Associative Constraint Propagation Across Heterogeneous Models
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Solution Overview
Problem
Maintaining consistency between heterogeneous models, such as 1D simulation and 3D CAD models, is challenging due to the lack of bidirectional propagation of changes, where alterations in one model are not reflected in the other.
Innovation Solution
A data processing system that accesses constraint expressions and maps to link properties between models, enabling bidirectional propagation of changes by using a processor to retrieve and update values based on constraint maps and expressions, allowing for associative constraint enforcement across models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heterogeneous models (1D simulation and 3D CAD) are maintained separately, then each model can be optimized for its specific purpose, but consistency between models deteriorates due to lack of bidirectional propagation
Solution Approach 1:
The patent introduces a constraint propagation system as an intermediary layer between heterogeneous models (1D simulation models and 3D CAD models). This system uses constraint maps and solvers to mediate changes between models, enabling bidirectional propagation while preserving each model's optimization for its specific purpose. The constraint propagation framework acts as the mediator that translates changes from one model type to another, maintaining consistency without requiring model homogenization.
2Reliability
If bidirectional propagation of changes is implemented, then model consistency is improved, but system complexity increases due to constraint mapping and propagation mechanisms
Solution Approach 1:
The patent segments the constraint propagation system into distinct modular components: constraint maps (which define relationships between models), constraint solvers (which compute propagated values), and propagation managers (which coordinate the process). This segmentation allows each component to be developed, tested, and optimized independently, reducing overall system complexity while maintaining bidirectional propagation capability. The modular architecture enables selective activation of propagation mechanisms based on specific modeling needs.
3Reliability
If manual synchronization of models is performed, then model consistency can be maintained, but productivity decreases due to time-consuming manual processes
Solution Approach 1:
The patent implements self-service through automatic constraint propagation, where the system autonomously detects changes in one model and automatically propagates corresponding constraints to related models without human intervention. The constraint propagation system monitors model changes, resolves constraints using predefined maps and solvers, and updates related models automatically. This eliminates manual synchronization tasks, significantly improving productivity while maintaining reliable model consistency through automated enforcement of associative constraints.
Data Source
AI summary
A system and method is provided that facilitates solving and enforcing associative constraints across heterogeneous models of the same system. The system may include a processor configured to: access an expression that specifies a relationship between at least one first constraint variable and at least one second constraint variable; access a first constraint map that maps at least one first model property of a first model to the at least one first constraint variable; and access a second constraint map that maps at least one second model property of a second model to the at least one second constraint variable. The processor may also be configured to: retrieve based on the first constraint map, at least one first model value of the at least one first model property from the first model by accessing a first modeling software component that is configured to access and manipulate the first model; and determine at least one second model value for the at least one second model property of the second model, based on the at least one first model value, the expression, the first constraint map, and the second constraint map.