Finite Element Analysis of Crankshaft Stress and Vibration in Internal Combustion Engines Using ANSYS
Bibliographic record
Abstract
Internal combustion engines are thermal engines in which a fuel-air mixture undergoes combustion within the combustion chamber. The high-pressure and hightemperature gases generated during this process expand, exerting force upon the pistons and converting chemical energy into mechanical energy. This retraction movement is transformed into a rotational motion that generates the necessary torque to rotate the wheels via the crankshaft. Common examples of these engines include four-stroke and two-stroke piston engines, gas turbines, jet engines, and most rocket engines. This study aims to develop a model of the piston-connecting rodcrankshaft system and investigate the thermal and mechanical stresses resulting from its operation. Utilizing the finite element method in conjunction with ANSYS software, the simulation of thermal and mechanical stresses is carried out, while random vibrations are applied to represent potential imperfections and errors during operation. The SOLIDWORKS program is employed for the design and illustration of the system. Simulation results indicate that maximum vertical stress and shear stress are primarily distributed along the piston heads and connecting rods.
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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) | 0.000 | 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) | 0.003 | 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".