Fluid Network Digital Twin for Event-Driven Flow Telemetry
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Solution Overview
Problem
Management of fluid infrastructure networks is time-consuming and financially burdensome due to the need for constant real-time monitoring and data transmission, which drains battery power and requires frequent replacements.
Innovation Solution
The implementation of a digital twin model that estimates flow attributes within fluid infrastructure networks, allowing for energy-efficient management by comparing actual sensor measurements with predicted values, reducing the need for frequent data transmission and extending battery life by only transmitting data when measurements fall outside a predefined tolerance or confidence interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor measurements are continuously transmitted to the server, then monitoring accuracy and reliability are improved, but energy consumption increases and battery life decreases
Solution Approach 1:
The patent implements a digital twin model that creates a virtual copy of the physical fluid infrastructure system. This digital twin continuously simulates and predicts flow attributes based on environmental conditions and historical data, allowing the server to have an accurate representation of the system state without requiring constant real-time data transmission from remote sensors. The digital twin serves as a predictive model that reduces the frequency of actual measurements and transmissions while maintaining monitoring reliability.
Solution Approach 2:
The system changes the parameter of data transmission frequency from continuous to event-driven. Instead of transmitting data at fixed intervals or continuously, the system only triggers data transmission when the digital twin's predicted values diverge from actual sensor measurements beyond a predefined tolerance threshold. This parameter change significantly reduces energy consumption while maintaining monitoring accuracy by focusing transmissions on meaningful deviations.
2Use of energy by moving object
If digital twin model is used to estimate flow attributes and reduce data transmission, then energy consumption decreases and battery life extends, but system complexity increases
Solution Approach 1:
The system segments the computational workload between the remote telemetry unit and the server. The digital twin model is divided into components: environmental condition forecasting is performed using available weather data, while the flow attribute estimation using the digital twin model occurs at the server. This segmentation allows the remote unit to remain simple with minimal processing requirements, while the server handles the complex simulations, thus reducing overall system complexity despite the sophisticated monitoring approach.
3Use of energy by moving object
If sensor measurements are transmitted only when outside tolerance thresholds, then data transmission frequency and energy consumption are reduced, but risk of missing critical data increases
Solution Approach 1:
The system performs preliminary estimation using the digital twin model before actual measurement and transmission occur. The digital twin continuously predicts what the sensor readings should be based on environmental conditions and system characteristics. By comparing these preliminary predictions with actual measurements, the system proactively identifies when transmissions are necessary, ensuring critical deviations are captured while avoiding unnecessary transmissions during normal operating conditions.
Data Source
AI summary
Fluid infrastructure management systems and methods are described. An exemplar method includes: (1) obtaining, inside a server and a remote telemetry unit, a digital twin model providing one or more estimates of a flow attribute inside a fluid infrastructure item; (2) receiving a forecast of an environmental condition around an area surrounding the sensor; (3) estimating, at the server and using the forecast and one of the digital twin models, flow attribute to produce one or more server's estimated flow attribute values; (4) obtaining, using the sensor and at the remote telemetry unit, the sensor measurement of the flow attribute; and reporting or more of the server's estimated flow attribute values as being or being an estimate of the sensor measurement, if the server does not receive from the remote telemetry unit the sensor measurement.