Efficient Modeling of Blades via Beam Element in the Multi-Objective Optimization of Small Wind Turbine Blades
Bibliographic record
Abstract
Abstract A study is conducted to propose an efficient structural analysis method to be included in the multi-objective optimization of small wind turbine (SWT) blades. For this purpose, initially aerodynamic optimization of a SWT blade is performed utilizing a multi-objective function including the power output and the starting time. Structural analyses of the aerodynamically optimized blade are performed utilizing different fidelity finite element models for justifying the use of the reduced-order finite element (FE) model with beam elements. As a reference, higher fidelity three-dimensional (3-D) FE analyses of the blade are performed and alternative 1-D beam-blade models are evaluated utilizing the results of 3-D FE solutions. In this respect, tapered and multi-section non-tapered 1-D beam-blade model alternatives are evaluated as potential lower fidelity reduced-order models to be employed as efficient structural solvers in a future multi-objective optimization of the small wind turbine (SWT) blade. It is shown that multi-section non-tapered beam-blade FE model of the SWT blade is a robust model which handles unsmooth section transitions, which are encountered in meta-heuristic optimization of blades, effectively unlike the tapered beam-blade model and it can be used in a future constrained optimization involving structural metrics as constraints and/or objective functions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".