Effects of wall heating on wall pressure fluctuations and flow noise in a low-Reynolds-number turbulent channel flow with temperature-dependent viscosity
Bibliographic record
Abstract
Abstract Wall pressure fluctuations and flow noise substantially degrade sonar detection performance and the acoustic stealth performance of underwater vehicles. This paper numerically investigates the effects of wall heating on wall pressure fluctuations in turbulent channel flow of water with temperature-dependent viscosity, exploring a novel method for controlling wall pressure fluctuations and flow noise in underwater vehicles. Large-eddy simulation (LES) is employed for the numerical calculation of the flow field, while a hybrid method combining LES with Lighthills acoustic analogy is employed to predict flow noise. The numerical results show that when the temperature difference between the wall and the incoming flow is 30 K and 50 K, the peak root-mean-square pressure fluctuations decrease by 6.76% and 8.91%, respectively. Wall heating stabilizes the pressure field near the wall, with the spectral levels of wall pressure fluctuations showing average decreases of approximately 1 dB and 2 dB. Wall heating weakens the energy-containing structures of wall pressure fluctuations and increases the overall convection velocity by 1.22% and 3.81%, respectively. Flow structure analysis reveals that the weakening of energy-containing structures results from the suppression of the vortex structures in the near-wall region. In the wall heating cases, peak turbulent kinetic energy decreases by 12.6% and 15.8%, respectively. Moreover, the sound pressure level of flow noise decreases with increasing wall temperature, with the maximum noise reduction exceeding 3 dB. Previous studies have not yet explored the effects of viscosity reduction caused by wall heating on wall pressure fluctuations and flow noise. This study demonstrates that wall heating is a promising method for reducing wall pressure fluctuations and flow noise.
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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) | 0.000 | 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) | 0.000 | 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 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".