Structural Digital Twin Modeling for Creep-Based Asset Maintenance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional approaches to managing industrial machinery and infrastructure for structural integrity are limited by uncertainty in operating conditions, leading to over-design, premature decommissioning, and increased health and safety risks.
Innovation Solution
A method for maintaining physical assets using a physics-based digital twin constructed from a composite model of components, employing a port-reduced static condensation reduced basis element approximation to analyze and update the model based on operational data, thereby providing recommendations for maintenance 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 margins are improved, but capital expenditure increases due to over-design
Solution Approach 1:
The system performs preliminary analysis at design time to establish a baseline digital twin model, but defers detailed structural integrity assessment to operational phase where actual data becomes available. This allows conservative design assumptions to be replaced with data-driven assessments later, avoiding over-design while maintaining safety.
Solution Approach 2:
The digital twin continuously receives feedback from operational sensors and inspection data, updating the structural integrity model in real-time. This feedback loop replaces static conservative assumptions with dynamic, evidence-based assessments, enabling optimal maintenance decisions without excessive safety margins.
2Reliability
If design-time analysis with conservative assumptions is used, then safety margins are improved, but asset lifetime is reduced due to premature decommissioning
Solution Approach 1:
A preliminary digital twin model is created at design time, but the system is configured to update its assessments during operation. This allows the asset to operate beyond conservative design limits when actual conditions prove favorable, extending asset lifetime without compromising safety.
Solution Approach 2:
The system transitions from static conservative assumptions to dynamic, real-time assessments of structural integrity. By continuously updating the digital twin with operational data, the system adapts safety margins to actual conditions, preventing premature decommissioning while maintaining reliability.
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 digital twin implements continuous feedback from operational sensors, inspection data, and environmental conditions to dynamically adjust safety assessments. This allows lean design to be compensated by real-time monitoring and adaptive risk management, maintaining safety without requiring excessive safety margins in the original design.
Solution Approach 2:
The system enables self-service monitoring and assessment through automated sensor data collection and digital twin updates. This continuous self-assessment of structural integrity allows lean designs to operate safely by detecting and responding to actual conditions rather than relying on conservative pre-designed safety margins.
4Ease of operation
If traditional design-based prescriptive methodology is used, then maintenance scheduling is simplified, but uncertainty about true operating conditions cannot be addressed
Solution Approach 1:
The digital twin implements continuous feedback loops that collect operational data, sensor readings, and inspection results to update the structural integrity model. This transforms static prescriptive maintenance schedules into dynamic, data-driven recommendations that adapt to actual operating conditions, reducing uncertainty while maintaining operational simplicity.
Solution Approach 2:
A preliminary maintenance schedule is established at design time based on prescriptive methodology, but the system is configured to refine and adjust this schedule during operation using actual performance data. This maintains the simplicity of scheduled maintenance while incorporating real-time information to address uncertainty.
Data Source
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 analyzing an error indicator associated with 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, from a sensor associated with the physical asset, first operational data associated with a region of the physical asset and updates the model, the operational data including data relating to a measurement of temperature and to a pressure load. The computing device determines a structural response of the physical asset to the thermal load and to the pressure load, including a determination of a level of creep strain. The computing device provides a recommendation for maintaining the physical asset.