Diesel-cng dual fuel combustion characterization using vibro-acoustic analysis and response surface methodology
Engine conversion process from any diesel vehicle to a diesel-CNG dual fuel system requires additional fuel management. The need for an engine monitoring is vital to ensure the dual fuel operation run smoothly without excessive knocking, which may shorten the life of the engine. Knock and air-fuel r...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2019
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| Online Access: | http://eprints.uthm.edu.my/10/1/24p%20ABD%20FATHUL%20HAKIM%20ZULKIFLI.pdf http://eprints.uthm.edu.my/10/2/ABD%20FATHUL%20HAKIM%20ZULKIFLI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10/3/ABD%20FATHUL%20HAKIM%20ZULKIFLI%20WATERMARK.pdf |
| Summary: | Engine conversion process from any diesel vehicle to a diesel-CNG dual fuel system requires additional fuel management. The need for an engine monitoring is vital to ensure the dual fuel operation run smoothly without excessive knocking, which may shorten the life of the engine. Knock and air-fuel ratio (AFR) sensors are commonly used for engine monitoring during fuel management setup. However, the engine output characteristics has been overlooked during the monitoring process. This study is aimed to explore a statistical approach by predicting the relationship between fuel management and engine output characteristics of diesel-CNG dual fuel engine using Response Surface Methodology (RSM). Two inputs which are CNG substitution rate and engine speed were used to predict the engine output characteristics in terms of engine performance, exhaust emissions, combustion pattern and combustion stability. Within the investigation, a statistical method was proposed to analyse the vibro-acoustic signal generated by a knock sensor installed at the outer cylinder wall of the engine. The frequency distribution analysis was applied to interpret the high variability of the vibro-acoustic signal. The results were used as the input for combustion stability in RSM analysis. It also provided useful information with regards to the engine stability. The response surface analysis showed that the CNG substitution rate and its properties significantly influenced the engine output characteristics. This study also describes the methodology to determine the accuracy and the significance of the developed prediction models. The prediction models were validated using confirmation test and showed good predictability within 95% confidence interval. Thus, it is concluded that RSM provide models that predict the engine characteristics with significant accuracy, which contributes to the effectiveness of diesel-CNG dual fuel engine conversion process. |
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