Analisis deformasi pada coupling element dari automatic mechanical coupler: studi kasus LRT Palembang
Bibliographic record
Abstract
Coupler merupakan komponen yang menyambungkan gerbong kereta. Kekuatan coupler terhadap beban eksepsional dan ketahanannya terhadap beban kerja normal menjadi parameter keamanan yang sangat penting dalam operasinya. Kajian ini melakukan analisis deformasi secara numerik terhadap coupling element dari automatic mechanical coupler yang digunakan pada tipe kereta ringan (Light Rail Transit, LRT) berdasarkan beban aktual yang diterima. Bagian coupling element yang diteliti adalah coupling link dan hooked plate. Model solid 3D dibuat tanpa penyederhanaan untuk hasil simulasi yang lebih mendekati fenomena aktual. Material isotropik yang digunakan adalah baja ASTM A633 Grade E. Simulasi statis dan transien dilakukan untuk mendapatkan faktor keamanan statik dan dinamik. Simulasi fatik dari analisis pembebanan transien dengan tiga mode pembebanan dilaksanakan untuk memperkirakan umur kerja coupling element. Hasil simulasi menunjukkan bahwa coupling element yang diobservasi aman terhadap beban eksepsional dan normal yang diberikan. Selain itu, simulasi mengindikasikan bahwa komponen yang kritis berdasarkan tegangan von Mises maksimum adalah coupling link dan komponen yang kritis berdasarkan siklus umur adalah hooked plate.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".