Date of Award

Spring 2018

Document Type


Degree Name

Master of Science (MS)


Mechanical Engineering

First Advisor

Singer, Simcha L.

Second Advisor

Borg, John P.

Third Advisor

Allen, Casey M.


Computational fluid dynamics (CFD) models for combustion of multicomponent hydrocarbon fuels must often prioritize computational efficiency over model complexity, leading to oversimplifying assumptions in the sub-models for droplet vaporization and chemical kinetics. Therefore, a computationally efficient hybrid droplet vaporization-chemical surrogate approach has been developed which emulates both the physical and chemical properties of a multicomponent fuel. For the droplet vaporization/physical portion of the hybrid, a new solution method is presented called the Coupled Algebraic-Direct Quadrature Method of Moments (CA-DQMoM) with delumping which accurately solves for the evolution of every discrete species in a vaporizing multicomponent fuel droplet with the computational efficiency of a continuous thermodynamics model. To link the vaporization model to the chemical surrogate portion of the hybrid, a Functional Group Matching (FGM) method is developed which creates an instantaneous surrogate composition to match the distribution of chemical functional groups in the vaporization flux of the full fuel. The result is a hybrid method which can accurately and efficiently predict time-dependent, distillation-resolved combustion properties of the vaporizing fuel and can be used to investigate the effects of preferential vaporization on combustion behavior.