The main goal of this research project was to develop and validate more accurate, physics-based, mathematical submodels for use in Computational Fluid Dynamics (CFD) software to enable better prediction of cavitation within fuel injectors. The research focused on the development of small scale Lagrangian models and larger scale Eulerian based models. Additionally, experimental research into the detection of cavitation in both idealized fuel injector nozzles and real fuel injectors was carried out. The outcomes of the research include (i) an improved cavitation model for use in large scale CFD modeling that accounts for variations in nuclei, (ii) a small scale Lagrangian model with an improved physical model of surface tension, (iii) neutron imaging data on cavitation in a fuel injector, and (iv) acoustic and optical cavitation detection methods. Controlled cavitation in fuel injectors can improve the atomization of the spray, which improves combustion and reduces emissions. However, excess cavitation can be detrimental to efficiency and can damage the injector. Therefore, the global motivation for research into cavitation in fuel injectors stems from the fact that improvements in fuel injection systems will increase fuel efficiency, reduce the emission of harmful pollutants and improve the lifetime and reliability of nozzles. To accomplish these goals better computational methods of predicting cavitation are needed and better methods of cavitation detection are needed.