Utah Engineers Journal 2021 Issue

20 So, What About Tomorrow? What does this presentation mean to you practically? I propose a few specific areas for you to think about, and then go help advance the state of the art! Legacy Data Migration Migrating legacy data is very expensive. It requires time and investment in digital engineering infrastructure, especially if your starting point is completely paper and you want to create a detailed model from it. The most important things to model early are those that represent high risk. For example, you should focus on modeling relationships, dependencies, interfaces, and aspects of the design that are most likely to change. As soon as you can complete even a partial model, it can help others execute their technical or program management responsibilities. Mixed Reality Virtual reality, or augmented reality, gives you the ability to immerse yourself in the data so you can understand it and how it interacts. The results can put modeling and simulation “on steroids”! For example, television shows like Star Trek showed us a holodeck where a simulation surrounded the actors. Other shows and movies have screens that can be pulled up and used by a hand gesture. These applications are becoming more and more available and practical. It is already possible to be surrounded by a simulation that isn’t just on a screen in front of you. You will be able to see how a very complicated system operates. You will also be able to examine a simulated object inside and out. Instead of being fixed in a cockpit, for example, you could go into an airplane wing and see how a hydraulic actuator is moving in flight and immediately see the impact on a change in design or in the system’s operation. There are very practical applications for mixed reality. Engineering used to be more about the operator and how the operator was going to interact. Engineering now is about system performance with or without the operator, and that’s the direction the simulations are going. The work has become more concerned about the system itself. Practical applications of a data- intensive, model-based environment are fairly self-explanatory. In a model-based world, you use system information to make products such as a Tesla sedan better and safer. The engineer is always looking ahead and uses the model-based environment to make continual process and product improvements. Operating Locations Engineers, from wherever they happen to be, can access data from many different operating locations. The pandemic has accelerated the process. The Cloud Information is ideally shared by a group of people who make decisions, but data growth has created the need for more intensive “data democratizing” capabilities. Of course, cloud security has become increasingly important. If radically comprehensive and collaborative data exists, then your adversary or your competition can get a hold of it as well. Facebook has shown us how hard it is to keep private data safe. Good data management and cybersecurity processes can ensure that people only see what they need to know. Rapid Virtual Prototyping The model-based world makes good engineers great and great engineers epic because they can see, comprehend, and act on so much data. They can look ahead, manage risks, see how to modify their work, and make and support decisions. For example, an “Iron Man” exoskeleton suit allows you to do inhuman feats. In that same vein, we will have AI for data access on the same level as Tony Stark’s AI system, Just a Rather Intelligent System (JARVIS), and we will be able to call up simulations very rapidly. For example, someone wearing a holo lens will be able to reach out and take apart a virtual system or look at its supply chain history. We currently do physical testing when we want to validate and verify the models. As our skill increases, we will need to do much less physical prototyping and testing, just as NASA moved from having mathematicians like Katherine Johnson do calculations by hand to using actual computers instead. Mathematicians continued to verify computations manually until NASA trusted computer accuracy. The same will be true for virtual simulations. We test when we need to anchor the model. The model drives simulations, but the simulations will become more powerful, and we will trust them more. Large virtual testing evaluations will become more common, and as it does, we will build fewer wind tunnels and test tracks. We will do fewer risky and expensive full-scale tests. Just Imagine … and then, MAKE IT HAPPEN! Data organized into models will be accessible to data tools, but this presentation about the expanding role of models and AI in engineering just scratched the surface. We will never reach a finish line, and the work currently being done — and your future work using these processes and technologies — is expanding human innovation. Go forward and innovate! Continued from the previous page