Aeroacoustic Noise Prediction and Acoustic Optimization of Mufflers
Bibliographic record
Abstract
Noise control of large diesel and natural gas generators is achieved through industrial mufflers. Design of such mufflers relies heavily on general guidelines. But these guidelines are not suitable for complex mufflers; instead, automated optimization provides an effective means of design. Optimization of a plug flow muffler (PFM) is conducted in this work with two different approaches: 1) a relatively simple gradient-descent algorithm (L-BFGS-B) maximizing the transmission loss of the PFM, and 2) a multi-objective (transmission loss and pressure drop) simulation-based optimization using the Efficient Global Optimization (EGO) algorithm. The EGO algorithm is shown to be well suited for muffler optimization, performing vastly better than the commonly used NSGA-II algorithm. In addition, the initial steps towards the prediction of aeroacoustic noise (self-noise) in mufflers is accomplished through the CFD simulation of the tandem cylinder benchmark experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".