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This paper presents the details of an engine emulation system utilized within a Hardware-in-the-Loop (HIL) test environment for aircraft power systems. The paper focuses on the software and hardware interfaces that enable the coupling of the engine model and the generator hardware. In particular, the rotor dynamics model that provides the critical link between the modeled dynamics of the engine and the measured dynamics of the generator is described in detail. Careful consideration for the measured torque is included since the measurement contains inertial effects as well as torsional resonances. In addition, the rotor model is equipped with the ability to apply power and speed scaling between the engine and generator.

With the advent of modern parallel computing systems, larger and more accurate simulation models have been developed to simulate real-world hardware. These models require verification and validation (V&V), the latter using data acquired from representative hardware to ascertain the uncertainty of the model. An understanding of the errors introduced by the measurement system into the validation assessment allows for the model assessor to attribute errors to the measurement system as opposed to the model or experimental setup. Once the model(s) have been through the validation process, decision makers can better understand the risk associated with using these models. This paper describes one possible procedure to quantify the uncertainty of the data acquisition (DAQ) system.

As technology for both military and civilian aviation systems mature into a new era, techniques to test and evaluate these systems have become of great interest. To achieve a general understanding as well as save time and cost, the use of computer modeling and simulation for component, subsystem or integrated system testing has become a central part of technology development programs. However, the evolving complexity of the systems being modeled leads to a tremendous increase in the complexity of the developed models. To gain confidence in these models there is a need to evaluate the risk in using those models for decision making. Statistical model validation techniques are used to assess the risk of using a given model in decision making exercises. In this paper, we formulate a transient model validation challenge problem for an air cycle machine (ACM) and present a hardware test bench used to generate experimental data relevant to the model.