Co-Simulation Model Auto-Configuration via Constrained Optimization
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Solution Overview
Problem
Current simulation technologies face challenges in efficiently managing complex systems by automatically selecting and configuring model components to meet objectives such as fidelity, execution speed, and memory usage, especially when dealing with hierarchical models and co-simulation environments.
Innovation Solution
The integration platform sets up and solves a constrained optimization problem to partition a high-level system design, selecting appropriate model components and configuring execution engines and solvers to achieve user-specified objectives, employing machine learning and statistical methods to optimize component selection and communication configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual model construction and configuration is performed, then model fidelity and accuracy can be optimized, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables automatic model construction where the simulation environment autonomously selects model components, configures parameters, and generates executable models without requiring manual intervention. The system self-configures the co-simulation environment by automatically partitioning the system design, selecting appropriate solvers and execution engines, and establishing communication interfaces between model components.
2Measurement precision
If complex hierarchical models are manually configured, then simulation accuracy improves, but device complexity and ease of operation deteriorate
Solution Approach 1:
The system automatically segments complex hierarchical models into manageable partitions, assigning different solvers and execution engines to appropriate sub-systems. The partitioning process divides the overall system design into coherent groups of model components that can be independently configured and simulated, reducing the operational complexity while maintaining simulation accuracy.
Solution Approach 2:
The simulation environment provides a universal automatic configuration framework that handles multiple types of model components, solvers, and execution engines through a unified process. The system can automatically configure diverse hierarchical models regardless of their specific structure or the types of components involved, simplifying the user interface while supporting complex simulations.
3Productivity
If automatic model construction is implemented, then productivity and ease of operation improve, but control over model configuration and reliability may be reduced
Solution Approach 1:
The system incorporates feedback mechanisms where the automatic configuration process continuously evaluates the simulated system performance against specified objectives and constraints. The configuration is iteratively adjusted based on simulation results, allowing the system to automatically optimize model parameters and component selections while maintaining reliability through objective-driven validation.
4Measurement precision
If co-simulation environments are manually set up, then communication configuration accuracy improves, but setup time and operational difficulty increase
Solution Approach 1:
The system performs preliminary automatic configuration of communication interfaces and data exchange mechanisms between model components before simulation execution. Communication protocols, data formats, and synchronization mechanisms are pre-configured based on the automatic partitioning and component selection, eliminating the need for manual setup while ensuring configuration accuracy.
Data Source
AI summary
Systems and methods automatically construct a realization of a model from an available set of alternative co-simulation components, where the realization meets one or more objectives, such as fidelity, execution speed, or memory usage, among others. The systems and methods may construct the realization model by setting up and solving a constrained optimization problem, which may select particular ones of the alternative co-simulation components to meet the objectives. The systems and methods may configure the realization, and execute the realized model through co-simulation. The systems and methods may employ and manage different execution engines and/or different solvers to run the realization of the model.