An Application of Random Response Analysis for Analyzing Vibration Fatigue Failures Due to Liquid Impingement Loads
Bibliographic record
Abstract
Abstract Vibration fatigue failures of components or structures subjected to liquid impingement loads are common in oil and gas, and petrochemical industries. The impulse force due to liquid impingement is random in nature as the magnitude of the force depends on factors such as the way in which liquid breaks-up as it impinges, the variation in the droplet sizes, and the frequency of the droplet’s impingement. Often the designers estimate the impingement load as an equivalent static force based on the principles of rate of change of momentum. While such static equivalent load is a good estimate for the time-averaged force, the use of such force alone in the design analyses is unconservative. A better way to design the components for liquid impingement loads is to treat the impingement loads as random loading and analyze the components using random response analysis as discussed in this paper. In the random response analysis, the liquid impingement load is defined in a statistical sense; power spectral density (PSD) as a function of frequency. The stresses from the random response analysis are a measure of root-mean-square (RMS) stress. Such RMS stresses can be compared to an RMS stress limit that can be obtained by integrating fatigue damage defined using a design fatigue (S-N) curve. This paper discusses an application of the random response analysis for identifying the root cause of a pressure vessel’s internal deflector support cracking and the design changes to the deflector for protection against such vibration fatigue failures in the future.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".