Spectral-Element/Kirchhoff Method for Fan-Tone Directivity Calculations
Bibliographic record
Abstract
A Kirchhoff integral method is coupled to a spectral-element radiation solver for efficient computations of fan-tone directivities at arbitrary distances from a turbofan inlet The Kirchhoff algorithm accurately constructs the acoustic solution at specified locations in the farfield by processing near-field data supplied by the spectral-element solver. This allows the use of a much smaller spectral-element domain concentrated near the engine that is not required to encompass the specified directivity radius. The use of a smaller computational domain translates into a reduction in overall CPU time and/or an increase in the frequency limit of the code. This paper presents a detailed description of the theory, implementation, and validation of the Kirchhoff algorithm. Numerical studies are performed to address the following issues: 1) optimal placement of the Kirchhoff integration surface in the presence of a locally non-uniform mean-flow; 2) impact of a partially opened Kirchhoff surface on the accuracy of the predicted directivity. The spectral-element/Kirchhoff system is further demonstrated by computing inlet radiation directivities up to a radius of 150 feet for practical engine power conditions and frequencies. Copyright © 2005 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.007 | 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".