Spline Segment Optimization for Automatic Meshing
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Solution Overview
Problem
The manual process of adjusting splines for fluid flow simulations in aerospace engine components is time-consuming and often results in overlaps or gaps, making the boundary unmeshable, which complicates the optimization of automatically meshable shapes.
Innovation Solution
A computer-implemented method that parameterizes shape properties of spline segments, links terminal points to maintain continuity, and uses an optimization algorithm to adjust these properties, ensuring the boundary remains continuous and automatically meshable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adjustment of splines is used to optimize shape, then shape optimization can be performed, but the process is time-consuming and may result in overlaps or gaps making the boundary unmeshable
Solution Approach 1:
The spline is divided into multiple segments, each with parameterized control points. This segmentation allows independent optimization of each segment while maintaining overall boundary continuity through constrained optimization that ensures terminal points remain coincident across segments.
Solution Approach 2:
Shape properties of spline segments are parameterized, allowing systematic variation of control point positions through optimization algorithms. The parameters enable continuous adjustment of segment shapes while maintaining boundary continuity constraints.
2Productivity
If automatic optimization algorithm is used to vary shape properties, then optimization speed increases, but maintaining terminal point coincidence requires additional constraints
Solution Approach 1:
Terminal points of adjacent segments are pre-linked with coincidence constraints before optimization begins. This preliminary setup ensures that during automatic optimization, the terminal points remain coincident without requiring continuous manual intervention or complex real-time constraint management.
Solution Approach 2:
The optimization algorithm incorporates feedback mechanisms that continuously monitor and enforce terminal point coincidence constraints. When terminal points deviate from coincidence during optimization iterations, the algorithm adjusts parameters to restore continuity, ensuring meshability is maintained throughout the optimization process.
Data Source
AI summary
A computer-implemented method of optimizing an automatically meshable shape is provided. The method includes the steps of: providing a continuous boundary of the automatically meshable shape, the continuous boundary being defined by a spline formed by two or more spline segments, wherein each segment has a terminal point at each end of the segment; parameterizing shape properties of the segments such that selected ones of the shape properties can be varied under operation of an optimization algorithm; linking each segment to its immediately adjacent segments such that the terminal points of the segment remain coincident with the terminal points of its immediately adjacent segments under operation of the optimization algorithm; and using the optimization algorithm to vary selected ones of the shape properties of the segments, whereby the spline defines an adjusted boundary that remains continuous so that the shape remains automatically meshable.