Numerical Investigations of the Long Blade Performance Using RANS Solution and FEA Method Coupled With One-Way and Two-Way Fluid-Structure Interaction Models
Bibliographic record
Abstract
The aerodynamic and mechanical performance of the last stage was numerically investigated using three-dimensional Reynolds-Averaged Navier-Stokes (RANS) solution and Finite Element Analysis (FEA) coupled with the one-way and two-way fluid-structure interaction models in this work. The part-span damping snubber and tip damping shroud of the rotor blade and aerodynamic pressure on rotor blade mechanical performance was considered in the one-way model. The two-way fluid-structure interaction model coupled with the mesh deformation technology was conducted to analyze the aerodynamic and mechanical performance of the last stage rotor blade. One-way fluid-structure interaction model numerical results show that the location of nodal maximum displacement moves from leading edge of 85% blade span to the trailing edge of 85% blade span. The position of nodal maximum Von Mises stress is still located at the first tooth upper surface near the leading edge at the blade root of pressure side. The two-way fluid-structure interaction model results show that the variation of static pressure distribution on long blade surface is mostly concentrated at upper region, absolute outflow angle of long blade between the 40% span and 95% span reduces, the location of nodal maximum displacement appears at the trailing edge of 85% blade span. Furthermore, the position of nodal maximum Von Mises stress remains the same and the value decreases compared to the oneway fluid-structure model results.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".