Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
Computational investigation of rotorcraft avionics bay cooling system
Download
index.pdf
Date
2019
Author
Akın, Altuğ
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
3
views
0
downloads
Computational investigation of a rotorcraft avionics bay cooling system is performed. Within the introduced system, the ambient air is supplied to the avionics-bay by a fan and exhausted back into the ambient after cooling the equipment inside. Depending on the fan and exhaust locations, hot zones may form around some of the equipment. The fan must provide a sufficiently high mass flow rate to keep the temperatures of the avionics equipment below the limits, while avoiding excessive amount of cooling to reduce power consumption. In this study, the effects of the fan and exhaust locations on the required mass flow rate are investigated. Prediction functions with Gaussian Process Regression and Artificial Neural Network methods are built to predict avionics surface temperatures using the results from a series of Computational Fluid Dynamics (CFD) analyses. The first method is selected over the latter as it yields more accurate results. The selected prediction function is used in conjunction with an optimization algorithm to determine the optimum fan and exhaust locations that minimize the required mass flow rate. It is found out that the required mass flow rate significantly depends on the fan location, while, the exhaust location has a relatively lessened effect. The required mass flow rate could be reduced to around half of its value with an even more significant reduction in power consumption. Additionally, the CFD analysis for the optimum fan and exhaust locations are repeated with different turbulence models to evaluate the effect of the selected model on the results.
Subject Keywords
Rotors.
,
Keywords: Avionics Cooling
,
Design of Experiment
,
Computational Fluid Dynamics
,
Optimization
,
Gaussian Process Regression
,
Artificial Neural Networks
,
Turbulence Model.
URI
http://etd.lib.metu.edu.tr/upload/12624314/index.pdf
https://hdl.handle.net/11511/44150
Collections
Graduate School of Natural and Applied Sciences, Thesis