Preferential vaporization impacts on lean blow-out of liquid fueled combustors

Won, Sang Hee; Rock, Nicholas; Lim, Seung Jae; Nates, Stuart; Carpenter, Dalton; Emerson, Benjamin; Lieuwen, Tim; Edwards, Tim; Dryer, Frederick L.

doi:10.1016/j.combustflame.2019.04.008

Cited by 48 publications

(18 citation statements)

References 32 publications

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“…The 10 fuels used in this study are a subset of those from the National Jet Fuels Combustion Program (NJFCP) [1,19,59]. Each of these fuels was carefully selected to accentuate the effect of a particular fuel property on lean blowout.…”

Section: Facilitymentioning

confidence: 99%

“…A summary of the findings from this study is shown in Figure 1, where the blowout boundaries are plotted against the DCN at 450 K (left) and the 90% boiling point temperature, T90, at 300 K (right). The DCN correlated best with the blowoff fuel-air ratios at 450 K and 550 K. The few fuels that didn't follow the DCN correlation (e.g., S2) have strong preferential vaporization characteristics; incorporating these effects into these correlations substantially improves the correlation [19,25]. Consistent with the findings of Burger et al [12], blowoff correlated best with fuel vaporization processes at 300 K. At this lower air temperature, the low T90 fuels were the most blowoff resistant and the high T90 fuels blew out the easiest.…”

Section: Introductionmentioning

confidence: 96%

“…For example, Colket et al [10], Stouffer et al [14], and Allision et al [17] found that blowoff boundaries could be correlated with the derived cetane number (DCN), a kinetic property. Although the DCN is directly related to the mixture's ignition delay time [18], it is also correlated with high-temperature kinetic properties, such as the flame speed and the extinction stretch rate [19]. For this reason, the DCN has often been used as a measure of global chemical kinetic reactivity [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Near-lean blowoff dynamics in a liquid fueled combustor

Rock

Emerson

Seitzman

et al. 2020

Combustion and Flame

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This paper describes an analysis of the near-lean blow off(LBO) dynamics of spray flames, including the influence of fuel composition upon these dynamics. It is motivated by the fact that, while reasonable correlations exist for predicting blowoffconditions, the fundamental reasons for why flames supported by flow recirculation actually blow offare not well understood. Prior work on gaseous systems has shown that the blowoffevent is a culmination of several intermediate processes, initiating with local extinction of reactions ("stage 1"), followed by large scale changes in flame and flow dynamics ("stage 2"), finally leading to blowoff. In this study, near-LBO dynamics were characterized for ten liquid fuels with widely varying kinetic and physical properties. Results were compared at two air inlet temperatures, 450 and 300 K, as this influences the relative importance of physical and kinetic properties in controlling LBO. Extinction, re-ignition, and recovery of the flame are evident from these data, and grow in frequency as blowoffis approached. Results show that after a near-blowoffevent, the flame can move upstream at velocities much faster than the flow velocity, corresponding to re-ignition. Nonetheless, the majority of the flame recovery events appear to be associated with convection of hot products back upstream, not re-ignition. In contrast, downstream motion of the flame faster than the flow, which would correspond to bulk flame extinction, was never observed. This indicates that "extinction events"actually correspond to convection of the flame downstream by the flow when it loses its stabilization point. The dependence of the equivalence ratio when these events appear, their frequency, and event duration were quantified as a function of fuel composition and air inlet temperature. For example, the data shows a higher percentage of recovery from near-blowoffevents through re-ignition for high DCN fuels at the 450 K air temperature condition. These extinction/re-ignition results suggest that high DCN fuels are harder to blow offthan low DCN fuels through two mechanisms: (1) by delaying the onset of LBO precursor events, and (2) because they are able to recover from these precursor events through re-ignition more often.

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Section: Facilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Near-lean blowoff dynamics in a liquid fueled combustor

Rock

Emerson

Seitzman

et al. 2020

Combustion and Flame

Self Cite

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“…As the ideal fuel for low temperature combustion (LTC) engines are likely to have auto-ignition quality in between diesel and gasoline; the octane and cetane scales have been correlated [10]. Recently a study [11] has also indicated the importance of DCN in understanding lean blow out (LBO) limits in gas turbine/jet engine applications. The LBO limit is critical parameter in combustion design for gas turbine applications, as the need for higher fuel efficiencies are pushing operation to lean limits where blow out in combustors can occur.…”

Section: Introductionmentioning

confidence: 99%

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Jameel

Oudenhoven

Naser

et al. 2021

SAE Int. J. Fuels Lubr.

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Artificial intelligence based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties. The present work deals with the development of an ANN model which can predict the derived cetane number (DCN) of oxygenated fuels containing alcohol and ether functionalities. Experimental DCN's of 499 fuels comprising of 116 pure compounds, 222 pure compound blends, and 159 real fuel blends were used as the dataset for model development. DCN measurements of sixty new fuels were carried out in the present work and the data for the rest was collected from the literature. Fuel chemical composition expressed in the form of eight functional groups namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic -CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups and ether O groups, along with two structural parameters namely, molecular weight and branching index (BI), were used as the ten input features of the model. The qualitative and quantitative determination of functional groups present in real fuels was performed using 1 H nuclear magnetic resonance (NMR) spectroscopy. A robust ANN methodology was followed to prevent over fitting using a multilevel grid search and genetic algorithm. The final developed model with two hidden layers was tested with 15 % of randomly generated unseen points from the dataset and a regression coefficient (R 2 ) of 0.992 was observed between the experimental and predicted DCN values. An average absolute error of 0.91 obtained from the test set indicates that the developed ANN model is successful in predicting the DCN of oxygenated fuels and captures the dependence of the fuel's ignition quality (i.e. DCN) on its constituent functional groups.

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“…Here, prevaporized liquid fuels have the potential to separate physical and chemical processes, allowing studying the combustion properties of different fuels based on their chemical properties alone. One important aspect is the lean blow-out (LBO) limit of different fuels [7][8][9] and hence also the dynamics of the lean blow-out process [10][11][12]. Since this process is highly dynamic and occurs on timescales on the order of milliseconds, optical diagnostics with high spatio-temporal resolution are necessary in order to fully resolve this phenomenon.…”

Section: Introductionmentioning

confidence: 99%