Skip to main content

A Predictive Model for Life Assessment of Automotive Exhaust Mufflers Subject to Internal Corrosion Failure due to Exhaust Gases Condensation.

Nazir, H., Khan, Z. A. and Stokes, K., 2016. A Predictive Model for Life Assessment of Automotive Exhaust Mufflers Subject to Internal Corrosion Failure due to Exhaust Gases Condensation. Engineering Failure Analysis, 63, 43-60.

Full text available as:

[img]
Preview
PDF
A Predictive Model for Life Assessment of Automotive Exhaust Mufflers Subject to Internal Corrosion Failure due to Exhaust Gases Condensation.pdf - Accepted Version

3MB

DOI: 10.1016/j.engfailanal.2016.02.014

Abstract

A study has been presented of pitting corrosion on internal walls of automotive exhaust muffler due to exhaust gases condensation. The problem mainly exists in the rear section of exhaust system close to tail end pipe such as muffler, especially when the temperature of muffler does not go up during short distance run or winter. The water vapor condenses on the muffler's inner wall in the form of water droplets. The dissolution of corrosive gases which are coming from internal combustion of engine as well as condensation of low-pH acidic vapors in the water droplet can cause severe pitting corrosion on standard exhaust steel. In this work, an experiment is reported for internal corrosion, by using mufflers as test bed subjected to different environmental conditions. Based on observations, a mechanistic model has been developed which involves three main techniques (i) the dropwise condensation technique predicts the condensation rate and is based on heat and mass transfer theory (ii) the species breakdown in the droplet is established through the main thermodynamic and chemical equilibrium (iii) the pitting corrosion involving pit depth is predicted using electrochemical kinetic reactions, species transport and chemical reactions occurring inside the droplet. Lastly, the accuracy of model has been validated by comparison between experimental and predicted results showing a good agreement.

Item Type:Article
ISSN:1873-1961
Group:Faculty of Science & Technology
ID Code:22940
Deposited By: Symplectic RT2
Deposited On:30 Nov 2015 15:44
Last Modified:14 Mar 2022 13:54

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -