Modelling of Wall Pressure Fluctuations Induced by Turbulent Boundary Layer Flow
Bibliographic record
Abstract
Semi-empirical models are used to predict the power spectral density of wall pressure fluctuations in the turbulent boundary layer.From the analysis of current semiempirical models and a large survey of experimental data, it was found that the models proposed by Goody and Smol'yakov have the lowest mean squared error when predicting the power spectral density for wind tunnel experiments.The Rackl and Weston model has the lowest mean squared error when predicting the power spectral density for flight test data.In addition, although current studies of the power spectra obtained in the wind tunnel are similar, they are not generally an accurate representation of flight test experiments.Current advancements in power spectral density wall pressure fluctuation prediction have focused on expanding the range of experiments that can be predicted to include adverse pressure gradient flows, however, favourable pressure gradient flows have not received much attention.An experiment was performed to capture the effects of the favourable pressure gradient on the power spectral density.A model was then created to improve upon some of the limitations of existing models.The proposed model improves upon the prediction of the high-frequency roll-off location, incorporates improvements made by past models, and captures the effects of favourable pressure gradients.I would like to thank the support I have received from the staff at Carleton.Specifically, Alex, and Kevin for their help with the manufacturing of components that I needed to complete this work and the advice I was given along the way; and Neil and Bruce for making working from home during the pandemic easy and reliable.I am also grateful to my research colleagues Yehia and Basim for the enlightening discussions we shared.The suggestions and teamwork within our group significantly improved the quality of the work and have been a breath of fresh air during the COVID 19 pandemic.Finally, I would like to thank my family and friends for all the support and encouragement.With a strong support network, even the most unforeseeable circumstances can be surmounted.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".