3D Fluid Model Training Using Aggregated Machine Data
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Solution Overview
Problem
Current fluid system design relies heavily on human interpretation of two-dimensional data, leading to inefficiencies, high costs, and maintenance burdens due to the need for human trial and error in analyzing fluid impact on device designs.
Innovation Solution
A system comprising a modeling component, machine learning component, and three-dimensional design environment that generates and refines three-dimensional models of mechanical devices using machine learning processes to predict fluid flow and physics behavior, reducing reliance on human interpretation by integrating real-time data collection and updating physics modeling data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human interpretation of two-dimensional data is used to analyze fluid flow, then flexibility in design analysis is maintained, but productivity is reduced and human error increases
Solution Approach 1:
The patent replaces the mechanical human interpretation process with an automated computer-based system that uses three-dimensional models and machine learning algorithms to analyze fluid flow characteristics, thereby eliminating human error while maintaining high-speed analysis capability
Solution Approach 2:
The system enables self-service automated analysis where the computer system independently performs fluid flow analysis without requiring human intervention, using trained machine learning models to predict fluid characteristics and generate design recommendations automatically
2Reliability
If multiple fluid model tools are employed to determine fluid impact, then analysis comprehensiveness is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent merges multiple fluid modeling capabilities into a single integrated three-dimensional modeling system that combines geometric modeling, fluid dynamics analysis, and machine learning prediction in one unified platform, eliminating the need for multiple separate tools while maintaining comprehensive analysis capability
Solution Approach 2:
The three-dimensional modeling system performs multiple functions including geometric design, fluid flow analysis, parameter optimization, and predictive modeling within a single universal platform, replacing the need for specialized separate tools for each function
3Adaptability or versatility
If human trial and error methods are used for fluid system design, then adaptability to different design scenarios is maintained, but loss of time and increased costs occur
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models with extensive fluid dynamics data before actual design work, enabling rapid prediction and analysis during the design phase without requiring time-consuming trial and error iterations for each new scenario
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously learn from simulation results and experimental data, improving prediction accuracy over time and enabling the system to adapt to different design scenarios more effectively with each iteration
Data Source
AI summary
A multiple fluid model tool for training and/or refining of fluid models using disparate and/or aggregated machine data is presented. For example, a system includes a modeling component, a machine learning component, a three-dimensional design component and a data collection component. The modeling component generates a three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a machine learning process associated with the three-dimensional model. The three-dimensional design component provides a three-dimensional design environment associated with the three-dimensional model. The three-dimensional design environment renders physics modeling data of the mechanical device on the three-dimensional model based on the one or more characteristics of the mechanical device. The data collection component collects machine data via a communication network to update the three-dimensional model associated with the three-dimensional design environment.