Data-Driven Approaches for Fuzzy Prediction of Temperature Variations in Heat Exchanger Process

Author(s):  
Oualid Lamraoui ◽  
Yassine Boudouaoui ◽  
Hacene Habbi
Author(s):  
Seyyed Khandani ◽  
Himanshu Pokharna ◽  
Sridhar Machiroutu ◽  
Eric DiStefano

Remote heat pipe based heat exchanger cooling systems are becoming increasingly popular in cooling of notebook computers. In such cooling systems, one or more heat pipes transfer the heat from the more populated area to a location with sufficient space allowing the use of a heat exchanger for removal of the heat from the system. In analsysis of such systems, the temperature drop in the condenser section of the heat pipe is assumed negligible due to the nature of the condensation process. However, in testing of various systems, non linear longitudinal temperature drops in the heat pipe in the range of 2 to 15 °C, for different processor power and heat exchanger airflow, have been measured. Such temperature drops could cause higher condenser thermal resistance and result in lower overall heat exchanger performance. In fact the application of the conventional method of estimating the thermal performance, which does not consider such a nonlinear temperature variations, results in inaccurate design of the cooling system and requires unnecessarily higher safety factors to compensate for this inaccuracy. To address the problem, this paper offers a new analytical approach for modeling the heat pipe based heat exchanger performance under various operating conditions. The method can be used with any arbitrary condenser temperature variations. The results of the model show significant increase in heat exchanger thermal resistance when considering a non linear condenser temperature drop. The experimental data also verifies the result of the model with sufficient accuracy and therefore validates the application of this model in estimating the performance of these systems.   This paper was also originally published as part of the Proceedings of the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems.


Author(s):  
Ouyang Wu ◽  
Ala E.F. Bouaswaig ◽  
Stefan M. Schneider ◽  
Fernando Moreno Leira ◽  
Lars Imsland ◽  
...  

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Tutpol Ardsomang ◽  
J. Wesley Hines ◽  
Belle R. Upadhyaya

One of the challenges in data-driven prognostics is the availability of degradation data for application to prognostic methods. In real process management settings, failure data are not often available due to the high costs of unplanned breakdowns. This research presents a data-driven (empirical) modeling approach for characterizing the degradation of a heat exchanger (HX) and to estimate the Remaining Useful Life (RUL) of its design operation. The Autoassociative Kernel Regression (AAKR) modeling was applied to predict the effect of fouling on the heat transfer resistance. The result indicates that AAKR model is an effective method to capture the HX fouling in the dynamic process. The AAKR residuals were fused to develop a prognostic parameter which was used to develop a General Path Model (GPM) with Bayesian updating. The results demonstrate the successful application of this approach for the heat exchanger RUL prediction.


Geothermics ◽  
2017 ◽  
Vol 67 ◽  
pp. 29-39 ◽  
Author(s):  
David Gordon ◽  
Tirupati Bolisetti ◽  
David S.-K. Ting ◽  
Stanley Reitsma

2011 ◽  
Vol 35 (3) ◽  
pp. 1470-1482 ◽  
Author(s):  
Hacene Habbi ◽  
Madjid Kidouche ◽  
Mimoun Zelmat

2016 ◽  
Vol 49 (7) ◽  
pp. 342-346 ◽  
Author(s):  
Shin Wakitani ◽  
Mingcong Deng ◽  
Toru Yamamoto

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