Bibliographic record
Abstract
A modular modeling approach was used to establish the design configuration for an exhaust gas recirculation valve, or EGR, intended to operate without an engine computer unit, or ECU signal. The proposed valve will be a fully mechanical device driven by means of a pressure differential across the valve and is intended to be completely independent of any electrical input. Modeling the valve involved the use of a combination of 3 relatively low cost software applications on a 64bit Dell Precision 690 with 16GB of RAM, to achieve what might otherwise call for a high end multi-physics FEA application and require much larger computational resources.At present, the standard method of controlling EGR is with an electrically actuated or vacuum actuated valve. These methods require expensive highly utilized ECU resources to control the valve position and regulate the EGR flow. The proposed valve would go between the exhaust and the intake manifolds. The model was formulated by balancing the spring stiffness and pre-load with the valve flow channel configuration and the pintle mass to attain a proper balance of forces. It took into consideration the inertial effects of the spring-mass system to predict valve operation under real-world conditions by using pressure and acceleration signals obtained in the field.Correlation work done indicates that the modeling approach is sound and may effectively be used in the design of such a valve.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".