Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.721832
Title: Experimental and chemical kinetic modelling study on the combustion of alternative fuels in fundamental systems and practical engines
Author: Agbro, Edirin Bruno
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2017
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Abstract:
In this work, experimental data of ignition delay times of n-butanol, gasoline, toluene reference fuel (TRF), a gasoline/n-butanol blend and a TRF/n-butanol blend were obtained using the Leeds University Rapid Compression Machine (RCM) while autoignition (knock) onsets and knock intensities of gasoline, TRF, gasoline/n-butanol and TRF/n-butanol blends were measured using the Leeds University Optical Engine (LUPOE). The work showed that within the RCM, the 3-component TRF surrogate captures the trend of gasoline data well across the temperature range. However, based on results obtained in the engine, it appears that the chosen TRF may not be an excellent representation of gasoline under engine conditions as the knock boundary of TRF as well as the measured knock onsets are significantly lower than those of gasoline. The ignition delay times measured in the RCM for the blend, lay between those of gasoline and n-butanol under stoichiometric conditions across the temperature range studied and at lower temperatures, n-butanol acts as an octane enhancer over and above what might be expected from a simple linear blending law. In the engine, the measured knock onsets for the blend were higher than those of gasoline at the more retarded spark timing of 6 CA bTDC but the effect disappears at higher spark advances. Future studies exploring the blending effect of n-butanol across a range of blending ratios is required since it is difficult to conclude on the overall effect of n-butanol blending on gasoline based on the single blend that has been considered in this study. The chemical kinetic modelling of the fuels investigated has also been evaluated by comparing results from simulations employing the relevant reaction mechanisms with the experimental data sourced from either the open literature or measured in-house. Local as well as global uncertainty/sensitivity methods accounting for the impact of uncertainties in the input parameters, were also employed within the framework of ignition delay time modelling in an RCM and species concentration prediction in a JSR, for analysis of the chemical kinetic modelling of DME, n-butanol, TRF and TRF/n-butanol oxidation in order to advance the understanding of the key reactions rates that are crucial for the accurate prediction of the combustion of alternative fuels in internal combustion engines. The results showed that uncertainties in predicting key target quantities for the various fuels studied are currently large but driven by few reactions. Further studies of the key reaction channels identified in this work at the P-T conditions of relevance to combustion applications could help to improve current mechanisms. Moreover, the chemical kinetic modelling of the autoignition and species concentration of TRF, TRF/n-butanol and n-butanol fuels was carried out using the adopted TRF/n-butanol mechanism as input in the engine simulations of a recently developed commercial engine software known as LOGEengine. Similar to the results obtained in the RCM modelling work, the knock onsets predicted for TRF and TRF/n-butanol blend under engine conditions were consistently higher than the measured data. Overall, the work demonstrated that accurate representation of the low temperature chemistry in current chemical kinetic models of alternative fuels is very crucial for the accurate description of the chemical processes and autoignition of the end gas in the engine.
Supervisor: Tomlin, Alison S. ; Hu, Li Sponsor: Tertiary Education Trust Fund (TETFUND) Nigeria ; COST (European Cooperation in Science and Technology)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.721832  DOI: Not available
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