Input Estimation Method in the Use of Electronic Device Temperature Prediction and Heat Flux Inverse Estimation

2007 ◽  
Vol 52 (9) ◽  
pp. 795-815 ◽  
Author(s):  
Tsung-Chien Chen ◽  
Shou-Jen Hsu
Author(s):  
Ming-Hui Lee ◽  
Tsung-Chien Chen ◽  
Tsu-Ping Yu

The innovative intelligent fuzzy weighted input estimation method (FWIEM) can be applied to the inverse heat transfer conduction problem (IHCP) to estimate the unknown time-varying heat flux as presented in this paper. The feasibility of this method can be verified by adopting the temperature measurement experiment. The experiment modular may be designed by using 4 copper samples with different thicknesses. Furthermore, the bottoms of the samples are heated by applying the standard heat source, and the temperatures on the tops are measured by using the thermocouples. The temperature measurements are then regarded as the inputs into the presented method to estimate the heat flux in the bottoms of samples. The influence on the estimation caused by the processing noise covariance Q, the weighting factor γ, the sampling time interval Δt, and the space discrete interval Δx, will be investigated by utilizing the experiment verification. The results show that this method is efficient and robust to estimate the unknown time-varying heat input.


2011 ◽  
Vol 54 (25-26) ◽  
pp. 5275-5285 ◽  
Author(s):  
David T.W. Lin ◽  
Ching-yu Yang ◽  
Jen-Chieh Li ◽  
Chi-Chang Wang

Author(s):  
X. Y. Xu ◽  
T. Ma ◽  
M. Zeng ◽  
Q. W. Wang

Due to the dramatic changes in physical properties, the flow and heat transfer in supercritical fluid are significantly affected by buoyancy effects, especially when the ratio of inlet mass flux and wall heat flux is relatively small. In this study, the heat transfer of supercritical water in uniformly heated vertical tube is numerically investigated with different buoyancy models which are based on different calculation methods of the turbulent heat flux. The applicabilities of these buoyancy models are analyzed both in heat transfer enhancement and deterioration conditions. The simulation results show that these buoyancy models make few differences and give good wall temperature prediction in heat transfer enhancement condition when the ratio of inlet mass flux and wall heat flux is very small. With the increase of wall heat flux, the accuracy of wall temperature prediction reduces, and the differences between these buoyancy models become larger. No buoyancy model can currently make accurate wall temperature prediction in deterioration condition in this study.


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