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Reconstruction of chemical structure of real fuel by surrogate formulation based upon combustion property targets

Published on Sep 1, 2017in Combustion and Flame4.12
· DOI :10.1016/j.combustflame.2017.04.032
Sang Hee Won3
Estimated H-index: 3
(USC: University of South Carolina),
Francis M. Haas15
Estimated H-index: 15
(Rowan University)
+ 2 AuthorsFrederick L. Dryer66
Estimated H-index: 66
(Princeton University)
Abstract
Abstract The global chemical character of complex chemical fuel mixtures is explicitly determined by evaluating the abundances of chemical functional groups present within them rather than by applying a traditional interpretation based upon molecular species composition. Statistical analyses of the relationships among each chemical functional group and specific combustion property targets (CPTs) of the fuel are rigorously developed. The results demonstrate that the four CPTs currently used in aviation kerosene surrogate formulation - H/C molar ratio, derived cetane number (DCN), average molecular weight (MW), and threshold sooting index (TSI) - effectively constrain the chemical functional group distribution of the fuel, and, hence, the global combustion behaviors of pre-vaporized fuel/air mixtures. Successful emulation of the CPTs for a target real fuel involves developing a surrogate mixture that defines an “equivalent” chemical functional group distribution to that of the target fuel. Among the CPTs used for real fuel surrogate development, DCN does not abide by a linear blending rule, which generally frustrates development of surrogates. However, a quantitative structure–property relation (QSPR) regression for DCN is demonstrated here using the chemical functional group approach. Results of the regression reveal that the (CH 2 ) n group plays the most significant role in determining the fuel autoignition propensity, followed by the influences of CH 3 and benzyl-type groups. The QSPR functional group approach extends to provide a powerful tool to address potential preferential vaporization effects dictated by fuel distillation characteristics. Further analysis of fuel chemical property variation (DCN and H/C ratio) over the distillation curve (and other physical properties) provides a foundation for understanding the complex combustion behaviors of multi-phase and multi-component fuels relevant to real gas turbine engine applications.
  • References (68)
  • Citations (12)
References68
Newest
#1Alessandro Stagni (Center for Turbulence Research)H-Index: 8
#2Lucas Esclapez (Center for Turbulence Research)H-Index: 5
Last.Matthias Ihme (Center for Turbulence Research)H-Index: 24
view all 6 authors...
#1Philippe Dagaut (CNRS: Centre national de la recherche scientifique)H-Index: 50
#2Pascal Diévart (CNRS: Centre national de la recherche scientifique)H-Index: 15
#1Doohyun Kim (UM: University of Michigan)H-Index: 5
#2Jason Martz (UM: University of Michigan)H-Index: 14
Last.Angela Violi (UM: University of Michigan)H-Index: 32
view all 3 authors...
#1Eduardo J. Barrientos (CTU: Czech Technical University in Prague)H-Index: 5
#2James E. Anderson (Ford Motor Company)H-Index: 25
Last.André L. Boehman (UM: University of Michigan)H-Index: 39
view all 4 authors...
Cited By12
Newest
#1Georg Eckel (DLR: German Aerospace Center)H-Index: 3
#2Jasper Grohmann (DLR: German Aerospace Center)H-Index: 2
Last.Manfred Aigner (DLR: German Aerospace Center)H-Index: 26
view all 9 authors...
#1Nicholas Rock (Georgia Institute of Technology)H-Index: 3
#2Ianko Chterev (Georgia Institute of Technology)H-Index: 5
Last.Tim Lieuwen (Georgia Institute of Technology)H-Index: 41
view all 6 authors...
#1Sang Hee Won (USC: University of South Carolina)H-Index: 3
#2Nicholas Rock (Georgia Institute of Technology)H-Index: 3
Last.Frederick L. Dryer (USC: University of South Carolina)H-Index: 3
view all 9 authors...
#1Yu Wang (Stanford University)H-Index: 1
#2Yu Wang (Stanford University)
Last.Ronald K. Hanson (Stanford University)H-Index: 74
view all 5 authors...
#1Nicholas Rock (Georgia Institute of Technology)H-Index: 3
#2Benjamin Emerson (Georgia Institute of Technology)H-Index: 8
Last.Tim Lieuwen (Georgia Institute of Technology)H-Index: 41
view all 4 authors...
#1Francis M. Haas (Princeton University)H-Index: 15
#2Sang Hee Won (Princeton University)H-Index: 27
Last.Cécile PeraH-Index: 1
view all 4 authors...
#1Stephen Dooley (Trinity College, Dublin)H-Index: 23
#2Sang Hee Won (USC: University of South Carolina)H-Index: 3
Last.Frederick L. Dryer (USC: University of South Carolina)H-Index: 3
view all 3 authors...
#1Karla Dussan (National University of Ireland, Galway)H-Index: 9
#2Sang Hee Won (USC: University of South Carolina)H-Index: 3
Last.Stephen Dooley (Trinity College, Dublin)H-Index: 23
view all 5 authors...
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