Digital twins only provide value when they reflect current operating conditions. This research examines architectures that ingest real-time data directly into simulation runtimes, enabling digital twins to remain synchronized with physical systems as conditions evolve.
When real-time data is integrated directly into the simulation runtime, several practical capabilities emerge:
Rather than operating as static replicas, digital twins become adaptive systems that evolve alongside their physical counterparts.
As digital twins are applied to larger and more complex systems, the cost of desynchronization increases. Small delays or inaccuracies in model state can propagate into significant errors when decisions depend on current conditions.
Architectures that support real-time data ingestion scale differently. They maintain relevance as systems grow in complexity and as data volume increases. Instead of requiring frequent resets or manual intervention, the digital twin remains continuously engaged with the physical system it represents.
Over time, this capability shifts the role of digital twins from retrospective analysis tools to active components of operational workflows. Simulations no longer describe what has happened; they help anticipate what will happen based on the system as it exists now.
Digital twins are often presented as continuously accurate representations of physical systems. In practice, many rely on static models updated periodically through manual intervention or scheduled data refreshes. While sufficient for retrospective analysis, this approach breaks down when systems operate under changing conditions.
As operations become more dynamic, the gap between a physical system and its digital counterpart grows quickly. Sensor data arrives continuously, boundary conditions shift, and system state evolves in ways that cannot be captured through infrequent updates. When digital twins lag behind reality, they lose predictive relevance and risk becoming misleading rather than informative.
This research exists because maintaining alignment between physical systems and their digital representations is no longer optional. For digital twins to support real-time decision-making, they must ingest live data and incorporate it into the simulation as it arrives—without disrupting execution or sacrificing stability.
A STRUCTURAL RETHINK OF DIGITAL TWIN ARCHITECTURE
Many digital twin implementations treat data ingestion as an external process. Live data is collected, processed, and then injected into a model through periodic updates or reinitialization. This workflow preserves legacy simulation architectures but introduces latency and discontinuity.
The core insight of this research is that data ingestion must be part of the runtime, not a preprocessing step.
By designing simulation architectures that incorporate real-time data streams directly into execution, digital twins can update internal state continuously. Boundary conditions, operating parameters, and system variables evolve in step with the physical system, allowing the simulation to remain coherent as conditions change.
This approach requires runtimes that tolerate ongoing modification, manage uncertainty in incoming data, and reconcile noisy or incomplete signals without destabilizing the model. Achieving this is fundamentally an architectural challenge rather than a data plumbing problem.