NUMERICAL ASSESSMENT OF REVERSE-FLOW MUFFLERS USING A SIMULATED ANNEALING METHOD
Bibliographic record
Abstract
Because of the necessity of maintenance and operation in industries in which the equipment layout is occasionally tight, the space for a muffler is constrained. An interest in maximizing the acoustical performance of mufflers within a limited space is of paramount importance. As mufflers hybridized with reverse-flow ducts may visibly increase acoustical performance, the main purpose of this paper is to numerically analyze and maximize their acoustical performance within a limited space. In this paper, a four-pole system matrix for evaluating the acoustic performance —sound transmission loss (STL)— is derived by using a decoupled numerical method. Moreover, simulated annealing (SA), a robust scheme used to search for the global optimum by imitating the metal annealing process, has been used during the optimization process. Before dealing with a broadband noise, the STL’s maximization with respect to a one-tone noise (300 Hz) is introduced for a reliability check on the SA method. Moreover, an accuracy check of the mathematical model is performed. Results reveal that the STL of a muffler with reverse-flow perforated ducts can be maximized at the desired frequency for pure tone elimination; moreover, the noise reduction for a broadband noise can reach 97.5 dB. Consequently, the approach used for the optimal design of the mufflers is simple and effective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".