Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data

碩士 === 國立成功大學 === 機械工程學系碩博士班 === 94 === The present study applies the hybrid method of the Laplace transform technique and finite-difference method in conjunction with least-squares scheme and experimental temperature data to predict the heat dissipation and the average convection heat transfer coef...

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Main Authors: Yong-Zhi Liu, 劉永智
Other Authors: Han-Taw Chen
Format: Others
Language:zh-TW
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/07670067954292036158
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spelling ndltd-TW-094NCKU54900962016-05-30T04:21:59Z http://ndltd.ncl.edu.tw/handle/07670067954292036158 Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data 利用逆算法和實驗溫度值估算CPU上之散熱鰭片的熱傳係數 Yong-Zhi Liu 劉永智 碩士 國立成功大學 機械工程學系碩博士班 94 The present study applies the hybrid method of the Laplace transform technique and finite-difference method in conjunction with least-squares scheme and experimental temperature data to predict the heat dissipation and the average convection heat transfer coefficient on CPU heatsink. Due to the thickness of the standard test material is very smaller than its length and width, the one-dimension mathematical model is applied to perform the present inverse analysis. The effects of the fin spacing and fin height on the present estimates can not be negligible for free convection and forced convection. The free convection heat transfer coefficient is increased with increasing the fin spacing and is decreased with increasing the fin height. A comparison of the forced convection heat transfer coefficient between the present estimates and previous results is made in order to evidence the accuracy and reliability of the present inverse scheme. Han-Taw Chen 陳寒濤 2006 學位論文 ; thesis 66 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 國立成功大學 === 機械工程學系碩博士班 === 94 === The present study applies the hybrid method of the Laplace transform technique and finite-difference method in conjunction with least-squares scheme and experimental temperature data to predict the heat dissipation and the average convection heat transfer coefficient on CPU heatsink. Due to the thickness of the standard test material is very smaller than its length and width, the one-dimension mathematical model is applied to perform the present inverse analysis. The effects of the fin spacing and fin height on the present estimates can not be negligible for free convection and forced convection. The free convection heat transfer coefficient is increased with increasing the fin spacing and is decreased with increasing the fin height. A comparison of the forced convection heat transfer coefficient between the present estimates and previous results is made in order to evidence the accuracy and reliability of the present inverse scheme.
author2 Han-Taw Chen
author_facet Han-Taw Chen
Yong-Zhi Liu
劉永智
author Yong-Zhi Liu
劉永智
spellingShingle Yong-Zhi Liu
劉永智
Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data
author_sort Yong-Zhi Liu
title Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data
title_short Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data
title_full Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data
title_fullStr Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data
title_full_unstemmed Application of the Inverse Method to Estimate Heat transfer Coefficient on CPU Heatsink using Experimental Temperature Data
title_sort application of the inverse method to estimate heat transfer coefficient on cpu heatsink using experimental temperature data
publishDate 2006
url http://ndltd.ncl.edu.tw/handle/07670067954292036158
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