A Simple Energy Approach to Assess Vibration and Fretting-Wear Damage in Process Equipment
Bibliographic record
Abstract
Excessive flow-induced vibration causing fretting-wear damage can seriously affect the performance of process equipment such as heat exchangers, condensers, nuclear steam generators, nuclear fuels, reactor internals, and piping systems. Fretting-wear damage generally takes place between a vibrating structure and its supports. It can be predicted with a fretting-wear coefficient obtained experimentally and a parameter called work-rate that formulates the dynamic interaction between structure and support. The work-rate is essentially the rate of mechanical energy dissipated at the support. On the other hand, the total available mechanical vibration energy in a structure is related to its mass, vibration frequency, mode shape, damping, and vibration amplitude. This leads to the development of a simplified formulation based on energy considerations to relate the vibration response of a structure to fretting-wear damage at its supports. The basic energy equations and the formulation of a simplified energy relationship to predict fretting-wear damage are outlined in this paper. The relationship is verified against experimental data for a multi-span heat exchanger tube. The energy approach is also compared to time domain calculations performed with a non-linear finite element code. The results indicate that the simple energy approach may be very useful to estimate fretting-wear damage in practical situations. Finally, the application of the method is illustrated for a typical heat exchanger tube and for nuclear fuels.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".