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Flame Characterization and NOx Emissions Prediction for Reactors Generated by Aeroengine-Derived Lean Direct Injectors, Operated on Hydrogen and Hydrogen/Natural Gas Blends.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Flame Characterization and NOx Emissions Prediction for Reactors Generated by Aeroengine-Derived Lean Direct Injectors, Operated on Hydrogen and Hydrogen/Natural Gas Blends./
作者:	Gomez Escudero, Iker.
面頁冊數:	1 online resource (78 pages)
附註:	Source: Masters Abstracts International, Volume: 85-02.
Contained By:	Masters Abstracts International85-02.
標題:	Energy. -
電子資源:	click for full text (PQDT)
ISBN:	9798380127530

Flame Characterization and NOx Emissions Prediction for Reactors Generated by Aeroengine-Derived Lean Direct Injectors, Operated on Hydrogen and Hydrogen/Natural Gas Blends.
Gomez Escudero, Iker.

Flame Characterization and NOx Emissions Prediction for Reactors Generated by Aeroengine-Derived Lean Direct Injectors, Operated on Hydrogen and Hydrogen/Natural Gas Blends. - 1 online resource (78 pages)

Source: Masters Abstracts International, Volume: 85-02.

Thesis (M.S.)--University of California, Irvine, 2023.

Includes bibliographical references

A set of 16 aeroengine micromixing injectors was adapted from a lean direct injection of fuel concept to be operated on hydrogen or hydrogen / natural gas blends. A statistically designed test plan was first employed to evaluate the lean blow-out limits of the test hardware, depending on the preheat temperature of the air flow, the air pressure drop across the airbox, and the fuel composition. This resulted in some clear trends in injector stability as a function of not only the combustion conditions, but also injector geometry.Fuel composition was the main factor influencing lean stability limits. The combustion conditions with a higher hydrogen content were those with a wider range of stability, because of the higher reactivity and diffusivity. On the injector geometry factors, both low levels of air split and air swirl expanded the lower flammability limits. A second statistical test plan was designed adding the adiabatic flame temperature as a fourth factor. Emissions and flame diagnostics were evaluated simultaneously. Regarding flame structure characterization, three different cameras were operated in parallel. A Nikon D90 was used to capture the visible spectrum of the flame, an FB-N9-U Dynacolor camera to record the OH* chemiluminescence, and a Phantom v7.1 to record the high-speed dynamics of the flame. The current thesis focused on the responses extracted from the OH* chemiluminescence imaging. Because of its direct correlation with the rate of heat release, OH* is commonly used to characterize the flame front. These responses were, for the most part, the flame area and its average brightness, the heat release area and its center of gravity and leading edge. An analysis of variance was performed for each response, with significant indicators of p-values lower than 0.05. The effect of the confinement ratio of the flame by the combustor quartz tube was also studied, since its dimensions were arbitrarily defined during the design of the setup. A reduction in its length and diameter concluded that no significant change occurred on the flame structure because of it. Finally, the thesis concludes with connecting the NOx emissions and the imaging flame characterization. A Random Forest machine learning algorithm was generated, yielding into 80%-accurate predictions of the NOx emissions as a function of injector geometry, combustion conditions and OH* chemiluminescence responses.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380127530Subjects--Topical Terms:

784773
Energy.
Subjects--Index Terms:

Flame imagingIndex Terms--Genre/Form:

554714
Electronic books.

Flame Characterization and NOx Emissions Prediction for Reactors Generated by Aeroengine-Derived Lean Direct Injectors, Operated on Hydrogen and Hydrogen/Natural Gas Blends.
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520 $a A set of 16 aeroengine micromixing injectors was adapted from a lean direct injection of fuel concept to be operated on hydrogen or hydrogen / natural gas blends. A statistically designed test plan was first employed to evaluate the lean blow-out limits of the test hardware, depending on the preheat temperature of the air flow, the air pressure drop across the airbox, and the fuel composition. This resulted in some clear trends in injector stability as a function of not only the combustion conditions, but also injector geometry.Fuel composition was the main factor influencing lean stability limits. The combustion conditions with a higher hydrogen content were those with a wider range of stability, because of the higher reactivity and diffusivity. On the injector geometry factors, both low levels of air split and air swirl expanded the lower flammability limits. A second statistical test plan was designed adding the adiabatic flame temperature as a fourth factor. Emissions and flame diagnostics were evaluated simultaneously. Regarding flame structure characterization, three different cameras were operated in parallel. A Nikon D90 was used to capture the visible spectrum of the flame, an FB-N9-U Dynacolor camera to record the OH* chemiluminescence, and a Phantom v7.1 to record the high-speed dynamics of the flame. The current thesis focused on the responses extracted from the OH* chemiluminescence imaging. Because of its direct correlation with the rate of heat release, OH* is commonly used to characterize the flame front. These responses were, for the most part, the flame area and its average brightness, the heat release area and its center of gravity and leading edge. An analysis of variance was performed for each response, with significant indicators of p-values lower than 0.05. The effect of the confinement ratio of the flame by the combustor quartz tube was also studied, since its dimensions were arbitrarily defined during the design of the setup. A reduction in its length and diameter concluded that no significant change occurred on the flame structure because of it. Finally, the thesis concludes with connecting the NOx emissions and the imaging flame characterization. A Random Forest machine learning algorithm was generated, yielding into 80%-accurate predictions of the NOx emissions as a function of injector geometry, combustion conditions and OH* chemiluminescence responses.
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