Towards a Universal Digital Twin Framework for Standardized Development

Digital twins (DTs) have gained increasing prominence as versatile solutions for real-time monitoring, predictive modeling, and autonomous control across diverse fields. However, each domain's unique requirements have led to a fragmented landscape where incompatible architectures and ambiguous terminology challenge broader adoption. In response, the National Academies of Science, Engineering, and Medicine introduced a set of guidelines that scope the foundational elements needed to support a robust DT ecosystem. Building on these guidelines, this paper not only unifies essential components into a cohesive DT framework, but also translates these highlevel principles into explicit, technically rigorous definitions. By systematically deconstructing and operationalizing the elements outlined in the report, the framework clarifies the roles, inputs, outputs, and integration patterns of each component. It demonstrates how ongoing data streams, high-fidelity virtual models, and iterative decision loops can be combined within a single environment to maintain consistent synchronization between physical assets and their digital counterparts. It also clarifies how commonly misunderstood elements-such as the historical repository-and newly introduced modules-like the query and response interface-interact to address state-of-theart challenges, enabling a minimal yet robust alignment with recognized DT principles. As a result, the proposed framework offers a structured foundation that can accommodate evolving operational demands and advance cross-domain DT innovation.