Structural Analysis of Compressor Blades Using Finite Elements Method
Bibliographic record
Abstract
The axial compressor considers the main component and plays a significant role in highspeed engines and it is subjected to different loads during the operations conditions. In this work, the simulation of the mechanical performance of the axial compressor under various loads was achieved. The simulation of the axial compressor (semi-open impeller type) was achieved with SOLIDWORKS 2016 while the structural numerical analysis was performed with ANSYS 2020 with three different materials which were (stainless steel, titanium, carbon fiber) to study the effect of different force values (5000, 10000, 15000) N on the mechanical performance of the axial compressor and to compare the behavior of modern material such as carbon fiber, which has been recently in the manufacturing of mechanical parts due to its significant properties like a high strengthweight ratio, with the other traditional materials. The output results were directional deformation, overall deformation, highest stress, highest shear stress and maximum strain. The results revealed that the carbon fiber showed the highest deformation more than the other two materials because of the type of failure that happened under loads such as delamination and cracks which may cause sudden failure for the compressor.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".