Multi-objective optimization of muffler for vehicle air-conditioning compressor pipeline
Bibliographic record
Abstract
The noise reduction of air-conditioning systems has gradually become an urgent problem linked to the requirement of driving comfort, and the muffler is a commonly used noise reduction equipment for air-conditioning pipelines. In this study, the transmission loss of a prototype muffler is co-simulated at different speeds. To optimize the muffler, a new method that combines orthogonal and detailed optimization was proposed. In orthogonal optimization, a multi-objective orthogonal test was used to analyze the effect of four structural parameters (shoulder height, cavity length, cavity diameter, and intubation length) on the average transmission loss, transmission loss at 1120 Hz, and frequency bandwidth below 4 dB. The influence of different factors on the transmission loss was studied at different speeds, and it was found that the length of intubation had a significant impact on the transmission loss. In a detailed optimization, the method is characterized by rapidity in the design of the air-conditioning system of a vehicle, and the final optimization model is determined. The results showed that the optimized structure was better than the original structure. The maximum reduction in the average noise can reach 11.99 dB, and the maximum noise reduction at 1120 Hz can reach 8.58 dB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".