Selective Simulation System for Production Process Modeling
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Solution Overview
Problem
Current prototype-based approaches for project planning are time-consuming, expensive, and fail to accurately represent complex systems, while simulations are needed to optimize operations and training, but require reliable and flexible methods to accurately model and test entire systems or their components.
Innovation Solution
A selective simulation system that allows for individual or combined simulation of system processes, enabling flexible testing, training, and evaluation by selectively executing simulation components and allowing operators to interact with simulated or actual processes, using customizable parameters and clock control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prototypes are built to model production systems, then system behavior can be visualized, but development time and cost increase significantly
Solution Approach 1:
The system divides the production system into discrete process components that can be individually simulated or executed. This segmentation allows selective simulation of specific components without requiring full system prototyping, reducing commissioning time while maintaining modeling accuracy for the components being simulated.
Solution Approach 2:
The patent creates virtual copies of process components through software simulation rather than physical prototypes. These digital twins replicate the behavior and characteristics of actual production components, providing accurate system modeling without the time and cost overhead of building physical prototypes.
2Reliability
If full system simulation is implemented to accurately represent complex systems, then system behavior can be evaluated, but computational complexity and resource requirements increase
Solution Approach 1:
The simulation system is divided into modular process components that can be independently configured and executed. This modular architecture allows users to simulate only the specific components needed for their analysis, reducing computational complexity while maintaining accuracy for those components. The system can scale from individual component simulation to full system simulation based on requirements.
Solution Approach 2:
The system enables partial simulation where only specific process components are simulated rather than the entire system. This partial action approach provides sufficient accuracy for many analysis scenarios without the computational burden of full system simulation, allowing users to achieve adequate results with reduced complexity.
3Productivity
If selective simulation of individual process components is enabled, then testing and training efficiency improves, but system integration and coordination become more challenging
Solution Approach 1:
The simulation system provides universal interfaces and standardized communication protocols that work across all process components. This universality allows individual components to be simulated independently for efficient testing and training, while seamlessly integrating with other components when needed. The same simulation infrastructure supports both isolated component analysis and full system simulation without requiring separate integration mechanisms.
Data Source
AI summary
A simulation model can individually and selectively simulate sub-processes of a full-scale production system. Such a customized simulator can directly simulate all the processes of a system, any combination of processes of a system, or a single process of a system. The remaining processes of the system are left to be executed by an operator or equivalent software program. The model and corresponding information can be used for test, design, evaluation, adjustment, certification, and training purposes.