Additive Manufacturing and Composite Materials for Marine Energy: Case of Tidal Turbine
Bibliographic record
Abstract
The global trend in additive manufacturing is the technology of three-dimensional (3D) printing with a high potential to avoid some of the weaknesses of conventional fabrication techniques. This new technology has been used to manufacture small tidal and wind turbines. In isolated areas, small turbines can be manufactured and assembled on-site for green energy production. The purpose of this document is to evaluate the thermomechanical behavior of a printed tidal turbine using Digimat-AM (Additive Manufacturing) with fused filament fabrication method. The finite element computes the mechanical deflection, temperature, residual stresses, and warpage fields of the printed part. The composites used during printing are thermoplastic polymers (acrylonitrile butadiene styrene, polyamide 6 [PA6], polyamide 12 [PA12], and polyetherimide [PEI]) reinforced with carbon and glass fillers in the form of fibers and beads (CF/GF and CB/GB). Through the simulation, one could show that the blade printed with PEI-CB/CF has excellent mechanical performance of low mechanical deflection and warpage, compared to PA6-CB/CF. In addition, the fiber-shaped fillers are better than the bead-shaped ones for the 3D printing process. In general, this study has shown the potential and feasibility of 3D printing as an excellent opportunity in the fabrication of small blades in the future, but more studies are required to understand this potential.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".