Digital Twin Calibration Using Gradient-Based Real-Time Synchronization
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Solution Overview
Problem
Conventional synchronization techniques fail to calibrate digital twins of complex systems in real-time due to computational expense and processing complexity, hindering their ability to accurately represent and respond to changes in physical systems.
Innovation Solution
A method utilizing differentiable hybrid models with gradient-based optimization to iteratively calibrate digital twins, leveraging sensor data to quickly adjust parameters and achieve real-time synchronization with physical systems, particularly for space-rich 3D models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional synchronization techniques are used to calibrate digital twins, then the digital twin can represent the physical system, but computational expense and processing complexity prevent real-time calibration
Solution Approach 1:
The patent segments the complex system into multiple subsystems, each with its own digital twin model. This allows parallel calibration of individual subsystems rather than attempting to calibrate the entire complex system simultaneously, reducing computational burden while maintaining overall calibration accuracy.
Solution Approach 2:
The patent transforms the calibration problem from solving the full complex system to solving for a smaller set of key parameters that govern the behavior of the complex system. By changing the parameter space from complete system states to essential governing parameters, computational complexity is reduced while calibration precision is maintained.
2Measurement precision
If detailed space-rich 3D digital twins are created to accurately represent physical systems, then measurement precision improves, but computational expense increases
Solution Approach 1:
The patent extracts only the essential spatial parameters and governing equations needed to represent the physical system's behavior, rather than modeling every detail of the 3D space. This extraction approach maintains spatial representation accuracy for critical regions while eliminating computationally expensive unnecessary details.
Solution Approach 2:
The patent employs dynamic model order reduction that adapts the level of spatial detail based on the current operational state and calibration needs. When full spatial fidelity is not required for calibration, the model dynamically reduces its spatial representation, lowering computational energy consumption while maintaining precision when needed.
3Measurement precision
If the digital twin model is updated iteratively with candidate parameters, then calibration accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary identification of the smaller set of key parameters before iterative calibration begins. This preliminary action establishes initial estimates and constraints that guide the subsequent iterative process, reducing the number of iterations needed to achieve convergence and thereby reducing total calibration time while maintaining accuracy.
Solution Approach 2:
The patent implements efficient feedback mechanisms that use sensor data to directly update the key parameters without requiring full system state reconstruction. This feedback approach allows rapid parameter adjustment with each iteration, maintaining calibration accuracy while minimizing the time spent per iteration through targeted updates rather than comprehensive recalculations.
Data Source
AI summary
Embodiments described below include a method for calibrating a digital twin, the digital twin being representative of a complex system governed by a set of parameters. The method includes receiving a subset of data from a plurality of sensors of the complex system then calculating an error that represents a difference in state between the digital twin and the complex system. In addition, a gradient of the calculated error is calculated and utilized to generate a set of candidate parameters for the complex system. The candidate parameters are generated using a gradient optimization. The candidate parameters are provided to the digital twin model and the error value is based on the candidate parameters. Steps may be repeated iteratively including calculating of the error and its gradient, generating of the candidate parameters, and applying the candidate parameter to the digital twin until the calculated error is smaller than a user-defined tolerance.