Advisor

Chamra, Louay M.

Committee Member

Janus, J. Mark

Committee Member

Mago, Pedro

Committee Member

Walters, Keith

Committee Member

Hodge, B. Keith

Date of Degree

1-1-2006

Document Type

Dissertation - Open Access

Department

Department of Mechanical Engineering

Abstract

The last few decades have seen a significant development of complex heat transfer enhancement geometries such as a helicallyinned tube. The arising problem is that as the fins become more complex, so does the prediction of their performance. In addition to discussing existing prediction tools, this dissertation demonstrates the successful use of artificial neural networks as a correlating method for experimentally- measured heat transfer and friction data of helicallyinned tubes.

URI

https://hdl.handle.net/11668/20315

Share

COinS