We invite you to NDIA Conference; meet experts in engineering analytics and uncertainty quantification, see demonstrations, and explore how SmartUQ can improve your analysis.
October 24 - 8:00 AM to 8:30 AM
Presented by Gavin Jones, Application Engineer
Essentially every government and private engineering group involved in the US Aerospace and Defense Industry has some form of ongoing digital engineering activity. The vision for full implementation of these digital engineering efforts is to connect research, development, production, operations, and sustainment to improve the efficiencies, effectiveness, and affordability of aerospace and defense systems over the entire lifecycle. As an example, the Department of Defense is converting their engineering practices for defense programs to a digital engineering approach. The transformation to digital engineering will involve a transition from documents to digital models and data, and the creation of authoritative digital truth sources. These developments all set the stage for the emergence of digital twin technologies and capabilities.
For a specific part, product, or system, an authoritative digital truth source can be created. This is a digital, interrogatable repository of all the accumulated data and knowledge concerning that part, product, or system. Using efficiently sampled data from the trade space of the system as well as from physical tests or experimental data a surrogate model of the system simulation may be created and calibrated to match real world system performance. Bayesian calibration is one technique that may be used in this process. Bayesian calibration has the advantage over other techniques of utilizing prior probability distributions for the parameters being calibrated. This statistical technique also has the advantage of accounting for the imperfect nature of all models by assuming discrepancy between the model being calibrated and the physical data set exists. Understanding the discrepancy between the simulation model and physical tests or experiments can help identify model form errors and aid in verification and validation of the simulation.
Through the use of uncertainty quantification (UQ) and other analytics techniques, this presentation will introduce attendees to the digital twin process workflow. The techniques discussed will be shown to be the solution to building and running an efficient and accurate digital twin. Both the industrial challenges and benefits that come from digital twin implementation will be addressed. The presentation will conclude by utilizing systems simulation data combined with UQ and analytics tools to demonstrate a digital twin example.
