Pump Performance Quantification via Multi-Fidelity Digital Twins
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in quantifying pump performance and predicting remaining service life due to factors like mechanical, hydraulic, and electromagnetic influences, lacking sensors, and incomplete operating data, making it difficult to establish a physical model for effective maintenance and replacement planning.
Innovation Solution
A method involving data fusion of design and operating data using a multi-fidelity digital twin model, combined with a data fusion algorithm, to generate a predicted performance curve, and calculate remaining life based on cost and operational flexibility models, optimizing operating conditions and predicting replacement times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physical model is established to quantify pump performance, then measurement precision is improved, but device complexity increases due to multiple factors (mechanical, hydraulic, electromagnetic, vibration, temperature)
Solution Approach 1:
The patent segments the complex pump performance modeling into multiple independent digital twin models, each representing a specific aspect (mechanical, hydraulic, electromagnetic, vibration, temperature). This allows each sub-model to be developed and validated separately, reducing the complexity of the overall system while maintaining comprehensive coverage of all influencing factors.
Solution Approach 2:
The patent introduces a data fusion algorithm as an intermediary that integrates data from multiple sensors and sources to feed the digital twin models. This intermediary layer processes and harmonizes heterogeneous data, enabling accurate performance quantification without requiring direct complex interactions between all system components.
2Measurement precision
If sensors are installed to collect operating data, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent designs a multi-functional sensor system where a single integrated platform performs multiple measurement functions (vibration, temperature, pressure, flow) simultaneously. This universal sensor system reduces the number of separate devices needed while maintaining comprehensive data collection capabilities for the digital twin models.
Solution Approach 2:
The system incorporates self-diagnostic and self-calibration capabilities through the digital twin models, which automatically validate sensor data quality and identify anomalies. This self-service functionality reduces the need for additional monitoring equipment and manual intervention, simplifying the overall sensor system while maintaining high measurement precision.
3Manufacturing precision
If detailed design data is collected for modeling, then manufacturing precision is improved, but ease of manufacture decreases due to data availability constraints
Solution Approach 1:
The patent collects and stores detailed design data during the pump manufacturing and design phases, creating a comprehensive digital archive before the pump enters operation. This preliminary data collection ensures that all necessary geometric, material, and performance parameters are captured upfront, enabling high-precision digital twin modeling without requiring difficult retroactive data gathering.
Solution Approach 2:
The patent creates digital copies (digital twins) of the physical pump and its components, replicating all design specifications and manufacturing parameters in virtual space. These digital copies serve as accurate models for performance prediction and analysis, eliminating the need to physically access or measure the actual pump components during operation.
4Reliability
If pump operation is monitored continuously, then reliability is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic monitoring cycles where the digital twin models update pump performance predictions at predetermined intervals rather than continuously. This periodic action maintains reliable performance tracking and remaining life prediction while significantly reducing the energy consumption associated with constant data acquisition and processing.
Solution Approach 2:
The system uses feedback from the digital twin models to dynamically adjust monitoring intensity based on pump condition. When the digital twin predicts normal operation, monitoring frequency is reduced; when degradation is detected, monitoring intensifies. This adaptive feedback mechanism maintains high reliability while minimizing unnecessary energy consumption during normal operation.
Data Source
AI summary
A product state quantification and remaining life prediction method, the method comprising:T1, acquiring design data and operating data of a product;T2, establishing a design model of the product on the basis of the design data, performing data fusion according to the design data and operating data to obtain a predicted performance curve of the product, and selecting an optimized operating state point in the predicted performance curve; andT3, establishing a cost model and an operating flexibility model of the product on the basis of the predicted performance curve, and calculating a remaining life prediction of the product according to a performance demand or operating cost of the product.