A Process for Modeling and Analysis of Prototyped Products and its Application to a Variable Fan Drive
Bibliographic record
Abstract
<div class="htmlview paragraph">It is not uncommon for complex engineering products to undergo several design iterations due to changing market expectations or inadequate performance. In such circumstances, a prototype is generally available that could be used for performance analysis before a revision to the design is made. The availability of a prototype can be an invaluable tool for the analysis of the impact of potential design changes on the system performance. In this paper, a process is proposed for the derivation of a physical model that could be used for design analysis. The process uses model identification for determination of model complexity and numerical optimization for estimation of model parameters.</div> <div class="htmlview paragraph">This process is applied to a new pneumatic fan clutch system that has been developed to improve the efficiency of engine temperature regulation in heavy-duty commercial vehicles. This system is currently in a prototype phase and its detailed physical model is required for design trade-off analysis</div>
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".