Multiscale Design Optimization of Hopper Cars Employing Functionally Graded Honeycomb Sandwich Composites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A novel multi-stage structural design optimization procedure has been developed for the weight minimization of hopper cars. The procedure has been tested under various loading conditions according to guidelines established by regulating bodies, as well as a novel load It is often said that we stand on the shoulders of giants, and throughout this thesis, thankfully, I had some big ones in my corner. First and foremost, thank you to my research adviser, Professor Mostafa ElSayed, who had the difficult and stressful task of navigating me through my research while simultaneously allowing me the freedom to pursue my interests. Thank you also to the folks at the MAE department who work in the background to keep things moving, and a special thanks to Neil McFadyen for his efforts in helping me with my computing issues. I was also surprised to find out during my research that there are, in fact, kind strangers on the internet. Thank you to all the members and staff of the Altair forums, particularly Simon Kriznik for helping me in the establishment of my explicit model. Thank you to my father, Bashar, and to my mother, Rola, for listening to all my woes non-stop and being my rock at my toughest moments. Thank you for instilling in me the vigor to pursue a career in STEM, giving me the drive to succeed, and for being the official sponsors to my dream of being an engineer.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it