Validation Framework of a Digital Twin: A System Identification Approach

Abstract

AbstractThe constant improvement and developments in Artificial Intelligence/Machine learning models coupled with increased computing power have led to the incorporation of AI/ML for simulating learning and problem‐solving in simple and complex engineering systems. This latent uncertainty and unpredictable characteristics of AI‐enabled systems challenges engineers and industry stakeholders who care about ensuring the right systems are built (system validation). Digital Twins are an excellent example of such AI‐enabled systems due to their data‐dependent nature when tasked with replicating, monitoring, and updating physical assets for structural health monitoring and control. However, Digital Twins' system validation has not been well‐researched. This study delves into existing research and frameworks for validating Digital Twins and proposes a novel model‐centric validation framework based on system identification techniques. As a case study, we apply this model‐centric validation framework towards partially validating a Digital Twin for a single‐heat‐pipe test article for a Microre‐actor Agile Non‐nuclear Experimental Testbed.