Performance Specifications for Real-Time Hybrid Testing of 1:50-Scale Floating Wind Turbine Models
Bibliographic record
Abstract
This paper presents performance requirements for a real-time hybrid testing system to be suitable for scale-model floating wind turbine experiments. In the wave basin, real-time hybrid testing could be used to replace the model wind turbine with an actuation mechanism, driven by a wind turbine simulation running in parallel with, and reacting to, the experiment. The actuation mechanism, attached to the floating platform, would provide the full range of forces normally provided by the model wind turbine. This arrangement could resolve scaling incompatibilities that currently challenge scale-model floating wind turbine experiments. In this paper, published experimental results and a collection of full-scale simulations are used to determine what performance specifications such a system would need to meet. First, an analysis of full-scale numerical simulations and published 1:50-scale experimental results is presented. This analysis indicates the required operating envelope of the actuation system in terms of displacements, velocities, accelerations, and forces. Next, a sensitivity study using a customization of the floating wind turbine simulator FAST is described. Errors in the coupling between the wind turbine and the floating platform are used to represent the various inaccuracies and delays that could be introduced by a real-time hybrid testing system. Results of this sensitivity study indicate the requirements — in terms of motion-tracking accuracy, force actuation accuracy, and system latency — for maintaining an acceptable level of accuracy in 1:50-scale floating wind turbine experiments using real-time hybrid testing.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".