Models Behaving Badly Costs Taxpayers Millions

Sorry, this is not an article about Hollywood celebrities causing trouble. This is actually about digital engineering transforming the aerospace and defense industries to provide a better future for the next generation of Americans. Do I still have your attention?

You may have heard that digital engineering is one of the latest trends in aerospace and defense due to its ability to dramatically reduce costs and delivery timelines for complex systems. Digital engineering includes using integrated data and process management systems, wide ranging, complex analytic simulations, and model-based systems engineering (MBSE).

One of the most challenging aspects of the traditional approaches is testing and understanding complex system behaviors without conducting expensive and time-consuming physical tests. National security systems make this even more challenging because it may be impossible to truly test the designed behavior in a realistic mission environment. Any inability to effectively test the behaviors of your system, under realistic conditions, can lead to expensive cost overruns, programmatic delays, or ineffective capabilities.

Advanced simulations with tools like Systems Tool Kit (STK) and Ansys HFSS have been shown to save millions of dollars by reducing the number of flight tests required for advanced aircraft systems.

Other studies have shown a 55% reduction in the development costs of complex projects by using MBSE practices.

You and your colleagues can realize the full potential of digital engineering when analytic simulations are driven by the same descriptive and behavioral MBSE models. This fundamentally enables you to simulate, quantify, and verify whether the digital prototypes you have designed can satisfy mission requirements. Linking MBSE behaviors to analytic simulations is what Moxie is designed to do.

The video above is from the Example Moxie Scenarios and shows a common occurrence: a satellite communications system experiencing jamming. 93% of satellite operators experience jamming like this annually, according to one report. The faster an operator can mitigate the effects of a jamming event, the faster they can get back to business. In the example above, the system is designed to react to an unknown signal source by creating a beam that searches for the interference source, and when it has located the source, apply a null direction. In this example, the satellite steers the beam through the field of view with a coarse search pattern, recording the signal power of the unknown signal at each grid point. It then performs a second, more refined search in the area surrounding the original grid point with the highest jamming power detected.

The combination of behaviors described via SysML state machines, driving analytic simulations in STK, enables you to execute hundreds or thousands of test cases to evaluate how the system actually responds while performing its designed behavior. If you make changes to the designed behavior, you can re-execute the system to verify performance, identify improvements, or find unexpected faults earlier, faster, and with much lower cost.

If you are starting your digital engineering and MBSE journey, you should know about Moxie; and we look forward to talking to you!