Digital Twin Model Updating for Structural Integrity
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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 increased health and safety risks due to unforeseen circumstances.
Innovation Solution
A method involving the creation of a physics-based digital twin of physical assets using a port-reduced static condensation reduced basis element approximation, which analyzes and updates models based on operational data to provide recommendations for maintenance and safety throughout the asset's lifetime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If design-time analysis with conservative assumptions is used to assess structural integrity, then safety margins are improved, but asset over-design and excessive capital expenditure occur
Solution Approach 1:
The patent implements a dynamic asset management system that transitions from static design-time assumptions to continuous operational monitoring. The digital twin updates structural integrity assessments in real-time based on actual operating conditions, allowing safety margins to adapt dynamically rather than relying on fixed conservative design factors throughout the asset lifecycle.
Solution Approach 2:
The system changes the parameters used for integrity assessment from conservative design-time estimates to actual operational data. By monitoring real-world conditions such as loading patterns, environmental factors, and actual stress levels, the system adjusts safety assessments to reflect true asset performance rather than worst-case design assumptions.
2Ease of operation
If fixed time intervals for inspection and maintenance are used, then operational simplicity is improved, but premature decommissioning may occur
Solution Approach 1:
The patent replaces static fixed-interval maintenance schedules with dynamic condition-based monitoring. The digital twin continuously assesses structural integrity and predicts remaining useful life based on actual asset condition and operational history, enabling maintenance decisions to be made based on real needs rather than predetermined timelines.
Solution Approach 2:
The system implements continuous feedback loops where operational data from sensors and monitoring systems feed into the digital twin, which then updates integrity assessments and provides feedback on asset health. This closed-loop system enables real-time adjustments to maintenance schedules based on actual asset condition rather than fixed intervals.
3Quantity of substance
If lean design with limited safety margins is used to reduce costs, then capital expenditure is reduced, but health and safety risks increase
Solution Approach 1:
The patent applies preliminary actions by continuously monitoring and assessing structural integrity before failures or safety incidents occur. The digital twin predicts potential issues and enables preventive maintenance or operational adjustments before actual safety risks materialize, allowing lean design to operate safely within reduced margins through proactive management.
Solution Approach 2:
The system implements continuous feedback monitoring of actual structural conditions, comparing real-time data against predicted performance and safety thresholds. This enables immediate detection and response to conditions that could compromise safety, allowing lean-designed assets to operate safely by continuously adapting to actual conditions rather than relying on large built-in safety margins.
4Reliability
If extensive design-time analysis is performed to cover all operational conditions, then comprehensive safety assessment is improved, but uncertainty about true operating conditions remains
Solution Approach 1:
The patent implements continuous feedback from actual operational conditions through sensors, monitoring systems, and the digital twin. This real-time data collection and analysis replaces uncertain design-time assumptions with measured operational reality, continuously updating the understanding of actual operating conditions throughout the asset lifecycle.
Solution Approach 2:
The system performs preliminary comprehensive analysis at design-time to establish baseline safety assessments and predicted performance under various conditions. This initial thorough analysis is then continuously refined and updated with actual operational data, combining the comprehensiveness of extensive design analysis with the accuracy of real-world observation.
Data Source
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AI summary
A method for maintaining a physical asset based on recommendations generated by analyzing operational data and a composite model of a plurality of models representing the physical asset includes constructing, by a computing device, using a port-reduced static condensation reduced basis element approximation of at least a portion of a partial differential equation, the composite model. The computing device analyzes an error indicator associated with at least one model within the composite model to determine that the error indicator exceeds a tolerance level and increases a number of basis functions in the port-reduced static condensation reduced basis element approximation accordingly. The computing device receives first operational data associated with at least one region of the physical asset and updates the composite model. The computing device provides a recommendation for maintaining the physical asset, based upon the updated composite model.