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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".