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MIL-STD-704 defines power quality in terms of transient, steady-state, and frequency-domain metrics that are applicable throughout a military aircraft electric power system. Maintaining power quality in more electric aircraft power systems has become more challenging in recent years due to the increase in load dynamics and power levels in addition to stricter requirements of power system characteristics during a variety of operating conditions. Further, power quality is often difficult to assess directly during experiments and aircraft operation or during data post-processing for the integrated electric power system (including sources, distribution, and loads). While MIL-STD-704 provides guidelines for compliance testing of electric load equipment, it does not provide any instruction on how to assess the power quality of power sources or the integrated power system itself, except the fact that power quality must be satisfied throughout all considered operating conditions.

The diverse and complex requirements of next-generation energy optimized aircraft (EOA) demand detailed transient and dynamic model-based design (MBD) to ensure the proper operation of numerous interconnected and interacting subsystems. In support of the U.S. Air Force's Integrated Vehicle Energy Technology (INVENT) program, several software tools have been developed and are in use that aid in the efficient MBD of next-generation EOA. Among these are subsystem model libraries, automated subsystem model verification test scripts, a distributed co-simulation application, and tools for system configuration, EOA mission building, data logging, plotting, post-processing, and visualization, and energy flow analysis. Herein, each of these tools is described. A detailed discussion of each tool's functionality and its benefits with respect to the goal of achieving successful integrated system simulations in support of MBD of EOA is given.

The combination of increasing performance demands, increasing system complexity, and the need for reduced program development schedule and budget costs in the aerospace industry is driving engineers to increasingly rely upon modeling, simulation, and analysis (MS&A) in the platform development cycle. One approach to ensuring that such integrated system simulations remain computationally tractable is co-simulation utilizing technology found in commercially available packages, such as PC Krause and Associates, Inc.'s (PCKA's) Distributed Heterogeneous Simulation (DHS) / FastSim software. In such co-simulation environments, dynamic models are executed in independent model spaces, with coupling between subsystems achieved by exchanging a minimal set of required data typically found at subsystem boundaries.

The trend of moving towards model-based design and analysis of new and upgraded aircraft platforms requires integrated component and subsystem models. To support integrated system trades and design studies, these models must satisfy modeling and performance guidelines regarding interfaces, implementation, verification, and validation. As part of the Air Force Research Laboratory's (AFRL) Integrated Vehicle and Energy Technology (INVENT) Program, standardized modeling and performance guidelines have been established and documented in the Modeling Requirement and Implementation Plan (MRIP). Although these guidelines address interfaces and suggested implementation approaches, system integration challenges remain with respect to computational stability and predicted performance over the entire operating region for a given component. This paper discusses standardized model evaluation tools aimed to address these challenges at a component/subsystem level prior to system integration.

Model based design is a standard practice within the aerospace industry. However, the accuracies of these models are only as good as the parameters used to define them and as a result a great deal of effort is spent on model tuning and parameter identification. This process can be very challenging and with the growing complexity and size of these models, manual tuning is often ineffective. Many methods for automated parameter tuning exist. However, for aircraft systems this often leads to large parameter search problems since frequency based identification and direct gradient search schemes are generally not suitable. Furthermore, the cost of experimentation often limits one to sparse data sets which adds an additional layer of difficulty. As a result, these search problems can be highly sensitive to the definition of the model fitness function, the choice of algorithm, and the criteria for convergence.