AEROSPACE ENGINEERING STRATEGIC VISION /// 2022-2027
Digital Engineering
Development time and cost of mant new aircraft, engines and spacecraft are accelerating at an unsustainable rate. Major technology developments in aerospace over the next decade will be in digital engineering and manufacturing. A much stronger digital-enabled connection between design/development and production/sustainment functions is needed, ultimately to enable certification-by-design.
Emerging computing paradigms will be exploited to increase fidelity of predictions of systems of higher complexity. Data will play an increasingly important role, and new tools are needed to enable integration of models and data for fast and real-time operational capabilities.
Systems engineering understanding will grow in importance on top of mastering the fundamentals, and universities must determine the appropriate depth of exposure to it. Engineers will be required to have a much broader and holistic perspective (encompassing operability, manufacturability, etc.) than in the past, and they must be able to think in this multidisciplinary environment.
WHY MICHIGAN?
Working alongside other experts at the University of Michigan, we collaborate in the areas of algorithms, numerical methods, and software with our robotics, computer science and mathematics departments. By leveraging our relationships with researchers in data science, manufacturing, digital manufacturing and supply chain engineering in the region, we can build interdisciplinary systems together.
Holistic Approach
Adopting industry 4.0 elements spanning design, development, production, and customer support considers the big picture of digital engineering aspects. This holistic approach will include design, manufacturing, and operations, from cradle to grave (digital twin)—the complete life cycle of the airspace system under the various societal, environmental, and commercial requirements.
Advanced Software
Our future requires advances in software that enable end-to-end design, analysis, optimization, simulation, and uncertainty quantification of complex multidisciplinary, multi-physics, and multi-fidelity systems.
We need new approaches for modeling and solving complex systems across all heterogeneous emerging computing architectures: embedded, local, edge,
supercomputers, quantum computers, as well as robust simulation algorithms that adapt to the problem and the experience of the user.
Integrating Operational Data
Our focus needs to involve new approaches for tightly integrating operational data with first-principles modeling: dealing/modeling complex experiments and their associated challenges, sparse data from variable fidelities, experimental design as a thread to integrate experiments and models. This must encompass both the airframe and its on-board software.