An Energy-Based Proper Model of an Automotive Fuel Delivery System
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Mathematical modeling is widely used throughout any scientific industry when trying to predict the behavior of dynamic systems. Oftentimes it is desirable that these models be simple and efficient, while still delivering accurate data.</div> <div class="htmlview paragraph">This paper builds and examines an energy-based (bond graph) model of an automotive fuel delivery system and suggests which elements are required to produce a Proper Model. The Model Order Reduction Algorithm (MORA) provides a mechanism to quantitatively rank each element in the model and determine its contribution to the system dynamics.</div> <div class="htmlview paragraph">Utilizing this approach, a model is developed that retains 98% of the complete system energy from 12 of the most active of 25 elements. This model requires 46.6% less simulation time while continuing to provide an adequate prediction of the system response.</div></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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".