Establishing Operating Points for a Linearized Model of a Load Sensing System
Bibliographic record
Abstract
A load sensing system is one in which the pump flow is adjusted to keep pressure across an orifice constant and independent of any variation in the load pressure. This ensures that the pressure losses across the orifice are kept to a minimum which increases efficiency substantially. Because the system is closed loop, stability can become a problem. To establish stability bounds, linearized analysis is often employed. However, to do this, operating points of all linearized parameters and coefficients must be established as a function of certain parameters such as load pressure. This can only be done by solving a series of nonlinear algebraic equations. This paper presents a set of equations for three special conditions. The experimental verification of operating points that are predicted for such a load sensing system is presented. The three regions are established theoretically and are verified experimentally. It is found that the operating points undergo a noticeable change when in transition from one region to another (as dictated by variations in load pressure or orifice area). It was also found that the agreement between the predicted and measured operating points was quite satisfactory and could be used with confidence in future studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".