Numerical and Experimental Investigation of the Design of a Piezoelectric De-Icing System for Small Rotorcraft Part 3/3: Numerical Model and Experimental Validation of Vibration-Based De-Icing of a Flat Plate Structure
Bibliographic record
Abstract
The objective of this research project is divided in four parts: (1) to design a piezoelectric actuator-based de-icing system integrated to a flat plate experimental setup and develop a numerical model of the system with experimental validation, (2) use the experimental setup to investigate actuator activation with frequency sweeps and transient vibration analysis, (3) add an ice layer to the numerical model and predict numerically stresses at ice breaking with experimental validation, and (4) bring the concept to a blade structure for wind tunnel testing. This paper presents the third part of the investigation in which an ice layer is added to the numerical model. Five accelerometers are installed on the flat plate to measure acceleration. Validation of the vibration amplitude predicted by the model is performed experimentally and the stresses calculated by the numerical model at cracking and delamination of the ice layer are determined. A stress limit criteria is then defined from those values for both normal stress at cracking and shear stress at delamination. As a proof of concept, the numerical model is then used to find resonant modes susceptible to generating cracking or delamination of the ice layer within the voltage limit of the piezoelectric actuators. The model also predicts a voltage range within which the ice breaking occurs. The experimental setup is used to validate positively the prediction of the numerical model.
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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 | 0.001 |
| 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".