Generalized Multi-Level Line Routing for Packaging Design of an Urban Air Mobility Nacelle Using Dijkstra’s Algorithm and Signed Distances
Bibliographic record
Abstract
Urban air mobility vehicle nacelles contain mechanical and electrical components of varying size and geometry, where interactions between components via pipes, cables, and wire harnesses are imperative to the fit, form, and function of the vehicle. There is a need for packaging design methods and tools to consider the design and modelling of system component interconnections to minimize production costs of these connections, including pipes, cables, and harnesses. A custom generalized line routing method was developed and applied to a nacelle system to minimize total length of pipes and cables. Utilizing Dijkstra’s algorithm to generate single line routes between two physical points, this method produced practical results for pipe and cable routes for multi-level branched routes. This method also leverages the mathematical signed distance function to generate a weighted graph for Dijkstra’s algorithm to prevent for geometric overlap between pipe and cable routes and system components. This paper outlines the methodology developed and final line routing results obtained for the nacelle case study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".