Novel Equivalent Circuit Model for a Load-Type Bi-Stable Supersonic Fluidic Oscillator
Bibliographic record
Abstract
Abstract This paper describes the development and evaluation of a novel equivalent fluid circuit model capable of accurately predicting the performance of a load-type bistable supersonic fluidic oscillator. The model utilizes some aspects of previous models that are available in the literature. It is based on a quasi-steady assumption and includes a special nonlinear element to account for certain aspects of the oscillator switching mechanism in addition to the traditional fluid resistance, capacitance, and inductance. A new technique for modeling a junction in a fluid duct network is also presented. Unlike previous studies which made use of empirical experimental data or analytical assumptions to estimate the fluid element parameter values and form of the nonlinearity, the current method utilizes steady, computational fluid dynamic techniques to evaluate the parameters and nonlinearity which cannot be accurately determined analytically. A simplification of the model is also used to establish the criteria for oscillations to exist. The transient solution of the model equations is then shown to give good quantitative agreement with previous experimental values of the oscillation frequency and amplitude. The model is also capable of predicting certain operational limitations and other trends in the data. Finally, the usefulness and robustness of the model are also demonstrated by showing the ease with which a parameter and design changes can be investigated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".