This paper was produced for the 2019 NAFEMS World Congress in Quebec Canada

Resource Abstract

The presentation will first discuss the role of analytics in the Digital Twin and the industrial challenges and benefits that come from it. Then it will present how Uncertainty Quantification and other analytics are the solution to building and running an efficient and accurate Digital Twin. These analytical solutions are then broadened to the Digital Engineering Ecosystem, which consists of the Digital Twin, the Digital Thread, and the Digital Tapestry.



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. Such a statistical calibration 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.



The presentation will include a demonstration example to create a response surface that can be calibrated using real world data. Various analyses conducted using the calibrated model (emulator) will be demonstrated including sensitivity analysis and an uncertainty propagation study to determine model form error.