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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Mechanical Engineering

Year Degree Awarded


Month Degree Awarded


First Advisor

David P. Schmidt

Subject Categories

Aerodynamics and Fluid Mechanics | Dynamics and Dynamical Systems | Energy Systems | Heat Transfer, Combustion | Propulsion and Power


Numerical modeling of advanced propulsion systems such as the Internal Combustion Engine (ICE) is of great interest to the community due to the magnitude of compute/algorithmic challenges. Fuel spray atomization, which determines the rate of fuel-air mixing, is a critical limiting process for the phenomena of combustion within ICEs. Fuel spray atomization has proven to be a formidable challenge for the state-of-the-art numerical models due to its highly transient, multi-scale, and multi-phase nature. Current models for primary atomization employ a high degree of empiricism in the form of model constants. This level of empiricism often reduces the art of predictive modeling, in the case of sprays to a mere data-fitting exercise to experimental observations by tuning model constants.

In this research, first a series of full three-dimensional (3D) Computational Fluid Dynamics (CFD) studies are presented that examine the factors affecting spray atomization, including the effects of nozzle geometry, transient injector needle motion and in-cylinder thermodynamic conditions on the spray atomization behavior. Informed by this study, a novel reduced order model - Eulerian Lagrangian Mixing Oriented (ELMO), with experimentally-informed inputs is presented that aims to replace current primary atomization models such as the Kelvin-Helmholtz Rayleigh-Taylor (KHRT). The ELMO fuel spray atomization model is coupled to a full 3D gas phase solver, and validation studies on single/multi-hole injector configurations and spray conditions (diesel/gasoline) are presented. In addition, validation studies for ELMO are presented for a standard diesel engine. In the last section of this thesis, the applicability of data-driven machine learning (ML) models are explored. First, a ML based turbulence closure is presented for the four-stroke Darmstadt engine and performance against experimentally validated ground-truth data measured. Sensitivity studies indicate the data-driven model preserves the functional characteristics of the turbulence closure, consistent with theory. In the final study, a machine learning based surrogate model for estimating the discretization error of coarser meshes is developed and integrated into OpenFOAM. This surrogate model enhances the quality of coarse-mesh CFD simulations by adding necessary source terms to retrieve the 'lost' resolved scale information, thereby providing a higher fidelity solution at almost the same compute cost. This model is tested on different OpenFOAM solvers, including for an engine cold flow setting, and the generalizability of the data-driven framework commented upon.