Turbulent Flow Estimation Using Primitive Shape Segmentation
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Solution Overview
Problem
Turbulent flow is challenging to model due to its random and time-variant behavior, as well as non-linear trajectories, and existing methods lack a memory-efficient description for estimation.
Innovation Solution
The method involves infusing a 3D flow with particles and using cameras or sensors to track their motion, characterizing eddy flows as either discrete polyhedrons or continuous shapes like spheres and toroids, and organizing datasets into primitive shapes to create a simplified model of turbulent flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to model turbulent flow by dividing it into laminar and turbulent regions characterized by Reynolds number, then the flow can be described using conventional approaches, but the model becomes complex and requires significant memory resources to capture the random and time-variant behavior
Solution Approach 1:
The patent segments the continuous turbulent flow into discrete primitive shapes (polyhedrons, spheres, toroids, ellipsoids) that represent individual eddies or flow structures. Each primitive shape captures local flow characteristics independently, allowing the complex turbulent flow to be represented as a collection of simpler geometric entities with defined parameters such as position, velocity, rotation, and size.
Solution Approach 2:
The patent creates simplified geometric copies (primitive shapes) that represent the essential characteristics of complex turbulent flow structures. Instead of modeling every detail of the random and non-linear trajectories, the invention uses standardized geometric forms that approximate the behavior of eddies and flow patterns, reducing computational complexity while maintaining adequate representation accuracy.
2Measurement precision
If detailed particle tracking is performed to capture random and non-linear trajectories of turbulent flow, then measurement precision improves, but memory resources and computational requirements increase significantly
Solution Approach 1:
The patent extracts only the essential characteristics of particle motion relevant to turbulent flow representation. Instead of storing complete particle trajectory data, the method identifies and extracts key parameters such as eddy position, velocity, rotation rate, and primitive shape geometry. This extraction process filters out redundant information while retaining the critical features needed for flow estimation.
Solution Approach 2:
The patent transforms detailed particle position and velocity data into a different parameter space representing primitive shape characteristics. By changing the representation parameters from individual particle coordinates to geometric shape parameters (center position, radius, orientation, rotation velocity), the system reduces data volume while preserving the essential turbulent flow behavior.
3Reliability
If continuous detailed modeling of turbulent flow trajectories is used, then flow behavior representation improves, but computational efficiency and processing speed decrease
Solution Approach 1:
The patent implements a dynamic model where primitive shapes can change their parameters over time, including position, velocity, size, and rotation. This allows the representation to adapt to the time-variant nature of turbulent flow while maintaining computational efficiency. The dynamic parameters are updated based on particle tracking data, enabling the model to capture flow evolution without requiring continuous detailed trajectory calculations.
Data Source
AI summary
A turbulent flow estimation apparatus and method provides for the measurement of turbulent flow through the introduction of particles which characterize the flow, the detection of extents of turbulent flow regions, the assignment of a plurality of primitive shapes to those turbulent flow regions, and the assignment of individual characteristics to each of the primitive shapes of the estimate, including center location, rotational velocity of the primitive shape, identification of the rotational axis of the primitive shape, and the temporal trajectory of the primitive shapes.