An optimization study for rotorcraft avionics bay cooling

2019-07-01
In this paper, a computational investigation of a rotorcraft avionics-bay cooling system is carried out. The introduced avionics cooling system utilizes a forced-convection method in which the ambient air is supplied to the avionics bay by a fan and then exhausted back into the ambient after cooling the equipment inside. The aim of this system is to keep the air temperature in the vicinity of the avionics equipment below the operational temperature limits. Depending on the locations of the fan and exhaust, local hot zones may form near some of the equipment. In order to overcome this issue, the fan capacity must be designed to provide a sufficiently high mass flow rate so that the temperature limits are not exceeded, while an excessive amount of cooling should be avoided to reduce power consumption. In this study, the effects of the fan and the exhaust locations on the amount of required mass flow rate are investigated. A prediction function is built by using Gaussian Process Regression method to predict the avionics surface temperatures. The Gaussian Process Regression method is trained by the results obtained from a large number of computational fluid dynamics (CFD) analyses. The prediction function is later used to determine the required mass flow rate depending on the fan and the exhaust locations. It is found out that the required mass flow rate changes significantly as the fan location changes. On the other hand, the exhaust location has a relatively lessened effect on the required mass flow rate. It is observed that depending on the locations of the fan and the exhaust, the required mass flow rate could be reduced to around half of its value. The amount of decrease in the electrical power consumption is even more significant. (C) 2019 Elsevier Masson SAS. All rights reserved.
AEROSPACE SCIENCE AND TECHNOLOGY

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Citation Formats
A. Akin and H. S. Kahveci, “An optimization study for rotorcraft avionics bay cooling,” AEROSPACE SCIENCE AND TECHNOLOGY, pp. 1–11, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34280.