Aerospace & Defense
Innovation and collaborative, synchronized program management for new programs
Predictive engineering analytics is the application of multidisciplinary engineering simulation and test with intelligent reporting and data analytics, to develop digital twins that can predict the real world behavior of products throughout the product lifecycle. Predictive engineering analytics includes both the tactics and tools that manufacturers can leverage to expand traditional design verification and validation into a predictive role in support of systems-driven product development. The ultimate goal of implementing a predictive engineering analytics strategy is to deliver innovation for complex products faster and with greater confidence.
Predictive engineering analytics represents the next evolution of product development processes which currently rely on independent workflows in computer-aided engineering (CAE) and testing. Predictive engineering analytics addresses the complex nature of today’s products and product development environments, which combine mechanical functions with electronics, software and controls. It integrates different technologies including 1D simulation, 3D simulation covering computational solid mechanics (CSM) and finite element analysis (FEA), computational fluid dynamics (CFD), and multibody dynamics, controls, physical testing, visualization, multidisciplinary design exploration, and data analytics in a managed context to support the engineering and development of complex systems.
In a predictive engineering analytics approach, the availability of sensor-based data combined with high-fidelity, physics-based simulations allows manufacturers to build and maintain digital twins of the product and to keep them in-sync with the product in use. That is of crucial importance to making more useful and realistic predictions of product performance that will enable these products to adapt to changing usage conditions, extend their useful life, and accommodate product degradation.
Deliver multi-fidelity models that are as close to reality as possible for each stage of development
Choose the best concepts and system architectures to carry forward in development
Implement faster simulation processes needed to drive design
Effectively balance multi-disciplinary requirements
Keep models in-sync with the actual product in use so simulations are more realistic
Leverage simulation and test approaches in concert to gain greater confidence in meeting requirements
Use data analytics combined with physics-based simulation to gain insights and drive innovation