The US Air Force’s two main aeropropulsion test centers, Arnold Engineering Development Center and the Air Force Flight Test Center, are developing a common suite of modeling and simulation tools employing advanced predictive modeling technologies. This common set of modeling and simulation tools incorporates real-time data validation, system identification, parameter estimation model calibration, and automated model updating as new test results or operational data become available. The expected benefit is improved efficiency and accuracy for online diagnostic monitoring of Air Force assets. These resultant models could also be used for flight manual development, determining compliance to specifications, or to aid in real-time equipment monitoring. This paper describes the integrated approach to system identification, parameter estimation, and model updating. Implementation of a software package to enable efficient model handoff between test groups and centers is discussed. An F/A-22 inlet model is used to demonstrate the approach. Compact polynomial function models of the distortion and recovery flow descriptors and 40-probe pressure values are derived from quasi-steady and instantaneous subscale wind tunnel data. The model parameters are then calibrated with F/A-22 flight test data. Results show that the modeling algorithm captures the relevant nonlinear physics of the application, and the calibration and updating procedure improves the model match to flight data. A companion paper provides preliminary results from integrating the calibrated total-pressure inlet distortion and recovery models into a real-time equipment health monitoring system to support test operations.
