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An allocation algorithm for optimally assigning the various subsystem simulations, within a distributed heterogeneous simulation, to a specific set of computational resources has been developed. This algorithm uses a cost function that approximates the simulation execution time for each of the subsystems based upon the model complexity and the performance parameters of the available computer resources. The cost function is then evaluated to determine the optimal allocation that ensures the overall simulation execution time is minimized. In this paper, the allocation algorithm is applied to a large-scale power-electronic-based aircraft electrical power system. This study system is comprised of ten component simulations that together are modeled by 85 state variables and include 74 switching devices. Both optimal and sub-optimal allocations are considered and the predicted simulation run times are verified experimentally.

In this paper, a parametric average-value modeling approach is applied to a high-frequency six-phase aircraft generation subsystem. This approach utilizes a detailed switch-level model of the system to numerically establish the averaged dynamic relationships between the ac inputs of the rectifier and the dc-link outputs. A comparison between the average-value and detailed models is presented, wherein, the average-value model is shown to accurately portray both the large-signal time-domain transients and the small-signal frequency-domain characteristics. Since the discontinuous switching events are not present in the average-value model, significant gains can be realized in the computational performance. For the study system, the developed average-value simulation executed more than two orders of magnitude faster than the detailed simulation.