Date of Award


Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Graduate Studies

Committee Chair

Dr. Sirish Namilae

First Committee Member

Dr. Richard Prazenica

Second Committee Member

Dr. Dahai Liu

Third Committee Member

Dr. Scott Parr


Hurricanes are powerful agents of destruction with significant socioeconomic impacts. A persistent problem due to the large-scale evacuations during hurricanes in the southeastern United States is the fuel shortages during the evacuation. Fuel shortages often lead to stranded vehicles and exacerbate the evacuation efforts. Computational models can aid in emergency preparedness and help mitigate the impacts of hurricanes. In this thesis, the hurricane fuel shortages are modeled using the Susceptible-Infected-Recovered (SIR) epidemic model. Crowd-sourced data corresponding to Hurricane Irma and Florence are utilized to parametrize the model. An estimation technique based on Unscented Kalman filter (UKF) is employed to evaluate the SIR dynamic parameters. Finally, an optimal control approach for refueling based on a vaccination analogue is presented to effectively reduce the fuel shortages under a resource constraint. The control model estimates the duration and the level of intervention required to mitigate this epidemic. This approach could be useful for emergency management of future hurricanes. A predictive model is then proposed where the UKF can be utilized to evaluate the SIR dynamic parameters from incoming fuel shortage during the initial stages of the hurricane. Due to the nature of the Ordinary Differential Equations (ODE) of SIR dynamics, only one of the parameters can be accurately estimated from the data collection of initial stages of the evacuation. The Basic Reproduction number (R0) value is then varied to produces predictive trends and the optimal refueling strategy is applied to these probable fuel shortage trends to demonstrate possible countermeasures.