Identification of Multimodal Dynamic Characteristics of a Decurrent Tree with Application to a Model-Scale Wind Tunnel Study
Bibliographic record
Abstract
Wind tunnel tests of scaled model trees provide an effective approach for understanding fluctuating wind loading and wind-induced response of trees. For decurrent trees, vague multimodal dynamic characteristics and ineffective estimation of leaf mass are two of the main obstacles to developing aeroelastic models. In this study, multimodal dynamic characteristics of the decurrent tree are identified by field measurements and finite element models (FEM). It was found that the number of branches swaying in phase determines the magnitude of effective mass fraction of branch modes. The frequencies of branch modes with larger effective mass fraction were considered as a reference for an aeroelastic model. In addition, an approach to estimate leaf mass without destruction was developed by comparing trunk frequency between field measurements and FEM. Based on these characteristics of the prototype, the scaled, aeroelastic model was constructed and assessed. It was found that the mismatch of leaf stiffness between the model and the prototype leads to mismatch of leaf streamlining and damping between them. The Vogel exponent associated with leaf streamlining provides a possible way to ensure consistency of leaf stiffness between the model and prototype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".