Insight Into the Interaction Between Flow Field and Acoustic Field of Turning Flow Into a Side Branch Against a Deadleg
Bibliographic record
Abstract
Abstract Acoustic resonances in internal flows can cause noise, vibration, and even equipment damage. One geometry prone to acoustic resonance is a deadleg at the point where the main flow turns into a side branch. Here, we report experimental and computational fluid dynamics (CFD) studies of flow-generated pulsation in such a geometry over a wide range of mean flow velocities. Experimental results of the normalized pulsation pressure amplitudes (P*) versus Strouhal number characterized the flow–acoustic field for acoustically tuned and detuned systems and for branch to main pipe diameter ratio of 1.0. The unsteady CFD simulations revealed the characteristic volume integral of the cross-product of the vorticity field and the velocity field (ω×U) in the vicinity of the T-junction which helped in quantifying the acoustic source power for different conditions. In addition, the cross-product (ω×U) in the direction along the main line is almost exactly out of phase with the acoustic velocity at the T-junction. The pressure amplitude at the closed end of the deadleg reaches maximum when two conditions are met: (i) the system overall acoustic resonance frequency matches closely the frequency of the oscillating component of the cross-product (ω×U) characterized by a Strouhal number and (ii) the deadleg length is tuned to an odd number of ¼ wavelength of this frequency such that maximum acoustic velocity is reached at the T-junction. Synchronized images generated from the unsteady CFD simulations revealed valuable insight into the velocity and vorticity field in the region of the T-junction in support of Howe's acoustic source power equation.
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 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 | 0.000 |
| 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) | 0.002 | 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".