Partition layout inside a muffler integrated with a thermoelectric generator: Multi-physics analysis and optimal design
Bibliographic record
Abstract
A multi-physics-analysis-based topology optimization (TO) method is proposed to optimally design the internal partition layout of a muffler integrated with a thermoelectric generator (TEG). The basic equations governing the acoustical behavior, heat transfer, and fluid flow in the muffler are introduced, and their interaction is designated for exact numerical analysis in terms of acoustics, heat transfer, and fluid mechanics. To implement density-based TO, one design variable is assigned to each finite element in the design domain, and interpolation functions suitable for each physics phenomenon are employed. In the TO problem formulation, the sum of the squared acoustic pressures at the outlet of the muffler for multi-target frequencies is selected as an objective function to achieve broadband noise attenuation. The temperature of the TEG and the pressure drop are constrained for high energy recovery efficiency and fluid passage, respectively. The optimization problem formulated for the muffler design is solved for various design conditions. Optimal partition layouts are obtained depending on the location and length of the TEG, the upper limit value of the pressure drop, and the number of target frequencies in the same frequency band. The noise attenuation performances of each partition layout are compared, and their expected recovery energies are calculated. One optimal partition layout is discussed in terms of acoustics, heat transfer, and fluid mechanics. The numerical results strongly support the validity of our proposed method for the optimal design of a muffler integrated with a TEG.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".