Procedures for finding optimal layouts of vehicle components with respect to durability
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
Abstract When designing complete systems or system components, it is of vital importance for the manufacturers to optimally fulfill the continuously increasing demands pertaining to safety, durability, reduction of energy consumption, noise reduction, improvement of comfort, accuracy, etc. This applies to all types of traffic and transportation systems like rail vehicles, automobiles, airplanes and ships. By combining structural analysis and simulation methods with optimization algorithms, required specifications can be met faster and more reliably, and hence the production development cycles can be substantially reduced. This paper shall give an overview on results of a method with the features of a damage approximation as precisely as possible on the one hand and, on the other hand, a load‐time history with few different load cycles so that a nonlinear calculation can be performed in the shortest possible time. Simulations with rigidly and elastically modeled components like bogie frames or carbodies show that depending on the type of modeling substantial differences may occur with respect to dynamic behavior and the interaction quantity between the bodies. This aspect has to be taken into consideration for quantitatively sufficient fatigue strength and durability calculation. Mathematical optimization procedures are in general an efficient tool to guarantee the optimal fulfillment of all required design objectives and constraints in all stages of the design process. Some of the procedures are illustrated at two examples (bogie frame, carbody). (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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) | 0.000 | 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