Structural Digital Twin Modeling for Real-Time Integrity Assessment
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Solution Overview
Problem
Traditional methods for managing industrial machinery and infrastructure based on structural integrity are limited by uncertainty about operating conditions, leading to over-design or premature decommissioning, and fail to provide real-time maintenance and safety recommendations.
Innovation Solution
A method using a physics-based digital twin created through a port-reduced reduced basis element approximation of partial differential equations, which updates models based on operational data to provide recommendations for maintaining physical assets, ensuring structural integrity and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design-time analysis with conservative assumptions is used to assess structural integrity, then safety factors are increased to compensate for uncertainty, but this leads to over-design and excessive capital expenditure
Solution Approach 1:
The system implements continuous feedback by monitoring actual operational conditions and asset state throughout the operational lifetime, then updating the digital twin model to reflect real-world performance. This feedback loop replaces static conservative design assumptions with dynamic, data-driven assessments, allowing the system to maintain safety while reducing unnecessary design margins.
Solution Approach 2:
The system performs preliminary actions by continuously collecting operational data and updating the digital twin model during the asset's operational lifetime. This ongoing preparation enables accurate assessment of actual structural integrity conditions, replacing the need for overly conservative preliminary design assumptions with evidence-based evaluations.
2Reliability
If design-time analysis with conservative assumptions is used, then safety margins are built in to compensate for unknown operating conditions, but this results in premature decommissioning compared to true asset capacity
Solution Approach 1:
Continuous monitoring of actual operational conditions and asset state provides feedback that updates the digital twin model, revealing the true structural capacity and degradation patterns. This enables accurate determination of when an asset actually needs replacement or major maintenance, preventing premature decommissioning based on conservative design assumptions.
Solution Approach 2:
The system transitions from static conservative design assumptions to dynamic, real-time assessment of structural integrity. By continuously updating the digital twin with operational data, the system adapts to actual asset performance and degradation, enabling optimal determination of asset lifetime extension opportunities.
3Loss of information
If design-time analysis is performed to assess all relevant operational conditions, then comprehensive coverage is attempted, but the large amount of uncertainty about true operating conditions limits effectiveness
Solution Approach 1:
The system continuously collects feedback from actual operational conditions through sensors and monitoring systems, updating the digital twin model with real-world data. This ongoing information gathering eliminates uncertainty about true operating conditions by replacing assumptions with measured evidence throughout the asset's operational lifetime.
Solution Approach 2:
The system enables self-service by automatically collecting operational data, updating the digital twin model, and generating maintenance recommendations without requiring extensive manual analysis. The asset's own operational data serves to continuously refine the model, reducing the need for comprehensive external assessment.
4Device complexity
If lean design with limited safety margins is used to reduce costs, then capital expenditure is reduced, but this increases the likelihood of the asset going outside its approved operating envelope
Solution Approach 1:
Continuous monitoring and updating of the digital twin model provides real-time feedback on actual structural integrity and operational conditions. This enables the system to safely operate lean-designed assets outside conservative design envelopes by providing evidence-based confirmation that safety margins are adequate, replacing predetermined safety limits with dynamic assessment.
Solution Approach 2:
The system performs preliminary actions by continuously assessing structural integrity and operational conditions before allowing operations outside the approved envelope. This ongoing preparation ensures that lean designs operate safely by verifying integrity in advance, replacing static safety margins with dynamic verification.
Data Source
AI summary
A method for maintaining a physical asset based on recommendations generated by analyzing operational data and analyzing at least one model representing the physical asset, includes constructing, by a computing device, using a port-reduced reduced basis element approximation of a partial differential equation, at least one model. The computing device analyzes an error indicator associated with the at least one model to determine that the error indicator exceeds a tolerance level and increases a number of basis functions in the port-reduced reduced basis element approximation accordingly. The computing device receives first operational data associated with a region of the physical asset and updates at least one model. The computing device provides a recommendation for maintaining the physical asset.