Spatial Area Normalization for Flow Field Efficiency
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Solution Overview
Problem
Existing methods fail to effectively analyze and quantify the structure and behavior of objects with apparent random patterns and pathways, such as blood vessels or river systems, which are crucial for predictive capabilities and comparative studies.
Innovation Solution
A method involving imaging, partitioning images into sub-regions based on metabolic need and function, generating Voronoi diagrams, calculating flow rates, and color-coding Voronoi cells to assess the efficiency of flow fields, allowing for the diagnosis of diseases and optimization of flow systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging methods are used to capture flow fields, then the visual representation is obtained, but the quantification of flow efficiency and structural analysis cannot be performed
Solution Approach 1:
The image is partitioned into multiple sub-regions based on metabolic need and function, with each sub-region containing one or more sources and sinks. This segmentation enables localized flow analysis while maintaining overall system context, allowing precise measurement of flow efficiency in different functional areas without requiring complex global analysis
Solution Approach 2:
Voronoi diagrams are generated from the Delaunay triangulation by subdividing sub-regions into Voronoi cells, transforming the two-dimensional image space into a structured geometric representation. This dimensional transformation enables quantitative analysis of flow paths and efficiency metrics that cannot be obtained from traditional imaging alone
2Productivity
If apparent random patterns in flow fields are not analyzed, then the system structure remains unquantified, but predictive capabilities and comparative studies cannot be performed
Solution Approach 1:
The system performs preliminary partitioning of the image into functionally-based sub-regions and generates Voronoi diagrams before conducting flow rate calculations. This preliminary structural organization transforms the apparent random patterns into a systematic framework that enables subsequent quantitative analysis and predictive modeling
Solution Approach 2:
Flow rates are calculated for each Voronoi cell and used to assign color codes, transforming the visual representation into a quantified parameter set. This parameter transformation enables comparative studies and predictive capabilities by converting qualitative visual patterns into measurable data that can be analyzed statistically
3Ease of operation
If uniform analysis methods are applied to all regions, then the processing is simple, but the metabolic needs and functional differences of different regions are not accounted for
Solution Approach 1:
Each sub-region is analyzed individually with its own Voronoi diagram and flow rate calculations, allowing the analysis to account for local metabolic needs and functional characteristics. This localized approach maintains measurement precision for each functional region while using a consistent methodology that preserves operational simplicity
Data Source
AI summary
Disclosed herein is a method comprising a method comprising imaging a network section through which flow occurs; where the flow is selected from a group consisting of fluid, electrons, protons, neutrons and holes; partitioning the image into sub-regions based on metabolic need and function; where each region comprises one or more sources and one or more sinks; where the flow emanates from the source and exits into the sinks; generating a Voronoi diagram from the Delaunay triangulation by subdividing the sub-regions into Voronoi cells, where each Voronoi cell contains exactly one sink or one source; and where the intersections of Voronoi cells are Voronoi cell vertices; calculating a flow rate in each Voronoi cell; and according a color to Voronoi cells based on their flow rates; where Voronoi cells having similar rates are accorded similar colors.