1. A method of developing a model for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft, the method comprising the steps of: (a) generating a data set from historical data representative of a plurality of VCS operating conditions over time, the generated data set comprising a plurality of data points, each data point comprising: (i) one or more values for a plurality of VCS operating variables reflecting operation of the VCS over a specific time period and corresponding to a specific set of operating conditions; and (ii) corresponding values for VCS refrigerant charge over the same time period; (b) identifying one or more steady-state data points in the generated data set, each steady-state data point corresponding to steady-state operation of the VCS; (c) forming a revised data set that includes at least the steady-state data points; (d) using principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables from the values for the plurality of VCS operating variables in the revised data set; (e) supplying the derived values for the plurality of minimally correlated input variables, and the corresponding values for the VCS refrigerant charge in the revised data set, to a nonlinear neural network model; and (f) deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.