Optimal Observer-Based Pressure Sensor Placement for Rigid Sails
Bibliographic record
Abstract
This paper investigates the optimal placement of pressure sensors for observer-based feedback on rigid domains, with a particular focus on rigid sails. Existing computational fluid dynamics (CFD) studies, supported by experimental validation, have shown promising results in analyzing sail aerodynamics using pressure sensors. Building on these developments, this study adapts the General Pressure Equation (GPE) into a linearized form, close to quasi-steady conditions, for pressure sensor placement analysis. Based on this model, an observer-based closed-loop strategy for optimal sensor placement is developed. A Lagrangian method is proposed to establish local optimality conditions in the infinite-dimensional setting without relying on reduced-order (lumped) models. The proposed strategy directly considers the state estimation efficiency within the optimal sensor placement process. The efficiency of the method to estimate the pressure field is illustrated by simulation results on a rigid sail with a symmetric profile and by experimental results on the jib (flexible) sail of a 6m sailboat.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 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".