Derivation of Design Requirements for Optimization of a High Performance Hydrostatic Actuation System
Bibliographic record
Abstract
The competitive global market dictates greater quality of product models produced at lower cost and in shorter duration. During the past two decades, the efficiency of production processes and the quality of products have been differentiating factors in establishing competitive advantage in mature industries such as fluid power. The survival of such industries is increasingly dependent on their ability of optimizing their component characteristics as well as integrating these in complex subsystems. Reduction of cost of poor quality is thus critical. This cost often originates from inadequate or sub-optimal design requirements. Mature industries involved in the design and production of complex systems, have recognized the importance of design requirements definition in reducing cost and increasing profitability.This paper considers linking of system requirements to design parameters for a high performance actuation system referred to as the Electro Hydraulic Actuator (EHA). EHA is based on the hydrostatic actuation concept. It has been prototyped and has demonstrated a very high level of performance. The mathematical model of EHA is reviewed and used for linking its performance to its design parameters through a set of mathematical functions. The actual and expected performances of the prototype are compared in order to validate the proposed mathematical functions and an improved design is proposed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".