Digital Twin for Cascading Failure Simulation in Interdependent Infrastructure
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Solution Overview
Problem
Current infrastructure modeling fails to effectively simulate the impact of adverse events on interdependent critical infrastructure elements managed by disparate organizations, lacking data sharing and connectivity, which hinders community preparedness for cascading failures across different service providers.
Innovation Solution
A method and system for generating and utilizing a CI protection blueprint in an interdependent CI architecture, involving the creation of a digital twin of heterogeneous CI elements, correlating sensor data, and simulating hypothetical sensor data to identify cascading effects, allowing for the development of remediation strategies and blueprint application across similar systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital twin modeling is implemented for interdependent CI elements across different organizations, then the ability to model cascading failures is improved, but data sharing and connectivity requirements increase system complexity
Solution Approach 1:
The patent introduces a centralized digital twin platform as an intermediary that receives and processes sensor data from multiple CI elements across different organizations. This mediator enables modeling of cascading failures without requiring direct integration between all participating systems, thus improving modeling accuracy while managing system complexity through a coordinated central system.
Solution Approach 2:
The digital twin framework creates a universal modeling platform that can represent multiple heterogeneous CI elements (power grids, water systems, communication networks) within a single system. This multi-functional approach allows the same infrastructure to model various types of critical infrastructure and their interdependencies, improving comprehensive modeling capability while using a standardized system architecture.
2Measurement precision
If comprehensive sensor data collection is implemented across all CI elements, then the correlation accuracy for modeling states is improved, but data acquisition and processing requirements increase
Solution Approach 1:
The patent extracts and correlates only the essential sensor data needed for modeling CI element states and their interdependencies, rather than processing all possible data from every sensor. The system identifies and processes key parameters (power consumption, water pressure, network traffic) that directly relate to system state and failure propagation, improving correlation accuracy while reducing unnecessary data processing.
Solution Approach 2:
The digital twin implementation applies different data collection and processing strategies to different CI elements based on their specific characteristics and importance. Critical elements with high interdependence receive more detailed monitoring, while less critical elements use simplified monitoring, optimizing the balance between measurement precision and data processing requirements across the heterogeneous infrastructure.
Data Source
AI summary
Critical infrastructure (CI) protection blueprint generation in an interdependent CI architecture includes constructing a digital twin of a heterogeneous collection of CI elements associated with respectively different services provided to a common community. Thereafter, hypothetical sensor data is specified in the digital twin for a target CI element in the hierarchy. In response, sensor data is read for other CI elements dependent upon the target CI element so as to identify impacted CI elements. For each impacted CI element, additional sensor data is read for further CI elements in the hierarchy dependent upon the impacted CI elements and the process repeats until no additional impacted CI elements are identified. A listing of all impacted CI elements is written to a blueprint for the hierarchy in association with the hypothetical sensor data in order to define a cascading effect of the hypothetical sensor data upon the hierarchy within the digital twin.