Session Category: AIAA SciTech Forum 2021

The number of new entrants into the aerospace community are many. The experience of the leaders, the business models, the driving technologies, and markets are a few variables represented on this panel. Hear about who these new entrants are, why aerospace, why now, and how the traditional community can foster these new entrants thus enabling a more robust and inclusive industry that can support solutions for the complex problems facing society.

To thrive in an ever-changing, hyper-connected, globally competitive, technology-enabled world, we are seeing huge potential in embracing “model-based everything” to help us better manage/understand complexity, increase speed, and improve agility/flexibility in dealing with uncertainty and pivots – but what’s the Holy Grail for digital models to enable us to “Design Faster?” The use of models is not new especially in our engineering world, but computing and technology have now enabled us to seriously accelerate multifidelity, multiphysics models, integrated with MBSE models to help us understand how trades in our individual engineering subteams impact our collective ability to meet larger requirements and even enable flexibility in operations through digital twins.

However, modeling is not just for engineering. We are seeing huge opportunities to use models in program/project management to better predict performance, estimate schedules and probable costs, not to mention forecast risks and effectiveness of mitigations, and even the probability of opportunities materializing. And we are also seeing modeling enable new insights in the way we manage organizations, such as prediction of facility readiness (through IoT tied to digital twins and analytics) as well as models of our workforce (including retirement /skill projections), not to mention supply chain vulnerability in procurements. The end game each of us might envision is becoming a model-based enterprise – where our engineering models, program/project management models, and institutional models are all integrated, sharing in real-time and operating on verified “single source of truth” data, powered by an interoperable model-based architecture. The Holy Grail for us collectively might be transforming to become a model-based ecosystem, thriving in an environment where we can seamlessly partner together facilitated through advanced integrated models, together creating societal breakthroughs through aerospace. To debate this future, we have assembled a panel of diverse stakeholder perspectives across the model-based space.

A cross-domain topic that focuses on AI/autonomy for both unmanned systems and general aviation (GA), especially within today’s context and the post COVID-19 world. Aviation is still in infancy on integrating machine learning/AI and autonomy. Over a number of years there have been ongoing efforts on robotic co-pilots, fully autonomous cargo planes, ground collision avoidance (auto GCAS), automated landing of GA aircraft, etc. This provides an excellent backdrop for a discussion of the acceleration of machine intelligence integration in the mainstream aerospace world. Further aspects to consider: the unmanned aviation technology and policy community is not taking advantage of the massively reduced sky traffic to try out new things right now. General aviation, charter, and Part 91-K flight operations should see a big boost from the current situation. Getting costs to a reasonable point will require going to one pilot initially, to remote pilots with one pilot per aircraft evolving to M pilots controlling N aircraft, and eventually to no pilots on board. The demand for that may have been created, or at least accelerated by COVID-19. .