Establishment of Methodology for Prediction of Fatigue Life of Connecting Rod through Virtual Simulation
Bibliographic record
Abstract
The application of virtual simulation of Engine components has become an integral part of design and development process. Virtual simulation offers opportunities to reduce number of physical tests during design verification and validation and thereby helps in achieving considerable reduction in development time and cost. This paper explains a case study that was essential for assessment of strength & fatigue analysis of diesel engine connecting rod as a part of engine development program for power upgrade through Turbo charging. The methodology adopted simulates major loading conditions for Compressive& Tensile stresses & fatigue life of connecting rod. Finite element analysis was done to calculate static displacement, strain and stresses under maximum compressive and tensile loading which were then used for critical point evaluation. Fatigue analysis and longevity is assessed through ANSYS. To validate the methodology developed; accelerated physical fatigue testing was carried out on the rig and results were compared. A very close correlation could be established between FEM results and failure on physical test samples. The repetitive results helped us to set appropriate design factor of safety for such applications. This methodology can be used to optimize the design of connecting rod and virtually validate in early stage of product design cycle thereby reducing no. of prototype tests.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".