱傳研究”,中華民國力學學會第三十一屆全國力學研討會,2007年。
[15] 馬偉平、王紀瑞、曾憲中、吳明潮,”具可變流向堆疊球流道之流體與溫度分佈分析”,輸送現象研討會,2007年。
[16] 吳明潮,”車輛排氣熱能回收系統之噪音與熱流特性實驗研究”,建國科技大學自動化工程系暨機電光系統研究所碩士論文,民國97年。
[17] Martin T. Hagan, Howard B. Demuth and Mark H. Beale, “Neural Network Design, Thomson Learning, 1996.
[18] Simon Haykin, ”Neural Networks: A Comprehensive Foundation”, Prentice Hall, 1999 .
[19] 葉怡成,"類神經網路模式應用與實作",儒林圖書公司,2000年。
[20] Kemal Ermis, Aytunc Erek, Ibrahim Dince, Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network, International Journal of Heat and Mass Transfer 50 (2007) 3163-3175.
[21] Refet Karadag, Omer Akgobek, The prediction of convective heat transfer in floor-heating systems by artificial neural networks, International Communications in Heat and Mass Transfer 35 (2008) 312-325.
[22] 羅華強,"類神經網路-MATLAB的應用",高立圖書有限公司,2005年。
[23] 蘇木春,張孝德,"機器學習:類神經網路、模糊系統以及基因演算法則",全華科技圖書股份有限公司,2004年。

[24] 李宗熹,” 利用類神經網路分析堆疊球流道之溫度分佈”,建國科技大學自動化工程系暨機電光系統研究所碩士論文,民國97年。
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