Study on the Structural Optimization of Diffuser Guide Vanes for LNG High-Pressure Pump Expander
Bibliographic record
Abstract
The guide vanes of the high-pressure pump at the Jingtang LNG receiving station frequently fail， potentially generating metal fragments that threaten the safety of downstream systems.In order to extend the service life of the guide vanes， ANSYS software was used to establish a model and conduct strength simulation calculations and fatigue simulation analysis on the diffuser guide vanes of the high-pressure pump.Results showed that cracks or fractures were more likely to occur near the hub of the impeller blades， with an increased risk of fatigue fracture at the root of the inlet edge.By changing the material from cast aluminum to forged aluminum 6061-T6， the mechanical properties were significantly improved.Additionally， it was found that when the thickness of the guide vane was increased from 3.5 mm to 5.0 mm， the membrane plus bending stress at the root was reduced by 40%to 50%.Enlarging the root fillet of the guide vane effectively reduced stress concentration，thereby decreasing the risk of crack initiation and significantly enhancing the fatigue resistance of the blade.Based on engineering application data， these improvements effectively resolved the operational issues encountered in the LNG high-pressure pump， enhancing its reliability and safety， and ensuring the stable operation of the LNG transmission system.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".