Dynamic Analysis of Long Floating Structures in Waves
Bibliographic record
Abstract
This paper describes a frequency domain dynamic analysis technique for calculating the response of long floating structures in waves. Due to the large dimensions of the structure, it is necessary to account for the correlation of wave forces along the structure length to accurately calculate the response. The sea surface is therefore modeled as short-crested and is described by a directional wave spectrum. The dynamic analysis technique uses a superposition principle in which the short-crested sea is composed of numerous harmonic wave components with different frequencies and directions of propagation and amplitudes calculated from the directional wave spectrum. Hydrodynamic coefficients are calculated using a 2-dimensional diffraction program. The structure is modeled using beam elements in the ANSYS finite element program and moorings are represented using spring elements. The response of the structure to each wave component is calculated in a series of harmonic analyses. Spectral analysis is used to calculate the variance of the responses in a given storm event. The responses of interest for a given exceedence probability are then determined. The covariance of the responses is calculated and coincident response combinations are produced based on the assumption that the responses have a multi-variate normal distribution. The magnitude of coincident responses is often of interest in structural design. For example, a member subject to bi-axial bending could be designed to have excess resistance if the maximum value of the orthogonal bending moments obtained from a dynamic analysis is used rather than the coincident bending moments.
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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 | 0.000 |
| 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) | 0.001 | 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".