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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.

As aircraft continue to increase their power and thermal demands, transient operation of the power and propulsion subsystems can no longer be neglected at the aircraft system level. The performance of the whole aircraft must be considered by examining the dynamic interactions between the power, propulsion, and airframe subsystems. Larger loading demands placed on the power and propulsion subsystems result in thrust, speed, and altitude transients that affect the aircraft performance and capability. This results in different operating and control parameters for the engine that can be properly captured only in an integrated system-level test. While it is possible to capture the dynamic interactions between these aircraft subsystems by using simulations alone, the complexity of the resulting system model has a high computational cost.

Next generation aircraft will require more electrical power, more thermal cooling, and better versatility. To attain these improvements, technologies will need to be integrated and optimized at a system-level. The complexity of these integrated systems will require considerable analysis. In order to characterize and understand the implications of highly-integrated aircraft systems, the effects of pulsed-power, highly-transient loads, and the technologies that drive system-stability and behavior, an approach will be taken utilizing integrated modeling and simulation with hardware-in-the-loop (HIL). Such experiments can save time and cost and increase the general understanding of electrical and thermal phenomena as it pertains to aircraft systems before completing an integrated ground demonstration. As a first step toward completing an integrated analysis, a dynamometer “drive stand” was characterized to assess its performance.

Aircraft power demands continue to increase with the increase in electrical subsystems. These subsystems directly affect the behavior of the power and propulsion systems and can no longer be neglected or assumed linear in system analyses. The complex models designed to integrate new capabilities have a high computational cost. Hardware-in-the-loop (HIL) is being used to investigate aircraft power systems by using a combination of hardware and simulations. This paper considers three different real-time simulators in the same HIL configuration. A representative electrical power system is removed from a turbine engine simulation and is replaced with the appropriate hardware attached to a 350 horsepower drive stand. Variables are passed between the hardware and the simulation in real-time to update model parameters and to synchronize the hardware with the model.

Experimental Hardware-in-the-loop (xHIL) testing utilizing signal and/or power emulation imposes a hard real-time requirement on models of emulated subsystems, directly limiting their fidelity to what can be achieved in real-time on the available computational resources. Most real-time simulators are CPU-based, for which the overhead of an instruction-set architecture imposes a lower limit on the simulation step size, resulting in limited model bandwidth. For power-electronic systems with high-frequency switching, this limit often necessitates using average-value models, significantly reducing fidelity, in order to meet the real-time requirement. An alternative approach emerging recently is to use FPGAs as the computational platform, which, although offering orders-of-magnitudes faster execution due to their parallel architecture, they are more difficult to program and their limited fabric space bounds the size of models that can be simulated.

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.

This paper introduces a method for conducting experimental hardware-in-the-loop (xHIL), in which behavioral-level models are coupled with an advanced power emulator (APE) to emulate an electrical load on a power generation system. The emulator is commanded by behavioral-level models running on an advanced real-time simulator that has the capability to leverage Central Processing Units (CPUs) and field programmable gate arrays (FPGA) to meet strict real-time execution requirements. The paper will be broken down into four topics: 1) the development of a solution to target behavioral-level models to an advanced, real-time simulation device, 2) the development of a high-bandwidth, high-power emulation capability, 3) the integration of the real-time simulation device and the APE, and 4) the application of the emulation system (simulator and emulator) in an xHIL experiment.

Cost and performance requirements are driving military and commercial systems to become highly integrated, optimized systems which require more sophisticated, highly complex controls. To realize benefits of those complex controls and make confident decisions, the validation of both plant and control models becomes critical. To quickly develop controls for these systems, it is beneficial to develop plant models and determine the uncertainty of those models to predict performance and stability of the control algorithms. A process of model and control algorithm validation for a dc-dc boost converter circuit based on acceptance sampling is presented here. The validation process described in this paper is based on MIL-STD 3022 with emphasis on requirements settings and the testing process. The key contribution of this paper is the process for model and control algorithm validation, specifically a method for decomposing the problem into model and control algorithm validation stages.