Wall-Pressure Spectrum Model Based on Artificial Neural Networks Predictions
Bibliographic record
Abstract
We propose machine learning approach using Artificial Neural Networks (ANNs) to model the wall-pressure spectra (WPS) beneath turbulent boundary layers. Classical (semi-empirical) wall-pressure models are based on scaling laws according to inner and/or outer parameters of the boundary layer. In this approach, the complete boundary layer profile (i.e. tangent velocity as a function to the wall-normal distance) is provided as an input into the ANN. The aim of this methodology is to obtain more insight on the relationships that may arise between the turbulent boundary layer and its corresponding WPS and that have not been assessed in the literature.The analysis and training of the ANN are performed on data from Large Eddy Simulations (LES) produced by the European SCONE project. The database consists on a set of LES simulations with mach number varying in between 0.3 and 0.7, as well as Reynolds numbers in between 8.3e5 and 2.4e6 and angles of attack from 1º to 7º. The set of data includes zero and adverse pressure gradient effects, including flows experiencing strong adverse pressure gradients. In order to produce the noise prediction, the approach that has been used is the following one. An autoencoder, composed by a encoder and decoder, is trained in order to compress the boundary layer profile into a minimal amount of parameters (reduced latent space) that permit to retrieve back the shape of the profile. This latent space is used together with the flow conditions to train the ANN to produce a prediction of the WPS. It has been found that the predictions on the WPS in the high-frequency content are more reliable than on the low frequency range since more data is available for training. Also, there is large effect of the recirculating regions on the boundary layer plays an important role in the noise prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".