This presentation was made at the Conference on Advancing Analysis & Simulation in Engineering (CAASE21). This event was a collaboration between NAFEMS Americas and Digital Engineering (DE).

Resource Abstract

A digital twin is an evolving virtual model that mirrors an individual physical asset throughout its lifecycle. Key to the digital twin concept is the ability to sense, collect, analyze, and learn from the asset's data. This talk will discuss the ways in which digital twins have the potential to transform design, manufacture, and operation of engineering systems. To make digital twins a reality, many elements of the interdisciplinary field of computational science, including physics-based modeling and simulation, inverse problems, uncertainty quantification, and scientific machine learning, have an important role to play. In this work, we develop a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset-twin system as a set of coupled dynamical systems, evolving over time through their respective state-spaces and interacting via observed data and control inputs. The abstraction is realized computationally as a dynamic decision network. Predictive capabilities are enabled by physics-based reduced-order models. We demonstrate how the approach is instantiated to create, update and deploy a structural digital twin of an unmanned aerial vehicle.



Key takeaways:

* A probabilistic graphical model provides a formal mathematical foundation for digital twins, on which we layer data assimilation, model updating, optimal control, and end-to-end uncertainty quantification.

* Predictive digital twins require a synergistic combination of physics-based modeling and data-driven learning.

* We demonstrate the definition, creation, updating and use of a structural digital twin for a custom-built unmanned aerial vehicle.