Simulation Controls for Model Variability and Randomness
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Solution Overview
Problem
Current simulation tools for industrial control systems are complex and time-consuming to execute, often requiring unnecessary variability and randomness features that complicate initial testing and model behavior prediction.
Innovation Solution
The development of simulation controls that allow for the selective enabling or disabling of model variability and randomness, enabling modifications to returned parameters and behaviors, such as converting stochastic models to deterministic ones, to facilitate testing and reduce complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If variability and randomness features are enabled in simulation models, then the simulation more accurately reflects real-world manufacturing conditions, but the model behavior becomes harder to predict and testing becomes more time-consuming
Solution Approach 1:
The simulation system dynamically adjusts the level of variability and randomness based on the testing phase. During initial model validation, variability is minimized or disabled to enable predictable behavior and faster testing. Once the model is validated, variability can be progressively increased to reflect more realistic manufacturing conditions, thereby resolving the contradiction between simulation accuracy and testing time.
2Reliability
If variability and randomness features are enabled in simulation models, then the simulation more accurately reflects real-world manufacturing conditions, but the model behavior becomes more complex to analyze
Solution Approach 1:
The simulation model is segmented into multiple layers or phases of complexity. The base layer contains deterministic behavior for initial validation, while additional layers of variability and randomness can be selectively activated. This segmentation allows users to start with simple, predictable models and progressively add complexity only when needed, thus maintaining both accuracy and analytical tractability.
3Reliability
If full variability is enabled in simulation models, then the simulation reflects realistic manufacturing conditions, but initial testing and model validation become less efficient
Solution Approach 1:
The system performs preliminary testing and validation with simplified models that have reduced variability before transitioning to full-variability simulations. This preliminary action with controlled complexity enables efficient model validation and debugging, after which the full realistic simulation can be executed with confidence that the underlying model structure is correct, thereby improving overall testing efficiency.
Data Source
AI summary
A tool for simulating an industrial control system is provided. The tool includes a simulation component to emulate one or more simulation models according to a simulated execution environment. A switch component is associated with the simulation component to selectively enable or disable variability in the simulation models.