Automated 3D CAD Point-Cloud Fluid Modeling
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Solution Overview
Problem
Conventional fluid system design relies heavily on human interpretation of two-dimensional data, leading to inefficiencies, high costs, and redundancy in analyzing fluid impact on device or product designs, as well as increased maintenance burdens.
Innovation Solution
A system incorporating a modeling component to generate three-dimensional models from point cloud data, a machine learning component to predict fluid flow and physics behavior, and a three-dimensional design component to render physics modeling data, facilitating an automated and accurate interdisciplinary fluid modeling process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human interpretation of two-dimensional data is used to analyze fluid flow, then flexibility in 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 computational system. The system uses point cloud data from three-dimensional CAD models to automatically generate control volumes and perform computational fluid dynamics analysis, eliminating manual intervention and its associated errors while significantly improving analysis productivity
Solution Approach 2:
The system enables self-service automation where the computational model automatically processes the three-dimensional CAD point cloud data to generate fluid flow predictions without requiring human operators to manually create control volumes or interpret two-dimensional data, thus improving both productivity and reliability
2Reliability
If multiple fluid model tools are employed to determine fluid impact, then comprehensive analysis is achieved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent merges multiple fluid model tools into a single integrated system. By combining the functionality of various computational fluid dynamics tools into one unified platform that processes three-dimensional CAD point cloud data, the system achieves comprehensive fluid analysis while reducing the complexity and maintenance burden associated with managing multiple separate tools
Solution Approach 2:
The system provides multi-functionality by integrating various fluid analysis capabilities into a single universal platform that can handle different fluid flow scenarios using the same three-dimensional point cloud data input, eliminating the need for multiple specialized tools while maintaining comprehensive analysis capabilities
3Device complexity
If two-dimensional data representation is used for fluid flow analysis, then data processing is simpler, but measurement precision and visualization accuracy are reduced
Solution Approach 1:
The patent transitions from two-dimensional data representation to three-dimensional point cloud data processing. By utilizing the full three-dimensional spatial information from CAD models to create control volumes and perform fluid flow analysis, the system achieves superior measurement precision and visualization accuracy while managing computational complexity through automated processing algorithms
Data Source
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AI summary
A multiple fluid model tool for utilizing a 3D CAD point-cloud to automatically create a fluid model is presented. For example, a system includes a modeling component 104, a machine learning component 104, and a three-dimensional design component 108. The modeling component 104 generates a three-dimensional model of a mechanical device based on point cloud data indicative of information for a set of data values associated with a three-dimensional coordinate system. The machine learning component 106 predicts one or more characteristics of the mechanical device based on input data and a machine learning process associated with the three-dimensional model. The three-dimensional design component 108 that 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 based on the input data and the one or more characteristics of the mechanical device on the three-dimensional model.