Simulation of Nonstationary and Nonhomogeneous Wind Velocity Field by Using Frequency–Wavenumber Spectrum
Bibliographic record
Abstract
The time history analysis is used to estimate the peak responses of structures subjected to stationary and nonstationary winds. The time histories of the fluctuating wind processes at multiple points can be simulated based on the spectral representation method for given target auto and cross power spectral density (PSD) functions. As the number of the processes of interest increases, the computation time for the simulation increases drastically. For the stationary homogeneous or nonhomogeneous wind fields, this problem can be overcome by using the frequency-wavenumber PSD function to simulate the stochastic propagating waves or fields. In the present study, a technique to simulate the amplitude modulated and frequency modulated nonstationary and nonhomogeneous stochastic propagating wind fields is presented. The technique relies on representing the nonstationary wind velocity by amplitude modulating a process that is time transformed from a stationary process. It is based on the established relations between the PSD functions of the nonstationary and of the stationary wind velocity. Simple to use and implement equations to carry out the simulation for one-dimensional line wind velocity field and two-dimensional nonstationary and nonhomogeneous wind velocity field are presented. The use of the developed technique and its adequacy is illustrated through numerical examples.
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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 | 0.001 |
| 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".