Integrated Platform for Real-Time Physical System Modeling
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Solution Overview
Problem
Current monitoring, modeling, and management systems for physical systems, such as data centers and buildings, lack integration and coordination between real-time data monitoring and theoretical or numerical modeling, limiting their ability to optimize operations effectively.
Innovation Solution
A platform that includes a data model builder, real-time data manager, physical model manager, analytic model manager, and control manager to create and update physical models based on real-time data, allowing for controlled operation of physical systems, leveraging Navier-Stokes equations for fluid flow modeling and incorporating sensors for dynamic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate monitoring and modeling systems are used for physical systems, then system complexity is reduced and ease of operation is improved, but integration and coordination between real-time data and theoretical modeling are lost, limiting optimization capability
Solution Approach 1:
The patent merges separate monitoring systems and modeling systems into an integrated platform that combines real-time data acquisition, physical system modeling, and optimization capabilities. The system integrates sensors, data management components, theoretical models (such as Navier-Stokes equations for fluid flow), and control interfaces into a unified architecture that enables both ease of operation and advanced optimization through coordinated use of real-time data and predictive modeling.
2Productivity
If integrated monitoring and modeling systems are implemented, then optimization capability and management efficiency are improved, but device complexity increases
Solution Approach 1:
The integrated platform employs universal components that perform multiple functions. For example, the data management system handles both real-time data acquisition and historical data analysis, the modeling framework supports various physical processes (fluid flow, heat transfer, structural mechanics), and the control interface can manage different types of physical systems. This multi-functionality reduces overall system complexity by eliminating redundant components while maintaining high management efficiency.
Solution Approach 2:
The system implements a nested architecture where modular components are organized in hierarchical layers. The core modeling framework nests various physical models (such as CFD solvers for fluid flow), which in turn nest specific application models for different physical systems. This nested structure allows complex functionality to be built from simpler, reusable modules, making the overall system more manageable despite its comprehensive capabilities.
3Measurement precision
If real-time data is continuously used to update physical models, then measurement precision and model accuracy are improved, but use of energy and computational resources increases
Solution Approach 1:
The system applies partial updating strategies where not all model parameters are continuously updated with real-time data. Instead, only critical parameters that significantly impact model accuracy are updated in real-time, while less sensitive parameters are updated periodically or remain static. This selective approach maintains adequate model accuracy while substantially reducing computational resource consumption compared to continuous full-model updating.
Solution Approach 2:
The platform implements periodic model updating cycles where real-time data is used to refresh physical models at predetermined intervals rather than continuously. The system alternates between data collection phases and model updating phases, allowing computational resources to be used efficiently while still maintaining model accuracy through regular updates driven by real-time sensor data.
Data Source
AI summary
Techniques for implementing system best practices are provided. In one aspect, a method for monitoring, modeling and managing a physical system is provided. The method includes the following steps. A physical data model of the physical system is provided. Real time data is obtained from the physical system. The physical data model is updated based on the real time data. An analytic model of the physical system is created based on the updated physical data model. Operation of the physical system is controlled based on output from the analytic model.