A hybrid methodology for uncertainty analysis of vibration response in fluid-filled pipes
Bibliographic record
Abstract
Reducing vibration and noise in fluid-filled pipeline systems is critical for enhancing the acoustic stealth of underwater vehicles. However, uncertainties inherent in the complex vibro-acoustic response and transmission of these systems render traditional deterministic methods inadequate. To address this, this paper proposes a hybrid methodology, named ISM-PCE, combining the impedance synthesis method (ISM) and polynomial chaos expansion (PCE), to efficiently estimate the low-order statistical moments of the frequency response function (FRF) of pipelines, validated by experiments and numerical simulations on homogeneous straight pipes. Results show that the normal ISM-PCE accurately estimates the mean FRF under single dimensional parameter (pipe inner diameter) uncertainty, but its variance estimation accuracy is insufficient in the resonance frequency band. Therefore, a stochastic frequency transformation method was introduced, significantly improving variance estimation accuracy and enabling successful multiple dimensional parameters uncertainty analysis. The results demonstrate that the normal ISM-PCE and its improved variant provide an efficient and accurate methodology for uncertainty quantification of vibration responses in fluid-filled pipeline systems. Although only fluid-filled straight pipes have been analyzed in this paper, the proposed methodology is generalizable and applicable to more complex fluid-filled pipeline systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".