Network Study of Plant Leaf Topological Pattern and Mechanical Property and its Application
Bibliographic record
Abstract
In order to explore the compliance structure and adaptability of the vein pattern of plant leaf, five fresh and mature leaf samples, which represent the typical leaf network in nature, are collected, and the finite element model of the samples are established and simulated. The results show that the topological pattern of plant leaf is self-adaptive to the multi-load fields. When considering the change of wind loads, it is found that the main vein consistently remains unchanged, and the lateral vein changes slightly along different wind load direction. Inspired by the similar work environment and structure, the bionic methodology of wind turbine blade is developed in this paper. Firstly, the wind turbine blade structure is optimized by using SIMP method. The results indicate that material distribution of wind turbine blade is similar to the leaf vein, where, the spar cap of the blade is equivalent to the main vein of leaf, and the skins are correspond to the lateral vein of leaf. Secondly, considering the similar stress environment, such as random wind loads, rain, snow, and self-weight, the topology structure of wind turbine blade was decided by referring the natural structure. Finally, the bionic method is used to design the spar cap region of the blade. The results show that the best fatigue life appears in blades with the ply angle in the range between 45° and 65°. It is not only coincident with the side vein angle of most plant leaves, but efficiently improves the blade fatigue performance.Keywords: Plant Leaf; Medial Axis; Self-Adaptability; Wind Turbine Blade; Bionic Design
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".