Elucidating the Properties of Surrogate Fuel Mixtures Using Molecular Dynamics

详细信息    查看全文

• 作者：Barbara L. Mooney ; Brian H. Morrow ; Keith Van Nostrand ; Dianne Luning Prak ; Paul C. Trulove ; Robert E. Morris ; J. David Schall ; Judith A. Harrison ; M. Todd Knippenberg
• 刊名：Energy & Fuels
• 出版年：2016
• 出版时间：February 18, 2016
• 年：2016
• 卷：30
• 期：2
• 页码：784-795
• 全文大小：671K
• ISSN：1520-5029

文摘

The wide compositional differences between conventional and alternative fuels have resulted in much research aimed at determining which alternative fuels can be used, and in what proportions, in conventional engines. Atomic-scale modeling is uniquely positioned to lend insight into this question without extensive large-scale tests. The predictive power such modeling affords could narrow the phase space that must be examined experimentally. This study utilizes molecular dynamics (MD) simulations to predict the properties of a set of pure hydrocarbons, as well as binary and multicomponent surrogate fuel mixtures for alternative fuels created from these pure components. The accuracy and transferability of the modified Lennard-Jones adaptive intermolecular reactive empirical bond-order potential (mod-LJ AIREBO) [Liu, A.; Stuart, S. J. J. Comput. Chem.lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve"> 2008lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve">, 29lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve">, 601−611] was assessed by calculating densities, heats of vaporization, and bulk moduli of pure hydrocarbons and the mixtures of these hydrocarbons, i.e., surrogate fuels. Calculated results were compared to experimentally determined values and to values obtained with the nonreactive, all-atom version of the optimized potential for liquid simulations (OPLS-AA) [Jorgensen, W. L.; Maxwell, D. S.; TiradoRives, J. J. Am. Chem. Soc.lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve"> 1996lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve">, 118lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve">, 11225−11236]. The mod-LJ AIREBO potential quantitatively predicts the densities of the pure hydrocarbons and binary mixtures of n-dodecane and 2,2,4,4,6,8,8-heptamethylnonane (isocetane). It is interesting to note, that despite doing an excellent job predicting the densities of the pure hydrocarbons, the performance of the mod-LJ AIREBO potential degrades when predicting the densities of the multicomponent surrogates and mixtures of n-hexadecane and isocetane, implying that it is not straightforward to extend potentials fit with pure compounds to mixtures. The OPLS-AA potential also has difficulty quantitatively predicting the densities of mixtures, although a new parameter set for long-chain hydrocarbons (L-OPLS) [Siu, S. W. I.; Pluhackova, K.; Bockmann, R. A. J. Chem. Theory Comput.lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve"> 2012lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve">, 8lass="NLM_x">lns:mml="http://www.w3.org/1998/Math/MathML" xmlns:ACS="http://namespace.acs.org/2008/acs" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:space="preserve">, 1459−1470] yields some improvement for binary mixtures. Heat of vaporization predictions using both potentials also agree reasonably well with experiment. Bulk moduli predictions using the mod-LJ AIREBO potential are consistently higher than, and do not quantitatively agree with, the experimental values. In contrast, bulk moduli predictions using the OPLS-AA potential are generally in good agreement with experimental values. Despite the success of the OPLS-AA potential predicting the bulk moduli of individual hydrocarbons, it is unable to quantitatively predict the bulk moduli of the multicomponent surrogates. Interestingly, the use of the L-OPLS parameter set improves density predictions but not predicted bulk moduli values.