Aircraft Engine Digital Twin Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current aircraft maintenance and operation optimization methods are ad hoc and time-consuming, failing to effectively manage the complex interplay of factors such as asset utilization, fuel costs, physical inspection, and service capacity in large industrial systems like aircraft engines.
Innovation Solution
A digital twin system is employed to generate data structures representing the states of industrial assets over time, using cumulative damage models to simulate the effects of exogenous factors and optimize operations for increased performance and reduced economic risk, with a computational control system dynamically adjusting asset assignments and maintenance schedules based on historical data and physics-based models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ad hoc methods are used to optimize aircraft operations, then flexibility in decision-making is maintained, but optimization efficiency and productivity are reduced
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the aircraft engine system that replicates the complex physical system's behavior, operations, and maintenance requirements. This digital replica allows optimization calculations to be performed on the copy rather than directly on the complex physical system, significantly improving computational efficiency while maintaining accuracy. The digital twin captures engine state, operational parameters, and maintenance history to enable rapid optimization scenarios.
Solution Approach 2:
The patent replaces manual ad hoc optimization methods with an automated computational control system that uses algorithms and physics-based models to dynamically optimize aircraft operations. This substitution of mechanical/manual processes with automated electronic systems dramatically improves productivity by continuously analyzing operational data and generating optimization recommendations without human intervention in the calculation process.
2Reliability
If comprehensive factors are considered in aircraft operations management, then decision quality is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and preprocessing operational data, maintaining up-to-date digital twin models, and pre-calculating optimization scenarios before they are needed for decision-making. The system proactively monitors engine state and prepares optimization recommendations in advance, so when decisions are required, the comprehensive analysis is already complete and ready for immediate use, eliminating time delays.
Solution Approach 2:
The patent implements continuous optimization by constantly analyzing operational data and updating the digital twin model in real-time as the aircraft operates. Rather than performing discrete batch optimizations, the system maintains continuous monitoring and calculation, ensuring that the most current and accurate optimization recommendations are always available. This continuous action eliminates idle time and ensures decisions are based on the latest data without interruption.
3Productivity
If dynamic optimization is performed over configurable time intervals, then operational performance is improved, but computational resource usage increases
Solution Approach 1:
The patent implements dynamic optimization where the optimization interval and computational intensity adapt based on operational conditions. The system can adjust the frequency of optimization calculations according to the configurable time intervals, engine state changes, and operational priorities. When conditions are stable, optimization may occur at longer intervals, conserving computational energy. When rapid changes occur or critical thresholds are approached, the system dynamically increases calculation frequency to maintain optimal performance.
Solution Approach 2:
The patent changes computational parameters such as optimization interval duration, model fidelity, and calculation depth based on operational context and priority. The system can switch between simplified and detailed models depending on the time interval and operational criticality, adjusting the computational energy consumption to match the actual need for precision while maintaining asset utilization optimization.
Data Source
AI summary
A method of providing a recommendation for optimizing operations of a set of industrial assets is disclosed. Digital twins of the set of industrial assets are generated. The digital twins include data structures representing states of each of a plurality of subsystems of the set of industrial assets over a time period. The states are estimated based on an application of simulations using cumulative damage models. The cumulative damage models model the effects of exogenous factors on the operation of the set of industrial assets over the time period. The digital twins are analyzed with respect to simulated operating performances to determine an optimized control of operations of the industrial assets. The optimized control of operations is calculated to jointly and severally to increase the specified operating performance criteria in time present and future of the industrial assets or decrease an economic risk associated with the operation of the industrial assets, within a specified probability. The recommendation is presented in a user interface for use in optimizing the operation of the industrial assets or automatically changing operating setpoints pertaining to the industrial assets.