IrvinGQ have a long history designing and manufacturing parachutes and aerial delivery equipment, primarily for the military market. IrvinGQ have developed many parachutes using empirical design principles and extensive testing programmes to ensure that parachutes meet the required performance characteristics. Physical testing is an expensive and time-consuming process and IrvinGQ want to research the validity of using more computational simulation and experimental data explorations in their parachute development process. This project is to (i) develop and validate a computational modelling methodology to predict parachute performance, such as rate of decent and carried mass, to improve on existing designs. The computational simulation, considered initially on a scaled parachute model being tested by IrvinGQ, and will simulate a full model afterwards to identify and guide performance optimisation features through design changes. In addition, and supported by company available full-scale parachute data, (ii) a machine learning tool will be developed for future design optimisation and enhanced performance of new parachutes.