Document Type

Article

Publication Date

3-2021

Publisher

Elsevier

Source Publication

Fuel

Source ISSN

0016-2361

Abstract

Liquid transportation fuels are composed of hundreds of species, necessitating the use of surrogates in CFD simulations. Surrogates composed of a few species are often formulated to emulate the combustion properties targets (CPTs) of pre-vaporized fuels but fail to reproduce their vaporization behavior, implying that such surrogates cannot replicate the CPTs in the presence of preferential vaporization. The prevailing approach to this problem proposes a physical–chemical surrogate formulated to match the fuel’s distillation curve in addition to its CPTs. However, the physical–chemical surrogate approach requires more species, may not reproduce the instantaneous (distillation-resolved) CPTs, and is not well-suited to conditions in which non-surrogate species surround the droplets. A recent hybrid approach addresses these shortcomings by combining a continuous thermodynamic model (CTM) for droplet vaporization with an adaptive chemical surrogate formulated using functional group matching (FGM). Whereas the hybrid model previously required a delumping calculation to recover discrete fluxes prior to FGM, the approach is modified here to directly predict the fluxes of functional groups using the CTM, increasing its flexibility for high-pressure applications. To this end, a novel, purely mathematical distribution variable is proposed to correlate key functional groups, in addition to thermophysical and transport properties. The accuracy and flexibility of both hybrid approaches compare favorably with the physical–chemical surrogate method. While droplet vaporization rates are well-represented by both methods, functional group fluxes and instantaneous CPTs are predicted more accurately by the hybrid methods, illustrating their potential for improving the accuracy of Eulerian-phase solvers in the presence of preferential vaporization.

Comments

Accepted version. Fuel, Vol. 288 (March 2021): 119821. DOI. © 2021 Elsevier. Used with permission.

Available for download on Wednesday, March 01, 2023

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