Modeling and Thermal-Mechanical Coupling Analysis of Piston in Car Engines
Bibliographic record
Abstract
In this paper, finite-element analysis (FEA) is carried out on the temperature field and stress field of automobile engine piston, as well as the thermal-mechanical load coupling stress field. Through the analysis, the authors grasped the thermal load and combined stress distribution of the piston, and thus optimized the piston design to improve its operational reliability. Specifically, a 1/4 solid model of the piston was constructed in the three-dimensional (3D) computer-aided design (CAD) software Pro/ENGINEER, and then converted into a finite-element model in Pro/Mechanica. Then, an alternating load was imposed on the piston model, and fatigue analysis was performed to identify the parts of the piston prone to fatigue failure, and judge whether the piston structure satisfies working requirements. Next, temperature field analysis was carried out on the piston model. The distribution of the steady-state temperature field as determined by applying temperatures and heat transfer coefficients as required by the boundary conditions of the third kind. Finally, the piston model was subject to thermal-mechanical coupling analysis. The stress and deformation distributions of the piston under the coupled stress field were ascertained under the boundary conditions of temperature field distribution and mechanical load. Through the above work, the authors obtained the basis for safety evaluation of piston, laying the foundation for further reducing the thermal load and optimizing the stress distribution of piston.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".