Different simulation tools for the evaluation of air temperature fields inside refrigeration spaces

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

Carlos Alberto da Conceição António (Author) FEUP; Clito F. Afonso (Author) FEUP

Conference proceedings International

Title: IRF’2009 – Integrity - Reliability - Failure Challenges and Opportunities

Pages: 577-596

IRF’2009 – Integrity - Reliability - Failure Challenges and Opportunities, Third International Conference ISBN: 978-972-8826-22-2 (Book) - 978-972-8826-21-5 (CD)

Faculty of Engineering, University of Porto, Porto, Portugal, July 20-24, 2009

Scientific classification

FOS: Engineering and technology > Other engineering and technologies

CORDIS: Technological sciences

Other information

Abstract (EN): In refrigerated spaces, the inside air is cooled by a heat sink either by forced or natural convection. The first situation is usually found on refrigerated stores while the second one is more frequent on small apparatus like domestic household refrigerators or on isothermal boxes to transport medical products, like vaccines or medicines. On the mentioned refrigerated spaces it is not frequent to monitored the inside air temperatures. So, the knowledge of the air temperature field inside them is almost unknown and often it can be found large air temperature gradients inside, which can put in risk the stored products. Usually there is only one thermostat located on some appropriate place whose bulb senses the temperature around it, assuming that the remainder inside air has the same temperature. As will be seen later on, in refrigerated spaces there is a wide air temperature field that must be known in order to better locate the perishable or other products inside, regarding their specific storage temperature. In order to accomplish this desiderate it was used on this work a commercial household refrigerator that was monitored with thermocouples on several points. The measured temperatures were then compared with the ones obtained from two different simulation tools, the Fluent and the other one based on an Artificial Neural Network (ANN) with supervised learning performed using a Genetic Algorithm (GA) supported by an elitist strategy. It was possible to conclude that, at least in this case, the last one presents a lower absolute error - 0.8K - when compared with the first one - 1K ¿-and also that the air temperature fields inside are more consistent with the reality.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 20